A sudden fault detection network based on Time-sensitive gated recurrent units for bearings

Abstract Mechanical fault diagnosis is an indispensable part of the modern industrial production process. The application scenario of fault diagnosis according to a neural network from theory to practical application is worth exploring. Most existing work focuses on remaining life prediction and fault classification, treating these two steps independently. This study aims at coherent real-time monitoring of bearing status and proposes a gated recurrent unit -based fault monitoring structure to obtain timely response and preliminary classification of sudden faults. In the existing fault classification research, more attention is paid to the classification accuracy of the fault details. The time sequence law followed by the sudden fault and the short-term prediction of that fault are easily overlooked. The proposed method is trained by key-frames of bearing data. These data frames first pass through the feature extraction layer which consists of two layers of 1D convolution. Then, the reset gates and update gates of developed units keep the valid information at the last moment and update the unit state at that instant. A sudden fault will trigger the detection network, and the detected fault frame will be extracted for further classification by the independently trained fault classification network. During the test, a prediction of the moment of failure occurrence is directly obtained. When the status is judged to be faulty, the fault frame is directly extracted and used as the input of the classification network. Experimental results confirm that the network quickly responds to sudden failures under an operating environment, and the classification accuracy rate can stably reach more than 98%.

[1]  Shuilong He,et al.  Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions. , 2021, ISA transactions.

[2]  Wei Jiang,et al.  Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. , 2018, ISA transactions.

[3]  Shichang Du,et al.  A double-layer attention based adversarial network for partial transfer learning in machinery fault diagnosis , 2021, Comput. Ind..

[4]  Ge Guo,et al.  Vehicle Localization During GPS Outages With Extended Kalman Filter and Deep Learning , 2021, IEEE Transactions on Instrumentation and Measurement.

[5]  Yun Liang,et al.  E-LSTM: Efficient Inference of Sparse LSTM on Embedded Heterogeneous System , 2019, 2019 56th ACM/IEEE Design Automation Conference (DAC).

[6]  Soumaya Yacout,et al.  Bidirectional handshaking LSTM for remaining useful life prediction , 2019, Neurocomputing.

[7]  Zhao Zhen,et al.  A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework , 2020 .

[8]  Yaguo Lei,et al.  Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .

[9]  Feng Gao,et al.  A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM , 2019, Shock and Vibration.

[10]  Ming J. Zuo,et al.  A novel knowledge transfer network with fluctuating operational condition adaptation for bearing fault pattern recognition , 2020 .

[11]  Gang Cheng,et al.  Research on Bearing Fault Diagnosis Method Based on Filter Features of MOMLMEDA and LSTM , 2019, Entropy.

[12]  Zhao-Hua Liu,et al.  A Stacked Auto-Encoder Based Partial Adversarial Domain Adaptation Model for Intelligent Fault Diagnosis of Rotating Machines , 2020, IEEE Transactions on Industrial Informatics.

[13]  Chuan Li,et al.  Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss , 2020, Sensors.

[14]  K. Bretonnel Cohen,et al.  Trigger Words Detection by Integrating Attention Mechanism into Bi-LSTM Neural Network - A Case Study in PubMED-Wide Trigger Words Detection for Pancreatic Cancer , 2018, CCL.

[15]  Yongzhi Qu,et al.  Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor , 2020, J. Intell. Manuf..

[16]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[17]  He Wang,et al.  A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox , 2020 .

[18]  Fanming Meng,et al.  An Improved Bearing Fault Diagnosis Method using One-Dimensional CNN and LSTM , 2018, Strojniški vestnik - Journal of Mechanical Engineering.

[19]  Yaguo Lei,et al.  Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .

[20]  Yong Zhi Liu,et al.  Transfer learning method for bearing fault diagnosis based on fully convolutional conditional Wasserstein adversarial Networks , 2021 .

[21]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[22]  Hee-Jun Kang,et al.  A survey on Deep Learning based bearing fault diagnosis , 2019, Neurocomputing.

[23]  Stefan Hinz,et al.  Review on Convolutional Neural Networks (CNN) in vegetation remote sensing , 2021, ISPRS Journal of Photogrammetry and Remote Sensing.

[24]  Yuanyuan Pan,et al.  Multi-Scale Stochastic Resonance Spectrogram for fault diagnosis of rolling element bearings , 2018 .

[25]  Jinrui Wang,et al.  A Review on the Signal Processing Methods of Rotating Machinery Fault Diagnosis , 2019, 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC).

[26]  Xiaoyang Zheng,et al.  Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder , 2021, Measurement.