A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

PLOS ONE, Dec 2019

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system.

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A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

et al. (2014) A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition. PLoS ONE 9(10): e109166. doi:10.1371/journal.pone.0109166 A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition Huaqing Wang 0 Ruitong Li 0 Gang Tang 0 Hongfang Yuan 0 Qingliang Zhao 0 Xi Cao 0 Dewen Hu, College of Mechatronics and Automation, National University of Defense Technology, China 0 1 School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology , Beijing , China , 2 School of Information Science and Technology, Beijing University of Chemical Technology , Beijing , China A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals' separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. - Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files. Funding: This work is supported in part by National Natural Science Foundation of China (Grant No. 51375037, 51075023), National Program on Key Basic Research Project (Grant No. 2012CB026000), Program for New Century Excellent Talents in University (NCET-12-0759). GT is also supported by China Fundamental Research Funds for the Central Universities (ZY1410) and the Public Hatching Platform for Recruited Talents of Beijing University of Chemical Technology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. A rolling bearing is one of the most widely used components in rotating machinery, whose running state directly affects the accuracy, reliability and service life of the whole machine. Therefore, the condition monitoring and fault diagnosis of a rolling bearing has extremely vital significance, and it is also very important to guarantee the production efficiency and the plant safety in modern enterprises [1]. Vibration signal detection is generally an effective method for fault diagnosis of rolling bearings. Ideally, it is better if a vibration signal contains only one defect when it is measured by an acceleration sensor under low-noise condition. In this case, features of the bearing defect can be extracted by Fast Fourier Transformation (FFT) comparing with the characteristic frequencies of the bearing. This approach can mainly be applied when the fault feature is relatively obvious. However, in practice, most bearing faults are often compounded by the outer-race defect, the inner-race defect or the rollers defect. Especially, in some cases, some strong noises may be mixed into a fault signal, which may lead to misrecognition of the useful information for equipment condition monitoring and fault diagnosis. In order to solve the problem issued above and improve the identification of fault types and the monitoring of rotating machinerys running state, it is critically important to separate the compound faults from measured signals. Blind Source Separation (BSS) developed by Herault [2] provides a new way to help solving the problem. BSS is a kind of new technique aiming at extraction of individual signal from mixed ones. In recent years, BSS problem becomes a popular issue in the field of unsupervised neural learning and statistical signal processing, especially on the theory itself and its further applications in practice. For example, Canonical Correlation Analysis (CCA) is applied to reveal underlying components with maximum autocorrelation from fMRI data [34]. Developed with BSS, without requirements of prior information about mixed signals under its original statistically independen (...truncated)


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Huaqing Wang, Ruitong Li, Gang Tang, Hongfang Yuan, Qingliang Zhao, Xi Cao. A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition, PLOS ONE, 2014, 10, DOI: 10.1371/journal.pone.0109166