# Presentation: 493996

## Applications of Data Mining in Automated ISHM and Control for Complex Engineering Systems 

- Karl M. Reichard
- Applied Research Laboratory
- Penn State University
- Voice: 814-863-7681
- Email: _[kmr5@psu.edu](mailto:kmr5@psu.edu)_

## Data Mining

**Data mining**, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data.
- Data mining tools are used to identify correlations and to predict behaviors and future trends from data.
- Businesses use data mining to search and analyze databases for hidden patterns and may find trends and predictive information that experts may miss because it lies outside their expectations or because they never thought to look for correlations there.

- ISHEM Forum - 9 Nov, 2005

## Data Mining

- Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for valuable minerals.
- For businesses, the economics of data mining have many analogies to traditional mineral mining – pay for access to  public data and make money from mining it.
- A **data warehouse** is a repository where data located in disparate databases are consolidated.  Data warehouses store large quantities of data by specific categories so users can easily retrieve, interpret and sort the contents.

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## Elements of Data Mining

- Data preparation
- Databases and data warehouses
- Machine learning
- Statistical models
- Clustering
- Data Models
- Knowledge Representation
- Association Rules
- Classification Rules
- Decision trees
- Data transformation

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## Empirical Science 
Scientist gathers data by direct observation
Scientist analyzes data
Analytical Science 
Scientist builds analytical model
Makes predictions.
Computational Science 
Simulate analytical model
Validate model and makes predictions 
Science - Informatics
Data captured by instrumentsOr data generated by simulator
Processed by software
Placed in a database / files
Scientist analyzes database / files

- Scientist gathers data by direct observation
- Scientist analyzes data
**Analytical Science **
- Scientist builds analytical model
- Makes predictions.
**Computational Science **
- Simulate analytical model
- Validate model and makes predictions
**Science - Informatics**
- Data captured by instrumentsOr data generated by simulator
- Processed by software
- Placed in a database / files
- Scientist analyzes database / files

**Data Mining as Scientific Evolution**

- Jim Gray, “Online Science the New Computational Science,” Microsoft Research.

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## Leveraging Technology Trends

- Moore’s Law
- Performance/Price doubles every 18 months
- 100x per decade

- Metcalfe’s law
- Utility of computer networks grows as the number of possible connections: O(N2)

- Computer storage capacity is actually beating Moore’s law

- Jim Gray, “Online Science the New Computational Science,” Microsoft Research.

- ISHEM Forum - 9 Nov, 2005

## Premise:  Most data is (or could be online) so, the Internet is the world’s best telescope:
It has data on every part of the sky, in every measured spectral band: optical, x-ray, radio..
It is up when you are up. The “seeing” is always great (no  working at night, no clouds no moons no..).
It’s a smart telescope: links objects and data to literature on them.

- It has data on every part of the sky, in every measured spectral band: optical, x-ray, radio..
- It is up when you are up. The “seeing” is always great (no  working at night, no clouds no moons no..).
- It’s a smart telescope: links objects and data to literature on them.

- _[http://www.us-vo.org](http://www.us-vo.org/)__[/](http://www.us-vo.org/)_**                    **_[http://](http://www.ivoa.net/)__[www.ivoa.net](http://www.ivoa.net/)__[/](http://www.ivoa.net/)_

**Example: World Wide Telescope****Virtual Observatory**

- Jim Gray, “Online Science the New Computational Science,” Microsoft Research.

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## Why are Computer Scientists interested in Astronomy Data?

- It has no commercial value
- No privacy concerns
- Can freely share results with others
- Great for experimenting with algorithms
- It is real and well documented
**High-dimensional data** (with confidence intervals)
**Spatial** data
**Temporal** data
- Many **different instruments** from many **different places** and many **different times**
- Federation is a goal
- There is a lot of it (petabytes)

- Jim Gray, “Online Science the New Computational Science,” Microsoft Research.

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## ISHM Development and Implementation

**Perform ****assessment**

**Make****diagnosis**

**Reduce****data**

**Implementation**** **

**Analyze****data**

**Sensing**

**Feature**
** ****Extraction**

**Fault****Detection**

**Damage**
**Estimation**

**Feature **
**Tracking**
**& Prognostics**

**Development**

**Sensor**
**Selection**

**Develop**
**Physical **
**Models**

**Transitional**
**Failure Data**

**Diagnostic**
**Prognostic**
**Algorithms**

**Systems**
**Interfaces**

- Open Systems Architecture

**Degrader**
**Analysis**

**ISHM Development and Implementation**

- Where can we apply data mining techniques in ISHM development and implementation?

