The Industrial Internet of Things
Volume T3: Analytics Framework
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Copyright © 2017, Industrial Internet Consortium, a program of the Object Management Group®

ACKNOWLEDGEMENTS
This document is a work product of the Industrial Internet Consortium Industrial Analytics Task
Group, co-chaired by K. Eric Harper (ABB), Wael William Diab (Huawei), and William Sobel(VIMANA). Portions of this technical report are contributed from an IIC white paper: “Industrial
Analytics – The Engine Driving the IIoT Revolution” (2017) 1.
EDITORSThe following persons (in alphabetical order) have edited the content in this document:
Diab, Wael William (Huawei)
Harper, K. Eric (ABB)
Lin, Shi-Wan (Thingswise, LLC)
Mellor, Stephen (IIC)
Waddell, Russel (AMT)
AUTHORSThe following persons (in alphabetical order) have authored the content in this document:
Anderson, Nina (AMT)
Diab, Wael William (Huawei)*
French, Terry (Denso Corporation)
Harper, K. Eric (ABB)*
Lin, Shi-Wan (Thingswise, LLC)*
Nair, Dinesh (National Instruments)*
Sobel, William (VIMANA)** Authored the original white paper and/or contributed substantive material to this framework

1

Diab, Harper, Lin, Sobel, “Industrial Analytics: The Engine Driving the IIoT Revolution”, March 2017, IIC,
http://www.iiconsortium.org/pdf/Industrial_Analyticsthe_engine_driving_IIoT_revolution_20170321_FINAL.pdf

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CONTRIBUTORSThe following persons have contributed valuable ideas and feedback that have significantly
improved the content and quality of this document:
Chatterjee, Chanchal (Dell EMC)
Guang, Liang (Huawei)

IIC ISSUE REPORTING
All IIC documents are subject to continuous review and improvement. As part of this process, we
encourage readers to report any ambiguities, inconsistencies or inaccuracies they may find in thisDocument or other IIC materials by sending an email to admin@iiconsortium.org.

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CONTENTS
1 Overview .......................................................................................................................... 1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8

Introduction ........................................................................................................................ 1
Purpose ............................................................................................................................... 1
Scope .................................................................................................................................. 1
Structure ............................................................................................................................. 1
Audience ............................................................................................................................. 2
Use ...................................................................................................................................... 2
Terms and Definitions .......................................................................................................... 2
Conventions......................................................................................................................... 2
1.8.1 Typographical and Linguistic Conventions and Style ............................................................. 3
1.9 Relationship with Other IIC Documents ................................................................................ 3

2 Business Viewpoint ........................................................................................................... 4
2.1
2.2
2.3

The Industrial Internet of Things........................................................................................... 4
Creating Business Value ....................................................................................................... 4
The Value of Industrial Analytics .......................................................................................... 5

3 Usage Viewpoint ............................................................................................................... 7
3.1
3.2

Analytics in Industry............................................................................................................. 8
Getting Started with Industrial Analytics .............................................................................. 8

4 Functional Viewpoint ........................................................................................................ 9
4.1
4.2

Analytics Architecture Objectives and Constraints .............................................................. 10
Analytics Functionality ....................................................................................................... 11

5 Implementation Viewpoint ............................................................................................. 13
5.1
5.2
5.3

Design Considerations ........................................................................................................ 13
Analytics Capacity Considerations ...................................................................................... 17
Analytics Deployment Models ............................................................................................ 17
5.3.1 Types of Analytics ................................................................................................................ 18
5.3.2 Deployment Location ........................................................................................................... 19
5.3.3 Deployment Phase ............................................................................................................... 19
5.4 Data Pre-Processing, Transformation and Curation ............................................................. 20

6 Artificial Intelligence and Big Data ................................................................................... 22
6.1
6.2

Big Data Analytics .............................................................................................................. 22
Artificial Intelligence .......................................................................................................... 23
6.2.1 Machine Learning ................................................................................................................ 24
6.2.2 Deep Learning ...................................................................................................................... 28

7 Analytics Methods And Modeling .................................................................................... 30
7.1

Analytic Methods and Algorithms....................................................................................... 30
7.1.1 Streaming Real-time Analytics ............................................................................................. 30
7.1.2 Batch-oriented Analytics...................................................................................................... 33
7.2 Analytics Model Building .................................................................................................... 36
7.2.1 Algorithm selection .............................................................................................................. 36
7.2.2 Cross-Validation ................................................................................................................... 37

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Contents

Performance metrics ........................................................................................................... 37

8 System Characteristics and Crosscutting Functions Related to Analytics ........................... 38
8.1
8.2
8.3
8.4

Safety ................................................................................................................................ 38
Security ............................................................................................................................. 39
Data Management ............................................................................................................. 39
Connectivity ...................................................................................................................... 40

Annex A

Example Analytics Standards ............................................................................. 41

Annex A Section 1 STANDARDS ECOSYSTEM ................................................................................ 41
Annex A Section 1, Subsection 1 Manufacturing Standards Ecosystem .......................................... 43

Annex B

Revision History ................................................................................................. 45

Annex C

Acronyms........................................................................................................... 46

Annex D

Glossary ............................................................................................................ 47

Annex E

References ......................................................................................................... 48

Index ................................................................................................................................... 48
Use of Information—Terms, Conditions and Notices ............................................................ 49

FIGURES
Figure 1-1 IIC Technical Publication Organization ........................................................................................ 3
Figure 2-1 Importance of industrial analytics considered by business leaders and industry analysts ......... 7
Figure 4-1. Analytics Mapping to the Industrial Internet Reference Architecture ..................................... 11
Figure 5-1 Types of analytics based on its applications .............................................................................. 18
Figure 5-2 A deployment pattern of various type of analytics ................................................................... 19
Figure 5-3 A deployment pattern of analytics during its different lifecycle stages .................................... 19
Figure 6-1 Example of Multi-Typed Data Processing in Big Data Analytic Systems .................................... 23
Figure 6-2 Artificial Intelligence (AI) ........................................................................................................... 24
Figure 6-3 Workflow for developing analytic models ................................................................................. 24
Figure 6-4 Mapping features to failure conditions for a motor.................................................................. 25
Figure 6-5 Spectrum feature engineering process...................................................................................... 26
Figure 6-6 Health assessment using Gaussian Mixture Model ................................................................... 27
Figure 6-7 Taxonomy of machine learning algorithms ............................................................................... 27
Figure 6-8 Deep learning workflow............................................................................................................. 28
Figure 6-9 Taxonomy of deep learning algorithms ........................................ Error! Bookmark not defined.

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Figure 7-1 Lambda Architecture ................................................................................................................. 31
Figure 7-2 Lambda Architecture - Streaming and Batch for IIoT ................................................................ 35
Figure 7-3 Kappa architecture .................................................................................................................... 35
Figure 7-4 The model building process ....................................................................................................... 36
Figure 7-5 Splitting data for cross validation .............................................................................................. 37
Figure 7-6 Confusion matrix showing types of classification errors for a binary classification problem ... 38
Figure 8-1 SysML for specifications, analysis, design, verification and validation of engineering models 41
Figure 8-2 Manufacturing has a numerous standards that relate to analytical methodologies ................ 43
Figure 8-3 Geometric design related standards ......................................................................................... 44

TABLES
Table 2-1: Ranking of future importance of advanced manufacturing technologies by executives ............ 5
Table 3-1: Industrial Analytics Requirements ............................................................................................... 9
Table 4-1: Industrial Analytics Capabilities ................................................................................................. 12
Table 6-1: Industrial Analytics Design Considerations ................................................................................ 16
Table 7-1 Machine-learning algorithm options for building analytic models............................................. 37

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1: Overview

1 OVERVIEWThe Industrial Internet of Things (IIoT) seeks to connect industrial assets and machines—thethings—to enterprise information systems, business processes and people who operate and use
them. Advanced analytics is at the core of this next-generation level of integration and, whenapplied to machine and process data, provides new insights and intelligence to optimize decisionmaking significantly and enable intelligent operations leading to transformational businessoutcomes and social value. These new insights and intelligence can be applied across any level of
any industry if the appropriate data can be collected and analytics applied correctly. If data is thenew oil, data analytics is the new engine that propels the IIoT transformation.

1.1

INTRODUCTION

As a fledgling discipline combining advances in mathematics, computer science and engineeringin the context of Information Technologies (IT) and Operational Technologies (OT) convergence,industrial analytics plays a crucial rule in the success of any IIoT system. Industrial analytics has
unique requirements, characteristics and challenges compared to business analytics and sorequires special considerations in its implementation. Since industrial analytics is in its early stageof development, much needs to be explored. This technical report describing an Industrial
Internet Analytics Framework is intended to spur discussions and research, and speed up thedevelopment and maturity of this indispensable technology.

1.2

PURPOSEThis Industrial IoT Analytics Framework document provides guidance and assistance in thedevelopment, documentation, communication and deployment of Industrial Internet of Things
Analytics Systems.

1.3

SCOPE

This document supplements the IIC Industrial Internet of Things Reference Architecture (IIRA)1
by detailing Industrial Analytics crosscutting concerns. It provides the concepts and components
required to create a viable analytical system and the characteristics of same so the technologies
employed provide the necessary services to perform successfully and correctly in an industrial
setting. This document presents an architectural framework of Industrial Internet of Things
Analytics systems using the same approach as the IIRA, specifically the architecture viewpoints(business, usage, functional and implementation), which in turn was based on ISO/IEC/IEEE
42010:2011 architecture concepts.

1.4

STRUCTURE

This document is organized as follows:1

http://www.iiconsortium.org/IIRA.htm

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•
•
•
•
•
•
•
•

1.5

1: Overview

Chapter 2 – Business Viewpoint
Chapter 3 – Usage Viewpoint
Chapter 4 – Functional Viewpoint
Chapter 5 – Implementation Viewpoint
Chapter 6 – Artificial Intelligence and Big Data
Chapter 7 – Analytics Methods And Modeling
Chapter 8 – System Characteristics and Crosscutting Functions Related to Analytics
Annex A – Example Analytics Standards

AUDIENCE

This document is intended for IIoT system architects and business leaders looking to successfully
deploy industrial analytic systems.

