Bearings fault diagnosis method based on multi-pathed hierarchical mixture-of-experts model

doi:10.11772/j.issn.1001-9081.2024010043

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (1): 59-68.DOI: 10.11772/j.issn.1001-9081.2024010043

• Artificial intelligence • Previous Articles Next Articles

Bearings fault diagnosis method based on multi-pathed hierarchical mixture-of-experts model

Xinran XU¹^,², Shaobing ZHANG¹^,²^,³(), Miao CHENG¹^,²^,³, Yang ZHANG¹^,²^,³, Shang ZENG¹^,²

^1.Chengdu Institute of Computer Application，Chinese Academy of Sciences，Chengdu Sichuan 610213，China
^2.School of Computer Science and Technology，University of Chinese Academy of Sciences，Beijing 100049，China
^3.Shenzhen CBPM-KEXIN Banking Technology Company Limited，Shenzhen Guangdong 518206，China

Received:2024-01-17 Revised:2024-03-21 Accepted:2024-03-21 Online:2024-05-09 Published:2025-01-10
Contact: Shaobing ZHANG
About author:XU Xinran， born in 1997， M. S. candidate. His research interests include artificial intelligence， predictive maintenance， industrial big data.
CHENG Miao， born in 1983， M. S.， senior engineer. His research interests include artificial intelligence， machine vision.
ZHANG Yang， born in 1985， M. S.， senior engineer. His research interests include machine vision， artificial intelligence， big data.
ZENG Shang， born in 1995， Ph. D. candidate. His research interests include artificial intelligence.
Supported by:
Sichuan Provincial Science and Technology Achievement Transfer and Transformation Demonstration Project(2023ZHCG0005);Sichuan Science and Technology Program(2023YFG0113)

基于多路层次化混合专家模型的轴承故障诊断方法

徐欣然¹^,², 张绍兵¹^,²^,³(), 成苗¹^,²^,³, 张洋¹^,²^,³, 曾尚¹^,²

^1.中国科学院成都计算机应用研究所，成都 610213
^2.中国科学院大学计算机科学与技术学院，北京 100049
^3.深圳市中钞科信金融科技有限公司，广东深圳 518206

通讯作者: 张绍兵
作者简介:徐欣然（1997—），男，四川成都人，硕士研究生，主要研究方向：人工智能、预测性维护、工业大数据；
成苗（1983—），男，四川成都人，高级工程师，硕士，主要研究方向：人工智能、机器视觉；
张洋（1985—），男，河南平顶山人，高级工程师，硕士，主要研究方向：机器视觉、人工智能、大数据；
曾尚（1995—），男，湖北荆门人，博士研究生，主要研究方向：人工智能。
基金资助:
四川省科技成果转移转化示范项目(2023ZHCG0005);四川省科技计划项目(2023YFG0113)

Abstract

Abstract:

In response to the issue of low accuracy in handling complex work conditions in rolling bearing fault diagnosis， a Multi-Task Learning （MTL） model naming as Multi-pathed Hierarchical Mixture-of-Experts （MHMoE）， and the corresponding hierarchical training mode were proposed. In this model， by combining multi-stage， multi-task joint training， a hierarchical information sharing mode was achieved. The model's generalization and fault recognition accuracy were further improved on the basis of the ordinary MTL mode， enabling the model to perform tasks on both complex and simple datasets excellently. Meanwhile， by incorporating the bottleneck layer structure of one-dimensional ResNet， the depth of the network was ensured while avoiding issues such as vanishing and exploding gradients， so as to extract relevant features of the dataset fully. Experimental results on the Paderborn University bearing fault dataset （PU） as the test dataset demonstrate that under varying degrees of working complexity， compared to the OMoE （One-gate Mixture-of-Experts） -ResNet18 model without MTL， the proposed model has the accuracy improved by 5.45 to 9.30 percentage points. Compared to the models such as Ensemble Empirical Mode Decomposition Hilbert spectral transform （EEMD-Hilbert）， MMoE （Multi-gate Mixture-of-Experts）， and Multi-Scale multi-Task Attention Convolutional Neural Network （MSTACNN）， the proposed model has the accuracy improved by 3.21 to 16.45 percentage points at least.

Key words: bearing fault diagnosis, Predictive Maintenance (PdM), Multi-Task Learning (MTL), deep learning, Convolutional Neural Network (CNN)

摘要：

针对滚动轴承故障诊断中处理复杂工况准确率较低的问题，提出一个多任务学习（MTL）模型，即多路层次化混合专家（MHMoE）模型，以及对应的层次化训练模式。该模型结合多阶段、多任务联合训练，实现了层次化的信息共享模式，并在普通MTL模式的基础上进一步提升了模型的泛化性和故障识别准确率，使模型能同时在复杂与简单的数据集上出色地完成任务，同时，结合一维ResNet的瓶颈层结构，在保证网络深度的同时，也规避梯度爆炸与梯度消失等问题，从而能充分地提取数据集的相关特征。以帕德博恩大学轴承故障数据集（PU）为测试数据集设计的实验的结果表明，在不同工况复杂度下，与不使用MTL的单任务混合专家单元结构（OMoE）-ResNet18模型相比，所提模型的准确率提升5.45~9.30个百分点；而与集成经验模态分解的Hilbert谱变换方法（EEMD-Hilbert）、MMoE （Multi-gate Mixture-of-Experts）和多尺度多任务注意力卷积神经网络（MSTACNN）等模型相比，所提模型的准确率至少提升3.21~16.45个百分点。

关键词: 轴承故障诊断, 预测性维护, 多任务学习, 深度学习, 卷积神经网络

CLC Number:

TP183

Xinran XU, Shaobing ZHANG, Miao CHENG, Yang ZHANG, Shang ZENG. Bearings fault diagnosis method based on multi-pathed hierarchical mixture-of-experts model[J]. Journal of Computer Applications, 2025, 45(1): 59-68.

