面向轴承早期故障检测的多尺度残差注意力深度领域适配模型

doi:10.11772/j.issn.1001-9081.2020030329

计算机应用 ›› 2020, Vol. 40 ›› Issue (10): 2890-2898.DOI: 10.11772/j.issn.1001-9081.2020030329

面向轴承早期故障检测的多尺度残差注意力深度领域适配模型

毛文涛^1,2, 杨超¹, 刘亚敏¹, 田思雨¹

1. 河南师范大学计算机与信息工程学院, 河南新乡 453007;
2. 智慧商务与物联网技术河南省工程实验室(河南师范大学), 河南新乡 453007

收稿日期:2020-03-21 修回日期:2020-05-14 出版日期:2020-10-10 发布日期:2020-06-05
通讯作者: 毛文涛
作者简介:毛文涛(1980-),男,河南新乡人,教授,博士,CCF会员,主要研究方向:机器学习、智能故障诊断;杨超(1994-),男,河南信阳人,硕士研究生,主要研究方向:机器学习、早期故障检测;刘亚敏(1995-),女,河南周口人,硕士研究生,主要研究方向:机器学习、故障检测;田思雨(1993-),女,河南安阳人,硕士研究生,主要研究方向:机器学习、早期故障检测。
基金资助:
国家重点研发计划重点专项（2018YFB1701400）；国家自然科学基金资助项目（U1704158）。

Deep domain adaptation model with multi-scale residual attention for incipient fault detection of bearings

MAO Wentao^1,2, YANG Chao¹, LIU Yamin¹, TIAN Siyu¹

1. School of Computer and Information Engineering, Henan Normal University, Xinxiang Henan 453007, China;
2. Engineering Laboratory of Intelligence Business and Internet of Things in Henan Province;(Henan Normal University), Xinxiang Henan 453007, China

Received:2020-03-21 Revised:2020-05-14 Online:2020-10-10 Published:2020-06-05
Supported by:
This work is partially supported by the Special Project of the National Key Research and Development Program of China (2018YFB1701400), the National Natural Science Foundation of China (U1704158).

摘要/Abstract

摘要： 针对由工作环境和设备状况的差异引起的轴承早期故障检测模型可靠性差、误报警率高的问题，根据早期故障检测的特点和需求，提出一种多尺度注意力深度领域适配模型。首先，将监测信号处理成由原始信号、希尔伯特-黄变换边际谱、频谱组成的三通道数据；然后，通过在残差注意力模块中增加不同尺寸的滤波器以提取多尺度深度特征，使用卷积-反卷积操作来重构输入信息从而获得注意力信息，并且将注意力信息与多尺度特征融合构建了一种多尺度残差注意力模块，用于提取对早期故障表征能力更强的注意力特征；其次，在所提取到的注意力特征基础上，构建基于交叉熵和最大均值差异（MMD）正则化约束的损失函数来实现领域适配；最后，采用随机梯度下降算法进行网络参数优化，构建端到端的早期故障检测模型。在IEEE PHM-2012数据挑战赛数据集上的实验结果表明，与8种代表性的早期故障检测和诊断方法以及迁移学习算法相比，所提方法能够在不延迟报警时间点的前提下，分别比8种方法的平均误报警率降低了62.7%和61.3%，有效提高了早期故障检测的鲁棒性。

关键词: 早期故障检测, 残差注意力网络, 迁移学习, 注意力机制, 深度学习

Abstract: Aiming at the problems of poor reliability and high false alarm rate of the fault detection models of bearings caused by the differences in working environment and equipment status, a multi-scale attention deep domain adaptation model was proposed according to the characteristics and needs of incipient fault detection. First, the monitoring signal was pre-processed into a three-channel data consisting of the original signal, Hilbert-Huang transform marginal spectrum and frequency spectrum. Second, the filters of different sizes were added into the residual attention module to extract multi-scale deep features, and the convolution-deconvolution operation was used to reconstruct the input information in order to obtain attention information, then a multi-scale residual attention module was constructed by combining the attention information and multi-scale features and was used to extract the attention features with stronger ability of representing incipient faults. Third, a loss function based on the cross entropy and Maximum Mean Discrepancy (MMD) regularization constraints was constructed to achieve the domain adaptation on the basis of the extracted attention features. Finally, a stochastic gradient descent algorithm was used to optimize the network parameters, and an end-to-end incipient fault detection model was established. Comparative experiments were conducted on the IEEE PHM-2012 Data Challenge dataset. Experimental results show that, compared with eight representative incipient fault detection and diagnosis methods as well as transfer learning algorithms, the proposed method can obtain the reduction of 62.7% and 61.3% in the average false alarm rate while keeping the alarm location not delayed, and effectively improves the robustness of incipient fault detection.