1.6

USE

The Industrial Internet of Things Analytics Framework is intended as an architectural templatefor system architects to create a concrete architecture tailored for the requirements of a specific
IIoT system; to assist in understanding and communicating the overall system among its
stakeholders; and to implement the architecture to meet the unique system requirements.

1.7

TERMS AND DEFINITIONS

Since this document is intended to address both IT and OT, the following terms and definitions
that are key to understanding this document are:
AI: Artificial Intelligence
Analytics: Analytics may be broadly defined as a discipline transforming data into information
through systematic analysis
Architecture Framework: conventions, principles and practices for the description ofarchitectures established within a specific domain of application and/or community of
stakeholders1
ICT: Information and Communications Technology
Industrial Analytics: Industrial Analytics is the use of analytics in IIoT systems
IT: Information Technology
OT: Operational Technology

1.8

CONVENTIONSGiven that the document is non-normative, all ‘must’, ‘may’ and ‘should’ statements are to beinterpreted as English language and not as in RFC 2119 [IETF-RFC2119].
1

ISO/IEC/IEEE 42010:2011

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1.8.1 TYPOGRAPHICAL AND LINGUISTIC CONVENTIONS AND STYLE
Terms that require definition are rendered in italics. (As the usage immediately preceding
demonstrates, italics may also be used as example, or for emphasis.)
Generally, only the first use of the term is italicized. However, when a term can be read in its
usual English language mode, the first use of the term may be italicized as the discussion becomes
technical. In the first example below, “safety” and “security” are used informally. In the second,
it introduces a definition.
“Among the key system characteristics that must be considered, safety is perhaps themost important, followed by security.”
Example“Safety is the condition of the system operating without causing unacceptable risk ofphysical injury or damage to the health of people, either directly or indirectly, as aresult of damage to property or to the environment.”

1.9

RELATIONSHIP WITH OTHER IIC DOCUMENTS

This document fits into the IIC Technical Publication Organization as shown in Figure 1-1.

Figure 1-1 IIC Technical Publication Organization

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2: Business Viewpoint

2 BUSINESS VIEWPOINT
The business viewpoint attends to the concerns of the identification of stakeholders and theirbusiness vision, values and objectives in establishing an industrial analytics system in its businessand regulatory context. It further identifies how the IIoT analytics system achieves the statedobjectives through its mapping to fundamental system capabilities.
These concerns are business oriented and so of particular interest to business decision-makers,
product managers and system engineers.

2.1

THE INDUSTRIAL INTERNET OF THINGSThe Industrial Internet of Things is a natural extension of the industrial and internet revolutions.
IIoT will be a major force driving economic growth for the coming decades, at a greater pace thanprior revolutions. As outlined by the World Economic Forum,1 “The first Industrial Revolution
used water and steam power to mechanize production. The Second used electric power to create
mass production. The Third used electronics and information technology to automate
production. Now, a Fourth Industrial Revolution is building on the Third, the digital revolutionthat is blurring the lines between the physical, digital and biological spheres.”
To accelerate this digital revolution the Industrial Internet Consortium (IIC) is advancing thetechnology of IIoT across a diverse set of application domains. The industrial internet integrates
the industrial assets and machines—the things—to enterprise information systems, businessprocesses and people who operate or use them. With these connections to the industrial assets
and machines, new technologies enable the application of advanced analytics to machine andoperational process data to gain insights into the operations, optimize them intelligently to boost
productivity, increase quality, reduce energy and material consumption, increase flexibility, andultimately create new business values, while maintaining commitments to safety, reliability,
resilience, security and data privacy as the trustworthiness of the systems, and conservation of
the environment as social values.
Industrial analytics, applied to machine data for operational insights, is as an engine driving theconvergence of OT and IT, and ultimately value creation for the Fourth Industrial Revolution.

2.2

CREATING BUSINESS VALUE

An enterprise needs to increase throughput, reduce expenses and inventory to generate higher
margins and thus create business value. Sales drives manufacturing throughput, but it cannot
exceed the enterprise’s capacity to produce, lest it compromise on-time delivery and disappoint
customers. One approach is to identify performance bottlenecks in overall operations
continuously and remove them one-by-one to meet the demand from sales and profit targets.

1

Schwab, K., “The Fourth Industrial Revolution: what it means, how to respond”, World Economic Forum(2016).

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This process needs to be grounded in reliable information about the demands, production,
inventory and operational processes. IIoT and specifically industrial analytics enables
stakeholders to gather data from machines and optimize processes more efficiently. The
opportunity is substantial since over 86% of companies do not have a corporate analytics
program that uses manufacturing data (LNS Research, 20161).
According to the 2016 Global Manufacturing Competitiveness Index report by Deloitte Touche
Tohmatsu Limited and the US Council on Competitiveness, Predictive Analytics and Smart
Connected Products are #1 and #2, respectively (see Table 2-1). This shows that the desire toleverage analytics is a high priority for most CEOs. However, the awareness of how thattechnology will be applied and the areas of greatest impact are not well understood.

Table 2-1: Ranking of future importance of advanced manufacturing technologies by executives1

As our business systems and analytical capabilities increase, some enterprises will adopt data andanalytical strategies; some will not. Enterprises that avail themselves of these technologies will
far outpace the others in their ability to produce products and services quickly and safely, andmaintain quality with full accountability for their processes. The cost of manufacturing will also
drop, allowing developed countries to compete effectively in global markets because the valueof skills and experience will more than compensate for the wage differential.

2.3

THE VALUE OF INDUSTRIAL ANALYTICS

Analytics may be broadly defined as a discipline transforming data into information throughsystematic analysis. Industrial analytics is the use of analytics in IIoT systems. It enables a better1

http://blog.lnsresearch.com/5-real-surprises-from-the-2016-metrics-that-matter-research-study

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understanding of a system’s operational states, performance and environment. It identifies andanalyzes emerging information patterns to enable industrial system assessments under varied
conditions. These assessments improve functionality and reduce inefficiency and operationalcost. For example, utility companies may optimize electricity output based on dynamic usage
patterns that factor in weather, season, events, pricing, resource availability, cost and electricity
generation asset availability; support vehicle and equipment fleet management; optimize smart
facility energy management and other unimagined capabilities. This is called dynamic operations
optimization.
Industrial analytics can also optimize system missions. For example, metropolitan-area real-timetraffic-pattern analysis combined with roadway conditions, roadway construction andmaintenance, weather condition, time and day, seasons, accidents and other events can lead tovehicle control systems determining optimal routes to reduce travel time, congestion, pollution
and energy consumption.
Industrial analytics can be applied to machine-streaming data received from disparate sources todetect, abstract, filter and aggregate event-patterns, and then to correlate and model them todetect event relationships, such as causality, membership, and timing characteristics. Identifying
meaningful events and inferring patterns can suggest large and more complex correlations sothat proper responses can be made to these events.
Industrial analytics can also be used to discover and communicate meaningful patterns in data
and to predict outcomes. Traditional business analytics are typically applied only to business data
to describe, predict and improve business performance.
To understand the value of industrial analytics, consider this example. In an industrial setting, amajor cause for unplanned downtime and expense is machine outage. This translates to billionsof dollars 1 lost in unplanned failures of equipment and unnecessary maintenance. Currently,
most companies use scheduled maintenance plans for preventive maintenance. This means thatmaintenance is performed on machines even if they are sitting idle, costing time and resources,
as well as requiring unnecessary disruptive procedures that can reduce equipment reliability. On
the other hand, critical problems are often missed due to insufficient diagnostics resulting in
unplanned downtime and often-costly repairs. Both the over- and under-maintenance of assets
contribute to higher operating expense.
To address these issues, maintenance needs to shift to prognostics that will schedulemaintenance based on component lifetime characteristics and its usage, rather than to apredetermined schedule. Next, the practice needs to become predictive where sensor andmachine operational data are analyzed to forecast the likelihood of certain failures in a given
period. With this information, machine maintenance can be scheduled optimally to avoid
interruption to the operations and at lower cost. Analytics can also be applied into the control

1

J. Manyika, et al, “The Internet of Things: Mapping the Value Beyond the Hype”, McKinsey & Company(June 2015)

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loop to adapt the machines to avoid operational conditions that can have detrimental effects on
the machines and processes.
Industrial analytics plays a vital role providing crucial insights needed for decision-making and for
optimal deployment of resources in industrial applications. Those capabilities in turn effectivelytranslate to an increase in the efficiency of labor and capital. Long-term GDP growth isfundamentally dependent on capital, labor and efficiency. Increasing the efficiency of capital andlabor is paramount for GDP growth and competitive advantages.
Business leaders have increasingly recognized the importance of industrial analytics. A recent
survey (see Figure 2-1) carried out by IoT Analytics GmbH 1 found that 69% of the surveyedbusiness leaders or industry analysts consider industrial analytics crucial for their businesses
within 5 years and want to apply analytics to strengthen their businesses. The same survey foundthat analytics on physical objects and machines (highlighted in Figure 2-1) rank high inimportance. Predictive and prescriptive maintenance of machines (at 79% of surveyed
considering it extreme or very important) ranks at the top, slightly surpassing consumer andmarket related analytics (at 77%).

Figure 2-1 Importance of industrial analytics considered by business leaders and industry analysts

3 USAGE VIEWPOINT
The usage viewpoint addresses the concerns of expected system usage. It is typically represented
as sequences of activities involving human or logical (i.e. system or system components) usersthat deliver the system’s intended functionality.
System engineers, product managers and those involved in the specification of the analytics
system representing the ultimate users are the typical stakeholders for these concerns.

1Knud Lasse Lueth, Christina Patsioura, Zana Diaz Williams and Zahra Zahedi Kermani, “INDUSTRIALANALYTICS 2016/2017”, IoT Analytics GmbH, December, 2016

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3: Usage Viewpoint

ANALYTICS IN INDUSTRY

Analytics as a discipline in applied mathematics has been widely adopted by organizations for
decades, most prominently as business analysis in finance, banking and ecommerce. In industrial
settings, analytics is currently used to identify and address potential faults in assets, improve
uptime and reduce repair costs. This is called condition-based monitoring, or CBM. With the rapid
development standards and innovations in sensor and computer technology, it is now possible
to extend advanced analytics to large numbers of machines across the globe. Advanced analytics
algorithms and techniques, including machine learning, are now used to analyze large amountsof data gathered from industrial control systems. The insights gained from the analytics can beapplied automatically to increase operational efficiency of machines by, for example, anticipating
peak usage, streamlining the supply chains for parts needed for preventative maintenance, andfor business planning and decision-making. Using insights drawn from the machine data to drive
intelligent operational and business processes, industrial analytics enables the convergence ofanalytics in the OT and IT worlds.

