Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve

Abstract

In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving the serious modal aliasing problem of local mean decomposition (LMD) and the dependence of permutation entropy on the length of the original time series. First, by adding a sine wave with a uniform phase as a masking signal, adaptively selecting the amplitude of the added sine wave, the optimal decomposition result is screened by the orthogonality and the signal is reconstructed based on the kurtosis value to remove the signal noise. Secondly, in the RTSMWPE method, the fault feature extraction is realized by considering the signal amplitude information and replacing the traditional coarse-grained multi-scale method with a time-shifted multi-scale method. Finally, the proposed method is applied to the analysis of the experimental data of the reciprocating compressor valve; the analysis results demonstrate the effectiveness of the proposed method.

An improved method for signal de‐noising based on multi‐level local mean decomposition

“…The results revealed that LMD is more suitable and performs better than EMD for the incipient fault detection. Song et al 38 proposed a fault feature extraction method that combines adaptive uniform phase local mean decomposition (AUPLMD) and refined time‐shift multiscale weighted permutation entropy (RTSMWPE) to recognize different categories and severities of reciprocating compressor valve faults. Yang and Zhou 39 utilized LMD and wavelet packet transform (WPT) to extract fault features of a diaphragm pump check valve.…”