Fault Diagnosis Method for Hydraulic Pump Based on Fuzzy Entropy of Wavelet Packet and LLTSA

Wang Fei, Fang Liqing, Qi Ziyuan


As the vibration signal characteristics of hydraulic pump present non-stationary and the fault features is difficult to extract, a new feature extraction method was proposed .This approach combines wavelet packet analysis techniques, fuzzy entropy and LLTSA (liner local tangent space alignment) which is one of typical manifold learning methods to extracting  fault  feature. Firstly, the vibration signals were decomposed into eight signals in different scales, then the fuzzy entropies of signals were calculated to constitute eight dimensions feature vector. Secondly, LLTSA method was applied to compress the high-dimension features into low-dimension features which have a better classification performance. Finally, the SVM (support vector machine) was employed to distinguish different fault features. Experiment results of hydraulic pump feature extraction show that the proposed method can exactly classify different fault type of hydraulic pump and this method has a significant advantage compared with other feature extraction means mentioned in this paper.



wavelet packet analysis; fuzzy entropy; LLTSA; feature extraction; hydraulic pump; fault diagnosis

Full Text:


International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
Creative Commons License
Scopus logo Clarivate Analyatics ESCI logo IET Inspec logo DOAJ logo DBLP logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo