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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Gao, Z. , Li, C. , Liu, B. , Huang, Z. , Eiji, T. , and Sadami, Y. , 2015, “ Detection of Engine Abnormal Combustion With Ion Current Method,” J. Xian Jiaotong Univ., 49(5), pp. 1–6.
Chang, J. , Kim, M. , and Min, K. , 2002, “ Detection of Misfire and Knock in Spark Ignition Engines by Wavelet Transform of Engine Block Vibration Signals,” Meas. Sci. Technol., 13(7), pp. 1108–1114. [CrossRef]
Sharma, A. , Sugumaran, V. , and Devasenapati, S. B. , 2014, “ Misfire Detection in an IC Engine Using Vibration Signal and Decision Tree Algorithms,” Measurement, 50, pp. 370–380. [CrossRef]
Williams, J. , 1996, “ An Overview of Misfiring Cylinder Engine Diagnostic Techniques Based on Crankshaft Angular Velocity Measurements,” SAE Paper No. 960039.
Chiatti, G. , Chiavola, O. , Palmieri, F. , and Piolo, A. , 2015, “ Diagnostic Methodology for Internal Combustion Diesel Engines Via Noise Radiation,” Energy Convers. Manage., 89, pp. 34–42. [CrossRef]
Connolly, F. T. , and Rizzoni, G. , 1994, “ Real Time Estimation of Engine Torque for the Detection of Engine Misfires,” ASME J. Dyn. Syst., Meas., Control, 116(4), pp. 675–686. [CrossRef]
Kiencke, U. , 1999, “ Engine Misfire Detection,” Control Eng. Pract., 7(2), pp. 203–208. [CrossRef]
Plapp, G. , Klenk, M. , and Moser, W. , 1990, “ Methods of On-Board Misfire Detection,” SAE Paper No. 900232.
Klenk, M. , Moser, W. , Mueller, W. , and Wimmer, W. , 1993, “ Misfire Detection by Evaluating Crankshaft Speed—A Means to Comply With OBD II,” SAE Paper No. 930399.
Taraza, D. , Henein, N. A. , and Bryzik, W. , 2001, “ The Frequency Analysis of the Crankshaft's Speed Variation: A Reliable Tool for Diesel Engine Diagnosis,” ASME J. Eng. Gas Turbines Power, 123(2), pp. 428–432. [CrossRef]
Geveci, M. , Osburn, A. W. , and Franchek, M. A. , 2005, “ An Investigation of Crankshaft Oscillations for Cylinder Health Diagnostics,” Mech. Syst. Signal Process., 19(5), pp. 1107–1134. [CrossRef]
Osburn, A. W. , Kostek, T. M. , and Franchek, M. A. , 2006, “ Residual Generation and Statistical Pattern Recognition for Engine Misfire Diagnostics,” Mech. Syst. Signal Process., 20(8), pp. 2232–2258. [CrossRef]
Cavina, N. , Cipolla, G. , Marcigliano, F. , Moro, D. , and Poggio, L. , 2006, “ A Methodology for Increasing the Signal to Noise Ratio for the Misfire Detection at High Speed in a High Performance Engine,” Control Eng. Pract., 14(3), pp. 243–250. [CrossRef]
Hu, C. , Li, A. , and Zhao, X. , 2011, “ Multivariate Statistical Analysis Strategy for Multiple Misfire Detection in Internal Combustion Engines,” Mech. Syst. Signal Process., 25(2), pp. 694–703. [CrossRef]
Helm, S. , Kozek, M. , and Jakubek, S. , 2012, “ Combustion Torque Estimation and Misfire Detection for Calibration of Combustion Engines by Parametric Kalman Filtering,” IEEE Trans. Ind. Electron., 59(11), pp. 4326–4337. [CrossRef]
Liu, B. , Zhao, C. , Zhang, F. , Cui, T. , and Su, J. , 2013, “ Misfire Detection of a Turbocharged Diesel Engine by Using Artificial Neural Networks,” Appl. Therm. Eng., 55(1–2), pp. 26–32. [CrossRef]
Jung, D. , Eriksson, L. , Frisk, E. , and Krysander, M. , 2015, “ Development of Misfire Detection Algorithm Using Quantitative FDI Performance Analysis,” Control Eng. Pract., 34, pp. 49–60. [CrossRef]
Boudaghi, M. , Shahbakhti, M. , and Jazayeri, S. A. , 2015, “ Misfire Detection of Spark Ignition Engines Using a New Technique Based on Mean Output Power,” ASME J. Eng. Gas Turbines Power, 137(9), p. 091509. [CrossRef]
Ma, X. , Xia, Z. , Wu, H. , and Huang, X. , 2015, “ Combined Frequency Domain Analysis and Fuzzy Logic for Engine Misfire Diagnosis,” SAE Paper No. 2015-01-0207.
