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A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis

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Abstract

Fault diagnosis of mechanical components has been attracting increasing attention. Researches have been carried out to reduce unnecessary breakdowns of machinery. Signal processing approaches are the most commonly used techniques for fault diagnosis tasks. Ontology and semantic web technology have great potential in knowledge representing, organizing and utilizing. In this paper, a hybrid fault diagnosis method for mechanical components is proposed based on ontology and signal analysis (HOS-MCFD). The method is a systematic approach covering the whole process of fault diagnosis: feature extraction from raw data, fault phenomenon identification using continuous mixture Gaussian hidden Markov model and fault knowledge modeling and reasoning using ontology and semantic web technology. A semantic mapping approach is presented to relate signal analysis results to ontology elements. The hybrid method integrates the advantages of signal analysis and ontology. It can be applied to deal with fault diagnosis more accurately, systematically and intelligently. This method is assessed with vibration data of rolling bearings. The experimental results prove the proposed method effective.

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Acknowledgements

The work was supported by the “2016 Smart manufacturing project of China (2016ZXFB2002)”. The authors would like to thank the anonymous reviewers for their valuable time and efforts in review.

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, Chongqing University, No. 174, Shazheng Street, Shapingba District, Chongqing, China

    Qiang Zhou, Ping Yan, Huayi Liu & Yang Xin

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  1. Qiang Zhou
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  2. Ping Yan
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  3. Huayi Liu
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Corresponding author

Correspondence to Ping Yan.

Appendix

Appendix

I. Common failure modes and detecting methods of rolling bearings

See Tables 8 and 9.

Table 8 Common failure modes of rolling bearings
Full size table
Table 9 Common detecting methods of rolling bearings
Full size table

In addition, there are also other detecting methods, including micro morphology analysis, oil film resistance diagnosis, ultrasonic testing, optical fiber monitoring and diagnosis, ray detection, penetrant testing and clearance measurement.

II. Common feature parameters in time domain of rolling bearings

See Table 10.

Table 10 Feature parameters in time domain
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III. Common feature parameters in frequency domain of rolling bearings

See Table 11.

Table 11 Feature parameters in frequency domain
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IV. Wavelet packet energy extraction process

For a given signal \(x\left( t \right) \), the wavelet packet energy extraction process is:

See Table 12.

Table 12 Three layer wavelet packet energy extraction process
Full size table

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Zhou, Q., Yan, P., Liu, H. et al. A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis. J Intell Manuf 30, 1693–1715 (2019). https://doi.org/10.1007/s10845-017-1351-1

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