The benefits and opportunities ofData-Driven Condition
Based MaintenanceREPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceContents
Executive Summary

3

Common shipboard maintenance methodologies

4

Condition-Based Maintenance (CBM) – A traditional approach

5

Data-driven condition based maintenance (DCBM)

6

Four key challenges of condition based maintenance7

Evolving technologies for modern analytics

9

Lessons from aviation

11

Machine Learning and Data-driven algorithms

12

Industry drivers for data-driven maintenance13

Macro-level maritime change drivers

13

Drivers for ship owners and operators

14

Real-life OPEX benefits of Condition-Based Maintenance

15

Drivers for other stakeholder groups

17

Implementing data-driven maintenance processes

20

Building a pathway to DCBM

21

Designing a DCBM process

22

Communication and goal alignment

23

Training machine learning models for DCBM

24

DCBM functional architecture

25

Incentive alignment and new business models

26

Innovation in business models

27‘Routine’ vs ‘Radical’ innovation

28

The maritime technology conundrum

29

Call to Action

30

Annexes31© Lloyd’s Register 2024

2

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Executive Summary
Timely and effective maintenance of seagoing vessels is fundamental to the maritime industry’s corebusiness of transporting cargo from A to B as safely and efficiently as possible. Well-maintained ships
experience fewer unexpected failures or breakdowns, reducing the potential for accidents, delays anddisruptions, while safeguarding the lives of the crews serving onboard.
While vessel maintenance necessarily incurs costs, careful asset management should ultimately leadto savings by preventing more expensive repairs or replacements that could result from neglect ordeferred action. Effective maintenance can also help to improve fuel efficiency and reduce otheroperational expenses by allowing the ship to sail in condition as close to optimal as possible, creating
benefits for both the shipping company and the environment.
The evolution of condition-based maintenance (CBM) has helped to increase the effectiveness ofvessel care processes, moving away from a reliance on averages and statistical assumptions to an
approach tailored specifically to the equipment or asset being maintained. Scheduling maintenanceactivities based on the actual condition of the equipment, rather than following a fixed calendarbased timetable, allows for resources to be deployed more efficiently and for the impact ofmaintenance procedures on vessel operations to be minimised.
Introducing and operating an effective set of CBM processes at a modern shipping company can bechallenging, but the adoption of analytics-driven methods can have a significant impact in delivering
evidence-based change and creating a clear path to improvements in the dependability and efficiencyof the merchant fleet.
This report aims to provide an overview of how data-driven condition based maintenance(DCBM) processes, utilising the latest analytical models, can help to deliver these benefits to themaritime industry, outlining the challenges that need to be overcome and suggesting potential
paths to successful implementation.

© Lloyd’s Register 2024

3

REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceCommon shipboard
maintenance methodologies
Current maintenance practices within the shipping industry typically involve a range of common
processes and requirements that present significant opportunities for optimisation.
Multiple stakeholders, including ship owners, classification societies and equipment manufacturers,
all have a role to play in managing maintenance, but processes suffer from a lack of direct integration
between the parties, exacerbated by the absence of shared common data platforms.
Despite the growing availability of data from modern vessels, this resource is often underutilised dueto the lack of an integrated strategy among stakeholders, who often independently accumulate vast
amounts of data without a clear plan for applying that data in conjunction with their partners in a
way that creates synergistic benefits. This lack of integration also limits the scalability of traditional
analytics methods, requiring extensive oversight from classification societies to ensure fleetwide compliance.
Current practices that do not take advantage of available data regarding equipment status
are consequently likely to remain inefficient, leading to both over-maintenance and undermaintenance, both of which carry their own cost implications. These legacy processes are alsoheavily dependent on the variable skill levels of human personnel, lack a proactive planningcapability for future improvements, and will frequently result in unscheduled downtime that disrupts
operational capabilities.

Dependenceof humanexpertise

LimitedScalability

Source ofInefficiencies

Lack ofproactiveness

Data
underutilization© Lloyd’s Register 2024

4

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Condition-Based Maintenance (CBM) – A traditional approach
Scheduled Inspections
Traditionally, vessel maintenance has primarily followed a programme of scheduledinspections on dates chosen by classification societies to ensure that ships comply withinternational regulations. These inspections are often based on calendar time or usage
metrics, such as running hours. During scheduled inspections engineers will manually
examine machinery and systems to assess their condition.
Subjective Assessments
This manual process involves human experts assessing the condition of shipboard machinery
based on their own training and experience, evaluating factors such as noise, vibration,
temperature, and visual indicators to create reports for classification societies to use todetermine compliance. These assessments are highly reliant on individual skill levels.
Reactive MaintenanceIn many cases, maintenance on ships is conducted on a reactive basis, rather than being
proactive. Reactive maintenance usually occurs when equipment shows signs of failure
and may require the manufacturer’s input on which parts and procedures are necessary tocomplete the process. When a system fails or shows signs of impending failure, immediate
action then will be required to repair or replace the malfunctioning component. Thisapproach can lead to downtime and increased operational costs.
Limited Use of Sensor Data
Some modern shipboard equipment will incorporate sensors, provided by the manufacturer
or installed by the vessel operator, which can monitor key parameters such as temperature,pressure, and RPM. However, where such data is available it is often underutilised. Sensors
may be applied to trigger alarms for out-of-range conditions, for example, but are rarely
integrated into a comprehensive maintenance strategy.
Documentation and Record-Keeping
Classification societies require comprehensive documentation and record-keeping to ensurecompliance with maintenance procedures, a process that usually needs to be managed by theship owner or manager themselves. Manual logs and paper-based reports are still commonlyused to document all maintenance activities, inspection results and repair histories, which
are prone to human error and do not readily support data analysis or the development ofpredictive maintenance strategies.

© Lloyd’s Register 2024

5

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Data-driven condition based
maintenance (DCBM)
The maritime sector undoubtedly faces a range of challenges, both technical and structural, in
deploying efficient and effective condition-based maintenance systems. However, many of thesechallenges could be substantially alleviated by integrating data and analytics-driven methods into themaintenance workflow and creating a data-driven condition based maintenance (DCBM) process.
By integrating data analytics with traditional maintenance methods, the industry has the potential
to significantly improve both the reliability and operational efficiency of marine vessels. This will
require the development of analytics-based diagnostic systems, real-time monitoring solutions, andcomprehensive digital platforms to remove errors and inefficiencies from traditional processes andoptimise workflows.
Presented below are four key challenges that have been identified in current condition-based
maintenance practices, along with potential improvements that could be created through theapplication of an analytics-driven approach.

