Condition Monitoring of a Reciprocating Air Compressor Using Vibro-Acoustic Measurements

Abstract

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Continual learning fault diagnosis method based on grid-based artificial immune system

“…The SGCM can still correctly identify sample classes even if the corresponding sample for the training set is zero and only a fixed number of classes are needed. For group 3, the number of clusters and the generation changes during the testing process can be observed from table 10. The first row of the table represents the degradation number of generations.…”

“…Through a simple analysis of the vibration signal of the cylinder head of the reciprocating compressor, the internal working state of the compressor is accurately judged [6,7]. Various methods for extracting non-stationary signals [8][9][10], such as local mean decomposition (LMD), empirical mode decomposition (EMD), wavelet and wavelet packet analysis, principal component analysis (PCA) analysis and Hilbert-Huang transform (HHT) signal processing, have been used for fault diagnosis of reciprocating compressors and achieved good results. However, these diagnostic methods still have some disadvantages, such as cross interference, poor adaptability, serious aliasing problems and so on.…”