In this paper, an artificial neural network (ANN) is introduced in order to detect the occurrence of misfire in an internal combustion (IC) engine by analyzing the crankshaft angular velocity. This study presents three reliable misfire detection procedures. In the first two methods, the fault features are extracted using both time domain and frequency domain techniques, and a multilayer perceptron (MLP) serves as the pattern recognition tool for detecting the misfiring cylinder. In the third method, a one-dimensional (1D) convolutional neural network (CNN) that combines feature extraction capability and pattern recognition is adopted for misfire detection. The experimental data are obtained by setting a six in-line diesel engine with different cylinder misfiring to work under representative operating conditions. Finally, all three diagnostic methods achieved satisfactory results, and the 1D CNN achieved the best performance. The current study provides a novel way to detect misfiring in IC engines.
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June 2019
Research-Article
Real-Time Angular Velocity-Based Misfire Detection Using Artificial Neural Networks
Wenzhi Gao,
Wenzhi Gao
State Key Laboratory of Engines,
Tianjin University,
Tianjin 300072, China
e-mail: gaowenzhi@tju.edu.cn
Tianjin University,
Tianjin 300072, China
e-mail: gaowenzhi@tju.edu.cn
Search for other works by this author on:
Lifeng Wei,
Lifeng Wei
State Key Laboratory of Engines,
Tianjin University,
Tianjin 300072, China
e-mail: weilifeng@tju.edu.cn
Tianjin University,
Tianjin 300072, China
e-mail: weilifeng@tju.edu.cn
Search for other works by this author on:
Ziqing Wei
Ziqing Wei
State Key Laboratory of Engines,
Tianjin University,
Tianjin 300072, China
e-mail: weiziqing@tju.edu.cn
Tianjin University,
Tianjin 300072, China
e-mail: weiziqing@tju.edu.cn
Search for other works by this author on:
Pan Zhang
Wenzhi Gao
State Key Laboratory of Engines,
Tianjin University,
Tianjin 300072, China
e-mail: gaowenzhi@tju.edu.cn
Tianjin University,
Tianjin 300072, China
e-mail: gaowenzhi@tju.edu.cn
Qixin Song
Yong Li
Lifeng Wei
State Key Laboratory of Engines,
Tianjin University,
Tianjin 300072, China
e-mail: weilifeng@tju.edu.cn
Tianjin University,
Tianjin 300072, China
e-mail: weilifeng@tju.edu.cn
Ziqing Wei
State Key Laboratory of Engines,
Tianjin University,
Tianjin 300072, China
e-mail: weiziqing@tju.edu.cn
Tianjin University,
Tianjin 300072, China
e-mail: weiziqing@tju.edu.cn
1Corresponding author.
Manuscript received June 8, 2018; final manuscript received October 25, 2018; published online January 10, 2019. Assoc. Editor: Alessandro Ferrari.
J. Eng. Gas Turbines Power. Jun 2019, 141(6): 061008 (10 pages)
Published Online: January 10, 2019
Article history
Received:
June 8, 2018
Revised:
October 25, 2018
Citation
Zhang, P., Gao, W., Song, Q., Li, Y., Wei, L., and Wei, Z. (January 10, 2019). "Real-Time Angular Velocity-Based Misfire Detection Using Artificial Neural Networks." ASME. J. Eng. Gas Turbines Power. June 2019; 141(6): 061008. https://doi.org/10.1115/1.4041962
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