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A Novel Multiple Feature-Based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine

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Abstract

Automotive engine knock is an abnormal combustion phenomenon that affects engine performance and lifetime expectancy, but it is difficult to detect. Collecting engine vibration signals from an engine cylinder block is an effective way to detect engine knock. This paper proposes an intelligent engine knock detection system based on engine vibration signals. First, filtered signals are obtained by utilizing variational mode decomposition (VMD), which decomposes the original time domain signals into a series of intrinsic mode functions (IMFs). Moreover, the values of the balancing parameter and the number of IMF modes are optimized using genetic algorithm (GA). IMFs with sample entropy higher than the mean are then selected as sensitive subcomponents for signal reconstruction and subsequently removed. A multiple feature learning approach that considers time domain statistical analysis (TDSA), multi-fractal detrended fluctuation analysis (MFDFA) and alpha stable distribution (ASD) simultaneously, is utilized to extract features from the denoised signals. Finally, the extracted features are trained by sparse Bayesian extreme learning machine (SBELM) to overcome the sensitivity of hyperparameters in conventional machine learning algorithms. A test rig is designed to collect the raw engine data. Compared with other technology combinations, the optimal scheme from signal processing to feature classification is obtained, and the classification accuracy of the proposed integrated engine knock detection method can achieve 98.27%. We successfully propose and test a universal intelligence solution for the detection task.

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Acknowledgements

This authors would like to thank the financial support from the University of Macao Distinguished Visiting Scholar Program. This research is funded by the Science and Technology Development Fund, Macau SAR (Nos. 0021/2019/A, 0018/2019/AKP, 0008/2019/AGJ), the Multi-Year Research Grant(No.MYRG2019-00137-FST), the National Natural Science Foundation of China (Nos. 61976172, 12002254) and the Natural Science Basic Research Program of Shaanxi (Nos. 020JQ-013, 2020JM-072). This work is also supported in part by the Macao Youth Scholars Program (No. AM201909).

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Authors and Affiliations

  1. State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Key Laboratory of Environment and Control for Flight Vehicle, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, China

    Zhao-Xu Yang & Hai-Jun Rong

  2. State Key Laboratory of Internet of Things for Smart City, Department of Electromechanical Engineering, University of Macau, Macao, China

    Zhao-Xu Yang & Zhi-Xin Yang

  3. Department of Electromechanical Engineering, University of Macau, Macao, China

    Pak Kin Wong & Chi Wai Chiu

  4. School of Computing and Communications, Lancaster University, Lancaster, United Kingdom

    Plamen Angelov

  5. Department of Computer and Information Science, University of Macau, Macao, China

    Chi Man Vong

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  1. Zhao-Xu Yang
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  2. Hai-Jun Rong
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  3. Pak Kin Wong
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  5. Chi Man Vong
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  7. Zhi-Xin Yang
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Correspondence to Pak Kin Wong.

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Yang, ZX., Rong, HJ., Wong, P.K. et al. A Novel Multiple Feature-Based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine. Cogn Comput 14, 828–851 (2022). https://doi.org/10.1007/s12559-021-09945-3

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  • DOI: https://doi.org/10.1007/s12559-021-09945-3

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