基于改进自编码网络的轴承振动异常检测

doi:10.3778/j.issn.1673-9418.2007042

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (1): 163-175.DOI: 10.3778/j.issn.1673-9418.2007042

基于改进自编码网络的轴承振动异常检测

李贝贝⁺(), 彭力

江南大学物联网工程学院,江苏无锡 214122

收稿日期:2020-07-07 修回日期:2020-09-04 出版日期:2022-01-01 发布日期:2020-09-15
通讯作者: + E-mail: jnlibeibei1995@163.com
作者简介:李贝贝（1995—）,男,山东滨州人,硕士研究生,主要研究方向为异常数据检测、不确定数据聚类。
彭力（1967—）,男,河北唐山人,博士,教授,博士生导师,CAAI会员,CCF会员,主要研究方向为视觉物联网、行为识别、深度学习。
基金资助:
国家重点研发计划(2018YFD0400902);国家自然科学基金(61873112)

Bearing Vibration Abnormal Detection Based on Improved Autoencoder Network

LI Beibei⁺(), PENG Li

School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China

Received:2020-07-07 Revised:2020-09-04 Online:2022-01-01 Published:2020-09-15
About author:LI Beibei, born in 1995, M.S. candidate. His research interests include anomaly data detection and uncertain data clustering.
PENG Li, born in 1967, Ph.D., professor, Ph.D. supervisor, member of CAAI and CCF. His research interests include visual Internet of things, action recognition and deep learning.
Supported by:
National Key Research and Development Program of China(2018YFD0400902);National Natural Science Foundation of China(61873112)

摘要/Abstract

摘要：

近年来,自编码器和神经网络技术已被广泛研究并应用于轴承振动等工业数据的异常检测问题上,但仍存在着训练数据量大、网络参数初始化、训练效率较低、异常检测效果较差等问题。为解决上述问题,提出了一种结合马氏距离和自编码网络的异常检测方法。利用轴承振动数据特征之间具有一定相关性的特点,通过数据的马氏距离快速检测出部分异常数据,减少了自编码网络的训练数据量;用自编码器结合分类器构建自编码网络,解决了网络参数初始化问题并且显著提高了训练效率;将数据的马氏距离作为特征加入训练中提升了自编码网络的异常检测效果;在自编码器中加入稀疏性限制并构造先升维再编码的结构,增强了自编码器的特征学习能力和收敛性。实验结果表明,针对低维轴承振动数据,提出的方法较其他异常检测方法具有较好的检测效果且具有一定的稳定性和泛化能力。

关键词: 自编码器, 轴承振动, 异常检测, 马氏距离, 自编码网络

Abstract:

In recent years, autoencoders and neural network technologies have been widely studied and applied to abnormal data detection problems of industrial data such as bearing vibration, but there are still problems such as large training data, network parameter initialization, low training efficiency, poor detection effect and so on. To solve such problems, this paper presents an anomaly data detection method combining Mahalanobis distance and autoencoder network. There is a certain correlation between bearing vibration data characteristics, so the Mahalanobis distance of the data is used to quickly detect some abnormal data, which reduces the amount of training data for the self-encoding network. In this research, the autoencoder and the classifier are combined to construct the autoencoder network, which solves the problem of network parameter initialization and significantly improves the training efficiency. The Mahalanobis distance of the data is added to the data features, which improves the anomaly detection effect of the autoencoder network. The sparseness restriction is added to the autoencoder, and a structure that first enhances the dimensionality and then encodes samples is constructed. The structure enhances the feature learning ability and convergence of the autoencoder. Experimental results show that the method presented has better detection results than other abnormal detection methods for low-dimensional bearing vibration data, and it has certain stability and generalization ability.

Key words: autoencoder, bearing vibration, abnormal detection, Mahalanobis distance, autoencoder network

中图分类号:

TP391

李贝贝, 彭力. 基于改进自编码网络的轴承振动异常检测[J]. 计算机科学与探索, 2022, 16(1): 163-175.

LI Beibei, PENG Li. Bearing Vibration Abnormal Detection Based on Improved Autoencoder Network[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(1): 163-175.

图/表 32

图1 自编码器结构

Fig.1 Structure of AE

图2 改进自编码器结构

Fig.2 Improved structure of AE

图3 有监督的微调

Fig.3 Supervised fine-tuning

图4 轴承振动异常检测模型

Fig.4 Bearing vibration abnormal detection model

表1 评价指标混淆矩阵

Table 1 Evaluation index confusion matrix

实际类别	预测为异常数据	预测为正常数据
实际异常数据	TP	FN
实际正常数据	FP	TN

图5 数据集1的轴承振动数据

Fig.5 Bearing vibration data from dataset 1

表2 数据集1样本大小

Table 2 Size of dataset 1

类别标签	类别名	样本数	数据占比/%
0	正常数据	537	54.7
1	异常数据	445	45.3

图6 正常轴承振动数据

Fig.6 Normal bearing vibration data

图7 正常数据的马氏距离

Fig.7 Mahalanobis distance for normal data

图8 所有数据马氏距离

Fig.8 Mahalanobis distance for all data

表3 不确定数据集

Table 3 Uncertain dataset

类别标签	类别名	样本数	数据占比/%
0	正常数据	537	86.3
1	异常数据	85	13.7

图9 自编码器训练误差随 β的变化

Fig.9 Training loss changes with β

图10 自编码器训练误差随 ρ的变化

Fig.10 Training loss changes with ρ

表4 自编码器参数设置

Table 4 Parameter setting of autoencoder

参数	数值
学习率	0.001
L2正则化惩罚因子	0.001
稀疏惩罚项权重系数 $β$	0.2
稀疏常数 $ρ$	0.04
一次训练选取样本数	10
输出层激活函数	sigmoid
输入层及隐层激活函数	ReLU

