Abstract
With the advent of modern computation, intelligent control and maintenance systems have become a viable option for complex engineering processes and systems. Such control and maintenance systems can be generically described as being composed of 5 analysis steps: (1) predict the expected system signals from their measured values, (2) use the residual of the measured and predicted value to determine if the system is operating in a nominal or a degraded mode, (3) if the system is operating in a degraded mode, diagnose the fault, (4) prognose the failure by estimating the remaining useful life (RUL) of the system, and (5) use the collected information to determine if an appropriate control or maintenance action should be performed to maintain the health and safety of the system performance. This chapter presents the development and adaptation of a single generic inference procedure, namely the nonparametric fuzzy inference system (NFIS), for monitoring, diagnostics, and prognostics. To illustrate the proposed methodologies, the embodiments of the NFIS are used to detect, diagnose, and prognose faults in the steering system of an automated oil drill. The embodiments of the NFIS were found to have similar performance to traditional algorithms, such as autoassociative kernel regression (AAKR) and k-nearest neighbor (kNN), for monitoring and diagnosis. The NFIS prognoser was also shown to estimate the remaining useful life of the steering system to within an hour of its actual time of failure.
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Garvey, D.R., Hines, J.W. (2008). An Integrated Fuzzy Inference-based Monitoring, Diagnostic, and Prognostic System for Intelligent Control and Maintenance. In: Lowen, R., Verschoren, A. (eds) Foundations of Generic Optimization. Mathematical Modelling: Theory and Applications, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6668-9_5
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