Abstract
Fault diagnosis can be divided into two main tasks: fault feature extraction and fault data classification. Firstly, aiming at the problem that the fault feature extraction method is not significant, this paper proposes a fault feature extraction method based on fuzzy distance (FD). The initial fuzzy distance calculation method is established by expert knowledge. The key parameters of fuzzy distance are optimised based on DE algorithm combined with historical test data. Secondly, aiming at the problem of low accuracy when classifying fault data, this paper proposes a fault data classification method based on grey target (GT) decision. The fault data classification is realised by calculating the target distance of each fault type with the current input signal set. Finally, the fuzzy distance- grey target (FD-GT) method is verified by an example. The results show that it can achieve more efficient and reliable fault diagnosis.
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