表4 自编码器参数设置

Table 4 Parameter setting of autoencoder

参数	数值
学习率	0.001
L2正则化惩罚因子	0.001
稀疏惩罚项权重系数 $β$	0.2
稀疏常数 $ρ$	0.04
一次训练选取样本数	10
输出层激活函数	sigmoid
输入层及隐层激活函数	ReLU

图11 改进自编码网络

Fig.11 Improved autoencoder network

图12 实验结果随 U层维度变化图

Fig.12 Experimental result changes with dimension of U layer

图13 实验结果随 h层维度变化图

Fig.13 Experimental result changes with dimension of h layer

表5 训练数据是否加入马氏距离的对比

Table 5 Comparison of training data with or without Mahalanobis distance

网络	Acc	Pre	Rec	F1
不加入马氏距离	0.875	0.875	1.000	0.934
加入马氏距离	0.976	0.973	1.000	0.986

图14 传统自编码网络

Fig.14 Traditional autoencoder network

图15 自编码器误差曲线比较

Fig.15 Comparison of autoencoder loss

表6 自编码器训练时间的对比

Table 6 Comparison of autoencoder training time

网络	一次训练所需平均时间/μs	训练次数	训练所需总时间/μs
传统AE	113	50	5 650
改进AE	142	15	2 130

图16 自编码网络误差曲线比较

Fig.16 Comparison of autoencoder network loss

图17 自编码网络准确率曲线比较

Fig.17 Comparison of autoencoder network accuracy

表7 自编码网络训练时间的对比

Table 7 Comparison of autoencoder network training time

网络	一次训练所需平均时间/μs	训练次数	训练所需总时间/μs
传统AN	121	60	7 260
改进AN	162	30	4 860

表8 传统自编码网络与改进自编码网络的对比

Table 8 Comparison between traditional autoencoder network and improved autoencoder network

网络	Acc	Pre	Rec	F1
传统AN	0.927	0.923	1.000	0.960
改进AN	0.989	1.000	0.979	0.989

表9 不同训练数据量下的实验结果对比

Table 9 Experimental results comparison under different amounts of training data

训练数据量	网络	Acc	Pre	Rec	F1
20%	传统AN	0.883	0.882	1.000	0.937
20%	改进AN	0.891	0.889	1.000	0.941
40%	传统AN	0.870	0.869	1.000	0.930
40%	改进AN	0.934	0.931	1.000	0.964
60%	传统AN	0.869	0.867	1.000	0.929
60%	改进AN	0.942	0.940	1.000	0.968
80%	传统AN	0.890	0.888	1.000	0.941
80%	改进AN	0.977	0.976	0.999	0.987

图18 实验结果随训练数据量变化图

Fig.18 Experimental result changes with amount of training data

表10 异常检测算法与所提方法的对比

Table 10 Comparison of anomaly detection algorithms with proposed method

检测方法	Acc	Pre	Rec	F1
iForest	0.644	0.606	0.998	0.754
KNN	0.931	0.927	1.000	0.962
LOF	0.920	0.965	0.885	0.923
SVM	0.960	0.964	1.000	0.981
K-means	0.968	0.982	1.000	0.981
DNN	0.976	0.973	1.000	0.986
本文方法	0.995	0.991	1.000	0.995

图19 数据集2的轴承振动数据

Fig.19 Bearing vibration data from dataset 2

图20 数据集3的轴承振动数据

Fig.20 Bearing vibration data from dataset 3

表11 3个数据集样本大小

Table 11 Size of 3 datasets

数据集	标签	类别名	样本数	数据占比/%
数据集2	0	正常数据	1 464	75.3
数据集2	1	异常数据	479	24.7
数据集3	0	正常数据	5 936	93.9
数据集3	1	异常数据	388	6.1
XJTU-SY数据集	0	正常数据	29 124	78.8
XJTU-SY数据集	1	异常数据	7 836	21.2

表12 在3个数据集上的实验结果

Table 12 Experimental results on 3 datasets

数据集	Acc	Pre	Rec	F1
数据集2	0.985	1.000	0.980	0.990
数据集3	0.993	0.994	0.998	0.996
XJTU-SY数据集	0.984	0.982	1.000	0.991

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基于改进自编码网络的轴承振动异常检测

Bearing Vibration Abnormal Detection Based on Improved Autoencoder Network

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