© Lloyd’s Register 2024

6

REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceFour key challenges of condition based maintenance
Imprecise Maintenance and Inspection Protocols
The challenge: A fundamental issue with current CBM processes is a lack of precision inmaintenance and inspection checklists, which often suffer from either an excessive numberof included tasks or an absence of crucial maintenance activities. These shortcomings cancreate unnecessary added labour costs but also increase the likelihood of errors during
maintenance. Furthermore, they can result in unexpected faults if essential upkeep steps
are neglected.
Data-driven improvement: These challenges could be mitigated by an analytics-driven
approach using historical and current operational data to revise and refine maintenanceand inspection schedules. Implementing such a strategy would necessitate the creationof analytics-based diagnostic systems incorporating advanced diagnostic algorithms andprecise measurement tools.
Deviations from Scheduled Maintenance and Inspections
The challenge: Another common issue in maintenance effectiveness is a frequent disregard
for prescribed maintenance and inspection timelines. A failure to correctly follow suggested
scheduled maintenance timings is often a primary causal factor in unexpected system failures.
Data-driven improvement: An analytics-driven remedy for this issue would be theimplementation of digital platforms to accurately record maintenance activities. This could becompleted in stages where complete automation of specific processes is currently impractical.
Accuracy in Condition Assessment
The challenge: The success of CBM can be compromised if the criteria used to identify
hazardous operating conditions are vague or undefined, or if essential diagnostic data is
scarce. These types of information gaps often culminate in operational failures and could leadto significant injuries if delays occur in identifying malfunctioning equipment.
Data-driven improvement: Analytics-driven methods can alleviate these issues by employing
data analysis to uncover trends or early warning signs of unsafe operating conditions andpotential system failures. To put this data into action, operators need to develop analyticsbased systems that include diagnostic algorithms as well as appropriate instrumentsof measurement.
Insufficient Troubleshooting Response
The challenge: A lack of an effective response strategy outlining steps to take when faced withsystem failures can lead to delays in initiating required corrective actions and could lead tosignificant injuries.
Data-driven improvement: This white paper does not recommend a specific analytics-driven
solution for this particular challenge, but the authors note that there is an urgent need for
additional research and development in this domain.

© Lloyd’s Register 2024

7

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

A summary of all of these challenges, with potential improvement paths incorporating DCBM, ispresented below.

Problem Category

Problem Details

Resulting issues

Inadequate timingof maintenance andinspection activities

Unnecessarily
excessive maintenanceand inspection
activities.

Increased labour
requirement.
Increased cost.
Increased risk ofmaintenance errors.

Required maintenanceand inspections
not carried out,or frequency is
insufficient.

Fault occurs.

Data-driven
improvement plans

Action required

Update the optimal
maintenance andinspection schedule byanalysing operational
data and maintenancehistory.

Development of
data-based condition
diagnosis methods(using applieddiagnostic logic,
measurement
devices).

Update the optimal
maintenance andinspection schedule byanalysing operational
data and maintenancehistory.

Development of
data-based condition
diagnosis methods(using applieddiagnostic logic,
measurement
devices).

Monitoring ofoperational data willallow detection ofabnormalities andprompt early action
even if scheduled
maintenancefrequency is
insufficient.
Failure to perform
maintenance andinspections

The listed
maintenance activities
and recommended
frequency are notcomplied with.

Fault occurs.

Failure to accurately
diagnose condition

Criteria for
determining
inappropriate (highrisk) operating
conditions are unclear.

Failure caused by
improper operation.

Necessary diagnostic
methods andtechniques are notproperly understood.

Failure caused by
improper operation.

Analyse operational
data to detect
inappropriate
operating conditions.

Development of
data-based condition
diagnosis methods(using applieddiagnostic logic,
measurement
devices).

Severe injury due todelayed detection of
malfunction.

Enable early detectionof malfunctions
through analysis ofoperational data.

Development of
data-based condition
diagnosis methods(using applieddiagnostic logic,
measurement
devices).

Digital data-based
managementof maintenancerequirements (withmanual input where
automation is
impractical).

Data necessary for
diagnosis may belacking.
Delayed detection of
occurrence of a failure.

Root cause of
malfunction cannot
be detected andcorrected.
Failure to respond to
breakdowns© Lloyd’s Register 2024

Appropriate response
to malfunctions has
not been defined.

Severe injury due todelayed breakdown
response.

8

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Evolving technologies formodern analytics
Until recently, the adoption of DCBM processes has typically been the preserve of a specialised nicheof organisations within the aerospace and defence sectors. However, modern advancements intechnology have served to make these systems more accessible to a wider range of industries thanever before.
Since the 1970s, the aerospace industry has employed a series of emerging technologies to increase
reliability, reduce failures and optimise maintenance. With a more diverse fleet of assets andownership, the marine industry is yet to adopt similar processes at scale, however technologicaladvances and the wider availability of operational data are expected to drive significant
improvements in this area.
Those early aerospace and defence adopters built their systems on proprietary algorithms, utilising
software and hardware engineered by dedicated subject matter experts using company-specific (andIP protected) engineering tools. Developing DCBM systems at that time required expensive expertiseand custom technology tools that put similar ambitions out of reach for most companies.
Today, many of those barriers have been eroded, with mainstream providers such as Ansys, Areva and
MathWorks providing the engineering and software engineering services required, including opensource code libraries and developer tool kits that allow companies to re-use and build upon the work
already done by external third parties.
In addition, a number of standards, handbooks and guidelines have been published that provide
access to many of the different engineering approaches in hardware, software and systems thatare required to successfully deploy DCBM. As a result, no team or company needs to start fromscratch, and many of the activities that previously required highly specialised expertise are much
more accessible.
The difficulty and expense of developing a DCBM infrastructure is expected to continue to reduce inthe future, already demonstrated by the fact that some of the simpler DCBM applications (e.g. forrotating equipment) have spawned marketplaces that allow users to buy and integrate third party
systems from providers such as Siemens, ABB and GE.

© Lloyd’s Register 2024

9

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Five of the key recent technological advancements supporting this expanding access to DCBM
systems include:
Connectivity – The availability of technology to support the collation and transmission of large
volumes of digital data in maritime has become the norm in recent years, as had previously been
the case with the aviation industry. This allows for comparative analysis at a depth and breadth notpreviously practicable and opens the door for alternative approaches to be explored.
Sensors – Equipment is now more commonly provided with sensors embedded as standard,
improving the availability and reliability of maritime data. Advanced sensors may also containcomputing elements to analyse outputs and further improve data quality, allowing greater confidence
in the analysis delivered.
Edge computing – Increased access to edge computing devices offers the capability to process data
at source with reduced latency, allowing for real-time analytics on connected and disparate systems
without external connectivity. This makes it possible to take immediate action based on data insights.
Data Quality – Sophisticated data cleaning and integration tools have created significant advances indata quality, allowing organisations to improve their ability to aggregate data from various sources,
including legacy systems, and create unified datasets for analysis.
Human-Machine interfaces – The role of human expertise in maintenance analytics is still crucial.
Combining human knowledge with machine-generated insights can allow maintenance teams to
combine the benefits of both worlds to make informed decisions.

