A novel knowledge transfer network with fluctuating operational condition adaptation for bearing fault pattern recognition

Abstract Data-driven based intelligent fault pattern recognition methods of rolling element bearings have made fruitful achievements in recent years. However, for real-world diagnostic occasions, a hypothesis of identical distribution between training and test datasets of current deep learning approaches is natural to be violated. Though reported transfer learning models have boosted diagnostic performance through knowledge transfer, yet most of them suffer from the following limitations: (a) Partial vibration datasets in the target domain are needed to assist the training of the models. (b) Operational conditions information is not adequately considered in the current diagnostic models. (c) Knowledge transfer-based methods of bearing fault pattern recognition are always implemented under stationary operational conditions. To remedy these limitations mentioned above, a new knowledge transfer network with a sparse auto-encoder and a deep convolutional neural network (KTN-SAEDCNN) is proposed in this paper. In KTN-SAEDCNN architecture, input instantaneous rotating speed (IRS), as operational condition information, is fed into the sparse auto-encoder (SAE) in the target domain so that the operational information can be included into the model training rather than make use of the partial vibration dataset only. Then deep convolutional neural network (DCNN) is utilized to extract features from raw vibrations. Finally, a knowledge transfer model of KTN-SAEDCNN is established through the combination of SAE and DCNN two sub-models. The proposed model enables the capacity of fault pattern recognition under fluctuating operational conditions for rolling bearing fault detection. Lastly, experiments result in bearing demonstrate the effectiveness of the proposed method.

[1]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[2]  Gaoliang Peng,et al.  A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load , 2018, Mechanical Systems and Signal Processing.

[3]  Ruqiang Yan,et al.  Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning , 2019, IEEE Transactions on Industrial Informatics.

[4]  Xiang Li,et al.  Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks , 2019, IEEE Transactions on Industrial Electronics.

[5]  Xin Sun,et al.  Improved multi-scale entropy and it's application in rolling bearing fault feature extraction , 2020 .

[6]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[7]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[8]  Weihua Li,et al.  Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network , 2017, IEEE Transactions on Instrumentation and Measurement.

[9]  Luis J. de Miguel,et al.  Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load , 2012 .

[10]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[11]  Ming Liang,et al.  A method for tachometer-free and resampling-free bearing fault diagnostics under time-varying speed conditions , 2019, Measurement.

[12]  Robert Sutton,et al.  A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions , 2015 .

[13]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[14]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[15]  Perry J. Hardin,et al.  Statistical Significance and Normalized Confusion Matrices , 1997 .

[16]  Liang Gao,et al.  A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[17]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[18]  Moncef Gabbouj,et al.  Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.

[19]  Tao Zhang,et al.  Deep Model Based Domain Adaptation for Fault Diagnosis , 2017, IEEE Transactions on Industrial Electronics.

[20]  Arun Ross,et al.  On automated source selection for transfer learning in convolutional neural networks , 2018, Pattern Recognit..

[21]  Yousef Hojjat,et al.  A comprehensive study on capabilities and limitations of roller–screw with emphasis on slip tendency , 2009 .

[22]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[23]  Tonphong Kaewkongka,et al.  A train bearing fault detection and diagnosis using acoustic emission , 2016 .

[24]  Adam Glowacz,et al.  Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals , 2018 .

[25]  Simon J. Godsill,et al.  Statistical gear health analysis which is robust to fluctuating loads and operating speeds , 2012 .

[26]  Walid G. Morsi,et al.  Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning , 2018, IEEE Transactions on Industrial Electronics.

[27]  Wenming Zheng,et al.  Feature Selection Based Transfer Subspace Learning for Speech Emotion Recognition , 2020, IEEE Transactions on Affective Computing.

[28]  Jose Antonino-Daviu,et al.  Application of Infrared Thermography to Failure Detection in Industrial Induction Motors: Case Stories , 2017, IEEE Transactions on Industry Applications.

[29]  Ming J. Zuo,et al.  An ameliorated synchroextracting transform based on upgraded local instantaneous frequency approximation , 2019 .

[30]  Bin Yang,et al.  An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings , 2019, Mechanical Systems and Signal Processing.

[31]  Jasbir S. Arora,et al.  Jan A. Snyman, Practical Mathematical Optimization: An introduction to basic optimization theory and classical and new gradient-based algorithms , 2006 .

[32]  Wei Jiang,et al.  Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks , 2018, Neurocomputing.

[33]  Yi Cao,et al.  Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions , 2016 .

[34]  Diego Cabrera,et al.  A review on data-driven fault severity assessment in rolling bearings , 2018 .

[35]  L. Renaudin,et al.  Natural roller bearing fault detection by angular measurement of true instantaneous angular speed , 2010 .

[36]  Hang Chang,et al.  Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Minping Jia,et al.  Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery , 2020 .