Diagnosis of residual bidirectional LSTM automotive motor bearings with attention mechanism

doi:10.3969/j.issn.1674-8484.2024.04.007

Abstract

Abstract:

In order to ensure the safe driving of vehicles and accurately diagnose and monitor motor bearing faults, this paper proposed an automotive motor bearing fault diagnosis method based on residual bidirectional Long Short-Term Memory (LSTM) network with attention mechanism. The feature extraction module combined LSTM groups that move in both forward and backward directions to fully perceive the fault features of automotive motor bearings. The signal diagnosis module adopted a residual bidirectional LSTM architecture and combined the local enhanced attention mechanism to optimize the weights and obtain the hidden state quantity. Global average pooling (GAP) method and SoftMax model are used in fault classification module to effectively detect faults. The results show that the detection accuracy of this method for automotive motor bearing fault detection reaches 93.1%. Under the condition of only 30 training samples, the accuracy reaches 66.3%. When the signal-to-noise ratio of the test set decreases from 10 dB to 2 dB, the accuracy only drops by 8.5%. Therefore, the proposed method has higher accuracy and stronger robustness.

Key words: vehicle safety, motor bearings, fault diagnosis, attention mechanism, feature extraction, accuracy rate, signal-to-noise ratio

CLC Number:

U469

JIANG Jian, WANG Ping. Diagnosis of residual bidirectional LSTM automotive motor bearings with attention mechanism[J]. Journal of Automotive Safety and Energy, 2024, 15(4): 511-519.

Figures/Tables 16

References 19

[1]	邵锦才, 何致远, 王子辉. 轮毂直驱电动汽车速度传感器的故障容错控制[J]. 汽车安全与节能学报, 2022, 13(1): 78-85.
	SHAO Jingcai, HE Zhiyuan, WANG Zihui, et al. Fault-tolerant control of speed sensors for direct drive in-wheel electric vehicle[J]. J Autom Safe Energ, 2022, 13(1): 78-85. (in Chinese)
[2]	张伟涛, 张东江, 纪晓凡, 等. 基于CCA和多通道循环维纳滤波的滚动轴承故障源分析[J]. 振动与冲击, 2023, 42(24): 317-325.
	ZHANG Weitao, ZHANG Dongjiang, JI Xiaodan, et al. Fault analysis of rolling element bearing based on CCA and a multichannel cyclic wiener filter[J]. J Vibr Shock, 2023, 42(24): 317-325. (in Chinese)
[3]	刘浩天, 魏洪乾, 时培成, 等. 基于帧间隔-总线电压混合特征的汽车ECU伪装攻击识别[J]. 汽车工程, 2023, 45(11): 2070-2081.
	LIU Haotian, WEI Hongqian, SHI Peicheng, et al. The masquerade intrusion detection technique for automotive ECUs based on the hybrid feature extraction of frame intervals and bus voltages[J]. Autom Engineering, 2023, 45(11): 2070-2081. (in Chinese)
[4]	张希, 廖宇兰, 李沁逸, 等. 安全行驶下的车用滚动轴承的数字孪生故障诊断[J]. 汽车安全与节能学报, 2023, 14(2): 232-238.
	ZHANG Xi, LIAO Yulan, LI Qinyi, et al. Digital twin fault diagnosis of automotive motor bearings under safe driving[J]. J Autom Safe Energ, 2023, 14(2): 232-238. (in Chinese)
[5]	张鹏博, 陈仁祥, 邵毅明, 等. 纯电动汽车电驱动系统故障诊断研究进展[J]. 汽车工程, 2024, 46(1): 61-74.
	ZHANG Pengbo, CHEN Renxiang, SHAO Yiming, et al. Research review of fault diagnosis for electric drive powertrain system of pure electric vehicles[J]. Autom Engineering, 2024, 46(1): 61-74. (in Chinese)
[6]	CUI Lingli, SUN Mengxin, ZHA Chunqing, et al. Early bearing fault diagnosis based on the improved singular value decomposition method[J]. Int’l J Advan Manufact Tech, 2023, 124(11-12): 3899-3910.
[7]	SU Naiquan, ZHANG Qinghua, ZHOU Lingmeng, et al. A fault diagnosis of rotating machinery based on a mutual dimensionless index and a convolution neural network[J]. IEEE Intel Syst, 2023, 38(4): 33-41.
[8]	ZHU Rui, WANG Mingxin, XU Siyu, et al. Fault diagnosis of rolling bearing based on singular spectrum analysis and wide convolution kernel neural network[J]. J Low Freq Noise V A, 2022, 41(4): 1307-1321.
[9]	Pavithra R, Ramachandran P. Deep convolution neural network for machine health monitoring using spectrograms of vibration signal and its EMD-intrinsic mode functions[J]. J Intel Fuzzy Syst, 2023, 44(6): 8827-8840.
[10]	SONG Xinmin, WEI Weihua, ZHOU Junbo, et al. Bayesian-optimized hybrid Kernel SVM for rolling bearing fault diagnosis[J]. Sensors, 2023, 23(11): No 5137.
[11]	REN Changan, LI He, LEI Jichong, et al. A CNN-LSTM-based model to fault diagnosis for CPR1000[J]. Nucl Tech, 2023, 209(9): 1365-1372.
[12]	ZHANG Shuo, LIU Zhiwen, CHEN Yunping, et al. Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis[J]. ISA Trans, 2023,133: 369-383.
[13]	朱雪峰, 冯早, 马军, 等. 基于MSCNN-LSTM的注意力机制U型管道缺陷识别模型[J]. 振动与冲击, 2023, 42(22): 293-302.
	ZHU Xuefeng, FENG Zao, MA Jun, et al. Siphon defect recognition model based on the MSCNN-LSTM and attention mechanism[J]. J Vibr Shock, 2023, 42(22): 293-302. (in Chinese)
[14]	YAN Xuyang, Sarkar M, Lartey B, et al. An online learning framework for sensor fault diagnosis analysis in autonomous cars[J]. IEEE Trans Intel Transport, 2023, 24(12): 14467-14479.
[15]	张英杰, 张彩华, 陆碧良, 等. 基于类别域自适应的滚动轴承故障诊断[J]. 振动与冲击, 2023, 42(24): 117-126.
	ZHANG Yingjie, ZHANG Caihua, LU Biliang, et al. Bearing fault diagnosis model based on class domain adaptation[J]. J Vibr Shock, 2023, 42(24): 117-126. (in Chinese)
[16]	SUN Meidi, WANG Hui, LIU Ping, et al. A novel data-driven mechanical fault diagnosis method for induction motors using stator current signals[J]. IEEE Trans Transport Elect, 2023, 9(1): 347-358.
[17]	XU Tao, LV Huan, LIN Shoujin, et al. A fault diagnosis method based on improved parallel convolutional neural network for rolling bearing[J]. Proceed Instit Mech Engi Part G-J Aero Engi, 2023, 237(12): 2759-2771.
[18]	JIA Sixiang, LI Yongbo, MAO Gang, et al. Multi-representation symbolic convolutional neural network: A novel multisource cross-domain fault diagnosis method for rotating system[J]. Struct Heal Monit, 2023, 22(6): 3940-3955.
[19]	刘延伟, 黄志明, 高博麟, 等. 车载视角下基于视觉信息的前车行为识别[J]. 汽车安全与节能学报, 2023, 14(6): 707-714.
	LIU Yanwei, Huang Zhiming, GAO Bolin, et al. Recognition of front vehicle behavior based on visual information from vehicle perspective[J]. J Autom Safe Energ, 2023, 14(6): 707-714. (in Chinese)