© Lloyd’s Register 2024

10

REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceLessons from aviation
Maritime should take the opportunity to learn lessons from the past experiences of the aviationindustry in its evolution of maintenance procedures, where previous calendar-based inspection
and maintenance regimes have now transitioned to prognostic decision-making based on collation
and analysis of data. This has resulted in a reported reduction in required inspection tasks while
reducing the incidence and severity of unexpected failures.
Advancement of Monitoring Technology

1970s

Exceedance recording
Temp, RPM, Vibes
Detection of exceedance events(Anomaly Detection

1980s

1990s

Damage accumulation,
Remaining Useful Life
(post flight calculation)

Goal: reduce uncertainty andretain conservatism in estimating
component life limit

Temp, RPM, Throttle position

Method: rainflow cycle counting,limists monitoring

Health and usage monitoring(HUMs systems) “lifing
algorithms, usage counting
algorithms, L10 algorithms,
other variants”
Digital Fuel Controls
Engine data collection
Condition Monitoring
Fault Detection and Isolation(Diagnosis)

2000s

Prognosis – time to failure,
recommended maintenanceactionAdvance fault detection &isolation and diagnosis (physics
based, models, machinelearning, etc.)

2010-present© Lloyd’s Register 2024

Goal: Extend time between
overhauls by automaticallydetecting incipient failures in realtime with embedded sensors
Method: vibration monitoring,oil debris detection, performancetrending

Prognostics and integrated
health management (e.g. system
digital twins, coupling withadaptive controllers

11

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Machine Learning and Data-driven algorithms
In addition to the technological advancements listed above, the emergence of new data-driven
algorithms and machine learning tools has also significantly widened potential access to DCBM,
removing the need for direct specific knowledge of equipment health and causes of failure to be
programmed into the system by software engineers.
Machine learning algorithms can be built by ‘learning’ from data sets that are representative ofdifferent operating conditions – such as ‘normal’, ‘abnormal’, ‘faulty’ and ‘failure’. These algorithms
can incorporate monitoring and reasoning capabilities that allow the software to recognise the statusof the equipment and the condition of its various parts based on observed operational data.
The ability to implement data-driven algorithms has been made possible by several recent
commercial and technological advancements, such as the development and commercialisation offoundational models like Generative Pre-Trained Transformers (GPTs) that are capable of a level ofreasoning, analysis and decision making (even creativity) not seen in previous machine learning
systems. These next-generation machine learning algorithms are also designed to be able to operate
across multiple applications and use cases rather than specialising in specific tasks.
The costs associated with the development of machine learning models has also drastically reduced.
In the past, data-driven algorithms were kept proprietary given the expertise, custom computing
technology and high costs involved in their development. Today, developer communities, technology
companies and organisations such as NASA provide public access to data-driven algorithms, including
their source code libraries, to the extent that open-source algorithms are now available at zero cost insome cases.
The cost of the computing power and memory required to run machine learning models has also
reduced dramatically in recent years. The emergence of cloud computing has eased the capital costs
for businesses looking to develop high performance computing facilities, while continued innovationin graphical processing units (GPUs) has accelerated computational capabilities, reducing the modeltraining time required.
For those that are new to the field of data-driven algorithms, a group that will include most maritime
organisations, ongoing standardisation and automation efforts in the sector are helping to remove
some of the barriers to entry when it comes to the engineering, production and deployment ofthese tools.
Various ML Ops (Machine Learning Operations) frameworks are available, supported by ISO and IEC
standards, to assist companies in building competence in the application of data-driven algorithms,
while additional ML DevOps (Machine Learning Development and Operationalisation) frameworks
and tools are available from engineering focused providers delivering services for the advanced
monitoring of equipment and systems.
Leveraging these resources can reduce the engineering burden for new users of these technologies
by providing proven monitoring-specific examples that can be adapted for maritime use, such as
anomaly detection algorithms that can be customised to specific monitoring use-cases.

A list of technologies and tools to support the development of data-driven algorithms and machine learning models is provided in Annex 1© Lloyd’s Register 2024

12

REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceIndustry drivers fordata-driven maintenance
The potential benefits from the enhancement of shipping maintenance through the applicationof data also align closely with several macro-level change drivers that are currently reshaping
the maritime industry, pushing shipping companies to adapt their operational processes to staycompetitive, improve efficiency, and meet evolving demands.
A combination of economic, regulatory, technological and competitive factors will require theshipping sector to continue to evolve in its approach to vessel management, with data-driven
maintenance likely to play an increasingly important role in ensuring that improved levels of
reliability, safety, and sustainability in maritime operations are achieved going forward.

Macro-level maritime change drivers
Cost Efficiency and Profitability: Shipping companies are under constant pressure to reduce
operational costs and improve profitability. Data-driven maintenance helps to optimise maintenancespending by reducing the number of inspection, survey and functional testing tasks required,allowing resources to be to be focused on carrying out required maintenance at the optimal time,minimising unplanned downtime and improving operational flexibility.
Environmental Regulations: With global goals to reduce emissions driving requirements formore efficient vessel operations, data-driven maintenance can help to reduce fuel consumption
and emissions by supporting greater efficiency in the operation of machinery, while avoiding theperformance of maintenance at inefficient times.
Optimised maintenance can also help to reduce the carbon footprint of the spare parts andmaintenance logistics activities required to service a vessel. Traditional planned maintenancetypically results in the provision and storage of a large number of spare parts, some of which often
remain unused beyond their expiry date. Optimised maintenance schedules will also minimise travel
hours for service engineers and technical personnel attending to equipment and systems for testing
and troubleshooting.
Safety and Risk Mitigation: The increasing public visibility of incidents that occur at sea, and theconsequent risks they carry for company reputations, mean that maritime operators can benefit frombeing seen to be adopting the latest technologies to enhance equipment safety. DCBM sits firmly
within that category, helping to identify and address potential safety risks by proactively managing
equipment health and reducing the likelihood of unscheduled downtime and failures.
Regulators and insurers are also showing an increasing interest in data-driven maintenanceprocedures as a means of providing transparency in compliance processes and reducing therisk of failures in service, delivering improved confidence in vessel operations and a reduction ininsurance claims.

© Lloyd’s Register 2024

13

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

As DCBM allows for the creation of more precise and adaptive risk models than traditional approaches
by continuously collecting and analysing data from various ship systems, the application of datadriven methodologies within the maritime industry could deliver far-reaching benefits beyond merely
ensuring safe operations.
Improved digitalisation would support the accelerated adoption of parallel technologies that could
improve operational efficiency and enable decarbonisation, such as the utilisation of ‘digital twin’models to identify new opportunities for cost and fuel reduction.
This could create savings that are similar to, if not better than those realised by early adopting
industries like aviation – where US Army and FAA sources have reported that data-driven conditionbased maintenance regimes for aero engines have delivered a 30% reduction in mission aborts, a 30%
reduction in maintenance costs and a 5-10% reduction in scheduled maintenance costs.
The interconnected nature of the maritime industry requires significant collaboration among different
actors to achieve the various benefits discussed above. These stakeholders can be classified within
the following categories, which will be explored in more detail below:• Ship Owners / Operators• Classification Societies / Regulators• Technology Providers (including OEMs – Original Equipment Manufacturers)• Cargo Owners• Insurance Providers• Financial Services

Drivers for ship owners and operators
Vessel owners and operators clearly stand to gain from optimised asset utilisation, through increased
equipment availability, reduced downtime and lower total maintenance costs, which collectively
contribute to a higher return on investment from their assets.
Safety is a major driver, and the adoption of proactive measures in maintaining vessels will
support the early detection of abnormalities, preventing major breakdowns and enabling flexible
maintenance planning not constrained by the methods commonly found in Traditional Breakdown
Maintenance (TBM) and Condition Monitoring Systems (CMS). DCBM can be expected to not
only reduce the frequency of maintenance activities, but to also significantly lower operational
expenditure (OPEX) and crew workload.
The increase in overall availability of the vessel, which can be predicted with a higher level of certainty
through analysis of actual operating conditions for each particular system that is dependent oncomponents that are subject to degradation over time, is a key consideration for owners whenbuilding a business case to demonstrate return on investment in DCBM technologies.

