Abstract
The idea of safety region was introduced into the rolling bearing condition monitoring. The safety region estimation and the state identification of the rolling bearing operational were performed by the comprehensive utilization of Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), and the Least Square Support Vector Machine (LSSVM). The collected vibration data was segmented according to a certain time interval, and then the Intrinsic Mode Functions (IMFs) of each piece of the data were obtained by EMD. The control limits of two statistical variables extracted by PCA were presented as state characteristics. The safety region estimation for the rolling bearing operational status was performed by two-class LSSVM. The states of normal bearing, ball fault, inner race fault, and outer race fault were identified by the multi-class LSSVM. The results show that the estimation accuracy for both the safety region and the states identification reached 95%, and that the validity of the proposed method was verified.
Highlights
Rolling bearings are widely used in some industries such as railway vehicle, automobile, construction machinery and so on
The concept of safety region was introduced into the rolling bearing condition monitoring and the estimation methods integrated with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), and Least Square Support Vector Machine (LSSVM) were proposed based on statistical features extraction
intrinsic mode functions (IMFs) components decomposed by EMD were processed by PCA
Summary
Rolling bearings are widely used in some industries such as railway vehicle, automobile, construction machinery and so on. Only 10%∼20% of the rolling bearings can achieve their design life [1]. Accurate and effective condition monitoring and identification of the rolling bearings are very important for safety, work efficiency and operating cost. Feature extraction and state identification are key issues while dealing with rolling bearing condition monitoring problems. Empirical Mode Decomposition (EMD) is a relatively new signal processing method [2]. It is a proper technique for non-stationary and non-linear signal processing such as mechanical vibration signal because of its self-adaptive and high signal-to-noise ratio [3]. The intrinsic mode functions (IMFs) which are obtained by EMD can be used to extract fault feature information. Calculation of the energy moment [1], energy entropy [4], Renyi entropy [5], and Shannon entropy [6] of IMFs and calculation of singular
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