3.2

GETTING STARTED WITH INDUSTRIAL ANALYTICS

Industrial analytics are used to identify and recognize machine operational and behavioral
patterns, make fast and accurate predictions and aid in optimal decision making with greater
confidence.
Analytics generally fall into three major categories:
Descriptive analytics gain insight from historical or current data streams including for status andusage monitoring, reporting, anomaly detection and diagnosis, model building or training.
Predictive analytics identify expected behaviors or outcomes based on predictive modeling using
statistical and machine-learning techniques, e.g. capacity demand and usage prediction, material
and energy consumption prediction, and component and system wear and fault predictions.
Prescriptive analytics uses the results from predictive analytics as guidance to recommend
operating changes to optimize processes and to avoid failures and the associated downtime. An
example of prescriptive analytics is on-demand production from a solid geometric assemblymodel to find the optimal set of manufacturing processes to achieve the final product.
The analytics results can be applied automatically to the machines and systems, or used tosupport human decisions through visualization of the analytics results to enhance humanunderstanding and generate confidence in a decision.
Industrial analytics has unique challenges because the results can alter the operation and safetyof things in the physical world. These effects may be undesirable or harmful, inadvertently
affecting the safety of people or damaging property and the environment. Moreover, because
industrial analytics often interpret data from different sensors and machines that may conflictwith one another, we need to understand and synthesize the diverse information streams toreach a correct conclusion. Table 3-1 below, identifies the requirements to consider whenplanning for industrial analytics.
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Topic

Description
Industrial analytics must satisfy a higher level of accuracy in its analytic results. Any system
Correctnessthat interprets and acts on the results must have safeguards against undesirable andunintended physical consequence.
Industrial analytics must satisfy certain hard deadline and synchronization requirements.
TimingNear instantaneous analytic results delivered within a deterministic time window arerequired for reliable and high quality actions in industrial operations.
When applying industrial analytics, and interpreting and acting on the result, strong safety
Safetyrequirements must be in place safeguarding the wellbeing of the workers, users and theenvironment.
The analysis of data within an industrial system is never done without the context in which
the activity and observations occur. One cannot construct meaning unless a fullContextualized understanding of the process that is being executed and the states of all the equipment andits peripherals are considered to derive the true meaning of the data and create actionable
information.
Industrial operations deal with the physical world and industrial analytics needs to bevalidated with domain-specific subject matter expertise to model the complex and causal
Causal-oriented relationships in the data. The combination of first principles, e.g. physical modeling, alongwith other data science statistical and machine learning capabilities, is required in manyindustrial use cases in order to provide accurate analytics results.
Many complex industrial systems have hierarchical tiers distributed across geographic areas.
Each of these subsystems may have unique analytic requirements to support theiroperations. Therefore, industrial analytics must be tailored to meet the local requirementsof the subsystems it supports. The requirements on timing (avoiding long latency) andDistributed
resilience (avoiding widespread outage of service because of faults in the network or in acentralized system) require a distributed pattern of industrial analytics in that the analytic
will be implemented close to the source of data it analyzes and to the target where its
analytic outcome is needed.
Industrial analytics can be continuous or batch processes. Because of continuous execution
in industrial systems, a large proportion of industrial analytics will be streaming in nature,
Streaming
performing analysis of live data and providing continuous flow of analytics results in supportof the operations. Traditional batch-oriented analytics will still be performed either forbuilding or improving analytic models, or for human decision-making.
For the industrial analytics to support continuous operations, the analysis of streaming data
and the application of analytic outcomes must be automatic, dynamic and continuous. As
Automatic
the technologies in industrial analytics advance, improvements in analytic modeling e.g.
through learning may also be automatic.
Analytical systems require data that has meaning and context. Unstructured data, whenreported without attribution to the source and the component or system it represents,
makes deriving value complex since it requires the analytics to guess or infer the meaning.
Semantics
Inference unnecessary adds significant uncertainty into the system. Most data can beproperly attributed at the source, and if this information is communicated, it cansignificantly increase the success and accuracy of the analytical systems.

Table 3-1: Industrial Analytics Requirements

4 FUNCTIONAL VIEWPOINT
The functional viewpoint focuses on the functional components in an industrial analytics system,
their structure and interrelations and the relation and interactions of the system with external
elements, to support the usages and activities of the overall system.
These concerns are of particular interest to system and component architects, developers andintegrators.

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4.1

4: Functional Viewpoint

ANALYTICS ARCHITECTURE OBJECTIVES AND CONSTRAINTS

Industrial analytics can be applied in different domains distributed across an IIoT system andtuned to varying time-scale horizons.
The required architecture can be analyzed in the context of the functional domains of theIndustrial Internet Reference Architecture (IIRA) developed by the IIC, as shown in Figure 4-1
above. An end-to-end IIoT system in the IIRA is functionally decomposed into five functional
domains:•••••

Control: sensing, communication, execution, motion, and actuation;
Operations: provisioning, management, monitoring, diagnostics and optimization;
Information: data fusion, transforming, persisting, modeling and analyzing;
Application: logic, rules, integration, human interface; andBusiness: enterprise and human resources, customer relationships, assets, service
lifecycle, billing and payment, work planning and scheduling.

The control domain is a collection of functions performed by the industrial assets or control
systems, in exercising the closed control loops within themselves. The operations domain is acollection of functions for assets and control systems management and maintenance to ensure
their continuing operations. The information domain is a collection of functions for collecting,transforming, analyzing data to acquire high-level intelligence of the entire system. The
application domain is a collection of functions for applying use-case-specific logic, rules andmodels based on the information obtained from the information domain to achieve system-wide
optimization of operations or other business objectives. The business domain is a collection offunctions for integrating information across business systems and applications to achievebusiness objectives.
Industrial analytics results can be applied to the control domain at the edge providing real-time
operational insights to the control loops in a machine-time horizon that typically requires analytic
response in milliseconds or less. Examples include autonomous vehicles and robotics. Analytics
in this time horizon tend to be streaming in nature and applied automatically. Industrial analytics
results can be applied to the application and operations domains to provide machine insightsthat enable advanced maintenance such as automatic fault-detection and diagnosis, andpreventive maintenance, or to drive optimal operations across fleets of machines or assets. The
analytics result is applied in an operation-time horizon that typically requires an analytic responsein the range of seconds or more. Analytics in this time horizon also tends to be streaming in
nature and applied automatically. Industrial analytics results can also be applied to the businessdomain as well, providing insights to enable in intelligent business processes, including aiding
business planning and engineering processes. The analytics result is applied in a planning-time
horizon that typically requires an analytic response in the range of days or more. It consists ofboth streaming analytics results that are applied automatically (e.g. to work and machine partscheduling for on-site repair) and batch analytics results based on on-demand queries.

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Figure 4-1. Analytics Mapping to the Industrial Internet Reference Architecture

4.2

ANALYTICS FUNCTIONALITY

Industrial analytics functionality is deployable throughout the IIoT architecture. The capabilities
needed for successful industrial analytics solutions are shown in Table 4-1, below. Each capability
is realized by a set of functions defined by use cases that meet the stakeholders’ expectations,especially with regard to non-functional requirements.
In a manufacturing use case, the plant operators visualize the conditions of the process line usinggraphical displays. When an alert appears, the operator drills down to the time-series records ofthe key parameters. Planning for the next run, the operator might use a similar drill-down todetermine if there are any concerns that need to be addressed based on anticipatedenvironmental conditions.
The fundamental prerequisite for industrial analytics is availability and access to data from theindustrial process and related assets. Data is collected close to the process through connections
and stored, at least temporarily, where the readings can be scanned and evaluated dependingon the type of analytics. The stored values may be discarded or archived for further calculations.
Data scientists can explore the archived data using statistics to compute correlations, and apply
algorithms to classify and cluster the evidence over time. Industry subject matter experts have agood understanding of the context and condition of the process and assets, and can interpretand validate the readings and recommend cleansing filters. It is this combination of data scienceand subject matter expertise that produces the best results.

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Capability
Visualize
ExploreDesignOrchestrate
Connect
CleanseComputeValidate
Apply
Store
ManageSupervise

4: Functional Viewpoint

Description
Display and manage data readings and analytics results using a common framework
Ad hoc experiments with historical data
Automation of the data analytics stages; data quality, data mining, and businessintelligence algorithm composition
Delegate work requests over a cluster of computing resources, and collect and aggregateintermediate and final results
Exchange data and work requests between components using a common framework
Merge data sets from different data sources based on suitable criteria; remove irrelevant
data and clean noise from data
Perform computation of statistical, first-principle and machine-learning model analytical
calculations, including live analysis on streaming data, batch or ad hoc data mining andoperation and business intelligence analysis
Ensure analytics results when applied in the context of the application and environment
will not harm people or property. This function should be independent from the core
analytics processing and act as a governor
Apply analytics results to various subsystems, including the automation systems (e.g.
adjusting control parameters or models), operations and business processes, increasingly
automatically or as information aiding human decision-making
Archive and reproduce measured and calculated data streams, especially time-series
sequences
Manage the information model, including data sources, computing resources and data
analytics metadata
Manage system reliability by ensuring processes are started and maintained, and thatcomputer resources are not exhausted

Table 4-1: Industrial Analytics Capabilities
The industrial analytics workflow can be automated as the data and relationships are better
understood. Given an appropriate framework, the automation is designed, configured andorchestrated to transform raw data into actionable results. The workflow and algorithmic
content is versioned and deployed both on premise and in the cloud as needed to meetstakeholder expectations. The entire process is supervised to ensure all steps are completed andvalidated. Ideally, the industrial analytics solution evolves over time to produce better results
using a minimum of resources, and to improve accuracy as more experience and historical data
are acquired.
The final step is to communicate and present the industrial analytics results in a compelling andeasily understandable format, including charts, graphs, and recommended actions. Most
important is to provide means for humans to interact with the results, starting with a summary
and allowing drill down into the evidence that supports the recommendations.
As analytics advances, more meaningful operational patterns, especially anomalies, will be
detected, identified and reported as alerts, along with relevant supporting data, automatically.
Root causes of faults can be automatically diagnosed and remedies or repairing actions can beprescribed; faults and failures can be prevented by ruling out improper operating parametersthat are outside of the normal ranges and be predicted based on the historical experience ofmaking similar parts. Machine operational efficiency can be monitored and optimized based on
analytics results as well as orchestration of manufacturing resources and coordination andinteraction of the prices of equipment and the people who operate them.
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Clearly, all these will improve the operational efficiency of manufacturing and operations and at
the same time reduce the stress on human operators in keeping the machines operating at theirbest capacity. On the other hand, analytics is no magic by itself—it requires a combination ofobtaining the proper data at the proper time, applying the proper analytics algorithms andmodels that are guided by the necessary engineering domain knowledge from both the machine
manufacturers, system integrators and the plant operators themselves. This is a process ofcontinuing learning and improvement. For example, the types of data collected can be extended
and their quality can be enhanced, the algorithms and models can be subjected to refinementand deeper domain knowledge can be injected into the models.

