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. 2013 Aug 16;13(8):10856-75.
doi: 10.3390/s130810856.

Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds

Ming Zhao  1 , Jing Lin, Xiaoqiang Xu, Yaguo Lei
Affiliations

Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds

Ming Zhao et al. Sensors (Basel). .

Abstract

Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of rolling element bearings. Conventional diagnostic methods are based on the stationary assumption, thus they are not applicable to the diagnosis of bearings working under varying speed. This constraint limits the bearing diagnosis to the industrial application significantly. In order to extend the conventional diagnostic methods to speed variation cases, a tacholess envelope order analysis technique is proposed in this paper. In the proposed technique, a tacholess order tracking (TLOT) method is first introduced to extract the tachometer information from the vibration signal itself. On this basis, an envelope order spectrum (EOS) is utilized to recover the bearing characteristic frequencies in the order domain. By combining the advantages of TLOT and EOS, the proposed technique is capable of detecting bearing faults under varying speeds, even without the use of a tachometer. The effectiveness of the proposed method is demonstrated by both simulated signals and real vibration signals collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Analyzed results show that the proposed method could identify different bearing faults effectively and accurately under speed varying conditions.

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Figures

Figure 1.
Figure 1.
Time-frequency distribution and frequency-bands of harmonics: (a) Small speed variation; (b) Large speed variation.
Figure 2.
Figure 2.
Flow chart of the proposed tacholess envelope order analysis technique.
Figure 3.
Figure 3.
Simulated signal: (a) Impulse signal; (b) Harmonics of shaft; (c) Noise signal; (d) Mixed signal.
Figure 3.
Figure 3.
Simulated signal: (a) Impulse signal; (b) Harmonics of shaft; (c) Noise signal; (d) Mixed signal.
Figure 4.
Figure 4.
The conventional envelope spectrum of simulated signal.
Figure 5.
Figure 5.
Kurtogram of simulated signal.
Figure 6.
Figure 6.
(a) Band-pass filtered signal; (b) The envelope signal of (a).
Figure 6.
Figure 6.
(a) Band-pass filtered signal; (b) The envelope signal of (a).
Figure 7.
Figure 7.
(a) ASTFT spectrogram zoomed in 0–40 Hz; (b) IF estimation result.
Figure 8.
Figure 8.
Extracting the 2nd harmonic by generalized demodulation: (a) Spectrogram of the generalized Fourier transformed signal; (b) Extracting the 2nd harmonic by BPF; (c) The 2nd harmonic is separated after BPF; (d) Restoring the 2nd harmonic by inverse GFT.
Figure 9.
Figure 9.
(a) Waveform of 2nd harmonic; (b) Instantaneous phase of shaft.
Figure 10.
Figure 10.
Envelope order spectrum obtained by proposed TLEOA technique.
Figure 11.
Figure 11.
(a) The arrangement of the locomotive bearing test bench; (b) Schematic view.
Figure 12.
Figure 12.
The raw vibration signal.
Figure 13.
Figure 13.
The envelope spectrum of the bearing signal.
Figure 14.
Figure 14.
The ASTFT spectrogram of the bearing signal: (a) Overview; (b) Zoomed in 100–160 Hz.
Figure 15.
Figure 15.
The ASTFT spectrogram of the extracted 19th harmonic.
Figure 16.
Figure 16.
The envelope order spectrum obtained by proposed technique.
Figure 17.
Figure 17.
The spall fault on the outer race.
Figure 18.
Figure 18.
(a) Inner race fault; (b) Roller fault.
Figure 19.
Figure 19.
Comparison with conventional method: (a) Inner race fault detection; (b) Roller fault detection.
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References

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