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research-article

Real-time Angular Velocity-Based Misfire Detection Using Artificial Neural Networks

[+] Author and Article Information
Pan Zhang

State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
perfect04@126.com

Wenzhi Gao

State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
gaowenzhi@tju.edu.cn

Qixin Song

State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
1812883953@qq.com

Yong Li

State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
li_yong@tju.edu.cn

Lifeng Wei

State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
weilifeng@tju.edu.cn

Ziqing Wei

State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
weiziqing@tju.edu.cn

1Corresponding author.

ASME doi:10.1115/1.4041962 History: Received June 08, 2018; Revised October 25, 2018

Abstract

In this paper, an artificial neural network 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 multi-layer perceptron (MLP) serves as the pattern recognition tool for detecting the misfiring cylinder. In the third method, a one-dimensional (1-D) 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 works under representative operating conditions. Finally, all three diagnostic methods achieved satisfactory results, and the 1-D CNN achieved the best performance. The current study provides a novel way to detect misfiring in IC engines. Keywords: misfire detection, internal combustion engine, artificial neural network (ANN), multi-layer perceptron (MLP), convolutional neural network (CNN)

Copyright (c) 2018 by ASME
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