一种基于深度迁移学习的滚动轴承早期故障在线检测方法

毛文涛; 田思雨; 窦智; 张迪; 丁玲

doi:10.16383/j.aas.c190593

一种基于深度迁移学习的滚动轴承早期故障在线检测方法

doi: 10.16383/j.aas.c190593

毛文涛^{1, 2,},
田思雨^1,,
窦智^{1, 2,},
张迪^1,,
丁玲^1,

1.
河南师范大学计算机与信息工程学院新乡 453007
2.
“智慧商务与物联网技术”河南省工程实验室新乡 453007

基金项目: 国家重点研发计划重点专项项目(2018YFB1701400)和国家自然科学基金(U1704158)资助

详细信息

作者简介:
毛文涛：河南师范大学计算机与信息工程学院教授. 主要研究方向为机器学习, 时间序列预测. 本文通信作者. E-mail: maowt@htu.edu.cn

田思雨：河南师范大学计算机与信息工程学院研究生. 主要研究方向为深度学习, 故障检测.E-mail: tianxiaosi77@163.com

窦智：河南师范大学计算机与信息工程学院副教授. 主要研究方向为图像处理, 可重构计算, 机器学习.E-mail: 2015160@htu.edu.cn

张迪：河南师范大学计算机与信息工程学院研究生. 主要研究方向为深度学习, 异常检测.E-mail: vencent8692@gmail.com

丁玲：河南师范大学计算机与信息工程学院研究生. 主要研究方向为深度学习, 故障检测.E-mail: ddll1029@163.com

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出版历程
- 收稿日期: 2019-08-18
- 录用日期: 2020-04-27
- 网络出版日期: 2022-01-11
- 刊出日期: 2022-01-20

A New Deep Transfer Learning-based Online Detection Method of Rolling Bearing Early Fault

MAO Wen-Tao^{1, 2
,},
TIAN Si-Yu^1
,,
DOU Zhi^{1, 2
,},
ZHANG Di^1
,,
DING Ling^1
,

1.
School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007
2.
Engineering Laboratory of Intelligence Business & Internet of Things, Xinxiang 453007

Funds: Supported by National Key Research & Development Program of China (2018YFB1701400) and National Natural Science Foundation of China (U1704158)

More Information

Author Bio:
MAO Wen-Tao　Professor at the School of Computer and Information Engineering, Henan Normal University. His research interest covers machine learning and prediction of time series. Corresponding author of this paper

TIAN Si-Yu　Master student at the School of Computer and Information Engineering, Henan Normal University. Her research interest covers deep learning and fault detection

DOU Zhi　Associate professor at the School of Computer and Information Engineering, Henan Normal University. His research interest covers image processing, reconfigurable calculations, and machine learning

ZHANG Di　Master student at the School of Computer and Information Engineering, Henan Normal University. His research interest covers deep learning and anomaly detection

DING Ling　Master student at the School of Computer and Information Engineering, Henan Normal University. Her research interest covers deep learning and fault detectio

摘要

摘要: 近年来, 深度学习技术已在滚动轴承故障检测和诊断领域取得了成功应用, 但面对不停机情况下的早期故障在线检测问题, 仍存在着早期故障特征表示不充分、误报警率高等不足. 为解决上述问题, 本文从时序异常检测的角度出发, 提出了一种基于深度迁移学习的早期故障在线检测方法. 首先, 提出一种面向多域迁移的深度自编码网络, 通过构建具有改进的最大均值差异正则项和Laplace正则项的损失函数, 在自适应提取不同域数据的公共特征表示同时, 提高正常状态和早期故障状态之间特征的差异性; 基于该特征表示, 提出一种基于时序异常模式的在线检测模型, 利用离线轴承正常状态的排列熵值构建报警阈值, 实现在线数据中异常序列的快速匹配, 同时提高在线检测结果的可靠性. 在XJTU-SY数据集上的实验结果表明, 与现有代表性早期故障检测方法相比, 本文方法具有更好的检测实时性和更低的误报警数.
- 早期故障检测 /
- 在线检测 /
- 迁移学习 /
- 异常检测 /
- 深度自编码网络
Abstract: In recent years, deep learning techniques have been successfully applied to fault detection and diagnosis for rolling bearings. However, for online detection of incipient fault without system halt, these techniques still have some shortcomings such as insufficient feature representation of incipient fault and high false alarm rate. To solve such problems, this paper presents a new deep transfer learning-based online detection approach on the perspective of temporal anomaly detection. First, a new deep auto-encoder network with multi-domain transferring is proposed by constructing a new loss function with the maximum mean discrepancy regularizer and Laplace regularizer. This model can adaptively extract the common feature representation among the data of different domains, and effectively improve the feature difference between normal state and early fault state as well. Second, with the obtained feature representation, a new online detection model based on temporal anomaly pattern is proposed. By utilizing the permutation entropy of normal state of offline bearings to build an alarm threshold, this model can match quickly anomaly sequence of the online monitoring data, and then improve the detection reliability. The experimental results on the XJTU-SY bearings dataset demonstrates that the proposed approach obtains better real-time detection performance and lower false alarm rate compared to some state-of-the-art methods of incipient fault detection.
- Incipient fault detection /
- online detection /
- transfer learning /
- anomaly detection /
- deep auto-encoder network