© Lloyd’s Register 2024

14

REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceSome of the key benefits in this area can be delivered through management of the uptime oravailability of different ‘functions’ of the ship, rather than individual components in isolation – forexample, is the ‘navigational function’ available for operation, or the ‘loading’?
This approach requires consideration of the ship as a ‘system of systems’ that undertakes a rangeof connected functions, which each involve a certain collection of components that are required todependably work together.
The level of digitalisation of such systems will determine the success of the adoption of data-driven
decision-making business models, and is dependent on a number of factors – including the maturityof the available technology, the cost of adapting the technology to specific requirements, the actual
adoption of the technology on board real ships, and the acceptance of the analysis provided in theoperational decision-making process (supervised by humans in the initial stages).

Real-life OPEX benefits of Condition-Based MaintenanceAmong all of the related stakeholder categories, the incentive for shipping companies to adopt DCBM
processes may be the easiest to quantify, as reduced maintenance spending can be directly expressed
as dollar amounts.
To highlight this point, the below data has been collected from operating vessels in a shipping
company fleet, from ships that have deployed CBM processes and those that have not. The vessels
have been split into categories based on their number of years in service, with the cost areas (OPEX)
directly impacted by the adoption of CBM methods similarly classified by type.
OPEX Comparison for 5 Years in Service with and without CBM

OPEX (Million USD)

5

Without CBMWith CBM

4
3
2
1
0

© Lloyd’s Register 2024

Lubrication

Special Survey

RepairServices

Spare Parts

Docking

15

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

OPEX Comparison for 10 Years in Service with and without CBM

OPEX (Million USD)

5

Without CBMWith CBM

4
3
2
1
0

Lubrication

Special Survey

RepairServices

Spare Parts

Docking

OPEX Comparison for 15 Years in Service with and without CBM
8Without CBM

OPEX (Million USD)With CBM6420

Lubrication

Special Survey

RepairServices

Spare Parts

Docking

OPEX (Million USD)OPEX Comparison for 20 Years in Service with and without CBM
12Without CBM

10With CBM

8
6
4
2
0

© Lloyd’s Register 2024

Lubrication

Special Survey

RepairServices

Spare Parts

Docking

16

REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceThis real-world data clearly illustrates the disparities in maintenance costs between vessels utilising
CBM and those without CBM – across all service categories and years of service, vessels implementing
CBM consistently demonstrate lower OPEX compared to their counterparts.
Over the years, the cumulative savings that could be accrued by vessels employing CBM become
increasingly evident. Notably, the gap in maintenance costs widens with the age of the ship,
underscoring the long-term financial benefits of CBM adoption. While certain services exhibit more
pronounced cost reductions from CBM implementation, others showcase moderate to significant
savings, contributing to overall fleet-wide cost optimisation.
The data underscores the tangible benefits that can be created through the integration of ConditionBased Maintenance strategies within the operational framework of a shipping fleet. By leveraging
predictive maintenance techniques and real-time monitoring, organisations stand to significantly
mitigate maintenance expenditure, enhance operational efficiency, and prolong asset lifespan.

Drivers for other stakeholder groups
Other stakeholder groups within the maritime sector also stand to benefit from the adoption of DCBM
techniques, with their individual drivers varying based on their interaction with vessel operators andwith one another.
Classification Societies – For classification societies, one of the most significant potential benefitsof an industry shift towards DCBM is the ability to develop more accurate and dynamic risk models,
which would facilitate targeted inspections with more efficiently allocated inspection resources, aswell as a streamlined certification process delivering a comprehensive, data-backed overview of thecondition of maritime assets.
By adopting a proactive, evidence-based strategy incorporating actual vessel data, class societies
can create greater transparency in their assessment criteria and further increase the credibility of
their methodologies with regulatory agencies. In addition, the insights gathered from data analysis
can be used to update classification rules and guidelines to more closely align with actual operating
conditions, increasing their relevance in a dynamic maritime environment.
Original Equipment Manufacturers (OEMs) – OEMs stand to benefit from the implementation of
DCBM through access to a greater lever of granular data from the improved digitalisation of vessel
systems, allowing those manufacturers to access more detailed equipment performance metrics tosupport innovation in design improvements at a level far exceeding current industry standards.
Equipment providers could also offer a higher level of customisation to customers in theirmaintenance plans, with timelines based on operational use. This could lead to increased sales ofspares for installation before a failure occurs and reduce the likelihood of non-original spare parts
being purchased, or third-party service providers being employed for maintenance management.

© Lloyd’s Register 2024

17

REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceOEMs could create better informed warranty contracts based on the use of machinery if equipment
follows a DCBM methodology, and make evidence-backed predictions when discussing the reliability
and operational lifecycle of the equipment. Manufacturers would also be in a better position topredict spare parts and servicing requirements across the customer base by making calculations
using actual machinery reliability and usage data, reducing the cost of providing such services andthe expense of associated logistics.
The time and cost involved in carrying out repair services would also be reduced if the nature ofa failure or issue can be more readily identified, with the complexity of specific repairs analysed
and used to improve processes so that the effectiveness and efficiency of maintenance efforts canbe benchmarked.
Increases in sales volume could result from improved design capabilities, particularly incorporatingchanges demonstrated to create higher reliability based on customers’ operational and failures
data. The adoption of DCBM could also create the opportunity to offer proactive model maintenanceservices to asset owners as an additional option with value-added service packages. These kindsof value-added services could help OEMs transition from suppliers to partners, businesses that aredemonstrably invested in their clients’ long-term success.
Cargo Owners – The application of DCBM processes within the maritime sector could have a
direct impact on cargo owners by providing a foundation for more transparent and collaborative
relationships with other stakeholders, like ship owners or classification societies.
The data delivered can serve as an impartial basis for performance reviews, contract renewals, oreven for identifying areas of shared investment to improve overall operational effectiveness. Reduced
disruptions and penalties through improved reliability in vessel operations could also free up capitalfor cargo owners to invest in other areas of their business, such as research and development,
marketing, or geographical expansion.
Insurance Providers – Insurers stand to significantly benefit from the adoption of data-driven
management of the maintenance of equipment on ships by allowing insurance premiums to be
calculated with more certainty, through the expansion of the pool of data available for use in theiranalysis of the risks involved. Access to a greater level of specific operational data from vessels would
help these companies to reduce pay-outs for machinery failures and to optimise overall premiums.
Financial Services – Shipping financiers and the various banks and other organisations providing
capital to maritime companies would be able to model their financial forecasts with greater certainty
if given access to the increased volume of vessel operational data provided by DCBM. A greater
understanding of the specifics of vessel operations will allow financial providers to make data-driven
decisions on where to allocate their investments.