LeCun, Y. , Bengio, Y. , and Hinton, G. , 2015, “ Deep Learning,” Nature, 521(7553), pp. 436–444. [CrossRef] [PubMed]
Ince, T. , Kiranyaz, S. , Eren, L. , Askar, M. , and Gabbouj, M. , 2016, “ Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks,” IEEE Trans. Ind. Electron., 63(11), pp. 7067–7075. [CrossRef]
Kiranyaz, S. , Ince, T. , and Gabbouj, M. , 2016, “ Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks,” IEEE Trans. Biomed. Eng., 63(3), pp. 664–675. [CrossRef] [PubMed]
Abdeljaber, O. , Avci, O. , Kiranyaz, S. , Gabbouj, M. , and Inman, D. J. , 2017, “ Real-Time Vibration-Based Structural Damage Detection Using One-Dimensional Convolutional Neural Networks,” J. Sound Vib., 388, pp. 154–170. [CrossRef]
Nielsen, M. A. , 2015, Neural Networks and Deep Learning, Determination Press.
LeCun, Y. , Bottou, L. , Bengio, Y. , and Haffner, P. , 1998, “ Gradient-Based Learning Applied to Document Recognition,” Proc. IEEE, 86(11), pp. 2278–2324. [CrossRef]
Chen, Z. , 1987, Chuanpo Tuijinzhouxi Zhendong, Shanghai Jiaotong University Press, Shanghai, China, pp. 37–116.
Chen, J. , and Randall, R. B. , 2015, “ Improved Automated Diagnosis of Misfire in Internal Combustion Engines Based on Simulation Models,” Mech. Syst. Signal Process., 64–65, pp. 58–83. [CrossRef]
Xiao, X. Y. , Xiang, Y. , Qian, S. C. , Li, R. , and Zhou, Q. , 2014, “ The Application of the Multi-Harmonic Phase Method to Fault Diagnosis of Diesel Engines,” J. Harbin Eng. Univ., 35(8), pp. 945–960.
Theodoridis, S. , and Koutroumbas, K. , 2008, Pattern Recognition, 4th ed., Academic Press, San Diego, CA, pp. 1–248.
Sheela, K. G. , and Deepa, S. N. , 2013, “ Review on Methods to Fix Number of Hidden Neurons in Neural Networks,” Math. Probl. Eng., 2013, p. 425740. [CrossRef]
Mirchandani, G. , and Cao, W. , 1989, “ On Hidden Nodes for Neural Nets,” IEEE Trans. Circuits Syst., 36(5), pp. 661–664. [CrossRef]
Reed, R. , 1993, “ Pruning Algorithms–A Survey,” IEEE Trans. Neural Networks, 4(5), pp. 740–747. [CrossRef]
Fahlman, S. E. , and Lebiere, C. , 1990, “ The Cascade-Correlation Learning Architecture,” Advances in Neural Information Processing Systems, Denver, CO, Nov. 26–29, pp. 524–532.
Shibata, K. , and Ikeda, Y. , 2009, “ Effect of Number of Hidden Neurons on Learning in Large-Scale Layered Neural Networks,” ICROS-SICE International Joint Conference, Fukuoka, Japan, Aug. 18–21, pp. 5008–5013. https://ieeexplore.ieee.org/document/5334631
Demuth, H. , Beale, M. , and Hagan, M. , 2009, Neural Network Toolbox™ User's Guide, The MathWorks, Natick, MA, pp. 5–50.
Rumelhart, D. E. , Hinton, G. E. , and Williams, R. J. , 1986, “ Learning Representations by Back-Propagating Errors,” Nature, 323(6088), pp. 533–536. [CrossRef]


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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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