![]() ![]() ![]() gave a data-driven rotating machinery fault diagnosis method based on compressed sensing (CS) and an improved multi-scale network (IMSN), which can effectively identify faults under different working conditions. They used the simulated annealing genetic algorithm fuzzy c-means clustering (SAGAFCM) method to eliminate redundant and invalid data. For the PEMFC system, a deep belief network (DBN) was adopted by Zhang et al. put forward a transparent, exploratory, and detailed data mining workflow based on data characterization, time window, association rule mining, and association classification. proposed a multivariable process fault diagnosis method based on data-driven, used the normalized transfer entropy (NTE) between the measured process variables and residual signal variation to estimate the strength of causality, which reduced the amount of calculation required for analysis. Since it can diagnose without a precise system model description, and the historical data can be obtained entirely and sufficiently, both academia and industry attach positive importance to this method. ![]() Data-driven strategies include information processing methods, statistical analysis methods, machine learning methods, et cetera. The way based on data-driven is to collect, analyze and diagnose the data generated during the operation of the equipment without knowing the accurate system model. Last but not least, the data-driven method. The limitation of the ES lies in relying on the domain knowledge acquisition of experts. Knowledge-based method as the second one, such as fault tree and Expert System (ES). For a large-scale system, it is arduous to establish an accurate mathematical model. First, the analytical model-based approach, for example, parameter estimation and equivalent space method, which is based on the system operation mechanism. Numerous methods have been proposed since the development of fault detection and diagnosis technology, including roughly three categories. Fault detection and diagnosis are essential measures to improve system reliability and availability. ![]()
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