Key words: incipient fault detection, residual attention network, transfer learning, attention mechanism, deep learning

中图分类号:

TP181

毛文涛, 杨超, 刘亚敏, 田思雨. 面向轴承早期故障检测的多尺度残差注意力深度领域适配模型[J]. 计算机应用, 2020, 40(10): 2890-2898.

MAO Wentao, YANG Chao, LIU Yamin, TIAN Siyu. Deep domain adaptation model with multi-scale residual attention for incipient fault detection of bearings[J]. Journal of Computer Applications, 2020, 40(10): 2890-2898.

参考文献

[1] LEI Y,LI N,GUO L,et al. Machinery health prognostics:a systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing,2018,104:799-834.
[2] YAN R,GAO R X. Machine health diagnosis based on approximate entropy[C]//Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference. Piscataway:IEEE,2004, 2054-2059.
[3] QIU H,LEE J,LIN J,et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration,2006,289(4/5):1066-1090.
[4] LIU R,YANG B,ZHANG X,et al. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis[J]. Mechanical Systems and Signal Processing, 2016, 75:345-370.
[5] 杨洪柏, 张宏利, 刘树林. 基于可视化异类特征优选融合的滚动轴承故障诊断[J]. 计算机应用,2017,37(4):1207-1211. (YANG H B,ZHANG H L,LIU S L. Rolling bearing fault diagnosis based on visual heterogeneous feature fusion[J]. Journal of Computer Applications,2017,37(4):1207-1211.)
[6] MAO W,CHEN J,LIANG X,et al. A new online detection approach for rolling bearing incipient fault via self-adaptive deep feature matching[J]. IEEE Transactions on Instrumentation and Measurement,2020,69(2):443-456.
[7] WEN L,LI X,GAO L,et al. A new convolutional neural networkbased data-driven fault diagnosis method[J]. IEEE Transactions on Industrial Electronics,2018,65(7):5990-5998.
[8] AKCAY S, ATAPOUR-ABARGHOUEI A, BRECKON T P. GANomaly:semi-supervised anomaly detection via adversarial training[C]//Proceedings of the 2018 Asian Conference on Computer Vision,LNCS 11363. Cham:Springer,2018:622-637.
[9] JIA F,LEI Y,GUO L,et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines[J]. Neurocomputing,2018,272:619-628.
[10] WEN L,GAO L,LI X. A new deep transfer learning based on sparse auto-encoder for fault diagnosis[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2019, 49(1):136-144.
[11] HAN T,LIU C,YANG W,et al. Deep transfer network with joint distribution adaptation:a new intelligent fault diagnosis framework for industry application[J]. ISA Transactions, 2020, 97:269-281.
[12] LU W,LIANG B,CHENG Y,et al. Deep model based domain adaptation for fault diagnosis[J]. IEEE Transactions on Industrial Electronics,2017,64(3):2296-2305.
[13] 雷亚国, 杨彬, 杜兆钧, 等. 大数据下机械装备故障的深度迁移诊断方法[J]. 机械工程学报,2019,55(7):1-8.(LEI Y G, YANG B,DU Z J,et al. Deep transfer diagnosis method for machinery in big data era[J]. Journal of Mechanical Engineering, 2019,55(7):1-8.)
[14] MAO W,DING L,TIAN S,et al. Online detection for bearing incipient fault based on deep transfer learning[J]. Measurement, 2020,152:No. 107278.
[15] WANG F,JIANG M,QIAN C,et al. Residual attention network for image classification[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2017:6450-6458.
[16] VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook,NY:Curran Associates Inc.,2017:6000-6010.
