Reliability Centered Asset Management (RCAM)
Predictive maintenance using operation data for Asset Management – with power system applications
The value of making smart decisions gives a reason for adopting Asset Management (AM). AM is a broadly used concept typically used either in the financial or engineering sector. “Asset Management is defined as a coordinated activity of an organization to realize value from assets”. The first step of AM is always the motivation. In this research the applications in focus all related to the infrastructure of the electric power system, which is a technical system that has been identified as one of the most important inventions of the past century. Access to electricity for lighting and transportation was fundamental for the industrial revolution. Today electricity is fundamental to the digital society. A future trend is the returning to use electricity for transportation making it possible to use renewable fuels. The electric power system is an enabler for the transition into a sustainable energy system, also referred to as Smart Grid (SG). The figure below gives an illustration of the impact of maintenance policies on life curves (Figure 2.1 Bertling Tjernberg, CRC press 2018) and consequently on the value of preventive maintenance for AM.
The Reliability Centered Maintenance (RCM) method is a structured approach that focuses on reliability aspects when determining maintenance plans. The concept RCM was established in 1978 by Nowlan and Heap. They were active in the commercial aviation industry. The need to improve reliability while containing the cost of maintenance was the driving factor behind this work. Maintenance and reliability are important because of the large costs associated with maintenance tasks and costs due to the loss of production and breakdowns. Breakdowns can also lead to consequences that affect the environment or personal safety. These aspects could also be taken into consideration when performing an RCM analysis.
This research present the Reliability Centered Asset Maintenance (RCAM) method. The RCAM method combines the proven method of RCM with quantitative maintenance optimization techniques. The RCAM method was originally developed for the application on electric power distribution systems by Bertling in 2002, and later shown promising for the maintenance strategy selection and optimization of wind turbines.
The figure below gives an overview of steps of the RCM and RCAM process. It is outgoing from the: IEEE, IEEE Recommended Practice for the Design of Reliable Industrial and Commercial Power Systems, The Gold Book. IEEE Std 493-2007, February 2007. (Figure 4.3 in Bertling Tjernberg, CRC press 2018). Note that the process includes the operation and support phase and not only the design and development.
The two figures below show results from ongoing research. The overall approach is to use large volumes of data from the operation phase, e.g. SCADA systems, for predicting potential failures for wind turbines. This information could be used for planning predictive maintenance and making life time estimations. Recent publications: Y. Cui, P. Bangalore, and L. Bertling Tjernberg, An anomaly detection approach using wavelet transform and artificial neural networks for condition monitoring of wind turbines’ gearboxes , In proceedings of Power Systems Computation Conference (PSCC), Dublin, 2018. , Y. Cui, P. Bangalore, and L. Bertling Tjernberg, An anomaly detection approach based on machine learning and SCADA data for condition monitoring of wind turbines, PMAPS, Boise, 2018.
