Fault diagnosis method for diesel engines based on MACNN

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (10) : 8-15.

CHENG Jiangang1，BI Fengrong1，ZHANG Lipeng2，LI Xin1，YANG Xiao1，TANG Daijie1

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Abstract

Efficient and accurate fault diagnosis can improve the safety and reliability of diesel engine. In the traditional mechanical fault diagnosis method, the degree of human participation is too high, which brings high uncertainty to results. To solve this problem, this paper proposes an end-to-end fault diagnosis method based on multiple attention convolutional neural networks (MACNN). In this method, multi-layer convolutional neural networks (CNN) and convolutional block attention module (CBAM) are used to extract features from the original time-domain data, and then the multi-dimensional feature map of convolutional output is recombined to retain its sequence information. Finally, the sequential attention mechanism is directly used to learn the sequence feature. The results show that MACNN can achieve recognition accuracy of 97.88% for eight-class fault data set of diesel engine, and the time taken to test 100 samples is only 0.35 seconds. Compared with other traditional fault diagnosis methods and end-to-end fault diagnosis methods, the proposed MACNN has better diagnosis effect.

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multiple attention / convolutional neural networks (CNN) / fault diagnosis / end to end

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CHENG Jiangang1，BI Fengrong1，ZHANG Lipeng2，LI Xin1，YANG Xiao1，TANG Daijie1. Fault diagnosis method for diesel engines based on MACNN[J]. Journal of Vibration and Shock, 2022, 41(10): 8-15

References

[1] BI F, MA T, WANG X. Development of a novel knock characteristic detection method for gasoline engines based on wavelet-denoising and EMD decomposition[J]. Mechanical Systems and Signal Processing, 2019, 117: 517-536.
[2] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 1998, 454(1971): 903-995.
[3] BI X, LIN J, TANG D, et al. VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals[J].Energies, 2020, 13(1): 228.
[4] BI X, LIN J, BI F, et al. Engine working state recognition based on optimized variational mode decomposition and expectation maximization algorithm[J]. IEEE Access, 2020, 8: 33545-33559.
[5] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE transactions on signal processing, 2013, 62(3): 531-544.
[6] LU C, WANG Z, ZHOU B. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification[J]. Advanced Engineering Informatics, 2017, 32: 139-151.
[7] WANG L, ZHAO X, WU J, et al. Motor fault diagnosis based on short-time Fourier transform and convolutional neural network[J]. Chinese Journal of Mechanical Engineering, 2017, 30(6): 1357-1368.
[8] XIA M , LI T , XU L, et al. Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks[J]. IEEE/ASME Transactions on Mechatronics, 2017, PP(99):1-1.
[9] 庄雨璇. 基于深度学习的旋转轴承端到端故障诊断研究[D].苏州大学, 2019.
[10] 张立鹏, 毕凤荣, 程建刚, 等. 基于注意力BiGRU的机械故障诊断方法研究[J]. 振动与冲击, 2021, 40(5): 113-118.
ZHANG Lipeng, BI Fengrong, CHENG Jiangang, et al. Mechanical fault diagnosis method based on attention BiGRU. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(5): 113-118.
[11] ZHOU C , SUN C , LIU Z , et al. A C-LSTM neural network for text classification[J]. Computer Science, 2015, 1(4):39-44.
[12] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[13] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
[14] IOFFE S, SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. Communications of the ACM, 2015: 448–456.
[15] WOO S, PARK J, LEE J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the European conference on computer vision (ECCV). Munich, 2018: 3-19.
[16] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. California, 2017: 5998-6008.
[17] ZHOU P, SHI W, TIAN J, et al. Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers). Berlin, 2016: 207-212.
[18] KINGMA D P, BA J. Adam: A method for stochastic optimization[J]. Computer Science，2017，9：1-15.