Sample Imbalanced Fault Diagnosis Method Based on Multi-channel Data Double Augmentation

doi:10.12382/bgxb.2024.0012

Abstract

Abstract:

In complex manufacturing processes,it is crucial to collect and analyze the multi-channel data for condition monitoring and fault diagnosis.The existing methods are used to difficultly handle the problems of complex spatial-temporal correlation and sample imbalance of the multi-channel data.To solve these problems,a sample imbalance fault diagnosis method based on multi-channel data double augmentation is developed.The proposed method has the advantages of two-stage data augmentation and global optimization.It first learns the fault features,and then converts them into the multi-channel data for the data augmentation.The distribution difference evaluation mechanism is introduced to effectively describe the correlation between different channels,and a multi-objective global optimization strategy is designed to improve the quality of generated data.The effectiveness of the proposed method is verified by studying a real-world case.The experimental results show that the data double augmentation method can effectively expand the multi-channel data with small samples,and the global optimization strategy can improve the performance of generated data in the fault diagnosis.Compared with existing methods,the proposed method has higher fault diagnosis accuracy in various sample imbalance scenarios.

Key words: multi-channel data, sample imbalance fault diagnosis, data augmentation, global optimization

CLC Number:

TH17

GUO Yiming, TONG Yifei, HE Fei, XIE Zhongqu, SONG Shida, HUANG Jing. Sample Imbalanced Fault Diagnosis Method Based on Multi-channel Data Double Augmentation[J]. Acta Armamentarii, 2025, 46(2): 240012-.

Figures/Tables 16

Fig.1 The overall structure of the proposed model

Fig.2 Fault feature learning network structure

Fig.3 Multi-channel data generation network structure

Fig.4 Global optimization strategy

Fig.5 Multi-step forging machine and technological process

Fig.6 Four-channel pressure data

Fig.7 Fault diagnosis results of imbalanced sample

Fig.8 Multi-channel data generated by two methods

Table 1 MSE value between generated and real data

数据通道	传统GAN	所提方法
1	0.0331	0.0039
2	0.0361	0.0065
3	0.0531	0.0048
4	0.0540	0.0054

Table 2 MSE value between different channels

生成数据	真实数据
生成数据	通道1	通道2	通道3	通道4
通道1	0.0039	0.0167	0.0177	0.0109
通道2	0.0193	0.0065	0.0182	0.0159
通道3	0.0237	0.0185	0.0048	0.0103
通道4	0.0133	0.0178	0.0115	0.0054

Fig.9 Generated fault feature

Fig.10 Visualization of fault features based on the t-SNE

Table 3 Diagnosis accuracy when Faults 1-5 are minority samples

对比方法	诊断准确率
对比方法	故障1	故障2	故障3	故障4	故障5
随机过采样	99.25	98.88	100	89.14	98.88
SMOTE	99.63	99.63	100	89.89	93.26
ADASYN	99.63	99.63	100	90.26	91.76
传统GAN	99.63	99.63	100	97.75	99.25
所提方法	99.63	99.63	100	99.25	100

Fig.11 Fault diagnosis result when Fault 4 samples are minority

Fig.12 Fault diagnosis result when Fault 5 samples are minority

Table 4 Diagnosis accuracy when Faults 4-5 are minority samples %

状态	对比方法
状态	随机过采样	SMOTE	ADASYN	传统GAN	所提方法
故障1	100	100	100	100	100
故障2	100	100	100	100	100
故障3	100	100	100	100	100
故障4	10.34	10.34	17.24	79.31	93.10
故障5	13.79	48.28	41.38	93.10	100
正常	100	100	100	90.98	100
整体	80.90	84.64	84.64	92.88	99.25