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## Data Mining in ISHM Development

**Selection**

**Develop**
**Physical **
**Models**

**Transitional**
**Failure Data**

**Diagnostic**
**Prognostic**
**Algorithms**

**Systems**
**Interfaces**

**Degrader**
**Analysis**

**Data Mining in ISHM Development**

- Parts usage
- Repair orders
- Maint. logs

- Initial design and development
- ISHM system verification and validation
- Continuous ISHM system improvement (borrowing techniques from manufacturing and quality control)

- Sensor reliability

- Operational data
- ISHM system data
- Maint. logs

*Biggest payoff may be in collection and analysis of transitional failure data across large numbers systems*

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## Data Mining in Prognostics

**Data Mining in Prognostics**

- RCM Analysis

- Detection  & Diagnostic Algorithms

- Prognostics

**Predicted Remaining Useful Life**

**Anticipated Demand**

- Transitional failure data from seeded fault experiments

- Transitional data from analogous systems

- Operational data

- Load history

- Transformation
- Model

- Data mining may allow access to much larger sets of transitional data for  assessing fault severity and predicting remaining useful life

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**Notes:**
Developing validated models and data fusion knowledge bases for CBM requires:
Normal, seeded, and transitional data 
Well documented, ground truth data
Prior to MURI IPD:
Lack of existing, high-fidelity data sets pertaining to faults initiation and evolution
Need for realistic data sets to develop CBM technology (e.g., failure models, diagnostic algorithms, predictive techniques, data fusion, reasoning)
As a result of MURI IPD:
Data sets are made available through cooperative arrangements with other research and developers
MDTB benchmark data sets for are used as Measures of Effectiveness/ Performance for CBM algorithms

## Managing System Health Information

- Platform Data Store

- PlatformData Store

- Platform Data Store

- PlatformData Store

- Platform Data Store

- PlatformData Store

- Platform Data Store

- RegionalData Store
- (CRIS)

- RegionalAnalysis

- Net-CentricCompliantService

- OSA-EAITech-XML

- GlobalData Store
- (CRIS)

- 3rd PartyAnalysis

- Net-CentricCompliantService

- OSA-EAI
- or
- COTS Replication

- DepotAnalysis

- _Net Centric Attributes_
- Only handle information once
- Tag and post before processing
- Store publicly and advertise
- Register metadata
- Tag applications for discovery

- PlatformData Store

- Platform Data Store

- PlatformData Store

- Platform Data Store

- PlatformData Store

- Platform Data Store

- PlatformData Store

- Platform Data Store

- RegionalData Store
- (CRIS)

- RegionalAnalysis

- Net-CentricCompliantService

- OSA-EAITech-XML

- OEMAnalysis

- Database

- Flat file or XML

- Database

- Flat file or XML

- PM IDEs

- _What types of data are we talking about?_
- Asset Register Management
- Work Management
- Diagnostic / Health / Rec (?)
- Static Trends / Alarms
- Dynamic Vibration / Sound
- Oil /Fluid / Gas / Solid Tests
- Binary Data / Thermography
- Reliability Data

**Managing System Health Information**

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## Leveraging Platform Asset Health Information

- SME

- Supply

- SmartMaintainer

- Maintenance

- Life Cycle
- Mgmt

- PM

- MC

- What is the status of all my platform Cs?

- What is broken or about to break on this vehicle?I need to report a problem.

- Give me battery health data for all of platform type As.

- What are the new maintenance requests?  Do I have the capacity?

- Operations

- Logistician

- Planner

- Do I have assets required for the upcoming mission?
- What risks do I mitigate or introduce?

- When do I need to buy material for systems that are predicted to fail?

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 2

- Vehicle
- Type A
- Block 2

- Vehicle
- Type A
- Block 2

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type B
- Block 2

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 1

- Vehicle
- Type A
- Block 2

- Vehicle
- Type A
- Block 2

- Vehicle
- Type C
- Block 2

- Vehicle
- Type B
- Block 1

- Vehicle
- Type C
- Block 1

- Operator

- How can I automatically report when the remaining useful life of an LRU drops below threshold?

- Integrated
- Architecture

**Leveraging Platform Asset Health Information**

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**Notes:**
CMMS = Computerized Maintenance Management System
LCMS = Life-Cycle Management System
MC = Maintenance Controller
SME = Subject Matter Expert
ISEA = In-Service Engineering Agent
PM = Program Manager
GCSS = Global Combat Support System
OPS = Operations

## USMC Autonomic Logistics Architecture

**Internal Message Manager**

**GCCS**

**GCSS**

**AL**

**Operator / Maintainer Presentation & Interface**

**EXternal Message Manager**

**USMC Autonomic Logistics Architecture**

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**Notes:**
Slide was taken from an PM AL presentation to depict basic software fuctions and information exchanges required to support future AL expected capabilities on system.  Capabilities document only functions in support of platform health monitoring and reporting.  Software, data and implied harware functions/capabilities listed do not necessarily reflect capability which will be provided by AL however atleast some portion would be required to support AL to work on the platform.

