A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification

Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment

“…In general, the time-domain statistical features are usually employed as the physical magnitude descriptors. is study calculates eight statistical features to build the original time-domain features [17], including the maximum peak value, kurtosis, absolute mean, crest factor, shape factor, root mean square (RMS), variance, and square root value. Simultaneously, as one of the famous feature generation techniques, wavelet transform (WT) has been widely used with its merit of multiresolution analysis.…”

“…Up to now, many advanced techniques have been working on the original features construction, which can be divided into three analysis domains: time-domain analysis, frequencydomain analysis, and time-frequency analysis. To further extract the sensitive features, feature selection (FS) [13][14][15] or feature extraction (FE) [16][17][18][19][20][21][22] techniques are often used as the preprocessing methods for bearing fault diagnosis and degradation assessment. e FS methods mainly focus on the selection of the most sensitive features based on the principle of the feature discrimination ability.…”

“…By utilizing the internal liner/nonlinear characteristics in the raw features, the FE methods employ some optimization strategies to enhance the sensitivity of the extracted features. ere are mainly two different kinds of FE methods: linear projection methods, such as principal component analysis (PCA) [13], linear discriminant analysis (LDA) [14], and locality preserving projections (LPP) [17] and nonlinear projection methods, including kernel PCA (KPCA), trace ratio linear discriminant analysis (TR-LDA) [20], local tangent space alignment (LTSA) [21], and locally linear embedding (LLE) [23]. Linear FE methods are practical and visualized but are not sufficient enough to extract the nonlinear structure in the nonstationarity data.…”

“…However, in the traditional framework, the normal data and the monitor data are always independent and the information will not reflect on the mappings/extracted features. Our recent work [17] has shown a significant difference between the feature space of healthy data and unhealthy data, which will effectively improve the clustering and distinguishability of multiclass features, including identification for some different degradation severities with similar feature distribution. In this manner, in our work in [18], we have tried to use reference features to evaluate the bearing performance degradation.…”

Multifault Diagnosis Method Applied to an Electric Machine Based on High-Dimensional Feature Reduction

“…Typically, it is estimated the root mean square (RMS) from the vibration signal as a numerical indicator to assess the general condition of the machine [16]- [18]. In order to consider improved characterization of the vibration signal, the numerical set of features is extended to additional statistical time-domain, frequency domain, and also time-frequency domain [19]- [21], [22].…”