Abstract-Within power generation, ageing assets and an emphasis on more efficient operation of power systems and improved maintenance decision methods has led to a growing focus on asset prognostics. The main challenge facing the implementation of successful asset prognostics in power generation is the lack of available run-to-failure data. This paper proposes to overcome this issue by use of full scope high fidelity simulators to generate the run-to-failure data required. From this simulated failure data a similarity based prognostic approach is developed for estimating the Remaining Useful Life of a valve asset. Case study data is generated by initializing prebuilt industrial failure models within a 970MW Pressurized Water Reactor simulation. Such full scope high fidelity simulators are mainly operated for training purposes, allowing personnel to gain experience of standard operation as well as failures within a safe, simulated operating environment. This research repurposes such a high fidelity simulator to generate the type of data and affects that would be produced in the event of a fault. The fault scenario is then run multiple times to generate a library of failure events. This library of events was then split into training and test batches for building the prognostic model. Results are presented and conclusions drawn about the success of the technique and the use of high fidelity simulators in this manner.Index Terms-Model-based prognostics, remaining useful life, high fidelity simulation, power generation,

Within the field of power generation, aging assets and a desire for improved maintenance decision-making tools have led to growing interest in asset prognostics. Valve failures can account for 7% or more of mechanical failures, and since a conventional power station will contain many hundreds of valves, this represents a significant asset base. This paper presents a prognostic approach for estimating the remaining useful life (RUL) of valves experiencing degradation, utilizing a similarity-based method. Case study data is generated through simulation of valves within a 400MW Combined Cycle Gas Turbine power station. High fidelity industrial simulators are often produced for operator training, to allow personnel to experience fault procedures and take corrective action in a safe, simulation environment, without endangering staff or equipment. This work repurposes such a high fidelity simulator to generate the type of condition monitoring data which would be produced in the presence of a fault. A first principles model of valve degradation was used to generate multiple run-to-failure events, at different degradation rates. The associated parameter data was collected to generate a library of failure cases. This set of cases was partitioned into training and test sets for prognostic modeling and the similarity based prognostic technique applied to calculate RUL. Results are presented of the technique’s accuracy, and conclusions are drawn about the applicability of the technique to this domain.