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

Author

Listed:
  • Qiang Zhou

    (Chongqing University)

  • Ping Yan

    (Chongqing University)

  • Huayi Liu

    (Chongqing University)

  • Yang Xin

    (Chongqing University)

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.

Suggested Citation

  • Qiang Zhou & Ping Yan & Huayi Liu & Yang Xin, 2019. "A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1693-1715, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1351-1
    DOI: 10.1007/s10845-017-1351-1
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    References listed on IDEAS

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    Cited by:

    1. Zilong Zhuang & Liangxun Guo & Zizhao Huang & Yanning Sun & Wei Qin & Zhao-Hui Sun, 2021. "DyS-IENN: a novel multiclass imbalanced learning method for early warning of tardiness in rocket final assembly process," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2197-2207, December.
    2. Rubén Medina & Jean Carlo Macancela & Pablo Lucero & Diego Cabrera & René-Vinicio Sánchez & Mariela Cerrada, 2022. "Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1031-1055, April.
    3. Yiping Gao & Liang Gao & Xinyu Li & Yuwei Zheng, 2020. "A zero-shot learning method for fault diagnosis under unknown working loads," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 899-909, April.
    4. He, Yuanbiao & Qiao, Zijian & Xie, Biaobiao & Ning, Siyuan & Li, Zhecong & Kumar, Anil & Lai, Zhihui, 2024. "Two-stage benefits of internal and external noise to enhance early fault detection of machinery by exciting fractional SR," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    5. Ke Zhao & Hongkai Jiang & Zhenghong Wu & Tengfei Lu, 2022. "A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 151-165, January.
    6. Dengyu Xiao & Chengjin Qin & Honggan Yu & Yixiang Huang & Chengliang Liu, 2021. "Unsupervised deep representation learning for motor fault diagnosis by mutual information maximization," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 377-391, February.

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