5 IMPLEMENTATION VIEWPOINT
The implementation viewpoint deals with the technologies needed to implement functional
components (functional viewpoint), their communication schemes and their lifecycle
procedures. These elements are coordinated by activities (usage viewpoint) and supportive of
the system capabilities (business viewpoint).
These concerns are of particular interest to system and component architects, developers andintegrators, and system operators.

5.1

DESIGN CONSIDERATIONS

To determine where analytics should be performed, the following considerations should be taken
into account:
Scope: Ultimately, it is the derived information (not the raw data) and how it can be acted on that
determines what kinds of analytics are deployed, and where. For instance, if the goal is to
optimize machine uptime at one site, then analytics performed on data gathered there may besufficient. In this case, the analytics can be performed anywhere, provided that, if done remotely,
the normal local operation is not critically dependent on network latency and the availability of
the analytics results. On the other hand, if the value proposition is to optimize production acrosssites requiring comparison of factory efficiencies then analytics needs to be performed on data
gathered from these sites and thus be available in a higher tier of the system architecture.
Response time and reliability: In an industrial setting, some problems require deterministic
analysis, computation and response; others can be done after the fact. The former almost always
requires analytics to be local for reliability and performance.
Bandwidth: The total amount of data generated by these sensors, together with data gatheredfrom the control systems can be huge. The increased network and infrastructure required to fusedata from one domain with others will be compensated by the creation of valuable insights.
Capacity: In some cases, it may be optimal to perform the analytics at a particular tier in a systemarchitecture, but the existing infrastructure may not be able to support it, so a different tier isselected. Key properties of the ICT infrastructure include latency, bandwidth and computational
capacity.
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Security: The value from moving data must be balanced with concerns for transferring raw data
outside of controlled areas, and the associated costs. It may be more efficient to perform someanalytics locally and share necessary summary, redacted or anonymized information with otherdomains.
Volume: All data needs to be stored, at least temporarily. The storage required depends on theapplication.
Velocity: Industrial measurements are typically captured cyclically. High-frequency data, such as
vibration or acoustics data from aircraft engines and wind turbines, can significantly increase thespeed of data processing needed. Another consideration is transient events where readings must
be captured with accurate time recording to determine order of occurrence, causality and root
cause. With these low latency requirements, analytics using high velocity data is better
performed close to where the data is measured.
Variety: When many pieces of equipment have been acquired over the years with dissimilar
controls, interfaces and available data, effective analytics depends on shared information models
to understand the data highly varying in both format (syntax) and content (semantics) to deliver
the expected insights.
Analytics maturity: Analytics involves the processing of raw data (measurements) into
descriptions (information) and then contextualizing the results (knowledge) and benefiting from
historical experience (wisdom). The maturity of this process is not limited to where the analytics
can be performed and as a design consideration is equally valid regardless of where the analytics
are performed.
Temporal correlation: One of the common issues in IIoT systems is correlating data between
multiple sensors and process control states, including the temporal order at which the data is
generated. Applying analytics closer to where the data is generated reduces the burden of
correlation when the analytics is applied.
Provenance: Performing analytics at a lower architecture tier maintains the genuine sources ofthe data as they progress through the IIoT system.
Compliance: To illustrate how compliance may impact the analytics as a design consideration,national security is used as an example. National security concerns may place restriction on thearchitectural decision about data management and sharing with government regulations inindustries like aerospace and defense. This will influence where the analytics must be placed tomeet the regulatory requirements, for example, possibly preventing performing large-scalecomputation in a public cloud facility to lower the cost.
Table 6-1 below is an example.
The combination of all these factors determines what capabilities are required and where theanalytics will be deployed. Generally speaking, which analytics and where the processing islocated depends on the maximum acceptable network latency and jitter in response to events,
criticality of the analytics in the normal operations (e.g. how bad it would be if the external
network is cut or an upper-tier system becomes unavailable) and the cost of uploading large
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amounts of data. From the deterministic response, reliability and resilience perspectives, it is
optimal to perform analytics close to the sources of the data, and the analytics results need tobe accessible for decision-making. In most systems, some form of hybrid approach with local andcentralized analytics will be required.

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Industrial Analytics Location

Evaluation Criterion
Analysis ScopeSingle site optimization
Multi-site comparison
Multi-customer benchmarking
Results Response Time
Control loop
Human decision
Planning horizon
Connectivity Reliability
Site
Organization
GlobalConnectivity Bandwidth
Raw data
Processed results
Summarized results
Storage and Compute Capacity
ServerMultiple servers
Data center
Data Security
Secret
Proprietary
SharedData Characteristics
VolumeVelocity
Variety
Analytics Maturity
Descriptive
Predictive
Prescriptive
Event Correlation
Sub-seconds
Seconds
Tens of seconds
Data ProvenanceSensorAsset
Site
Regulatory Compliance
Asset
Process
Industry

Plant

Enterprise

Cloud

✓

✓
✓

✓
✓
✓

✓
✓
✓

✓
✓

✓

✓
✓
✓

✓
✓

✓

✓
✓
✓

✓
✓

✓

✓

✓
✓
✓

✓
✓

✓
✓

✓
✓

✓
✓

✓

✓

✓
✓
✓

✓
✓
✓

✓
✓
✓

✓
✓
✓

✓
✓

✓

✓
✓
✓

✓
✓

✓

✓

✓
✓

Table 6-1: Industrial Analytics Design Considerations

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5.2

5: Implementation Viewpoint

ANALYTICS CAPACITY CONSIDERATIONS

The functionality for industrial analytics is informed by experience and wisdom from both IT and
OT. Both expect reliable operation and repeatable response times. However, the way these goals
are achieved is different. Information technology relies on elasticity to provide the required
capacity and operational technology ensures determinism with engineered capacity.
Elasticity is a cloud-technology measure of “the degree to which a system is able to adapt toworkload changes by provisioning and de-provisioning resources in an autonomic manner, suchthat at each point in time the available resources match the current demand as closely as
possible.” 1 For example, most retail businesses generate significant revenue during holiday
seasons. All the IT systems need to be prepared and have enough capacity to avoid affecting
profitability. The remainder of the year those resources can be scaled back or repurposed toreduce costs.
Determinism is the ability to support computation and transmission of data within apredetermined time between connected devices and applications. Deadlines must be met withexpectations that a work request will complete within the same response time for every request.
The analytics and results must be communicated in a defined time-period and confirmation must
be provided. Operational systems are designed with a capacity for continual processing,regardless of the state or condition of the plant. So, when a production plant starts up or shuts
down, which can generate rapidly changing values and multiple alarms, the requirement for
deterministic response in the IIoT system is no different from when the plant is at steady state.
These two philosophies are in many ways complementary. Close to industrial processes, it isappropriate to provide reliability and predictability using dedicated resources. In the cloud withmultiple tenants sharing the same set of resources dynamically, available capacity can be shiftedto support different service level agreements. There are early signs that the manufacturingindustry is moving in the direction of on-demand services to deliver just-in-time parts using
shared assets. The World Economic Forum, in collaboration with Accenture, have predicted that
this will become the long-term trend of manufacturing and there will be a convergence between
the concerns between shared IT capacity on demand and manufacturing services. 2 In otherindustries, such as transportation and energy, there is movement towards services and sharedcapacity.

5.3

ANALYTICS DEPLOYMENT MODELSThe design and capacity considerations described above determine where the analytics will be
deployed. Most IIoT systems use a hybrid approach to analytics deployment with analytics that1

Herbst, Nikolas Roman; Samuel Kounev; Ralf Reussner (2012). "Elasticity in Cloud Computing: What It Is,
and What It Is Not" (PDF). Proceedings of the 10th International Conference on Autonomic Computing(ICAC 2013), San Jose, CA, June 24–28.
2
O’Halloran, et. al., (2015). “Industrial Internet of Things: Unleashing the Potential of Connected Products
and Services” (PDF). World Economic Forum in collaboration with Accenture.

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5: Implementation Viewpointneed to be performed with very low latency and in a deterministic manner deployed closer tothe I/O at the edge; predictions that are not time sensitive or require data from sources that are
distributed or historical in nature are deployed in the cloud.
5.3.1 TYPES OF ANALYTICS
Figure 5-1 illustrates the different types of analytics that can be performed and the types of datarequired to perform the analytics.

Figure 5-1 Types of analytics based on its applications

Baseline analytics detect irregular behavior of the asset within milliseconds of the actual event.
The data used to perform these analytics is usually local to the asset under consideration andrelies on data acquired from the asset when it was working normally.
Diagnostic analytics that identify the root cause of the anomaly such as a failing bearing in a
motor requires previous knowledge of fault states. Diagnostics results can be returned in theorder of minutes.
Prognostic analytics that tell you the remaining useful life of a bearing can take in the order ofhours to return a result and requires access to multiple types of data and from multiple sources
to make the prediction.

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5.3.2 DEPLOYMENT LOCATION
Figure 5-2 is an example of where these analytics could be deployed.

