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A Novel Multiple Feature-Based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine
Yang, Zhao Xu1,2; Rong, Hai Jun1; Wong, Pak Kin3; Angelov, Plamen4; Vong, Chi Man5; Chiu, Chi Wai3; Yang, Zhi Xin2
2022-03-01
Source PublicationCognitive Computation
ISSN1866-9956
Volume14Issue:2Pages:828-851
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.

KeywordEngine Knock Detection Multiple Feature Learning Sample Entropy Sparse Bayesian Extreme Learning Machine Variational Mode Decomposition
DOI10.1007/s12559-021-09945-3
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000744388000001
Scopus ID2-s2.0-85123160672
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Faculty of Science and Technology
Affiliation1.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
2.State Key Laboratory of Internet of Things for Smart City, Department of Electromechanical Engineering, University of Macau, Macao
3.Department of Electromechanical Engineering, University of Macau, Macao
4.School of Computing and Communications, Lancaster University, Lancaster, United Kingdom
5.Department of Computer and Information Science, University of Macau, Macao
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yang, Zhao Xu,Rong, Hai Jun,Wong, Pak Kin,et al. A Novel Multiple Feature-Based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine[J]. Cognitive Computation,2022,14(2):828-851.
APA Yang, Zhao Xu,Rong, Hai Jun,Wong, Pak Kin,Angelov, Plamen,Vong, Chi Man,Chiu, Chi Wai,&Yang, Zhi Xin.(2022).A Novel Multiple Feature-Based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine.Cognitive Computation,14(2),828-851.
MLA Yang, Zhao Xu,et al."A Novel Multiple Feature-Based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine".Cognitive Computation 14.2(2022):828-851.
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