基于多通道数据双层增强的样本不平衡故障诊断方法

doi:10.12382/bgxb.2024.0012

摘要/Abstract

摘要：

在复杂制造过程中常需要采集并分析多通道数据以实现状态监测和故障诊断,针对现有方法难以处理多通道数据复杂时空相关结构和样本不平衡的问题,提出了一种基于多通道数据双层增强的样本不平衡故障诊断方法。所提模型具有2阶段数据增强和全局优化的特点,通过先学习故障特征再转化为多通道数据的方式实现数据增强,引入分布差异评估机制有效地描述不同通道之间的数据相关性,基于多目标的全局优化策略来提高生成数据的质量。通过实际案例验证所提方法的有效性,实验结果表明:双层增强方法能有效扩充多通道数据的样本量,全局优化策略可以提高生成数据在故障诊断中的性能。与现有模型相比,所提方法在多种样本不平衡场景下均具有较高的故障诊断准确率。

关键词: 多通道数据, 样本不平衡故障诊断, 双层数据增强, 全局优化

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

中图分类号:

TH17

郭一鸣, 童一飞, 何非, 谢中取, 宋世达, 黄静. 基于多通道数据双层增强的样本不平衡故障诊断方法[J]. 兵工学报, 2025, 46(2): 240012-.

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-.

图/表 16

图1 所提模型整体结构

Fig.1 The overall structure of the proposed model

图2 故障特征学习网络结构

Fig.2 Fault feature learning network structure

图3 多通道数据生成网络结构

Fig.3 Multi-channel data generation network structure

图4 全局优化策略流程

Fig.4 Global optimization strategy

图5 多工步锻压设备结构与工艺流程

Fig.5 Multi-step forging machine and technological process

图6 4通道压力数据

Fig.6 Four-channel pressure data

图7 样本不平衡故障诊断结果

Fig.7 Fault diagnosis results of imbalanced sample

图8 两种方法生成的多通道数据

Fig.8 Multi-channel data generated by two methods

表1 生成数据和真实数据间MSE值

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

表2 不同通道数据间MSE值

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

图9 生成的故障特征

Fig.9 Generated fault feature

图10 基于t-SNE的故障特征可视化

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

表3 故障1-5分别为少数样本时的诊断准确率 %

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

图11 故障4为少数样本时的诊断结果

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

图12 故障5为少数样本时的诊断结果

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

表4 故障4-5均为少数样本时的诊断准确率

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

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