© Lloyd’s Register 2024

18

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

It is important to note that incorporating DCBM as an integral part of a comprehensive maritime
operations ecosystem will not be achieved without overcoming significant challenges. The tablebelow offers a summary of some of the key opportunities and challenges for a selection of the
major stakeholders.

OEM

Class

Owner/Ship© Lloyd’s Register 2024

Opportunity

Challenge

Expand sales of condition monitoring
services.

Additional risk management
capabilities needed.

Increase in information collected, suchas operational data and maintenancerecords, leading to better productdevelopment.

Additional labour resources needed for
expanded DCBM operations.

Increase in product value, promoting
sales.

Potential reduction in revenue fromcomponent sales.

Optimised operations can prevent
occurrence of serious problems.

Additional risk management
capabilities needed.

Reduced frequency of inspectors
visiting vessels.

New skillsets required for DCBM
inspections.

Increased flexibility in maintenanceimplementation, leading to reduced
maintenance costs and workload forcrew members.

Cost and labour investment in new
maintenance processes to share data,
maintenance records, etc. with relevant
stakeholders.

Onboard systems are operated
appropriately, preventing serious
problems.

Improper operation may increase therisk of unexpected problems.

19

REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Implementing data-driven
maintenance processes
Implementation of data-driven processes for ship maintenance has the potential to delivertransformative benefits for the maritime industry when compared with the limitations of current
practices, impacting ship owners, classification societies, equipment manufacturers and a range ofother stakeholders.
Enhanced predictive capabilities allow for pre-emptive maintenance activities to be carried out,
significantly reducing downtime and operational costs, while the utilisation of resources can be
optimised through the application of data analytics to allow ship owners to prioritise maintenanceactivities based on the likelihood and severity of potential failures.
This analytical insight would be invaluable for vessel operators in discussions with classificationsocieties and equipment manufacturers to create more effective maintenance schedules. Ideally,
predictive models could be created that align with the guidelines set by classification societies andincorporate best practices recommended by equipment manufacturers, providing the opportunity tostandardise and scale maintenance operations across fleets.
Creating synergies between classification societies, ship owners and equipment manufacturers by
integrating various data sources into a shared centralised ecosystem platform would enable a holistic
approach to maintenance, ensuring better decision-making and overall maritime safety.
However, as mentioned in previous sections of this whitepaper, many challenges remain in
implementing data driven condition-based maintenance processes in the maritime sector. The
development of new data-based condition diagnosis methods (with applied diagnostic logic, andappropriate measurement equipment) in place of traditional practices will require commitment frommultiple stakeholders, while applying DCBM will also necessitate investment in sensor technology tocollect data from the asset of interest.
The integration of sensor technology and analysis instruments into maritime operations will requiresignificant expenditure, both for initial setup and ongoing maintenance. This expense may also becompounded by a subsequent potential increase in monthly ship-to-shore communication charges
for data transmission and monitoring. To create a scalable and sustainable business model, detailed
cost analyses are essential, especially when tailoring solutions to specific use-cases.
Aside from the technical challenges, there is also a potential disconnect between Original Equipment
Manufacturers (OEMs) and ship owners in their pathways towards effective DCBM, particularly when it
comes to data collection units.
Many ship owners already possess their own data collection systems, which might not be compatible
with OEM requirements to connect their own machinery. For OEMs to leverage these existing systems
a set of industry standards for data collection and sharing needs to be established.
OEMs also require access to digital maintenance records to monitor spare parts usage for DCBM to beeffectively implemented, which will require ship owners to prioritise digitisation of maintenance logs
to facilitate data sharing and to streamline overall operational workflows.

© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Building a pathway to DCBM
Once a shipping company is committed to implementing DCBM, ideally in cooperation with its
industry partners, there are three key areas that should be comprehensively analysed to make surethat value creation will be maximised – technology, methodology, and operational workflows.
Technology – Value can only be created from data-driven decision-making if it is based on accurate
digital models that can replicate reality with a high level of fidelity. These models, or ‘digital twins’,should provide the basis for analysis that all parties can trust, so that decision-making accountabilitylies with the technology, not the human. The use of suitable modelling equipment, systems, andoperating environments are all integral to the creation of value-creating data-driven business models.
Methodology – An effective DCBM process must be built on an operating model where data can betrusted to generate the desired business outcome. The critical key parameters for operation should
be identified and then monitored and measured by digital models. At a basic level, this would include
critical data such as equipment reliability, as well as the connectivity status of the machinery to allow
data to be captured.
Operational workflows – There are a number of different options for interacting with the datacollected, so a preferred operational workflow should be chosen that suits the company’s specificrequirements. This could involve sharing of datasets themselves with approved parties, or sharing ofdata outcomes with chosen partners that have been granted access to provide support in decision
making processes.
Depending on which technology, methodology and operational workflows are adopted, there may be
several implementation pathways that could be followed to create an effective DCBM process that will
generate the outputs required to support improved maintenance decisions.
We envisage four potential pathways that may be practicable, depending on the companies involved
and their own specific circumstances:• Digitising and creating digital models of existing equipment.
• Utilising AI, based on machine learning that analyses testing data and operational data from therelevant equipment.
• Re-engineering current equipment designs to support high fidelity data collection and digitaluse cases.
• A hybrid pathway, consisting of the re-engineering of all or parts of equipment based on
operational data to create a ‘phygital’ (physical and digital) model of the equipment. Data isshared, from design to operation, linked to a commercial contract and value sharing model.
It should be noted that value generation through DCBM can be multiplied by applying any of theabove pathways to whole ‘systems’ rather than individual pieces of equipment – for example, creating
a data-driven maintenance process pathway for the ‘navigation system’ as a whole, or the ‘shafting
system’, the ‘propulsion system’ etc.

© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Designing a DCBM process
When it comes to designing a data-driven CBM process, there are typically two desired outcomes to choose from –proactive failure prevention, or failure minimisation. The type of strategies that will be followed will differ slightly ineither case.
Strategies for Proactive Failure Prevention

Strategies for Failure Minimisation• Establish Maintenance and Operational Guidelines:
Develop clearly defined criteria for both the maintenanceand operation of the equipment, aimed at averting
system breakdowns and malfunctions.

• Confirm Anomaly Detection Logic: Develop algorithms
or logic-based sequences to be used to promptly identify
anomalies and determine their underlying causes.

• Identify Consequences of Non-Compliance: Clearly
describe the adverse outcomes or major issues that may
arise if the established guidelines are not followed.