Figure 5-2 A deployment pattern of various type of analytics

5.3.3 DEPLOYMENT PHASE
Deployment of analytics typically consists of three steps:•••

train a (predictive) analytics model,
test and validate the model on previously unseen data anddeploy the model to make predictions on real (streaming) data.

Training and deployment of analytics models can be broadly categorized into the following three
workflows:
1. training of the model and the deployment (or inference) of the model is done in the cloud,2. training of the model is done in the cloud, while the model is deployed at the edge and3. training and deployment of the model is done at the edge.

Figure 5-3 A deployment pattern of analytics during its different lifecycle stages

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Figure 5-3 illustrates the three workflows for the training and deployment of analytic models.
For the first workflow, once the model is trained in the cloud and the deployment of the modelis usually deployed as a web service. Data acquired from sensors at the edge is sent to the model
via the web service. Predictions from the model can be returned to the edge through the webservice if required.
In the second workflow, once the model is trained in the cloud the model is deployed to the edge.
For deployment, the model is usually exchanged between the cloud and analytic algorithms
running at the edge using a standard interchange formal such as PMML 1 or JSON2.
Analytic models can be deployed at the edge on heterogeneous computing elements, such as aCPU, GPU or FPGA. For example, FPGAs are ideal deployment targets for very low latency
applications.

5.4

DATA PRE-PROCESSING, TRANSFORMATION AND CURATION

Data preparation accounts for about eighty percent of the work of data scientists.3 “Messy data
is by far the most time-consuming aspect of the typical data scientist’s workflow”. Exploratory
analysis of raw data will often reveal distributions that are skewed, affecting how relationships
in the data can be effectively characterized with machine learning classifiers. The relationships inthe data may be inconclusive, however, transforming the records may clarify these relationships.
The same survey shows that data scientists spend 60% cleaning and organizing the data, and 19%of their time getting access to and collecting the records. Identifying the significant features of adata set is the first step to transform raw data into information. It is also beneficial to reason
about relationships that may exist in the data. For example, if a feature is correlated with anothervariable in the dataset, converting it into a ratio that cancels the effect of that bias may improve
the accuracy of the analytics results.
Several researchers go into more detail regarding their work4 with big data. They recognize thatdata is imperfect due to missing values, inaccurate measurements, and too many independent
variables. To address these challenges, techniques are used to simplify and clarify thedimensions. For example, the number of independent variables used in the analysis can be
reduced using statistical techniques to identify correlations. Consulting with subject matter
experts helps to understand why variables depend on one another, providing guidance which
combinations of variables should be used as features to train machine learning models. Anothertechnique is to reduce the number of records that need to be processed by classifying the statesand maintaining the distributions of the readings. On the other hand, curated data can be usedin artificial ways to reinforce the original measurements to fill regions of the domain of the1

http://dmg.org/pmml/v4-1/GeneralStructure.html
http://www.json.org/
3“Data Science Report”, CrowdFlower (2016).
4
Garcia, S., Ramirez-Gallego, S., Luengo, J., Benitez, J.M., Herrera, F., “Big data processing: methods andprospects”, Big Data Analytics, 1:9 (2016).
2

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problem which have no representative examples in the original data. Finally, raw data can bediscretized into buckets to reduce the noise of the measurements and reduce the complexity of
the algorithms.
When reviewing the time dimension in historical data, a combination of transformations mayneed to be considered based on either: seasonality and trend patterns, or to stabilize the variancein the data. This reduces the impact of time in order for the data to be better statisticallyassessed. These transformations aid in generating forecasted values with greater accuracy.
Transformations can be mathematical or based on adjustments such as using indexes torepresent a current or scaled value of the series. Additionally, differencing is a type oftransformation that adjusts for the seasonality and trend patterns to stabilize the mean of theseries prior to using the data in certain time series algorithms. Differencing seasonality produces
a representation of the current data with its corresponding data from the previous year.
These transformation approaches optimize how to measure information by selectively altering
the shape of the distribution in a way that is still aligned with the broader business and strategic
perspectives of each use case. The goal of the data transformation phase in machine learning is
to simplify complexities which may exist in the data such that the information more appropriately
falls within the parameters of the algorithms.

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6 ARTIFICIAL INTELLIGENCE AND BIG DATA
Innovation in artificial intelligence and big data is expected to play an increasingly important rolein industrial analytics.
This chapter looks at taxonomies of artificial intelligence and emerging computational techniquesin big data in relation to industrial analytics.

6.1

BIG DATA ANALYTICS

Big data analytics cuts across IT (information technology) and OT (operational technology), data
and roles. Big data requires computational systems and networks to be designed around thedata. It will transform how businesses operate and the digital/physical divide.
There are numerous emerging definitions for big data. One definition is “data set(s) withcharacteristics (e.g. volume, velocity, variety, variability, veracity, etc.) that for a particular
problem domain at a given point in time cannot be efficiently processed using
current/existing/established/traditional technologies and techniques to extract value”.1
Big data differs from traditional data storage and processing applications in five ways:
•
•
•
•
•

volume: too big,velocity: arrives too fast,variability: changes too fast,
veracity: contains too much noise andvariety: too diverse.

Applications generating these data or requiring their analysis may have one or more of the above
aspects present.
In addition to the “5Vs” above, the machine and operational data have their own features suchas higher correlation, sensitivity to time order and historical context. Industrial big data areprocessed and analyzed for various application scenarios and purposes such as industrial
automation, system health monitoring, predictive maintenance and remote operation.
To support these application scenarios, diverse big data analytics functions are performed,
including but not limited to:
•
•
•
•

1

complex aggregation analysis: to profile information of different time periods or
locations,multi-dimensional query and analysis: to examine and deep-mine the machine data fromdifferent perspectives,
log data analysis: to monitor system and operational health,time-window based stream data analysis: to identify temporal features and trends andISO/IEC JTC 1 Big Data Preliminary Report 2014

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complex event processing: to detect patterns and anomalies.

As shown in Figure 6-1, the big data analytics platform need to address and support theprocessing of multi-typed input data from a large quantity of sensors or machines. To thoroughly
analyze and mine the data (either real-time or historical) for value, diverse types of queries andanalyses need to be applied. For in-time condition detection and decision making, these big data
analyses need to be completed under throughput and latency requirements.

Figure 6-1 Example of Multi-Typed Data Processing in Big Data Analytic Systems

Analytics functions usually face stringent requirements in an industrial environment such as:
•
•
•
•

6.2

high-performance in data loading,query and analysis,a single copy of input data for different types of analytics andfast response to concurrent queries and commands.

ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) technology, with its rapid development, is increasingly used in industrial
analytics to improve the analysis efficiency and accuracy.
The term artificial intelligence (AI) describes the analytics algorithms and frameworks used in
IIoT. Two branches of AI are considered:• machine learning (ML) and

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deep learning (DL).

Figure 6-2 Artificial Intelligence (AI)In IIoT applications, machine learning and deep learning provide new approaches to build
complex models of a system or systems using a data-driven approach. Instead of using thephysics-based models of the systems to describe the behavior of the system, ML and DL containalgorithms that can infer the function of the model from sample input data. These models arethen used to make predictions on the state of the system. This is commonly referred to as
predictive analytics.
6.2.1 MACHINE LEARNING
The workflow for developing and deploying models based on machine learning can be divided
into a number of steps.
Figure 6-3 illustrates the steps involved in deploying a machine-learning model.

Figure 6-3 Workflow for developing analytic models

Data collection is the first step and involves data acquisition and data preparation.
Feature engineering extracts important information from the raw data that have been collected.
This step usually comprises two parts: feature extraction and feature reduction.
Feature extraction is the process of converting raw data to information that relates to thephysical state of source of the data (asset). Consider an analytics model that is created to monitor
the health of a motor by measuring the motor’s vibration using an accelerometer. Information in
the frequency domain expresses more information about the state (normal and failure modes)of the motor than the time domain. Therefore, in the feature extraction step the waveform
information is converted to the frequency domain and the amplitudes at certain harmonics areused as the features that are input to the model. In a typical application, data are collected from
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multiple sources and this may result in redundant information in the features that are extracted
from the data.
The feature reduction step reduces the number of features by selecting a few features ortransforming the features to a lower dimensional space without reducing the discriminatory
properties of the features. The resulting reduced set of features is usually represented as a vector(or array) and input to the machine-learning algorithm in the next step.

Figure 6-4 Mapping features to failure conditions for a motor

Figure 6-4 is an example of a feature-engineering step. It shows how the spectrum informationof the current signal measured from a motor can be used to classify failure conditions in the
motor. Figure 6-5 shows an example of a spectrum feature engineering process.
The analytics methods and algorithms used to create the models can be broadly classified in two
categories: supervised and unsupervised. This classification is based on the nature of information
contained in the input data that algorithms use to infer the function of the model. Both types
need validation.

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In supervised methods, the algorithm is presented with pairs of input data and desired output.
This data is referred to as labeled data. The algorithm learns the function that maps the inputs to

Figure 6-5 Spectrum feature engineering process

the outputs. Consider the example of a model that is created to monitor a water pump for acavitation fault. The pump is instrumented with sensors that measure various parameters of thepump such as the horizontal and vertical vibration of the motor and the in-flow and out-flowpressures. The training data for a supervised algorithm will contain example features extracted
from sensor measurements for both normal and fault condition of the pump, with each example
clearly indicating the operating state.
Supervised algorithms are useful when it is feasible to acquire training data for the different
states (or classes) that need to be modeled. The training data needs to be balanced, that is, there
should be enough samples for each output state or class. In the pump example, supervised
methods can be used when data for both the normal and the cavitation operating state isavailable. In the case, there are two classes. Supervised algorithms can be easily extended tolearn multiple classes or states. For example, a supervised machine learning algorithm can beused to detect various fault conditions in a bearing such as outer race fault, inner race fault etc.
Unsupervised methods do not use labeled data. These algorithms find structure in the input dataon its own. These methods are good for discovering hidden patterns in the data. Unsupervised
methods are typically used when labeled data is not readily available; where the instances ofnormal behavior are more frequent than faults. A common use of unsupervised learningalgorithms is for density estimation of a function. For example, an unsupervised learningalgorithm such as the Gaussian Mixture Model (GMM) can be used to estimate the density
function that represents the normal operating condition of an asset. In this case, the model islearned based on data acquired during the normal operation of the asset. Once the function has
been learned, the GMM model can then be used to determine the health of the asset bycomputing the distance of the current estimate of the function based on the latest data from thecenter of the estimate of the normal (or base) function. This distance (or probability) is used todetect when the asset starts to deviate from normal operation.

