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Fault Diagnosis Analysis of Angle Grinder Based on ACD-DE and SVM Hybrid Algorithm

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  • Jiangming Jia

    (Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China
    Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province, Hangzhou 310018, China)

  • Chenan Zhang

    (Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Jianneng Chen

    (Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China
    Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province, Hangzhou 310018, China)

  • Zheng Zhu

    (Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Ming Mao

    (Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

Due to the complex structure of the angle grinder and the existence of multiple rotating parts, the coupling phenomenon of the data results in the complexity and chaos of the data. The market scale of angle grinder is huge. Manual diagnosis and traditional diagnosis are difficult to meet the requirements, so a fault diagnosis method of angle grinder that is based on adaptive parameters and chaos theory of dual-strategy differential evolution algorithm (ACD-DE) and SVM model hybrid algorithm is proposed by combining a chaos-mapping algorithm, dynamic and adaptive scale factor, and crossover factor. The effectiveness and robustness of the algorithm are proven by solving eight test functions. The acceleration signal is decomposed by wavelet packet decomposition and reconstruction, and a variety of sensor signals are processed and constructed as feature vectors. The training set and the test set of the fault diagnosis model are divided. SVM model is used as the fault diagnosis model and optimized by ACD-DE. Based on the fault data of the angle grinder, the hybrid algorithm is compared with other optimization algorithms and other machine learning models; the comparison results show that the performance of the improved differential evolution algorithm is improved, in which the precision rate is 98.81%, the recall rate is 98.74%, and the F1 score is 0.9877. Experiments show that the hybrid algorithm has strong diagnosis accuracy and robustness.

Suggested Citation

  • Jiangming Jia & Chenan Zhang & Jianneng Chen & Zheng Zhu & Ming Mao, 2022. "Fault Diagnosis Analysis of Angle Grinder Based on ACD-DE and SVM Hybrid Algorithm," Mathematics, MDPI, vol. 10(18), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3279-:d:911218
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    References listed on IDEAS

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    1. Deepam Goyal & Anurag Choudhary & B. S. Pabla & S. S. Dhami, 2020. "Support vector machines based non-contact fault diagnosis system for bearings," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1275-1289, June.
    2. Tuan Vu Dinh & Hieu Nguyen & Xuan-Linh Tran & Nhat-Duc Hoang, 2021. "Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-20, February.
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