• Systematise Anomaly Handling: Formalise theprocedures and systems that will be used to deal with
detected anomalies to ensure a standardised andeffective response.

• Create a Compliance Monitoring Framework: Implementa system to continuously monitor and assess adherence
to maintenance and operational guidelines.

• Data Acquisition for Diagnostic Purposes: Accumulatecomprehensive sets of operational data to facilitate
precise and actionable diagnosis of issues.

• Data Acquisition for Diagnostic Purposes: Collectcomprehensive sets of data from vessels in operationthat can be utilised for diagnostic evaluations ofactual conditions.

Management to prevent failure occurrence(preventive management)

Optimization ofmaintenance/
inspection work list

Detection ofunperformed work

Planned Maintenance
Execute and manage thenecessary work to prevent
failures from occurring

Deciding how to respond

Identification of abnormal locations

Analysis and response to
failure risk
Analyze the risk of failure
occurring and take necessary
measures

Monitor the risk of failure due
to inappropriate operating
conditions© Lloyd’s Register 2024

Risk diagnosis

Detection of abnormal parameters

Operational data monitoring

Definition of
inappropriate
operatingconditions

Analyze data
to determine if
unperformed workneeds to be done,
and if so, when

Management to minimize the effects of failure(corrective management)

Detection of
inappropriate
operatingconditions

Minor
failureMajor
failureJudgment of
measure tominimize riskin response to
inappropriate
driving conditions

22

REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceCommunication and goal alignment
To develop a methodology that best fits as many use cases as possible for the relevant stakeholder
groups, it is important to be able to discuss the DCBM process and develop a mutual understanding of
how these methodologies can best be implemented for each organisation’s particular objectives.
One way to design and communicate how value can be delivered from DCBM is to use risk-assessment
tools such as the ‘Bow-tie’ model below. In this example, causes of failure (Threat) and preventive as
well as mitigation actions (Barrier) can be analysed to determine how major failures that all parties
want to avoid (Consequences) can be prevented.
Identified and agreed methods to neutralise Threats can be utilised in the development of the DCBM
system, to improve procedures or to inform future changes in the design of the equipment itself.

hazardLack of lubrication
LO inlet pressure to bemonitored

LO sump tank level tobe monitored

Description

Description

threatManagement of Mainbearings

consequencebarrier

Improper conditionof Lub Oil
LO inlet temp to bemonitored
Description

Minor damageof bearing
metals

Improper quality of
Lub OilLO quality to beanalyzed periodically

Water content in Lub
Oil to be measured
continuously

Description

Description

top eventMajor damage ofbearing metals
Bearing temp
is monitoredcontinuously

Bearing clearance tobe measured
periodically

Description: Replace bearing
metals if it is exceeded

Description: Replace bearing
metals if it is exceeded

thresholdthresholdSludge catcher to bechecked periodically
Description: If metal particles
are found inside sludge
catcher, search the locationof metal damage and replace
bearing metal

Excess load incoured
to the bearing metals
Engine load (Cyl press)
to be monitored
Description

In order to determine effective monitoring points and preventive measures, it is also necessary
to understand the causes of failures and their risk of occurring. Performing a Failure Mode EffectAnalysis (FMEA) as part of the DCBM design process is recommended, to properly organise preventive
measures and anomaly detection methods that can be used for the various identified failure modes
and potential causes of malfunction for the components.
Additional preventive measures and detection methods can be derived by quantifying risk within thisprocess, which can then be utilised in the selection of the parameters to be monitored by the sensor,
or other parameters such as sound or images that may indicate a failure.

© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceFailures that trigger
other failures

Management to prevent failure
occurrence

Failure α

Failure α’

Cause offailurePreventive
measures

FailureManagement to minimize theeffects of failure

Mitigating
measures

Failures due to failure
propogation
Failure ꞵ

Cause offailureFailure ꞵ’

Training machine learning models for DCBM
Development and training methods for the machine learning models and algorithms required to
power data-driven CBM can be divided into four categories – supervised learning, unsupervised
learning, reinforcement learning and semi-supervised learning.
The type of DCBM algorithm created is influenced by the availability of labelled input and output data
sets, meaning inputs in the form of time series sensor data that can be matched with outputs that
describe the state or condition of the equipment, as well as its sub-assemblies and replaceable parts(including failure modes).
Supervised learning algorithms explicitly require labelled time series data that corresponds toexamples of ‘normal’, ‘abnormal’, ‘faulty’ and ‘failure’ conditions, with the algorithm’s capabilities andperformance directly influenced by the availability of multi-variate data sets.
From a fault detection and isolation perspective, this requires unique data sets that represent eachdifferent failure condition – if a piece of equipment has 100 unique failure modes, then unique timeseries data for each failure type should be made available for algorithm training.
However, in reality those required training data sets can be difficult to acquire, for a variety of reasons:• Lack of identifiable failure taxonomy for the candidate equipment. Failure descriptions canbe subjective if there are no formal templates and guidance for their identification. Incorrectly
described failure modes, e.g. an incorrectly identified affected part, incorrect failure type orincomplete failure description, may be erroneously ‘learned’ by the data-driven algorithm,
resulting in false positives or false negatives during deployment.
• Exceeding minimum/maximum thresholds of sensors may not automatically mean a failure.
Similarly, incorrect labelling of events where thresholds are exceeded as being representative of
failure within the training data, where that threshold breach is not a legitimate failure event, could
result in false negatives or false positives.
• Training data set imbalance. While large data sets may be collected by local control systems(e.g. SCADA systems) and integrated control systems (e.g. propulsion management, SMS), most ofthese data sets are representative of normal operations with occasional instances of anomalous
operations (e.g. exceedance events, intermittent faults, unexpected outliers). Very few cases ofactual failure modes may be recorded within the data. Furthermore, the availability of training data
sets that do include specific failures are typically a small subset of all potential failure modes.

© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance• Fault and failure complexity. A complex piece of equipment could consist of several subassemblies and hundreds of replaceable parts. The operational and environmental stressors
experienced by the system can trigger multiple faults with various causes that can progress todifferent failure modes. Capturing these data sets for training is impossible without a very clear
failure taxonomy and precise labelling of faults and failures.
• Big data is not representative of the diversity of the operational environment. Having
large data sets does not automatically deliver representative examples of ‘normal’, ‘abnormal’and ‘failure’ operating conditions. A data driven algorithm will only learn from the uniqueexamples in the training data that has been provided. This is one of the major reasons why
consumer technology companies are at the forefront of data-driven algorithm/machine learning
development, as the general public is constantly producing data at a scale that supports moreaccurate machine learning.
In contrast to the supervised learning process above, unsupervised learning algorithms automaticallyextract specified features (for anomaly detection, fault detection, diagnostics and prognostics) fromthe time series data presented and use that information to estimate and distinguish different states ofhealth, which can then be labelled as outputs.
While unsupervised learning can be a powerful method for performing anomaly detection, it canface challenges if there are multiple failure modes that appear in isolation, or if there are different
permutations and combinations that lead to the same failures in different cases.

