A Fault Diagnosis Method for Rotating Machinery Based on PCA and Morlet Kernel SVM

Abstract

A novel method to solve the rotating machinery fault diagnosis problem is proposed, which is based on principal components analysis (PCA) to extract the characteristic features and the Morlet kernel support vector machine (MSVM) to achieve the fault classification. Firstly, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. So, the PCA is introduced to extract the characteristic features and reduce the dimension. The characteristic features are input into the MSVM to train and construct the running state identification model; the rotating machinery running state identification is realized. The running states of a bearing normal inner race and several inner races with different degree of fault were recognized; the results validate the effectiveness of the proposed algorithm.

Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

“…Selection. Canonical discriminant analysis (CDA) is a dimension reduction method developed from the principal component analysis (PCA) method [19] and canonical correlation analysis (CCA) method [20]. Given the label values and quantitative variables, it is possible to extract the canonical components that are linear combinations of the quantitative variables.…”

Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

“…On the contrary, in the previous paper [4], the authors developed the AVPCA algorithm and used it as a feature extraction and then for classification to detect and diagnose the different bearing faults using only the rotor-speed signal. Furthermore, the PCA method was used for the condition monitoring systems (CMSs) [29][30][31] and for bearing fault detection and diagnosis [4,17,32,33]. However, it was never used as an image recognition tool to BFDD as proposed in this paper.…”

Local Prediction of Chaotic Time Series Based on Polynomial Coefficient Autoregressive Model

“…By using those combining techniques, the parameters can be obtained faster and the analysis of residual can be underestimated if residuals are not randomness. Such as SVM combined with neurofuzzy model [21], SVM combined with PCA [26], ARMA combined with RESN [8], and neural network combined with neurofuzzy model [34]. Those methods can obtain generally better results than those obtained with single model, but they are complex, affected by personal experience and easy to be overfitted.…”