Abstract
To timely detect bearing operating condition, and accurately identify bearing fault type and fault severity, a novel multi-stage hybrid fault diagnosis strategy for rolling bearing is proposed in this paper, which mainly consists of three stages (i.e. fault initial detection, fault type recognition and fault severity assessment). Firstly, the procedure of permutation entropy (PE)-based fault initial detection is performed to estimate bearing operating condition. If the bearing fault exists, the next two stages are conducted for fault type recognition and fault severity assessment. Specifically, in the second and third stages, for each dataset under different fault conditions, hybrid-domain features including time-domain, frequency-domain and time-frequency domain are firstly extracted to establish high-dimensional feature space based on statistical analysis and variational mode decomposition (VMD). Then, locality preserving projection (LPP) is introduced to compress high-dimensional dataset into low-dimensional feature space which can reflect preferably intrinsic information of the raw signal and remove information redundancy embedded in hybrid-domain features. Finally, the obtained low-dimensional dataset is imported into Fuzzy C-means (FCM) clustering for recognizing bearing fault type and fault severity. The efficacy of the proposed approach is verified by experimental bearing data under different working conditions. The results indicate that our proposed method can both assess effectively bearing health status and recognize accurately bearing fault type and fault severity. In addition, our proposed approach has higher diagnosis precision than traditional single-stage diagnosis method mentioned in this paper.
Highlights
Research on fault detection of rolling element bearing has drawn much attention in recent years
Yan and Jia [8] proposed an improved multiscale dispersion entropy to obtain bearing fault feature information, and the mRMR method is used for feature selection and ELM is adopted for intelligent fault diagnosis of rotating machinery
In this paper, a neoteric multi-stage hybrid fault diagnosis scheme is proposed for identifying different health conditions of rolling bearing, which is divided into three stages
Summary
Research on fault detection of rolling element bearing has drawn much attention in recent years. Successful results have been achieved by the application of many time-frequency analysis techniques (e.g. Wigner-Ville distribution (WVD), wavelet transforms (WT), empirical mode decomposition (EMD), local mean decomposition (LMD) and intrinsic time-scale decomposition (ITD)) in fault detection [21] These approaches involve some inherent drawbacks for multiscale feature extraction [22]. For a bearing vibration signal with inner race fault from Case Western Reserve University (CWRU), the mutual information and false nearest neighbor (FNN) method reported in [43] and [44] are employed to determine two key parameters (time delay τ and embedding dimension m) of PE, respectively. Time delay τ and embedding dimension m of PE determined by different methods for a bearing vibration signal: (a) Mutual information method and (b) false nearest neighbor method. It is worth mentioning that determination of time delay τ and embedding dimension m is a difficult problem, more specific and effective selection criteria of PE parameters (time delay τ and embedding dimension m) for different signals will be the focus of our future work
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