[17] 孔子迁, 邓蕾, 汤宝平, 等. 基于时频融合和注意力机制的深度学习行星齿轮箱故障诊断方法[J]. 仪器仪表学报,2019,40(6):221-227.(KONG Z Q,DENG L,TANG B P,et al. Fault diagnosis of planetary gearbox based on deep learning with timefrequency fusion and attention mechanism[J]. Chinese Journal of Scientific Instrument,2019,40(6):221-227.)
[18] 吴静然, 丁恩杰, 崔冉, 等. 采用多尺度注意力机制的旋转机械故障诊断方法[J]. 西安交通大学学报,2020,54(2):51-58. (WU J R,DING E J,CUI R,et al. A diagnostic approach for rotating machinery using multi-scale feature attention mechanism[J]. Journal of Xi'an Jiaotong University,2020,54(2):51-58.)
[19] EL-SAWY A,EL-BAKRY H,LOEY M. CNN for handwritten Arabic digits recognition based on LeNet-5[C]//Proceedings of the 2016 International Conference on Advanced Intelligent Systems and Informatics,AISC 533. Cham:Springer,2016:566-575.
[20] KRIZHEVSKY A,SUTSKEVER I,HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook, NY:Curran Associates Inc.,2012:1097-1105.
[21] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2015:1-9.
[22] HE K,ZHANG X,REN S,et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:770-778.
[23] GRETTON A,BORGWARDT K,RASCH M,et al. A kernel method for the two-sample-problem[C]//Proceedings of the 9th International Conference on neural Information Processing Systems. Cambridge:MIT Press,2006:513-520.
[24] RAI V K,MOHANTY A R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2007,21(6):2607-2615.
[25] YANG B,LEI Y,JIA F,et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings[J]. Mechanical Systems and Signal Processing,2019,122:692-706.
[26] MEDJAHER K, ZERHOUNI N, BAKLOUTI J. Data-driven prognostics based on health indicator construction:application to PRONOSTIA's data[C]//Proceedings of the 2013 European Control Conference. Piscataway:IEEE,2013:1451-1456.
[27] LI G,HU Y,CHEN H,et al. An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm[J]. Energy and Buildings,2016,116:104-113.
[28] PEZZOTTI N,THIJSSEN J,MORDVINTSEV A,et al. GPGPU linear complexity t-SNE optimization[J]. IEEE Transactions on Visualization and Computer Graphics,2020,26(1):1172-1181.
[29] LU W,LI Y,CHENG Y,et al. Early fault detection approach with deep architectures[J]. IEEE Transactions on Instrumentation and Measurement,2018,67(7):1679-1689.
[30] MA H,HU Y,SHI H. Fault detection and identification based on the neighborhood standardized local outlier factor method[J]. Industrial and Engineering Chemistry Research,2013,52(6):2389-2402.
[31] DOMINGUES R, FILIPPONE M, MICHIARDI P, et al. A comparative evaluation of outlier detection algorithms:experiments and analyses[J]. Pattern Recognition,2018,74:406-421.
[32] 郭小萍, 刘诗洋, 李元. 基于稀疏残差距离的多工况过程故障检测方法研究[J]. 自动化学报,2019,45(3):617-625.(GUO X P,LIU S Y,LI Y. Fault detection of multi-mode processes employing sparse residual distance[J]. Acta Automatica Sinica, 2019,45(3):617-625.)
[33] LI Y,XU M,LIANG X,et al. Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings[J]. IEEE Transactions on Industrial Electronics,2017,64(8):6506-6517.
[34] PAN S J,TSANG I W,KWOK J T,et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks,2011,22(2):199-210.
[35] GONG B,SHI Y,SHA F,et al. Geodesic flow kernel for unsupervised domain adaptation[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2012:2066-2073.

面向轴承早期故障检测的多尺度残差注意力深度领域适配模型

Deep domain adaptation model with multi-scale residual attention for incipient fault detection of bearings

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