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Another commonly used category of unsupervised learning algorithms is clustering. Clusteringgroup objects (input data) together in such a way that objects that belong to a group or cluster

Figure 6-6 Health assessment using Gaussian Mixture Modelare more like each other than those in another cluster. We can use clustering where the asset
may operate in different “normal” regimes (for example when the load on a motor may increaseor decrease over time). In this case, clustering is used to detect different regimes and GMMs areused to estimate the normal behavior within each regime.

Figure 6-7 Taxonomy of machine-learning algorithms

The taxonomy of machine learning algorithms is given in Figure 6-7 for a brief overview.
One of the challenges with traditional machine-learning techniques is the need for features to be
extracted from the raw data for creating good models. Feature extraction is usually a brittle
process and requires the knowledge of a domain expert. It is usually the point of failure in theanalytics workflow.
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6.2.2 DEEP LEARNING
Deep-learning algorithms have recently gained popularity because they remove the need for thefeature-engineering step. Figure 6-8 illustrates the deep learning workflow.

Figure 6-8 Deep learning workflow

Note that in contrast to the machine learning workflow, the feature-engineering step is notpresent. Data acquired from the sensors (raw measurements) can be directly input to the deeplearning algorithms.
Deep learning algorithms are largely based on artificial neural networks (usually called neuralnetworks). Artificial neural network learning algorithms are inspired by the structure andfunctional aspects of biological neural networks. These algorithms are structured in the form of
an interconnected group of computational nodes (artificial neurons) organized in layers.
The first layer is the input layer that interfaces to the input signal or data. The last layer is theoutput layer and the neurons in this layer output the final prediction or decision. In between the
input and the output layer there are one or more hidden layers. By using multiple hidden layers,
deep learning algorithms learn the features that need to be extracted from the input data
without the need to input the features to the learning algorithm explicitly as in the case ofmachine-learning algorithms. This is called feature learning.
Deep-learning algorithms require large amounts of training data and are computationallyintensive. Deep learning has seen recent success in IIoT applications mainly due to the coming ofage of technological components:•••

advances in computation power with GPUs, FPGAs and CPUs,
availability of large data repositories with labeled data to train these networks (e.g.
ImageNet) andaccess to established open source deep learning software frameworks such as
TensorFlow, Caffe, Theano, CNTK etc.

There are many deep-learning algorithms, 1 and the selection depends on the problem to besolved. The two most commonly used deep-network topologies for IIoT applications areconvolutional and recurrent.
Convolutional Neural Networks (CNNs) are designed to take advantage of the structure of input
signals such as an input image or a speech signal. A convolutional network comprises one or more1

http://www.asimovinstitute.org/neural-network-zoo/

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convolutional layers (filtering layers) followed by a fully connected multilayer neural network.
These networks are successful in problems such as image classification and object recognition.
Recurrent Neural Networks (RNNs) are based on algorithms that make use of sequential (orhistorical) information to make predictions. These networks are good for time-series analysis. A
traditional neural network assumes that all inputs and outputs are independent of each other intime or order of arrival. RNNs on the other hand record state information that stores information
about the past and uses the information calculated so far to make the next prediction. RNNs aregood for learning historical behavior and predicting events in the future such as remaining usefullife (RUL). The most commonly used type of RRN is the long short-term memory network.1

1

https://en.wikipedia.org/wiki/Long_short-term_memory

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7: Analytics Methods And Modeling

7 ANALYTICS METHODS AND MODELINGThis chapter provides a survey of methods, models, algorithms and frameworks used forindustrial analytics applications.

7.1

ANALYTIC METHODS AND ALGORITHMS

Streaming analytics is performed on data-in-motion to get high-throughput, low-latency
actionable information so that industrial applications can respond to events in a timely manner.
The speed layer, as shown in Figure 7-1, in the system performs fast, incremental algorithms ondata as they are received and is usually performed close to the data sources to manage large data
throughput, latency, reliability and security more effectively. For example, streaming analytics
can be used to analyze wind turbine operations parameters (such as rotational speed, torque,blade pitch) and environmental data (such as wind speed and direction) to adjust the pitch values
dynamically to maximize the electricity generation without causing damage to the turbines.
Batch analytics on the other hand is applied on all available data and aims to provide very
accurate results, but with higher latency. This data can be historical in nature from a given
source—months or years of data collected for a given wind turbine, and from sources that are
spread out geographically—data from all wind turbines in a fleet. There are no restrictions on thetype of computations that can be done in the batch layer, but they make take hours or days tocomplete. Batch analytics, usually performed in big data platforms, are widely used to identify
and capture hidden patterns in the data set. Increasingly, it is used to build models, for example,
to train and test deep learning models, before they are deployed back to streaming analytics
environment to capture patterns in near-real time.
7.1.1 STREAMING REAL-TIME ANALYTICS
The lambda architecture is a common pattern in industrial analytics that splits the data analysis
into two separate concerns: master/batch/serving and speed. This section addresses the latter.
Stream analytics are not unique to the industrial internet. The financial industry used them forhigh-speed trading and arbitrage, and they have since spread to industry and media. Streaming
analytics aggregate data sources comprising discrete events, digital, analog and waveform data.
Each type of data requires special treatment and statistical processes to transform the data intoa usable event stream that can be aggregated into higher semantic information models. Stream
processing also takes repetitive data produced from sensors and control systems and reduces
the repetition to make more efficient use of bandwidth. Statistical downsampling and outlier
elimination are also used to increase the efficiency of connected devices where the raw data are
not required for higher-level business systems.

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Cloud
II Services
Master / Batch / Serving

Queries
Statistics
ML
Analytics
Models
Rules
Data Services
Short-Term (Speed)
Queries
Statistics
CEP

Figure 7-1 Lambda Architecture

One of the primary tools commonly used in stream analytics is complex event processing (CEP).
CEP also started in the finance industry where high-speed analysis of market data and orders
made deterministic statistical analytics a necessity and led to many of the tools that are now
being applied to IIoT. CEP uses a combination of statistics and patterns expressed as rules tocombine multiple event streams from multiple sources and create actionable higher-level events.
The events can be re-introduced to the event stream and combined with additional data sources
to provide context and infer higher-level understandings that are of greater value. CEP has beenused in many production systems successfully and many open-source tools are available.
One of the main differences between stream analytics and batch analytics is the amount of data
used for the analysis. Stream analytics assume that they will be operating on time series data-inmotion where the necessary reference data is known a priori and cached. Aside from the context
of the data feeds coming into the analytical system, it is usually desirable to have minimal data
access. The principal concern of stream analytics is fast computation of events where theintermediary computation and events are often kept in–memory and reference data is cached toreduce latency as much as possible.
In deterministic stream-based analytics, the end-to-end analysis and detection of actions must
be completed within the window of time available to prevent or actuate the changes to ensure
proper operation. For example, in CNC machining, CEP can be used to detect events like tool
break in certain conditions by collecting contextualized data from the CNC controller and tool

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7: Analytics Methods And Modelingmanagement system to determine when a certain set of events occur.1 Rules of this type willneed to be designed around the target process considering the material types and properties of
the tooling and the machine. The action that occurs as a result of a rule can have direct effect on
the equipment or may require a human to validate the inference. As the analytics become morereliable and the inferences are trusted, the human validation step can be removed and theanalytics can directly change the operational state of the equipment.
7.1.1.1

CONCERNS ABOUT TIME

Time windows are a common construct of stream processing and CEP. They allow the application
to apply a statistic (for example maxima, minima, average, counts) that spans an interval. All
statistical operations in a stream analytics system have an implied time-period on which they act.
Windowing also allows for the reduction of data that must be retained since only the necessary
history is kept. For example, streaming analysis will capture data for a given period of time andthen compute either at a given frequency (average over the last minute) or by using a rolling
statistic (average every second using a one minute window).
Streaming data by its nature is temporal and must have a timestamp associated with eachobservation. The time stamp should be applied as close to the source as possible to ensure its
accuracy. Causal relationships can be then be asserted across multiple data sources, which
requires synchronization of clocks. In practice, assigning a timestamp at the time the data has
been reported or observed is simplest for both statistical and time-series data sets.
The temporal dimension is a primary concern of stream processing. All the event-based patterns
are built around time-dimensional, causal and contextual relationships between the data
streams. The expression of the relationships between events and intervals allows theinterpretation on whether an observation occurs before, after, within, or concurrently, thus
constraining the analysis to “when” a certain state was observed or active.
7.1.1.2

STATISTICAL ANALYSISThe first step in stream processing is to preprocess the data into a form that it can be analyzed.
This usually requires various statistical- and signal-processing techniques such as Fast-Fouriertransformation (FFT) and high- and low-pass filters for analog data. Simple statistical methods,
such as means and averages, are common as well as linear algebra kernel transformations forimage and other complex data. The resulting data is a set of events and time series observations
that can be used for pattern matching or higher-level analytics.
Deriving the initial transformation is often one of the harder tasks in stream processing. The
development of the transformations is usually performed using batch systems to experimentwith various methods on historical data to find the most effective method that will produce
events that with highest certainty with respect to their use case. With many industrial use cases,
there is substantial context that must be considered when performing the analysis. What may

1

Such as a load less than 10 percent when the spindle is spinning and the machine is moving in acontouring type of motion for more than 30 seconds.

