A condition based reliability simulator framework based on a heuristic fault model

Abstract

There are significant concerns as well as remedial efforts by the SA Power Utility to improve the generation plant performance that showed a significant decline over the past number of years. There is general consensus on the significant opportunity for the power utility to leverage Condition Monitoring (CM) and Advanced Analytics (AA) technologies to assist in the turn–around of technical performance of the power station fleet. Over the years, extensive online-monitoring technology and predictive condition monitoring capability have been introduced on high-value assets like the generator, turbine, transformer and boiler; with much less focus on Balance of Plant (BoP) equipment and machine trains. The research study developed a Condition Based Reliability Simulator (CBRS) Framework that uses a heuristic fault and failure analytical model based on the Design Basis of plant systems and equipment. By developing integrated advanced analytical and predictive fault models based on a thorough understanding of critical Design Basis parameters and typical failure behaviors, it is possible to better identify impending failures and apply the most appropriate CM technology at the right time. The research considered operating philosophy and maintenance strategy impacts and demonstrated how the CBRS Framework and its associated heuristic fault model approach can significantly enhance predictive capability and assist with asset management activities to achieve the lowest total asset cost (Figure 1). Due to the generic nature of BoP equipment and machine trains, it would have application to the wider process plant industry that uses similar equipment. The successful and practical application of this CBRS Analytical Framework and Fault Model development and implementation methods are discussed in this paper