DCBM functional architectureData-driven condition-based maintenance can deliver significant improvements in the safety,
reliability and availability of assets. An example of a typical functional architecture to implement
DCBM is described below:
1. Availability of Electronic Sensors (fitted as standard in some equipment)
2. Additional Sensors to improve Fault-Failure Observability
3. Sensor Data Validation (Data Quality Management)
4. Data Acquisition
5. Descriptors/Fault Feature Extraction
6. Anomaly Detection
7. Fault Detection and Isolation
8. Diagnostics
9. Prognostics
10. Equipment State of Health Advisory
11. Generation© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Incentive alignment andnew business models
For data-driven condition-based maintenance (DCBM) to be effective from an industry perspective,
relevant interconnected stakeholders will need to make a move away from working with isolated
systems and adopt integrated platforms. To motivate these stakeholders to implement these new
ways of working it is important that the incentives of all parties involved are aligned, so that benefits
accrue for all actors.
Close collaboration between stakeholders is essential in understanding the impact of DCBM andhighlighting those benefits. Metrics should be established to evaluate the effectiveness of DCBM indelivering value to each of the parties involved, both quantitatively and qualitatively.
Examples of how value can be delivered when incentives are aligned can be seen in some earlycollaborative projects exploring potential DCBM pathways, where shipping companies, OriginalEquipment Manufacturers (OEMs) and class societies have already begun working together to explore
specific demand-side use cases and make the relevant adjustments to match the capabilities of OEMtechnologies.12
Small success stories like these can play a vital role in building confidence and trust in the DCBM
concept, breaking down barriers to open collaboration and innovation by demonstrating how
improvements can be achieved. This collaborative approach also enables consensus-building,scope coordination and capacity building, allowing partners to jointly develop new ways of working
together for mutual benefit.
With the individual CBM systems typically offered by OEMs and other solution providers today, theburden of responsibility for operation and management still largely falls on the ship owner, which
increases the risk of issues arising. To address these challenges, a more integrated data-driven CBM
system is proposed (outlined in the diagram below).
This new approach, backed by class approval of DCBM methodologies, would provide an increased
level of assurance for ship owners by providing data-backed evidence of a vessel’s condition,
which could then be further supported by Hull & Machinery (H&M) insurance coverage in case of
unforeseen issues.
Additionally, this integrated multi-stakeholder DCBM approach would have the potential to reduce
both planned and unplanned maintenance costs significantly. As a result, a more accurate returnon investment (ROI) can be calculated for the different parties and the responsibilities of each actor
defined, opening up a pathway to new business models for Original Equipment Manufacturers (OEMs)and transforming operational processes for both classification societies and ship owners.

1
2

NYK Group Joint Research on CBM Adopted in ClassNK Guidelines – https://www.nyk.com/english/news/2021/20210514_01.html
NYK pilots Wärtsilä predictive maintenance service – https://www.wartsila.com/jpn/media/local-news/13-04-2021-wartsilas-next-levelpredictive-maintenance-service-can-lead-to-fleet-wide-implementation-by-japanese-shipping-group© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Reach to TBM Running Hour (RH)

1

Instead of postponing opening indefinitely until anabnormality if observed, there is a reference TBM and theoperating time is used as the basis to determine whether
the opening can be extended beyond the TBM deadline.

2

Determine the specific operating time for the next condition
diagnosis and whether the opening can be extended.

3

In the workflow described above, after the TBM operation
time has elapsed, a decision is made each time whether
or not to extend open maintenance by dividing theoperation time.

OEM and Class evaluate the conditionof subject machinery then commit if
overhaul can be postponed or not.
OEM and Class instruct clearly when next
evaluation of machinery condition must
be implemented, which is necessary forfurther extention of overhaul is available
or not. For example, after 2000hrs ofoperation, next evaluation must be done.
Once RH is reached to above mentioned
RH, condition evaluation is done by OEM
and Class.
Repeat above.
Plan of next overhaul is decided withagreement of all related parties(OEM/Class/Shipowner).
5

The three parties
agree on the timingof open maintenanceand inspections.
Implement overhaul.

Shipowner suggests to OEM/Class how
long next overhaul is expected to be
postponed by considering timing of nextdock, vessel schedule, etc..
New overhaul must be advanced if any
abnormality is observed by continuous
condition monitoring.

4

If the shipowner plans tocarry out open maintenanceat a convenient time, suchas at the next dock, theshipowner informs the OEM/Class of this schedule and acondition assessment planis developed with the aim ofextending the period to thatpoint.

Existing service that remotemonitoring by OEM

Continuous condition monitoring.

Countermeasure to improve the condition
to be implemented according to the advice
from the OEM.

Innovation in business models
The broader implementation of DCBM processes in shipping will require the creation and sharing ofvalue across networks of digitally connected maritime stakeholders, at sea and on land. The scopeof work required to maximise the value to be derived from DCBM will include the digitalisation of adifferent combination of: Selected Equipment; Selected Systems; Selected Functions; and Selected
Value Chains.
An intermediate ‘quick win’ could involve the digitalisation of systems that are currently managed bysoftware onboard ships, and/or systems with design characteristics that are already well defined, tothe extent that they would support accurate measurement of their operational status.
One example of this would be the shafting system. Traditionally the condition of propeller shafts has
been assessed by visual examination. This method has largely been replaced by remote examination
based on available data, including oil analysis, bearing temperatures and measurement of wear, toreduce the need for withdrawal of the shaft.
Analysis of the data allows the company to determine the likelihood of deterioration of the shaft
bearing having occurred, and whether further examination is required. However, this means that theissue is only addressed after the fact. DCBM, incorporating design parameters and operational datain conjunction with existing data sets, could deliver additional value by potentially identifying issues
before damage occurs, enabling preventive measures to be taken.

© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance‘Routine’ vs ‘Radical’ innovation
While use cases like those described in this paper might be categorised as innovative models within a maritime context,
they represent an incremental level of innovation from the way we operate today rather than a radical shift. According to aJune 2015 Harvard Business Review (HBR) paper titled ‘You need an innovation strategy’, the ‘Innovation Landscape Map’
can be visualised within the following matrix:
The Innovation Landscape Map
When creating an innovation strategy, companies have a choice about how much to focus on technological innovationand how much to invest in business model innovation. This matrix, which considers how a potential innovation fits with acompany’s existing business model and technical capabilities, can assist with that decision.

