The optimized local sparse parallel multi-channel deep convolutional neural network-LSTM and the application in bearing fault diagnosis under noise and variable working condition
Restricted accessResearch articleFirst published online January, 2025
The optimized local sparse parallel multi-channel deep convolutional neural network-LSTM and the application in bearing fault diagnosis under noise and variable working condition
Since the actual operation of the bearing inevitably exists in both noise and variable working conditions, most of the traditional networks can only deal with them alone, and the fault identification result will be significantly reduced under such complex conditions. Therefore, Parallel Multichannel Deep Convolution Neural Network (PMDCNN) and Long Short-Term Memory (LSTM) are proposed as PMDCNN-LSTM model to enable better performance. And a local sparse structure is used to greatly reduce the number of model parameters, the Exponential Linear Unit (ELU) activation function is used to further improve accuracy and stability. The results show that the proposed method is strongly resistant to noise and variable working conditions, as verified by the Case Western Reserve University (CWRU) bearing dataset and bearing fault simulation experimental platform dataset. The comparison with other model shows that the proposed model has good applicability, strong stability and high accuracy of bearing fault identification.
WangSQiaoZNiuP.Coupled Hybrid stochastic resonance with multiobjective optimization for machinery dynamic signature and fault diagnosis. IEEE Sens J2023; 23(11): 11825–11837.
2.
XuYZhenDGuJX, et al. Autocorrelated envelopes for early fault detection of rolling bearings. Mech Syst Signal Process2021; 146: 106990.
3.
LuoJShaoHLinJ, et al. Meta-learning with elastic prototypical network for fault transfer diagnosis of bearings under unstable speeds. Reliab Eng Syst Saf2024; 245: 110001.
4.
XiaoYShaoHWangJ, et al. Bayesian variational transformer: a generalizable model for rotating machinery fault diagnosis. Mech Syst Signal Process2024; 207: 110936.
5.
YangCQiaoZZhuR, et al. An intelligent fault diagnosis method enhanced by noise injection for machinery. IEEE Trans Instrum Meas2023; 72: 1–11.
6.
FengZWangSYuM.A fault diagnosis for rolling bearing based on multilevel denoising method and improved deep residual network. Digit Signal Process2023; 140: 104106.
7.
ChenLMaYHuH, et al. An effective fault diagnosis approach for bearing using stacked de-noising auto-encoder with structure adaptive adjustment. Measurement2023; 214: 112774.
8.
LiGWuJDengC, et al. Parallel multi-fusion convolutional neural networks based fault diagnosis of rotating machinery under noisy environments. ISA Trans2022; 128(Pt A): 545–555.
9.
ZouLLiYXuF.An adversarial denoising convolutional neural network for fault diagnosis of rotating machinery under noisy environment and limited sample size case. Neurocomputing2020; 407: 105–120.
10.
YuZZhangCDengC.An improved GNN using dynamic graph embedding mechanism: a novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions. Mech Syst Signal Process2023; 200: 110534.
11.
AnYZhangKChaiY, et al. Domain adaptation network base on contrastive learning for bearings fault diagnosis under variable working conditions. Expert Syst Appl2023; 212: 118802.
12.
WangZLiuQChenH, et al. A deformable CNN-DLSTM based transfer learning method for fault diagnosis of rolling bearing under multiple working conditions. Int J Prod Res2021; 59(16): 4811–4825.
13.
FuanWHongkaiJHaidongS, et al. An adaptive deep convolutional neural network for rolling bearing fault diagnosis. Meas Sci Technol2017; 28(9): 095005.
14.
LuCWangZ-YQinW-L, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process2017; 130: 377–388.
15.
SuKLiuJXiongH.Hierarchical diagnosis of bearing faults using branch convolutional neural network considering noise interference and variable working conditions. Knowl Based Syst2021; 230: 107386.
16.
ProsvirinAEMaliukASKimJ-M.Intelligent rubbing fault identification using multivariate signals and a multivariate one-dimensional convolutional neural network. Expert Syst Appl2022; 198: 116868.
17.
LiGYaoQFanC, et al. An explainable one-dimensional convolutional neural networks based fault diagnosis method for building heating, ventilation and air conditioning systems. Build Environ2021; 203: 108057.
18.
WangXMaoDLiX.Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement2021; 173: 108518.
19.
YeMYanXChenN, et al. Intelligent fault diagnosis of rolling bearing using variational mode extraction and improved one-dimensional convolutional neural network. Appl Acoust2023; 202: 109143.
LiuHZhouJZhengY, et al. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Trans2018; 77: 167–178.
22.
JiangHLiXShaoH, et al. Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network. Meas Sci Technol2018; 29(6): 065107.
23.
ZhangJChenYLiN, et al. A weak fault identification method of micro-turbine blade based on sound pressure signal with LSTM networks. Aerosp Sci Technol2023; 136: 108226.
24.
HeSXuXXieJ, et al. Fault detection and fault-tolerant control of autonomous steering system for intelligent vehicles combining Bi-LSTM and SPRT. Measurement2023; 212: 112708.
25.
XiangLWangPYangX, et al. Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism. Measurement2021; 175: 109094.
26.
GuoXLuKChengY, et al. Research on fault diagnosis method for hydraulic system of CFETR blanket transfer device based on CNN-LSTM. Fusion Eng Des2022; 185: 113321.