References 34

[1]	ZHAO B, ZHANG X M, WU Q Q, et al. A novel unsupervised directed hierarchical graph network with clustering representation for intelligent fault diagnosis of machines[J]. Mechanical Systems and Signal Processing, 2023, 183:109615.
[2]	雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5):94-104. doi: 10.3901/JME.2018.05.094
	LEI Y G, JIA F, KONG D T, et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering, 2018, 54(5):94-104. (in Chinese) doi: 10.3901/JME.2018.05.094
[3]	ZHANG Z W, JIAO Z H, LI Y, et al. Intel ligent fa ult diag nosis of bearings driven by doub le-level data fusio n based on multic hannel sample fusion and f eature fusion un der time -varying speed conditions[J]. Reliability Engineering & System Safety, 2024, 251:11036.
[4]	邵海东, 李伟, 刘翊, 等. 时变转速下基于双阈值注意力生成对抗网络和小样本的转子-轴承系统故障诊断[J]. 机械工程学报, 2023, 59(12):215-224. doi: 10.3901/JME.2023.12.215
	SHAO H D, LI W, LIU Y, et al. Fault diagnosis of rotor-bearing system under time-varying speeds by using dual-threshold attention-embedded GAN and small samples[J]. Journal of Mechanical Engineering, 2023, 59(12):215-224. (in Chinese) doi: 10.3901/JME.2023.12.215
[5]	AZAMFAR M, SINGH J, INAKI B I, et al. Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis[J]. Mechanical Systems and Signal Processing, 2020, 144:106861.
[6]	CHEN H P, HU N Q, CHENG Z, et al. A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes[J]. Measurement, 2019, 146:268-278.
[7]	吴春志, 冯辅周, 吴守军, 等. 一种有效的不均衡样本生成方法及其在行星变速箱故障诊断中的应用[J]. 兵工学报, 2019, 40(7):1349-1357. doi: 10.3969/j.issn.1000-1093.2019.07.003
	WU C Z, FENG F Z, WU S J, et al. An effective method for imbalanced sample generation and its application in fault diagnosis of planetary gearbox[J]. Acta Armamentarii, 2019, 40(7):1349-1357. (in Chinese) doi: 10.3969/j.issn.1000-1093.2019.07.003
[8]	GUO Y M, ZHOU Y F, ZHANG Z S. Fault diagnosis of multi-channel data by the CNN with the multilinear principal component analysis[J]. Measurement, 2021, 171:108513.
[9]	SHI J C, PENG D K, PENG Z X, et al. Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks[J]. Mechanical Systems and Signal Processing, 2022, 162:107996.
[10]	ZHANG S, LIU Z W, CHEN Y P, et al. Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis[J]. ISA Transactions, 2023, 133:369-383.
[11]	徐鹏, 皋军, 邵星. 基于AMCNN-BiGRU的滚动轴承故障诊断方法研究[J]. 振动与冲击, 2023, 42(18):71-80.
	XU P, GAO J, SHAO X. Fault diagnosis method for rolling bearings based on AMCNN-BiGRU[J]. Journal of Vibration and Shock, 2023, 42(18):71-80. (in Chinese)
[12]	何强, 唐向红, 李传江, 等. 负载不平衡下小样本数据的轴承故障诊断[J]. 中国机械工程, 2021, 32(10):1164-1171.
	HE Q, TANG X H, LI C J, et al. Bearing fault diagnosis method based on small sample data under unbalanced loads[J]. China Mechanical Engineering, 2021, 32(10):1164-1171. (in Chinese)
[13]	QIN Y, LIU H Y, WANG Y, et al. Inverse physics-informed neural networks for digital twin-based bearing fault diagnosis under imbalanced samples[J]. Knowledge-Based Systems, 2024, 292:111641.
[14]	PENG P, ZHANG W J, ZHANG Y, et al. Cost sensitive active learning using bidirectional gated recurrent neural networks for imbalanced fault diagnosis[J]. Neurocomputing, 2020, 407:232-245.
[15]	HE H, Garcia A. Learning from imbalanced data[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9):1263-1284.
[16]	WU Z Y, ZHANG H K, GUO J C, et al. Imbalanced bearing fault diagnosis under variant working conditions using cost-sensitive deep domain adaptation network[J]. Expert Systems with Applications, 2022, 193:116459.
