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