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7: Analytics Methods And Modelinghold true when a physical system is in one state may not hold true when it is in another. This
process is most often curated by domain experts who verify the behavior of the models.
7.1.1.3

EDGE, DATA AGGREGATION AND DOWNSAMPLING

In the cloud, analytics is usually a combination of both stream and batch, whereas at the edge, it
is almost exclusively stream processing because of the constraints in computational capability,
accessibility to data and the need for determinism. Common example use cases of edge
streaming analytics include: downsampling of data, data aggregation, adaptive control, statisticalanalysis, process control, maintenance and error detection and correction.
By performing stream processing at the edge, the communications and computational overhead
on upstream systems can be greatly reduced. Additional benefits may include the removal ofunwanted detail for anonymity of the data and privacy of the supplier, balanced with the loss ofdetail in the data and the inability to reproduce a complete account of the events. It is difficult tobalance the various concerns to collect just enough data to provide desired business value.
In many systems, there will be an initial dump of highly detailed raw data with no filtering andtransformation. From this raw data, the analytical models will be developed and tested to ensurethat the transformation are not too aggressive leading to missing critical events. When themodels are sufficient for the purpose, they can be deployed in the streaming architecture. The
models may not be complete, but they will be continually evolving and replaced as new
discoveries arise. Most streaming platforms can deploy new models as needed in a continuous
improvement cycle.
7.1.2 BATCH-ORIENTED ANALYTICS
Batch analytics can be used in multiple ways:
To improve the accuracy of the analytics (or models) deployed in the streaming layer: The
analytics used in the streaming layer are sometime fast approximations of the exact analytics
that need to be run on the data. This may be due to limited capacity available or the limited
exposure to long-term trends in the data. The batch layer enables improvement of the model
deployed in the streaming layer by periodically updating the model as more data are observed.
This allows the approximate models running in the streaming layer to be corrected over time.
Perform analytics that require data from multiple sources that may be distributed geographically.
In IIoT applications streaming analytics is usually performed at the edge, close to the physicalassets. These analytics may run on an edge-computing node1 that acquires data from sensors
measuring information from one asset, such as a wind turbine or a single automated test system.
The analytics running on an edge-computing node has limited exposure to data and state of othersimilar assets that are being monitored. This information on a class of assets can be useful inmaking more accurate predictions. For example, the anomaly detection analytics running on awind turbine has limited knowledge on the current operating conditions of other turbines in a1

It in fact can be a controller, an IoT gateway, a server or even a cluster of servers, deployed in proximity
to the assets.

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fleet. By comparing the current state of a turbine to the others in a fleet, batch analytics can beused to detect true anomalous behavior of a turbine from operating conditions that may beaffecting the entire fleet.
The master/batch/serving layer performs two functions. First, it manages an immutable masterdataset that contains all the data that has been collected up to the current time. Second, it
enables low latency analytics to perform on the entire master dataset on-demand. Instead of
performing the analytics on the entire dataset when information is demanded through a query,the batch layer precomputes batch views of the data over time. The batch views can then bequickly queried to provide low-latency results. The master/batch/serving layer indexes the batch
views to serve queries with low latency.
Here are some considerations when designing the master/batch/serving layer:
Data fidelity: In the batch layer, storing the data in the rawest form available is very valuable as
keeping the raw data provides the flexibility to run new types of analytics and obtain new insightsfrom the data. Storing only the preprocessed data (such as statistical features extracted from thedata) limits the amount of information available from the data. The tradeoff is the cost of storage
and data transportation.
Analytics: There are no restrictions on the type of machine learning or deep learning algorithms
that can be performed in the batch layer. Deep learning frameworks rely on large amounts ofdata and are more effective to train on machines equipped with GPUs or FPGAs. There are anumber of open source machine learning and deep learning tools that integrate well with open
source big data platforms, the discussion of which is outside of the current scope of thisdocument.
Reconciling speed and batch: Periodically, the results from the batch layer will override insightsfrom the streaming layer. Given a query, the serving layer will merge real-time results with thebatch views.
Drawbacks: The main drawback of the lambda architecture is the need to maintain two separate
code paths and execution modules (speed and master/batch/serving). Emerging architectures
unify both streaming and batch processing under a single execution model.
Figure 7-2 illustrates the application of the lambda architecture to a predictive maintenanceapplication. The analytics (machine learning and deep learning) models are in the batch layer
because of the time required to train these models, computational resources and the amount ofhistorical data required for training. They are then run either in the speed or batch layer. The
choice of the layer to deploy the analytics is based on criteria such as the maximum latency within
which the predictions are expected, the sources of data (local or distributed) and security of thedata. For example, event and anomaly detection analytics can run in the speed layer because of

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7: Analytics Methods And Modeling

Figure 7-2 Lambda Architecture - Streaming and Batch for IIoT

the need to make predictions with low latency, while diagnostic and prognostic analytics can be
run in the batch layer.
The kappa architecture is an emerging software architecture pattern that simplifies the lambda
architecture. In this architecture, the batch layer is removed as shown in Figure 7-3.
The data is stored in an immutable, append-only log database using a distributed stream
management system that start multiple stream processing jobs that can be used to combine
speed processing as well as batch processing. For example, one job could be evaluating only
current data and writing the output to the serving database. Another job could be looking at
historical data in the stored log to get more accurate results.

Figure 7-3 Kappa architecture

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7.2

7: Analytics Methods And Modeling

ANALYTICS MODEL BUILDINGThe analytics process is shown in Figure 7-4. The first step is to collect and prepare the data by
removing or filling in missing data, extracting features from the data that are relevant to theapplication and optionally reducing the number of features.

Figure 7-4 The model building process

7.2.1 ALGORITHM SELECTION
Analytics model building is next. Analytics models fall into three major categories: descriptive,
predictive or prescriptive, as described in Getting Started with Industrial Analytics above. For
each category, there a multiple machine learning or deep learning algorithms that can be used
to build the model and the performance of these algorithms differ depending on the types ofdata being analyzed and the predictions to be made. So we should select a few algorithms in eachanalytics category and then iterate through them to identify the one that works the best for thecurrent data and application. Table 7-1 shows some machine-learning algorithms that can beused to build analytics models, classified based on the major categories. For example, an anomaly
detection model can be built using a one-class support vector machine (SVM) or a Gaussian
Mixture Model (GMM).
Along with algorithm selection there are two other import aspects of the model building process.
First is on how the data is used to train the model. The second is how to compare theperformance of these algorithms and select the best one.

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Table 7-1 Machine-learning algorithm options for building analytic models.

7.2.2 CROSS-VALIDATION
To be able to test the performance of a model, we need two sets of data. One set is for training
and the other is for validating the performance of the model on data it has not seen before.
Using the same data for training and testing the model can result in the model “memorizing” oroverfitting the data. Overfitting manifests itself as a model that performs with high accuracy
during training but poorly on new data. Cross-validation mitigates this issue.
With cross-validation the available dataset is split into training and testing data as shown in Figure7-5. During cross-validataion, the master dataset is randomly divided into k folds (subsets) of
equal size. k-1 of these subsets are used to train the model and remaining fold is used to test themodel. This process is then repeated k times. The model’s performance is then the average of all
k test sets. A split of 70% (training) to 30% (testing) is typical.

Figure 7-5 Splitting data for cross validation

7.2.3 PERFORMANCE METRICS
For supervised learning algorithms, the performance of the model is usually measured by looking
at the classification errors. Because the training and testing datasets are labeled data, we knowwhat the expected (or true) output is. This information can be used to bin the predictions results
into different categories as shown in Figure 7-6, which shows the types of error for a binaryclassification. For an anomaly detection model condition positive is the detection of a failure or
anomaly.
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Analytics Framework 8: System Characteristics and Crosscutting Functions Related to Analytics

Figure 7-6 Confusion matrix showing types of classification errors for a binary classification problem

8 SYSTEM CHARACTERISTICS AND CROSSCUTTING FUNCTIONS RELATED TO
ANALYTICS
The business, usage, functional and implementation viewpoints facilitate a systematic way to identify
IIoT analytics system concerns and their stakeholders, to bring similar or related concerns together
so they can be analyzed and addressed effectively. The deliberation of the concerns is often
performed within each of the viewpoints to which they belong, but they should not be resolved inisolation to those in other viewpoints.

Industrial analytics requires many services from components of an industrial system, including
connectivity and data management. The closest dependency will be the data management
system that provides the raw material for the analytics. This Industrial Analytics Frameworkprovides business and market drivers with guidelines in respect to the contexts and correctness,but stops short of addressing the ownership and chain of custody (data management) issues
required. These concerns will be fully addressed in other documents. This framework provides aset of dependencies and requirements for both the collection, storage, and communication ofdata to other parts of the industrial processes.

8.1

SAFETYThe industrial internet has the potential to deliver value, but also to cause unintended loss. Unlike
traditional information technology, the unintended harm from industrial analytics could result inhuman injury or loss of life. The mitigation of this risk is to design industrial analytics processes
and computations to prevent unintended operation and independently validate that theresulting actions do not harm life or property.
The first line of defense comes with the security expectations for authentication andauthorization. Role-based administration and control prevents an unauthorized actor frommanipulating industrial analytics data or processes. The second line of defense is provided by thedata management expectations for information models, reducing the unintended consequencesof ad hoc configurations of system components. A common representation for types and their
attributes enables simpler industrial analytics application design and realizations that businessowners can verify and validate. The third line of defense comes with the connectivity
expectations for reliable data synchronization between tiers. Missing or incomplete data sets
could contribute to out-of-bound industrial analytics calculations. The fourth line of defense is to
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Analytics Framework 8: System Characteristics and Crosscutting Functions Related to Analytics
post-process industrial analytics results to confirm they are in the expected range, and engage ahuman in the loop to review anomalies.

8.2

SECURITY

The industrial internet is only as secure as its weakest link and should provide defense in depth
so that if a malicious or unintended action compromises one security or accountability measurethen another measure still guards the assets. On the other hand, there are architectural quality
penalties such as reduced performance and scalability if security is the only consideration. The
compromise is to secure only the sensitive (as defined by the business) data and govern who hasaccess to critical resources. This approach increases the operational overhead to identify thesensitive content and manage it separately from everything else. These security mechanisms
should be common across tiers and leverage a federated identity infrastructure for ownership,
authentication and authorization.
First, the data management components should encrypt sensitive data-at-rest, and theconnectivity protocols should perform the same functions for data-in-motion. Encryption is usedboth within a tier and for synchronization between tiers to prevent improper access. Only
authorized clients, with appropriate credentials, should be able access the data based on
established policies and protocols. Second, stakeholders can configure security domains toprotect and manage access to industrial analytics processes and data based on defined attributes
and scopes. The owner configures this role-based administrative control for authenticatedclients, defining the policies by which sharing is allowed. This capability protects intellectual
property and sensitive information. These policies synchronize across tiers so transfer of data
does not implicitly cause transfer of ownership.

