Orthogonal Wavelet Transform-Based Gaussian Mixture Model for Bearing Fault Diagnosis

Abstract

The Gaussian mixture model (GMM) is an unsupervised clustering machine learning algorithm. This procedure involves the combination of multiple probability distributions to describe different sample spaces. Principally, the probability density function (PDF) plays a paramount role by being transformed into local linear regression to learn from unknown f failure samples, revealing the inherent properties and regularity of the data, and enhancing the subsequent identification of the operating status of the machine. The wavelet transform is a multiresolution transformation that can observe the signal gradually from coarse to fine, highlighting the localization analysis of nonstationary signals. Orthogonal wavelet transform selects the appropriate orthogonal wavelet function to transform so that the local characteristics of the signal in the time domain and frequency domain can be specifically described and the feature information of the original data can be mastered more effectively. In this study, a diagnostic method based on the Gaussian mixture model (OWTGMM) of orthogonal wavelet transform is proposed, in which orthogonal wavelet transform (OWT) is used to extract each detailed fault signal, the signal peak-to-peak value eigenvector is used as the construction model, and the GMM is used for fault classification. Based on the classification result from the rolling bearings’ test data, the use of detail signals extracted through OWT as the training data of the Gaussian mixture model promotes fast classification of bearing faults. Compared with the GMM without the extraction of the characteristic values, this method can reliably distinguish the categories of bearing faults about 100% of the time, which is consistent with the service life test chart. Furthermore, the unknown fault data is subject to classification with the orthogonal wavelet Gaussian model, and the bearing fault data is well distinguished, with an overall recognition rate of over 95%.

Multidimensional clustering based fault diagnosis method for wind turbine transmission chain

“…However, this method focused solely on gearbox faults and did not consider the specificity of transmission chain faults. Li et al [18] employed the orthogonal wavelet transform method to extract detailed signals as training samples and utilized the K-centre point clustering algorithm for classifying rolling bearing faults. However, the robustness of the clustering results could be improved, as the boundaries between different fault types appear fuzzy, and more support from engineering data is required.…”