Rolling Bearing Reliability Assessment via Kernel Principal Component Analysis and Weibull Proportional Hazard Model

Abstract

Reliability assessment is a critical consideration in equipment engineering project. Successful reliability assessment, which is dependent on selecting features that accurately reflect performance degradation as the inputs of the assessment model, allows for the proactive maintenance of equipment. In this paper, a novel method based on kernel principal component analysis (KPCA) and Weibull proportional hazards model (WPHM) is proposed to assess the reliability of rolling bearings. A high relative feature set is constructed by selecting the effective features through extracting the time domain, frequency domain, and time-frequency domain features over the bearing’s life cycle data. The kernel principal components which can accurately reflect the performance degradation process are obtained by KPCA and then input as the covariates of WPHM to assess the reliability. An example was conducted to validate the proposed method. The differences in manufacturing, installation, and working conditions of the same type of bearings during reliability assessment are reduced after extracting relative features, which enhances the practicability and stability of the proposed method.

Kernel PCA in nonlinear visualization of a healthy and a faulty planetary gearbox data

“…It is possible to continue the analysis using some more sophisticated methods like in [8], permitting to find the kind of the fault. In any case, the main answer about state of the device, can be easily perceived from the simple scatterplots of few kernel PCs.…”

Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip‐EEMD‐GM(1, 1) Model

“…Figure 4 shows the pictures of bearing components after a test; the bearing failure in different forms. From the vibration signal, 11 features that could reflect the degradation process are selected to form a feature set [23].The features were as follows:…”

“…The KPCA was used to reduce the dimension of the bearing feature set and to select the first three-dimensional kernel principal components, whose cumulative contribution rate was greater than 85%. The WPHM parameters were estimated and shown in Table 1 [23].…”

“…The hazard rate was calculated by using the variation data series of the KPC1, the KPC2, and the KPC3. The different degradation stages [23] were used to select the data from four time periods in order to predict the trend with the GM(1,1) model. The characteristics of the data were shown by amplifying and marking each time period in Figure 6.…”

An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder

“…The input space is mapped to a highdimensional feature space using a nonlinear transform and the calculation in the high-dimensional features space is performed using a kernel function; this approach reduces the computational complexity. Some conventional methods have been improved by various approaches of the kernel function method, including the kernel principal component analysis (KPCA) [17], the kernel independent component analysis (KICA) [18], and the kernel discriminant analysis (KDA) [19]. Hence, a novel KAE network based on a kernel function combined with an AE is proposed.…”