## US Army Common Logistics Operating Environment

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**Notes:**
Distributed Processing is further define in the Off platform portion of the architecture
Distributed Processing Capability – Platform Data is Distributed & Shared Throughout The Brigade and Higher As Required
Maintenance Data & Status  to the Combat Repair Team & Forward Maintenance Company
Log data (Fuel, Ammo, & Personnel) to the Combat Trains and Brigade Support Area 
Distributed Data Processing – Off Platform processes include 
The current Unit Level Logistics System automatically receives and processes platform data to add fault and order parts
Decision Support Tools (PMM) provides CBM & Fleet Status
 National Level data stored in PMM until Data Hub is in place
Reach as Required Capability - Has two functional requirements
 Drill Down Capability for Combatant Commanders and Logisticians to obtain additional information
 Remote Diagnostics provides a reach back capability to perform troubleshooting from the FMC or CRT to the platform
Future Plans – Linked to ERP Data Hub and WIN-T

## US Army Common Logistics Operating Environment

**Maintain on-platform persistent health data store using MIMOSA OSA-EAI CRIS or Tech-XML**

**Bulk health data transfer using MIMOSA OSA-EAI Tech-XML**

**Manage off- platform persistent health data store using MIMOSA OSA-EAI CRIS**

**Emergency Resupply Request using JVMF K07.xx messages**

**Replicate to CONUS using COTS tools**

**Query persistent health data store using MIMOSA OSA-EAI Tech-XML**

- ISHEM Forum - 9 Nov, 2005

**Notes:**
Distributed Processing is further define in the Off platform portion of the architecture
Distributed Processing Capability – Platform Data is Distributed & Shared Throughout The Brigade and Higher As Required
Maintenance Data & Status  to the Combat Repair Team & Forward Maintenance Company
Log data (Fuel, Ammo, & Personnel) to the Combat Trains and Brigade Support Area 
Distributed Data Processing – Off Platform processes include 
The current Unit Level Logistics System automatically receives and processes platform data to add fault and order parts
Decision Support Tools (PMM) provides CBM & Fleet Status
 National Level data stored in PMM until Data Hub is in place
Reach as Required Capability - Has two functional requirements
 Drill Down Capability for Combatant Commanders and Logisticians to obtain additional information
 Remote Diagnostics provides a reach back capability to perform troubleshooting from the FMC or CRT to the platform
Future Plans – Linked to ERP Data Hub and WIN-T

## CACE - Coherent AnalyticalComputing Environment

**CACE - Coherent Analytical****Computing Environment**

- CACE is an example of a data mining application which taps into databases of maintenance requests, repair  schedules, and training schedules to optimize flight operations

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## Access to Data

- Need access to data for all information customers
- Embedded ISHM systems
- Operators
- Maintainers
- Planners
- Program managers
- Design Engineers
- Latency of information will determine value to different customers

- Who is responsible for maintaining the data warehouse?

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## Open Systems Architecture for Condition Based Maintenance

**Open Systems Architecture for Condition Based Maintenance**

- Standardized architecture for health and condition monitoring systems
- Breaks monitoring system  into functional layers
- Defines inputs and outputs required for each  layer
- Modules not confined to one locale
- Middleware independent

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## Standards for Enabling System Management

- _MIMOSA Technology Types_
- REG (Physical Asset Register Management)
- WORK (O&M Agent Work Management)
- DIAG (Diagnostics / Prognostics / Health Assessment)
- TREND* *(Operational Scalar Data & Alarms)
- DYN (Dynamic Vibration/Sound Data & Alarms)
- SAMPLE (Oil/Fluid/Gas/Solid Test Data & Alarms)
- BLOB (Binary Data/Thermography Data & Alarms)
- REL (RCM/FMECA/Model Reliability Information)
- ALGORITHM (Algorithm Management Information)
- AGENT (Intelligent Agent Management Information)
- TRACK (Physical Asset GeoSpatial Tracking Info.)
- FORECAST (Capability Forecasting & Projections)

**Standards for Enabling System Management**

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## Beyond System Health Monitoring

*Need techniques to capture decisions and actions in addition to data and permit machines to mine those high level control “lessons learned”*

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## Other Data Mining Challenges

- Data management
- Data storage
- Indexing data for efficient computer access
- Ownership of data, data rights, and security
- Distributed processing architectures
- Data summarization, trend detection anomaly detection are key technologies
- Translation models

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## Conclusions

- Data mining is the search for hidden relationships and meaning in data
- Applications in ISHM include modeling, diagnostics, and prognostics
- Useful for system development and for closed-loop improvement of systems
- Biggest payoff could be in creation of virtual transitional failure data sets
- Information customers are driving for network-centric applications so databases and data warehouses will be there – we need to be sure the data is useful for ISHM

- ISHEM Forum - 9 Nov, 2005