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Research Papers

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
e-mail: perfect04@126.com

Wenzhi Gao

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

Qixin Song

State Key Laboratory of Engines,
Tianjin University,
Tianjin 300072, China
e-mail: 1812883953@qq.com

Yong Li

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

Lifeng Wei

State Key Laboratory of Engines,
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

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 141(6), 061008 (Jan 10, 2019) (10 pages) Paper No: GTP-18-1242; doi: 10.1115/1.4041962 History: Received June 08, 2018; Revised October 25, 2018

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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Figures

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Fig. 1

Diesel internal combustion engine test rig

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Fig. 2

Speed variation contrast under normal and misfire conditions

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Fig. 3

Sis under normal and misfire conditions at 1000 r/min, 150 N·m

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Fig. 4

Comparison of Sis under different operating conditions. Mark color meanings: red—S1, black—S2, cyan—S3, green—S4, magenta—S5, and blue—S6. Mark shape meanings: +—cylinder 1 misfire, ○—cylinder 2 misfire, △—cylinder 3 misfire, *—cylinder 4 misfire, ◻—cylinder 5 misfire, and ◇—cylinder 6 misfire.

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Fig. 5

Excitation torque vectors at each harmonic order (≤3) of a four-stroke, six-cylinder engine: (a) l = 0.5, (b) l = 1, (c) l = 1.5, (d) l = 2, (e) l = 2.5, and (f) l = 3

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Fig. 6

Amplitudes of the first six harmonic orders for normal and misfire conditions under 1000 r/min and 300 N·m

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Fig. 7

Polar diagram of angular speed variation with normal and misfire conditions under 300 N·m (above) and no load (below). Mark color meanings: red—800 r/min, black—1000 r/min, blue—1200 r/min, magenta—1400 r/min, cyan—1600 r/min, green—1800 r/min, and yellow—2000 r/min. Mark shape meanings: △—normal condition, .—cylinder 1 misfire, ○—cylinder 2 misfire, +—cylinder 3 misfire, *—cylinder 4 misfire, ◻—cylinder 5 misfire, and ◇—cylinder 6 misfire.

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Fig. 8

The diagram of MLP with three layers

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Fig. 9

Overview of a sample conventional CNN

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Fig. 10

Architecture of the 1D CNN

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