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  • COGN-D-20-00117_R3__1_ (1)

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s12559-021-09945-3

    Accepted author manuscript, 5.45 MB, PDF document

    Embargo ends: 19/01/23

    Available under license: Other

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

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • Zhao Xu Yang
  • Hai-Jun Rong
  • Pak Kin Wong
  • Plamen Angelov
  • Chi Man Wong
  • Chi Wai Chiu
  • Zhi-Xin Yang
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Article number9945
<mark>Journal publication date</mark>19/01/2022
<mark>Journal</mark>Cognitive Computation
Number of pages23
Pages (from-to)1-23
Publication StatusE-pub ahead of print
Early online date19/01/22
<mark>Original language</mark>English

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.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/s12559-021-09945-3