Structured CMMS Implementation Approach for Wind Farms (Part 6 of 6)

Structured CMMS Implementation Approach for Wind Farms
Having understood the role of a CMMS solution in wind farm maintenance, it is equally important to take the right approach for the implementation of CMMS solution. Below is the holistic, structured, and futuristic CMMS implementation approach for long term wind farm sustenance, generating maximum value from the investments. It takes into consideration the current maturity levels of maintenance practices and accordingly recommends to gradually advance the depth of the maintenance practices leveraging advanced technology solutions to drive maximum values from wind farms.

Below picture depicts the suggested implementation approach for the CMMS solution. The approach aligns to start with “Basic” and then gradually advancing to “Optimize” level of maintenance. The approach is adjustable to align with the existing maturity level of wind farm maintenance practices.

Wind Turbines are heavy and complex asset structures which require appropriate monitoring, tracking and maintenance. Even if wind turbines are covered for service by manufacturer warranty or maintenance is outsourced to 3rd party, it is still important to manage and capture the data in the CMMS for future use.

Basic: This is the initial step towards moving to a formal CMMS solution implementation. This applies to wind farms where:

  • Preventive Maintenance is paper or excel based
  • Work Order and Inspections planning & execution are paper based
  • Limited or no Asset Setup and asset life cycle tracking
  • Legacy CMMS system is outdated

As part of this stage, following is proposed for the CMMS implementation:

Preventive Maintenance (PM): Setup the PM schedules for all the wind turbines at large and for their critical subassemblies as per the OEM PM guidelines as well as company specific maintenance guidelines established through experience. The PM can be setup as Time-Based where the turbines and sub-assemblies are inspected and maintained based on definite schedule (like every quarter\year), or PM can be setup as Meter-Based which means that after specific amount of usage (like # of rotations, hours of operations, amount of Power generated, etc) they are maintained. Typically, all major CMMS software have ability to generate the PM Work Order in advance (by few days or weeks which is configurable) giving planners enough time to plan for resources, spares, equipment needed for maintenance.

Asset Setup and Life Cycle Management: Setup all the wind turbines including their sub-assemblies as assets in CMMS. The depth of the sub-assembly hierarchy should be driven by the criticality of operations and maintenance requirements. Then track key aspects of asset life cycle closely like asset maintenance planning, tracking maintenance costs, asset criticality, downtime, replacement costs, asset design changes all the way till asset retirement and asset disposal.

Work Management: Record and track all the maintenance and inspection work executed for the wind turbines including capturing tasks executed with duration, failure details, inspection results, labor and materials used. The way data is captured is critical, so CMMS system should provision structured and consistent data capture which can be used for future reporting and analysis. Link important aspects of PM, work management and asset management for efficient tracking and reporting.

Advance: After the “Basic” CMMS setup is done, the next step is a gradual movement towards the “Advance” stage. In this stage, the base setup done as part of the “Basic” stage is leveraged to move towards an advanced maintenance planning as proposed below:

Condition Driven Maintenance: Leverage data from installed sensors on wind turbines to track various operational parameters like vibration frequency, sound, temperature among other and whenever these parameters are out of operational range as per prescribed limit, indicating a signal of potential future failure, then trigger an automated work order for maintenance. Early detention and resolution of potential failures helps to avoid both costly critical failures and unplanned downtime.

Crew Mobility: The nature of work with wind farm maintenance includes working in large area wind farms and at extreme heights. Typically, wind farm technicians rely on paper and carry bunch of papers related to work they execute, or inspection results they capture manually on paper. At times they might discover some anomalies and might need to fix something on the go. A mobile device can help in multiple ways by carrying all the information related to their work, carrying a questionnaire driven inspection form to easily capture data and looking for additional details (related to previous work history or asset details) on the go as needed. Often connectivity in such areas is limited, so a mobile solution with ability to work in offline mode is a better option.

Data Repository: There is a lot of data generated by the wind farms. For instance, SCADA integrations provide a lot of operational data, the installed sensors provide the condition data, the inspections and work order provide the inspection and failure data. Then there is additional external weather data like wind speed, wind direction, temperature, humidity. All these data are gold and there should be appropriate provision to capture this data which can be leveraged in future to derive insights.

Optimize: After the appropriate setup and use of “Advance” CMMS setup, the next step is a gradual movement towards the “Optimize” stage. The “Optimize” stage is a journey and a not a destination where the operations and maintenance are continuously improved. Following is proposed for the CMMS implementation as part of Optimize stage:

Predictive Maintenance (PdM): Predictive Maintenance (PdM) is an advanced level of Condition Based Maintenance where the Artificial Intelligence (AI) and Machine Learning (ML) techniques are applied along with the real time data from installed sensors to discover patterns of possible failures. Leverage the historical condition and failure data and combine it with the current operational data to derive predict the failure and hence generate a Predictive Maintenance work order. The AI\ML model learns with more data and becomes better with time.

Analytics: On the data repository, which is established as part of “Advance” stage, apply analytics to derive insights from historical data which can assist in enhancing both operations and maintenance practice. It could be related to farm operations, maintenance data, failure data.

Benchmarking: Set benchmarks for critical aspects of operations and maintenance related to power generated per turbine, capacity utilization, downtime, mean time to repair, mean time between failures among others. Compare turbines against these benchmarks both internally within farms as well as with other wind farms owned by same utility and by others. These help to understand, compare, and continuously improve both operations and maintenance.

IBM Maximo Application Suite for Wind Farm
IBM Maximo is one of the leaders in this space because along with core functionality of a standard CMMS solution they have a specialized Asset Performance Management [APM] solution for utilities operators with capabilities like:

  • Ability to manage the complex composition of parts needed to ensure continuous operation and support the preventive and condition-based maintenance required for wind turbines
  • The connection and monitoring of real-time sensor data with included AI based anomaly detection for identifying potential problems before they occur. This can include operating characteristics as well as energy generation data. Custom dashboards can be created to visualize the current and historical data associated with the assets.
  • Visual insights provided by IBM’s AI technology to recognize and categorize cracks from drone footage or mobile snapshots.
  • Ability to identify and visualize on a map, the overall health and risk of individual wind turbines and/or components based on a combination of sensor data, inspections, maintenance records and age. Includes detailed view of each asset along with scores and KPIs.
  • Predicted failure dates and probability of asset failures based historical and current data.

Conclusion
To summarize, wind energy presents exciting prospects in renewables segment. It is a cleaner, safer source of energy available in abundance from nature. Technology advancements are happenings while lowering initial costs and increasing reliability of wind turbines, making it much more financially viable which will only improve with time. Utilities taking lead in operations and maintenance leveraging advanced Operations Technology and CMMS solutions will be differentiated from others creating greater value for their shareholders and fast tracking the Net Zero carbon footprint targets. Wind energy opportunity is literally in the air for the world to grab.

Author Details

Ganesh Murai

Ganesh is a seasoned consultant with over 19 years of experience in Process Design, Solution Design, Integrated Solution Architecture and Technical Solution Implementation in various functional areas like Supply Chain, Business Spend Management, Work & Asset Management, Field Mobility, Warehousing & Logistics. He has worked for clients across various industries like Utilities, Railroad, Oil & Gas, Logistics and Retail customer. He has presented in global forums like Maximo World and GO Maximo on multiple topics. He is a certified Maximo consultant and certified Coupa Consultant.

Vijeth Shanbhag

Vijeth Shanbhag is a Senior Consultant with Infosys and has 8+ years of experience in Supply Chain Management with industry experience in Energy & Utilities and Manufacturing sector.

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