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. 2015 Oct 9;15(10):25648-62.
doi: 10.3390/s151025648.

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

Gang Tang  1 , Wei Hou  2   3 , Huaqing Wang  4 , Ganggang Luo  5 , Jianwei Ma  6
Affiliations

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

Gang Tang et al. Sensors (Basel). .

Abstract

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments.

Keywords: compressive sensing; fault detection; harmonic detection; matching pursuit; roller bearing.

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Figures

Figure 1
Figure 1
Scheme of the proposed strategy.
Figure 2
Figure 2
A simulated vibration signal induced by (a) outer race faults and (b) a typical section with four cycles within the red box in (a) is shown from zoom-in view.
Figure 2
Figure 2
A simulated vibration signal induced by (a) outer race faults and (b) a typical section with four cycles within the red box in (a) is shown from zoom-in view.
Figure 3
Figure 3
Envelope of the signal as shown in Figure 2.
Figure 4
Figure 4
10% samples of the signal as shown in Figure 3.
Figure 5
Figure 5
The first two detected harmonic components and their frequencies: (a) the first harmonic and its frequency; and (b) the second harmonic and its frequency.
Figure 6
Figure 6
Experiment platform with fault rigs of a roller element bearing.
Figure 7
Figure 7
The vibration waveform of a bearing with an outer race fault.
Figure 8
Figure 8
Harmonics detected and their frequencies from envelope samples of the signal shown in Figure 7: (a) the first harmonic and its frequency, and (b) the second harmonic and its frequency.
Figure 9
Figure 9
Vibration waveform of a bearing with an inner race fault.
Figure 10
Figure 10
Harmonics detected and their frequencies from envelope samples of the signal shown in Figure 9: (a) the first harmonic and its frequency; (b) the third harmonic and its frequency; (c) the fifth harmonic and its frequency.
Figure 11
Figure 11
Probability of the compressive fault detection.
See this image and copyright information in PMC

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