8.3

DATA MANAGEMENT

The industrial internet creates business value for a wide range of industrial processes and assets.
Each asset has a set of static and dynamic characteristics tracked over time to gain insight, e.g.
from historical records captured in the form of data, both raw and computed. The curated data
(i.e. those stored in an historian) should be common across tiers and accessible using a federated
information model that supports search, classification and markup to enable rapid industrial
analytics application development.
First, a best practice for industrial analytics is to collect and store data in their rawest form,
because the data-cleaning process to curate the data could remove the most important anomaly
needed at a later date. But it is inefficient to clean data repeatedly for each computation if theresults of that process are the same. The compromise is to store the data twice, once in its raw
form and again in its curated form, updated periodically. The industrial analytics expectation forthis curated form is common across the tiers. For example, the rules and controls must apply tothe data uniformly. Second, asset types and instances are crucial aspects of the industrial
ecosystem: discoverable, navigable and organized independent of naming conventions. It is
unrealistic that all industrial internet applications will agree on a common taxonomy andattributes, but the mechanisms for organizing them can be common. Classification and markup
of types in this information model apply to related instances and property values. The
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Analytics Framework 8: System Characteristics and Crosscutting Functions Related to Analytics
expectation is that federation of multiple information models within a tier provide a broad viewof the available storage. Third, semantics must be maintained for the information to continue tobe meaningful. Proper context of the data increases the success and accuracy of the analytics
systems. Fourth, data lineage (provenance) provides considerable value to analytics as it supplies
attribution of the data sources. For example, this lineage allows for quicker debugging and for
tracking error propagation.

8.4

CONNECTIVITY

The industrial internet is a distributed architecture by design. This is necessary because, unlike
traditional cloud computing (for example associated with online retail), the system produces raw
data geographically separate from where the resulting information delivers business value. A
distributed architecture requires connectivity between components, not only between
collocated processes but also across wide-area and global networks.
First, connectivity within a tier is expected to be more reliable than communications across tiers.
It is more likely that applications deployed within a tier, including local access to data andprocessing, will deliver valuable results compared to applications depending on resources acrossmultiple tiers. The industrial analytics expectation is that there needs to be a capability for
efficient and reliable synchronization of data between tiers when connectivity is available.
Second, IIoT systems typically incorporate multiple vendors in a deployment, potentially with ad
hoc connectivity relationships and more than one communication protocol. There should bemechanisms for industrial analytics components within a tier to synchronize with each otherwithout needing to know the deployment configuration or end-to-end protocols.
Third, response time is expected to be quicker within a tier compared to across tiers. If theanalytics need to produce value close to real-time, then the expectation is that they need theconnectivity at that tier.

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Annex A: Example Analytics Standards

Annexes
Annex A EXAMPLE ANALYTICS STANDARDSThis annex provides an example of an analytics standards ecosystem using the manufacturing
vertical as an example.

ANNEX A SECTION 1 STANDARDS ECOSYSTEM
There are three primary categories of standards relating to industrial analytics that range fromcross-domain to domain-specific: standards that address cross-domain systems engineering andsimulation, those that provide domain specific performance metrics, and those that specify
operational analytics for asset maintenance and function. There are other standards that willaffect the analytics, such as design standards that are used to model the form and engineeringfunction of the product and can be used throughout the lifecycle to provide a digital surrogate.
Only the first category, system-engineering standards, can be applied to almost any domain. The
most prevalent standard in this category is SysML that is developed, owned and maintained by
OMG. SysML provides a general-purpose language for systems engineering and a framework forexpressing the specifications, analysis, design, verification and validation of engineering models(Figure 8-1). It was designed to address the concerns of model exchange between software

Figure 8-1 SysML for specifications, analysis, design, verification and validation of engineering models

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Annex A: Example Analytics Standards

systems and is the most widely adopted standard to date. There are other standards that are
more domain-specific, such as STEP (ISO 10303 – AP233) in the manufacturing domain, but AP233
has been largely subsumed into SysML.
Beyond the engineering model exchange standards, the remaining standards tend to be focusedon domain-specific concerns. In many domains these include specifications for operational
metrics that are expressed in a set of objective and subjective formulae that can be applied todata from given systems to provide a business value metric. Since the business value metrics are
all domain specific, the formulae are domain specific as well. These metrics can be applied atvarious degrees of specificity narrowly within a given domain.
For example, the OEE metric defined the Overall Equipment Effectiveness metric for
manufacturing equipment comprises three components: the availability or utilization of theequipment, the performance of the equipment, and the quality of the parts. The components arethen multiplied to provide a singular value that is a rough indicator of a piece of equipment’s
production capability. The standard is used across continuous process and discrete
manufacturing, but in many cases, it fails to provide actionable information in discrete
manufacturing. This is an example that even domain standards will have certain use cases withinthose domains where they perform better.
There are many standards in manufacturing that provide efficiency and effectiveness metrics thatspan from production performance to quality. These standards are often not known beyond
practitioners in the domain and often have little applicability outside the confines of that domain.
Therefore, the consideration of standards must evaluate the ecosystem in which the application
lives and review the available standards within that ecosystem.
It is useful to evaluate other domains to see if there are developments within that domain thatare applicable in other domains. For example, there may be standards or methodologies inmaintenance that apply to manufacturing equipment that can be used to develop standards intransportation. There are similar ideas in each, both have actuators and mechanical components
but the operational metrics and the failure scenarios are often different. An aircraft engine will
have a different set of operational metrics than a machine tool since an engine failure is a more
serious incident than a scrapped part—in most cases.

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Annex A: Example Analytics Standards

ANNEX A SECTION 1, SUBSECTION 1 MANUFACTURING STANDARDS ECOSYSTEM
Manufacturing has a numerous standards that relate to analytical methodologies, and four areas
where standards apply: geometric design, system engineering and simulation, operational
efficiency, measurement and verification, and equipment maintenance. Geometric design has
been primarily represented by the STEP (ISO 10303) standards and the current revision,
AP242ed2, takes the basic core geometric representation given in AP203 and adds productinformation and geometric tolerance.

Figure 8-2 Manufacturing has a numerous standards that relate to analytical methodologies

Figure 8-2 is an illustrative example of some of the numerous standards in the manufacturing
space.
Geometric design also needs to be coupled with engineering models to express the requirementsof the product and simulate its function. That is where standards like SysML are applied toprovide the foundation for communicating the functional information necessary to develop
digital surrogates of the final product. The question may be raised about how this applies to theindustrial internet that is primarily concerned with data from devices. The reason these models,often first-principles based, are necessary is they provide the initial intent from which theempirical data can be compared and the models refined.
Figure 8-3 illustrates geometric design related standards within the context of manufacturing
IIoT.

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Annex A: Example Analytics Standards

Figure 8-3 Geometric design related standards

The standards ecosystem extends through the production of the product and standards thatprovide measurements of quality such as the Risk Priority Number (RPN) that evaluates theimportance, stability, and the detectability of defects in a manufacturing process to standards
that provide reports of the products lifecycle such as STEP AP239. There are multiple standards
that also constrain manufacturing processes and material like ASME and ASTM that provide theinformation required to validate the correctness of a part and a product.
Analytics require the expression of the semantic intent of the product as well as the as produced
and as built instances of the design. The models need to be communicated between systems
using standards and open technologies.
The existing cross-domain and domain-specific standards as described above will help thesemantic understanding for analytics, new standards with common abstract modeling of parts,
products, equipment and processes may need to be developed to enable better communicationof and greater degree of semantic understanding of them and their interactions.

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Annex B: Revision History

Annex B REVISION HISTORY
Revision
V1.00

Date

EditorDiab, Wael William (Huawei)
Harper, K. Eric (ABB)
2017-09-14 Lin, Shi-Wan (Thingswise, LLC).

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Annex C: Acronyms

Annex C ACRONYMS
IETF
IIC
IIoT
IIRA

Internet Engineering Task Force
Industrial Internet Consortium
Industrial Internet of Things
Industrial Internet of Things Reference Architecture

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Annex D: Glossary

Annex D GLOSSARY
Industrial Internet Consortium (IIC)
an open membership, international not-for-profit consortium that is setting
the architectural framework and direction for the Industrial Internet.
Founded by AT&T, Cisco, GE, IBM and Intel in March 2014, the consortium’s
mission is to coordinate vast ecosystem initiatives to connect and integrate
objects with people, processes and data using common architectures,interoperability and open standards.
Industrial Internet of Things (IIoT)
describes systems that connects and integrates industrial control systems
with enterprise systems, business processes, and analytics.
Note 1: Industrial control systems contain sensors and actuators.
Note 2: Typically, these are large and complicated system.
Internet Engineering Task Force (IETF)
The Internet Engineering Task Force (IETF) develops and promotes voluntary
Internet standards, in particular the standards that comprise the Internet
protocol suite (TCP/IP). It is an open standards organization, with no formal
membership or membership requirements.

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Annex E: References

Annex E REFERENCES[IIC-IIRA2015]

IIC: The Industrial Internet, Volume G1: Reference Architecture Technical
Report, version 1.7, 2015-June-04
http://www.iiconsortium.org/IIRA.htm[IIC-IIV2015]

IIC: The Industrial Internet, Volume G8: Vocabulary Technical Report, version
1.0, 2015-May-07
http://www.iiconsortium.org/vocab/index.htm[IETF-RFC2119]

IETF, Bradner, S.: Key words for use in RFCs to Indicate Requirement Levels,
1997. Accessed 2016-06-29.
http://ietf.org/rfc/rfc2119.txt

INDEX
I

O

IIC ......................... see Industrial Internet Consortium
Industrial Internet Consortium ................................56

Object Management Group .....................................56
OMG ......................... see Object Management Group

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Analytics Framework

Use of Information—Terms, Conditions and Notices

USE OF INFORMATION—TERMS, CONDITIONS AND NOTICESThis is an Industrial Internet Consortium document (the “Document”) and is to be used inaccordance with the terms, conditions and notices set forth below. This Document does not
represent a commitment by any person to implement any portion or recommendation containedin it in any products or services. The information contained in this Document is subject to change
without notice.

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