故障位置	DPD / μm		样本数目	标记	标签
正常	0		1 000	0	J
滚动球	178		1 000	1	GQ07
	356		1 000	3	GQ14
	533		1 000	4	GQ21
内圈故障	178		1 000	5	IT07
	356		1 000	6	IT14
	533		1 000	7	IT21
外圈故障	178	3点钟	330	8	OT07_03
		6点钟	330	9	OT07_06
		12点钟	340	10	OT07_12
	356	3点钟	330	11	OT14_03
		6点钟	330	12	OT14_06
		12点钟	340	13	OT14_12
	533	3点钟	330	14	OT21_03
		6点钟	330	15	OT21_06
		12点钟	340	16	OT21_12

故障位置	DPD / μm		样本数目	标记	标签
正常	0		1 000	0	J
滚动球	178		1 000	1	GQ07
	356		1 000	3	GQ14
	533		1 000	4	GQ21
内圈故障	178		1 000	5	IT07
	356		1 000	6	IT14
	533		1 000	7	IT21
外圈故障	178	3点钟	330	8	OT07_03
		6点钟	330	9	OT07_06
		12点钟	340	10	OT07_12
	356	3点钟	330	11	OT14_03
		6点钟	330	12	OT14_06
		12点钟	340	13	OT14_12
	533	3点钟	330	14	OT21_03
		6点钟	330	15	OT21_06
		12点钟	340	16	OT21_12

方法	第1次	第2次	第3次	平均值
ResFDSVD方法	3.75	3.84	3.72	3.77
FDWK方法	1.65	1.75	1.79	1.73
本文方法	1.81	1.85	1.83	1.83

方法	第1次	第2次	第3次	平均值
ResFDSVD方法	3.75	3.84	3.72	3.77
FDWK方法	1.65	1.75	1.79	1.73
本文方法	1.81	1.85	1.83	1.83