REQUIRES NEW
BUSINESS MODEL

LEVERAGESEXISTING
BUSINESS MODELDISRUPTIVEARCHITECTURAL• Open source software
FOR SOFTWARE COMPANIES• Personalized medicine
FOR PHARMACEUTICAL COMPANIES• Video on demand
FOR DVD RENTAL SERVICES• Digital imaging
FOR POLAROID AND KODAK• Ride-sharing services
FOR TAXI AND LIMO COMPANIES• Internet search
FOR NEWSPAPERS

ROUTINE

RADICAL• A next-generation 3 series
FOR BMW• Biotechnology
FOR PHARMACEUTICAL COMPANIES• A new index fund
FOR VANGUARD• Jet engines
FOR AIRCRAFT MANUFACTURERS• A new 3-D animated film
FOR PIXAR• Fiber-optic cable
FOR TELECOMMUNICATIONS COMPANIESLEVERAGES EXISTING TECHNICAL COMPETENCESREQUIRES NEW TECHNICAL COMPETENCES

Source: Corning: Gary P. Pisano, from “You Need an Innovation Strategy”, June 2015

When creating an innovation strategy, companies have a choice in how much to focus on technological innovation and how
much to invest in business model innovation.
In maritime, the data-driven condition-based maintenance business model is not a ‘Routine’ innovation. DCBM could beconsidered ‘Disruptive’ once the industry has developed all of the technical competency required to create and implement
such processes. Over time, OEMs are expected to develop the required capabilities and technology to deliver this new
business model, however ‘digitising equipment’ may not, by itself, be sufficient to deliver the minimum required economic
value demanded by investors.
Where that economic value falls short in delivering the required ROI, we may find ourselves in a situation where newtechnology stakeholders need to enter the industry in order to generate the required returns. This would require an‘Architectural’ level of innovation – the most challenging pathway for well-established industries, according to the model.

© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

The maritime technology conundrum
In a 2017 article for the Harvard Business Review, Greg Satell categorises technological innovationaccording to two parameters – how well the domain is defined, and how well the problem is defined.

WELL

BREAKTHROUGH INNOVATIONNOT WELL

HOW WELL IS THE
PROBLEM DEFINED?

Mavericks
Skunk Works
Open innovation/prizes
BASIC RESEARCHSUSTAINING INNOVATION
Roadmapping
R&D labs
Design thinking
Acquisitions
DISRUPTIVE INNOVATION

Research divisions
Academic partnerships
Journals and conferences

VC modelInnovation labs
15%/20% rule
Lean launchpad

NOT WELLWELL
HOW WELL IS THE DOMAIN DEFINED?

Source: Greg Satell

In the case of data-driven condition-based maintenance, the problem is considered well defined,
although the opportunities that exist to solve the problem have not yet been fully defined; similarly,
the domain is also well defined, although the domain within a future operating model could be
different from today.
We believe the industry sits within the ‘Sustaining innovation’ quadrant at our current DCBM
starting point, and will evolve towards the ‘Breakthrough innovation’ sector through our technology
development pathway. From there, we should move towards fully data-driven decision-making businessmodels for vessel equipment, before improving further to viewing the ship as a ‘system of systems’ andsubsequently reaching the end goal of having the ship as an integral part of a wider value chain network.
Technology readiness is typically seen as a fundamental pilar in transforming operating businessmodels – once the technology exists, we can create business cases that apply that technology tocreate new ways of working.
However, the technology required to support maritime maintenance decision making based purely on
data does not yet exist on board ships at the required level of maturity. Where the technology does exist,
it has been applied at a small fraction of the scope required to create significant operational efficiencies.
Solving the remaining challenges requires two further developments to occur – we must implement
technology that addresses our requirements at the ‘function’ and ‘system’ level, and we must ensurethat such technology demonstrates a level of accuracy and reliability capable of meeting the levels of
performance and safety compliance required.
Resolving the first of these points and implementing technology that addresses our requirements isperhaps the most crucial in changing the way our industry performs maintenance, kickstarting the movetowards a digital business model. It is the most important step, and yet also the most difficult to achieve,
as it requires a re-balancing of levels of involvement by stakeholders and a move towards placing
systems integrators centre stage in the management of operational performance aboard digital ships.

© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based Maintenance

Call to Action
The maritime industry is in the midst of a major shift in operational processes that will challenge ourability to adapt to new global demands, with the twin drivers of decarbonisation and digitalisation
creating disruption in legacy processes and forcing shipping to embrace new ways of working.
As such, it is an appropriate time to seize the opportunity to collaboratively transform maritime
industry operations through the implementation of data-driven Condition-Based Maintenance(DCBM) processes for the benefit of all industry stakeholders.
For Classification Societies, these technologies can offer a pathway to new methods of risk
assessment, allowing inspection regimes to become more intelligent and effective. For Ship Owners,
the shift to data-driven processes offers an opportunity to achieve significant ROI by investing in new
methods of maintenance management that can reduce operational downtime and maximise thevalue of fleet assets.
Original Equipment Manufacturers (OEMs) have the opportunity to elevate their product offeringsand become an integrated industry partner by delivering data-driven value-added services. For the
insurance and finance industries, these changes will help to provide increased levels of confidence
in assessments of vessel condition, lowering coverage risks. Cargo Owners can also benefit fromincreased predictability in supply chain timelines and scheduling due to the predictive improvementsthat can be delivered by DCBM.
The authors encourage all industry stakeholders to explore the possibilities of these technologies andtake their first steps on this journey, so that together we can embrace a data-driven future to createa range of improvements beyond just enhancing safety – securing competitive advantage, reducing
overheads, and delivering excellence in maritime operations.

© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceAnnexes
Annex 1
Technologies and tools to support the development of data-driven algorithms and machinelearning models• Generalised machine learning algorithms designed to operate across multiple use cases
Introducing Pathways: A next-generation AI architecture (blog.google)• Paid-for and open-source data-driven algorithms and source code libraries
Amazon SageMaker JumpStart – Amazon Web Services
GitHub – The NASA Prognostic Python Packages
Machine Learning – Google AI• ISO/IEC standards for AI and Machine Learning
ISO/IEC TR 24030:2021 Information technology — Artificial intelligence (AI) — Use cases
ISO/IEC 23053:2022 Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)
ISO/IEC 24668:2022 Information technology — Artificial intelligence — Process management
framework for big data analytics• Examples of ML Ops Frameworks
ML Ops: Machine Learning Operations (ml-ops.org)
Microsoft Azure
Machine Learning with MATLAB – MATLAB & Simulink (mathworks.com)• Examples of ML DevOps Frameworks
Ansys Twin Builder | Create and Deploy Digital Twin Models
What is Digital Twin Software | Digital Twin Solutions | AVEVA
Predictive Maintenance Toolbox – MATLAB (mathworks.com)• Industrial AI marketplaces
ABB Ability. Industrial Internet of Things (global.abb)
KI-Marktplatz
Siemens Xcelerator© Lloyd’s Register 2024

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REPORT The benefits and opportunities of Data-Driven Condition Based MaintenanceAcknowledgements
NYK Line
Shogo Yamada
Kanefumi Uematsu
Monohakobi Technology Institute
Hideyuki Ando
Hideaki Nomura
Shinichi Inoue
Putu Hangga Nan Prayoga
Shuma Kochii
Lloyd’s Register
Jim Smith
Joseph Morelos
Luis Benito
Mark Warner
Sally Ann Sadler
Smart Maritime Network
Rob O’ Dwyer (Editor)© Lloyd’s Register 2024

32

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