徐欣然, 张绍兵, 成苗, 张洋, 曾尚. 基于多路层次化混合专家模型的轴承故障诊断方法[J]. 《计算机应用》唯一官方网站, 2025, 45(1): 59-68.

Figures/Tables 16

References 26

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编号	名称	类别编号	标签名称
0	工况	0	N15_M01_F10
		1	N09_M07_F10
		2	N15_M07_F10
		3	N15_M07_F04
1	故障外因	0	No fault
		1	drilling
		2	EDM（Electrical Discharge Machining）
		3	electric engraver
		4	fatigue： pitting
		5	plastic deforms
2	复合故障	0	No fault
		1	Artificial damage
		2	Single damage
		3	Repetitive damage
		4	Multiple damage
3	故障等级	0	No fault
		1	0-2 mm fault
		2	2-4.5 mm fault
		3	4.5-13.5 mm fault
4	内圈故障	0	No fault
4	内圈故障	1	With fault
5	外圈故障	0	No fault
5	外圈故障	1	With fault

编号	名称	类别编号	标签名称
0	工况	0	N15_M01_F10
		1	N09_M07_F10
		2	N15_M07_F10
		3	N15_M07_F04
1	故障外因	0	No fault
		1	drilling
		2	EDM（Electrical Discharge Machining）
		3	electric engraver
		4	fatigue： pitting
		5	plastic deforms
2	复合故障	0	No fault
		1	Artificial damage
		2	Single damage
		3	Repetitive damage
		4	Multiple damage
3	故障等级	0	No fault
		1	0-2 mm fault
		2	2-4.5 mm fault
		3	4.5-13.5 mm fault
4	内圈故障	0	No fault
4	内圈故障	1	With fault
5	外圈故障	0	No fault
5	外圈故障	1	With fault

工况名称	负载扭矩/（N·m）	轴转速/（r·min^-1）	径向力/N
N15_M01_F10	0.1	1 500	1 000
N09_M07_F10	0.7	900	1 000
N15_M07_F10	0.7	1 500	1 000
N15_M07_F04	0.7	1 500	400

工况名称	负载扭矩/（N·m）	轴转速/（r·min^-1）	径向力/N
N15_M01_F10	0.1	1 500	1 000
N09_M07_F10	0.7	900	1 000
N15_M07_F10	0.7	1 500	1 000
N15_M07_F04	0.7	1 500	400

组别	数据文件	轴承工况	说明
组1	K001，KA04，KA15，KA16，KA22，KA30，KB23，KB24，KB27，KI14，KI16，KI17，KI18，KI21	N15_M01_F10	自然损毁的单一工况轴承
组2	K001，KA04，KA15，KA16，KA22，KA30，KB23，KB24，KB27，KI14，KI16，KI17，KI18，KI21	所有工况	自然损毁的多种工况轴承
组3	所有文件	所有工况	全数据集

Bearings fault diagnosis method based on multi-pathed hierarchical mixture-of-experts model

基于多路层次化混合专家模型的轴承故障诊断方法

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Figures/Tables 16

References 26

Related Articles 15

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Metrics

参数	默认值
DWA温度	10
单条数据长度	1 024
批大小	64
EU输出维度	64
单个EU的ResNet18单元数	3
数据读取总条数	200 000
学习率衰减步长	20
学习率衰减率	0.3

名称	配置信息
CPU	AMD Ryzen 7 5800X
GPU	GeForce RTX3090（24 GB）
操作系统	Ubuntu 18.04.6
编程语言	Python 3.8.0
框架	torch 1.13.1+cu117

模型	分类准确率
模型	组1	组2	组3
WDCNN	86.47	69.43	46.93
EEMD-Hilbert+FWA-SVM	91.42	79.25	51.75
ResNet18	93.21	74.74	49.59
MMoE+ResNet18	93.73	75.24	51.78
MSTACNN	94.03	75.40	52.27
MHMoE（本文模型）	97.24	89.13	68.72

组别	任务	准确率
组别	任务	MHMoE	OMoE+ResNet18
组1	故障等级	99.69	95.21
	内圈故障	98.72	92.42
	外圈故障	98.61	93.03
组2	故障等级	96.45	92.27
	内圈故障	96.77	90.03
	外圈故障	94.92	88.60
组3	故障等级	84.37	77.02
	内圈故障	87.65	78.31
	外圈故障	89.33	78.11

组别	任务	准确率
组别	任务	CEU+EEU	CEU
组1	故障等级	99.69	99.01
	内圈故障	98.72	97.47
	外圈故障	98.61	97.12
组2	故障等级	97.39	93.95
	内圈故障	96.77	92.58
	外圈故障	95.33	93.32
组3	故障等级	84.37	82.31
	内圈故障	87.65	81.23
	外圈故障	89.33	82.01

组别	任务类别	准确率
组别	任务类别	O+M+U	U	O+U	M+U
组1	故障等级	99.69	97.17	99.23	99.17
	内圈故障	98.72	97.63	97.03	97.85
	外圈故障	98.61	96.34	98.06	97.14
组2	故障等级	97.39	93.42	94.03	93.39
	内圈故障	96.77	91.03	93.32	94.56
	外圈故障	95.33	92.86	92.77	91.13
组3	故障等级	84.37	80.34	82.06	82.12
	内圈故障	87.65	82.21	82.33	83.57
	外圈故障	89.33	82.13	83.65	83.76

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