[17]	张玉彦, 张永奇, 孙春亚, 等. 不平衡样本下基于生成式对抗网络的风机叶片开裂状态识别[J]. 计算机集成制造系统, 2023, 29(2):532-543.
	ZHANG Y Y, ZHANG Y Q, SUN C Y, et al. Identification method of cracking state of wind turbine blade based on GAN under imbalanced samples[J]. Computer Integrated Manufacturing Systems, 2023, 29(2):532-543. (in Chinese)
[18]	REN Z J, ZHU Y S, KANG W, et al. Adaptive cost-sensitive learning:improving the convergence of intelligent diagnosis models under imbalanced data[J]. Knowledge-Based Systems, 2022, 241:108296.
[19]	REN J J, WANG Y P, CHEUNG Y M, et al. Grouping-based oversampling in kernel space for imbalanced data classification[J]. Pattern Recognition, 2023, 133:108992.
[20]	GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Gener-ative adversarial nets[J]. Advances in Neural Information Processing Systems, 2014, 27:2672-2680.
[21]	PAN T Y, CHEN J L, ZHANG T C, et al. Generative adversarial network in mechanical fault diagnosis under small sample:a systematic review on applications and future perspectives[J]. ISA Transactions, 2022, 128 Part B:1-10.
[22]	GUO Z Q, PU Z Q, DU W L, et al. Improved adversarial learning for fault feature generation of wind turbine gearbox[J]. Renewable Energy, 2022, 185:256-266.
[23]	LIU Y P, JIANG H K, WANG Y F, et al. A conditional variational autoencoding generative adversarial networks with self-modulation for rolling bearing fault diagnosis[J]. Measurement, 2022, 192:110888.
[24]	郭伟, 邢晓松. 基于改进卷积生成对抗网络的少样本轴承智能诊断方法[J]. 中国机械工程, 2022, 33(19):2347-2355.
	GUO W, XING X S. Intelligent fault diagnosis of bearings with few samples based on an improved convolutional generative adversarial network[J]. China Mechanical Engineering, 2022, 33(19):2347-2355. (in Chinese) doi: 10.3969/j.issn.1004-132X.2022.19.009
[25]	WANG Y R, SUN G D, JIN Q, et al. Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network[J]. Applied Soft Computing, 2020, 92:106333.
[26]	YAN H, PAYNABAR K, SHI J J. Real-time monitoring of high-dimensional functional data streams via spatio-temporal smooth sparse decomposition[J]. Technometrics, 2018, 60(2):181-197.
[27]	SHI J J. In-process quality improvement:Concepts,methodologies,and applications[J]. IISE Transactions, 2022, 55(1):2-21.
[28]	LECUN Y, BOSER B E, DENKER J, et al. Handwritten digit recognition with a back-propagation network[J]. Advances in Neural Information Processing Systems, 1989, 2:396-404.
[29]	李晓雄, 张淑宁, 赵惠昌, 等. 基于1DC-CGAN和小波能量特征的引信小样本地形目标识别[J]. 兵工学报, 2022, 43(10):2545-2553.
	LI X X, ZHANG S N, ZHAO H C, et al. Identification of fuzzy small-sample terrain targets based on 1 DC-CGAN and wavelet energy features[J]. Acta Armamentarii, 2022, 43(10):2545-2553. (in Chinese)
[30]	LEI Y, ZHANG Z S, JIN J H. Automatic tonnage monitoring for missing part detection in multi-operation forging processes[J]. Journal of Manufacturing Science and Engineering, 2010, 132(5):051010.
[31]	VAN DER MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9:2579-2605.
[32]	REZVANI012-11 S, WANG X Z. A broad review on class imbalance learning techniques[J]. Applied Soft Computing, 2023, 143:110415.
[33]	CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE:Synthetic minority over-sampling technique[J]. Journal Artificial Intelligence Research, 2002, 16:321-357.
[34]	HE H B, BAI Y, GARCIA E A, et al. ADASYN:Adaptive synthetic sampling approach for imbalanced learning[C] // Proceedings of the IEEE International Joint Conference on Neural Networks.Hong Kong, China:IEEE, 2008:1322-1328.