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. 2018 May 8;18(5):1466.
doi: 10.3390/s18051466.

Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings

Jie Duan  1 , Tielin Shi  2 , Hongdi Zhou  3 , Jianping Xuan  4 , Yongxiang Zhang  5
Affiliations

Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings

Jie Duan et al. Sensors (Basel). .

Abstract

Bearing fault features are presented as repetitive transient impulses in vibration signals. Narrowband demodulation methods have been widely used to extract the repetitive transients in bearing fault diagnosis, for which the key factor is to accurately locate the optimal band. A multitude of criteria have been constructed to determine the optimal frequency band for demodulation. However, these criteria can only describe the strength of transient impulses, and cannot differentiate fault-related impulses and interference impulses that are cyclically generated in the signals. Additionally, these criteria are easily affected by the independent transitions and background noise in industrial locales. Therefore, the large values of the criteria may not appear in the optimal frequency band. To overcome these problems, a new method, referred to as multiband envelope spectra extraction (MESE), is proposed in this paper, which can extract all repetitive transient features in the signals. The novelty of MESE lies in the following aspects: (1) it can fuse envelope spectra at multiple narrow bands. The potential bands are selected based on Jarque-Bera statistics of narrowband envelope spectra; and (2) fast independent component analysis (fastICA) is introduced to extract the independent source spectra, which are fault- or interference-related. The multi-band strategy will preserve all impulse features and make the method more robust. Meanwhile, as a blind source separation technique, the fastICA can suppress some in-band noise and make the diagnosis more accurate. Several simulated and experimental signals are used to validate the efficiency of the proposed method. The results show that MESE is effective for enhanced fault diagnosis of rolling element bearings. Bearing faults can be detected even in a harsh environment.

Keywords: Jarque-Bera statistic; blind source separation; fault diagnosis; narrowband amplitude demodulation; rolling element bearing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of frequency band segmentation.
Figure 2
Figure 2
REB fault signal with small noises. (a) Temporal waveform; (b) Traditional envelope spectrum of the signal; (c) Envelope spectra at different narrow frequency bands.
Figure 3
Figure 3
Kurtosis and JB statistics of narrowband envelope spectra with possible shapes: (a) Without harmonics; (b) One harmonic; (c) Two harmonics having the same amplitudes; (d) Two harmonics having descending amplitudes; (e) Three harmonics having the same amplitudes; (f) Three harmonics having descending amplitudes.
Figure 4
Figure 4
Flowchart of the proposed method for bearing fault diagnosis.
Figure 5
Figure 5
(a) Periodic impulses and the noise signal; (b) Envelope spectrum of the simulated fault signal.
Figure 6
Figure 6
(a) Amplitude spectrum of the simulated signal; (b) JB statistics calculated at different bands; (c) Envelope spectrum extracted by MESE.
Figure 7
Figure 7
(a) Simulated signal with heavy interferences; (b) Envelope spectrum of the signal.
Figure 8
Figure 8
Analyzed results of signals with heavy interferences: (a) Amplitude spectrum; (b) JB statistic diagram; (c) Envelope spectra extracted by MESE.
Figure 9
Figure 9
Bearing test rig and sensor placement illustration.
Figure 10
Figure 10
Bearing vibration signals measured at different points with inner-race defect: (a) Point 1; (b) Point 2; (c) Point 3.
Figure 11
Figure 11
Analyzed results of signal with inner-race defect measured at Point 1 by different methods: (a) The fast Kurtogram; (b) Protrugram; (c) MESE.
Figure 12
Figure 12
Analyzed results of signal with inner-race defect measured at Point 2 by different methods: (a) The fast Kurtogram; (b) Protrugram; (c) MESE.
Figure 13
Figure 13
Analyzed results of signal with inner-race defect measured at Point 3 by different methods: (a) The fast Kurtogram; (b) Protrugram; (c) MESE.
Figure 14
Figure 14
Bearing vibration signals measured at different points with outer-race defect: (a) Point 1; (b) Point 2; (c) Point 3.
Figure 15
Figure 15
Analyzed results of signal with outer-race defect measured at Point 1 by different methods: (a) The fast Kurtogram; (b) Protrugram; (c) MESE.
Figure 16
Figure 16
Analyzed results of signal with outer-race defect measured at Point 2 by different methods: (a) The fast Kurtogram; (b) Protrugram; (c) MESE.
Figure 17
Figure 17
Analyzed results of signal with outer-race defect measured at Point 3 by different methods: (a) The fast Kurtogram; (b) Protrugram; (c) MESE.
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