The use of novelty detection techniques for monitoring high-integrity plant

Total care schemes are now a common feature in the sales of power generation and propulsion plant. To mitigate risk of financial penalties and maximise profit, many suppliers will rely on health usage and condition monitoring techniques. Intelligent condition monitoring is a relatively new concept in this field and introduces prognostic capability. One key obstacle in this approach is the implementation of some form of rule-base that encapsulates possible fault conditions. The difficulty here is that a given fault scenario will not necessarily manifest itself in the same manner twice and will require complex rule-sets to describe possible variations in the development of the fault. In addition, due to the robustness of current high-integrity plant, example fault conditions are very rare and hence difficult to model using data driven approaches. Seeding faults during development is one approach often used, however, this can never be entirely representative of an in-service failure in addition to being a costly exercise. This paper describes the practical implementation of novelty detection schemes that aim to overcome the limitations described above.