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图 1 多域迁移深度自编码网络结构图

Fig. 1 Architecture of deep auto-encoder network with multi-domain transferring

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图 2 本文方法所提取的公共特征及其对应的排列熵值

Fig. 2 The extracted common features and their corresponding permutation values

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图 3 图2的局部放大图

Fig. 3 Partial enlarged view of Fig. 2

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图 4 所提方法流程图

Fig. 4 Flow chart of the proposed method

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图 5 实验平台^[23]

Fig. 5 Experiment platform^[23]

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图 6 采用多层深度自编码器提取的三种工况下轴承正常状态数据的特征分布

Fig. 6 Feature distribution of bearing normal state data extracted by multi-layer deep auto-encoder under three working conditions

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图 7 采用本文方法提取的三种工况下轴承正常状态数据的特征分布

Fig. 7 Feature distribution of bearing normal state data extracted by the proposed method under three working conditions

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图 8 本文方法训练损失变化趋势图

Fig. 8 Changing trend of training loss of the proposed method

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图 9 三种工况下15个轴承正常状态数据的排列熵

Fig. 9 Permutation entropy of 15 bearings in normal state under all three working conditions

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图 10 工况1下轴承5的特征走势及对应的排列熵值 (其中红线与蓝线分别为加入和未加入Laplace正则项的本文方法所对应效果)

Fig. 10 Feature trend and the corresponding permutation entropy value of bearing 5 under working condition 1 (where the red and blue curves show the effect with or without Laplace regularizer, respectively)

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图 11 工况2下轴承4的特征走势及对应的排列熵值 (其中红线与蓝线分别为加入和未加入Laplace正则项的本文方法所对应效果)

Fig. 11 Feature trend and the corresponding permutation entropy value of bearing 4 under working condition 2 (where the red and blue curves show the effect with or without Laplace regularizer, respectively)

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图 12 Bearing1-5轴承的检测结果

Fig. 12 Detection results of bearing 1-5

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图 13 Bearing2-4轴承的检测结果

Fig. 13 Detection results of bearing 2-4

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图 14 本文方法与LOF算法的检测结果对比图 (其中, 本文方法横坐标为序列编号(即样本号除以100), 标签值大于0表示样本识别为正常样本, 小于0表示识别为异常样本)

Fig. 14 Comparative detection results between the proposed method and LOF algorithm (while x-coordinate of the proposed method denotes sequence number (equal to the sample number divided by 100), the label value greater than 0 means the corresponding sample is recognized as normal sample, else as anomaly)

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表 1 本文所用符号与对应描述

Table 1 Symbols and corresponding descriptions used in this paper

符号	描述	符号	描述
${\cal D}{^{ s}}$	源域	${\cal D}{^{ t}}$	目标域
$W,b$	自编码器的权重矩阵和偏置	$f,g$	激活函数
${s_c}$	工况数量	$m$	监测数据的组数
$X$	数据矩阵	$Y$	标签矩阵
${\boldsymbol{x}}$	单个样本	$y$	单个样本的标签
$n$	样本数量	$H$	特征矩阵
$p$	数据分布	$W'$	$W$的转置

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表 2 XJTU-SY数据集中三种工况描述

Table 2 Description of three working conditions in XJTU-SY dataset

运行条件	径向力 (kN)	转速 (r/min)	轴承数据集
工况1	12	2100	Bearing 1_1 Bearing 1_2 Bearing 1_3 Bearing 1_4 Bearing 1_5
工况2	11	2250	Bearing 2_1 Bearing 2_2 Bearing 2_3 Bearing 2_4 Bearing 2_5
工况3	10	2400	Bearing 3_1 Bearing 3_2 Bearing 3_3 Bearing 3_4 Bearing 3_5

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表 3 7种方法对比检测结果

Table 3 Comparative detection results of seven methods

检测方法	Bearing1_5检测结果	误报警数	Bearing2_4检测结果	误报警数
BEMD-AMMA^[25]	1110	0	无	0
局部异常值检测(LOF)^[26]	1096	35	962	1
iFOREST^[27]	1250	10	960	13
SDFM^[28]	1150	0	980	0
DAFD^[18]	1210	0	1100	0
SRD^[29]	1170	6	965	2
本文方法	1090	0	960	0

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