Mass production of wind turbines (on an industrial scale) is a relatively young industry (less than a decade old) and, as such, robust operational data is hard to come by. It is not uncommon for experts to rely on anecdotal data to manage predictive maintenance (what component is likely to fail, on which turbine, and how to avert it). Inadequate documentation, for example the lack of maintenance comments recorded on completed work orders, and inconsistent review of comments provided, is a systemic problem.
Capturing equipment history data is extremely important at this early stage in the life cycle of these critical assets. Harnessing historical and current asset performance data and providing an electronic repository of machine condition, spare parts and equipment failure data can greatly reduce wind turbine maintenance costs, but under-utilization of computerized maintenance management software (CMMS), which supports consistent, enterprise-wide, accurate data-capture, remains a consistent trend throughout the industry.
Current estimates of successful maintenance software systems implementation are between 20 and 60 percent, despite the findings of the CMMS Best Practices Study conducted by Reliabilityweb.com (and a plethora of supporting evidence) which demonstrated that CMMS’ core functions (work order management, reporting and inventory) are “very important to users."
Challenges in the Wind Industry
Operations and maintenance (O&M) is the nexus of lean manufacturing, affecting everything from profitability, safety, environmental compliance and asset life to consumer confidence. Three areas for improvement that are adversely affecting the ascendancy of the wind industry are unruly maintenance costs, lack of historical maintenance data, and underutilization of the maintenance software systems that can redress the first two issues. Currently, US $40 billion worth of wind installations are at (or near) the end of the original equipment manufacturer (OEM) warranty periods, with current energy demands such that more operational turbines were out of warranty than covered in the previous year, according to an extensive study commissioned by Sandia National Laboratories. Wind farmers whose warranties have expired are weighing the pros and cons of managing maintenance internally or outsourcing it to OEMs or ISPs.
A study conducted by Electric Power Research Institute found that the annual maintenance cost of repairing machinery after it breaks down (corrective maintenance) is $17 to $18 per horsepower, while the annual cost per horsepower using preventive maintenance is between $11 and $13, and using predictive maintenance is between $7 and $9 – a substantial savings in both the short and long terms. The study revealed a potential maintenance cost savings of 47 percent by using predictive maintenance techniques. CMMS allows for maintenance and reliability (M&R) specialists to more accurately visualize the “P to F Curve”, also known as the Potential to Failure diagram, which illustrates the period between when equipment begins to fail and when it has completely done so. By providing alerts and appropriate work orders earlier on the curve, O&M specialists can detect and correct equipment anomalies prior to complete failure and reduce the costly downtime associated with it.
A report by the business intelligence firm GBI Research indicates that technological advancements in wind power mechanisms will allow wind farms to run far more efficiently and reap more profits as older turbines are upgraded. Similarly, as older workers retire and more tech-oriented workers enter the workforce, those upgrades will enable the industry to better engage them. David Berger, a recognized CMMS/enterprise asset management (EAM) system expert, sums it up: “Many senior management teams have come to the realization that, given the aging workforce being replaced with younger, less experienced technical resources, modern knowledge management tools such as a CMMS/EAM are critical to help smooth the transition. These tools retain much of the knowledge lost when technicians and other maintenance staff retire or leave – for example, standard operating procedures and job plans, failure analysis data, diagnostic techniques and a complete asset history. Furthermore, younger technical resources have come to expect these tools, and are comfortable and proficient in their use.”
CMMS and Wind Power Case Studies
Case Study 1:
Industry: Energy and Technology
Situation: The company needed to eliminate the inconsistencies its various data sources had created and sought a system to facilitate a master data validation to ensure an accurate and efficient data migration.
Solution: The selected CMMS provider conducted extensive analysis of the company’s myriad legacy data sources, isolated crucial inconsistencies, and facilitated the development of data standards. Data was captured, consolidated, and standardized via the CMMS. Descriptions for approximately 500,000 material masters were created. Additionally, programs to support the reconciliation, preparation, and data transfer of all asset-related data, work orders and PM procedures was prepared.
Results: 92,600,000 error-free records were loaded, which facilitated standardized master data across all facilities. Once the data was loaded into the CMMS, the consistent asset classification and data validation enhanced the client’s reliability processes via standardized reporting, metrics, and KPI generation. This, is in turn, alleviated the excessive spending (e.g., excess inventory purchasing), which is often caused by inaccurate, missing or incomplete data.
Case Study 2:
Industry: Power Generation
Situation: A large multi-fuel and technology energy company continuously lost revenue due to poor and inconsistent data quality and technology platforms.