Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction f...

Full description

Bibliographic Details
Main Authors:	Xiangang Cao, Fuqiang Zhang, Jiangbin Zhao, Yong Duan, Xingyu Guo
Format:	Article
Language:	English
Published:	MDPI AG 2024-03-01
Series:	Applied Sciences
Subjects:	multi-domain mixed features remaining useful life prediction temporal convolutional networks feature dimensionality reduction rolling bearing
Online Access:	https://www.mdpi.com/2076-3417/14/6/2354

Similar Items

Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
by: Guanwen Zhang, et al.
Published: (2024-02-01)

A Framework for Predicting Remaining Useful Life Curve of Rolling Bearings Under Defect Progression Based on Neural Network and Bayesian Method
by: Masashi Kitai, et al.
Published: (2021-01-01)

On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor
by: Christoph Bienefeld, et al.
Published: (2022-04-01)

Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network
by: Feiyue Deng, et al.
Published: (2021-11-01)

Digital Twin-Driven Remaining Useful Life Prediction for Rolling Element Bearing
by: Quanbo Lu, et al.
Published: (2023-06-01)

A Domain-Adversarial Multi-Graph Convolutional Network for Unsupervised Domain Adaptation Rolling Bearing Fault Diagnosis
by: Xinran Li, et al.
Published: (2022-12-01)

A Novel Method for Multistage Degradation Predicting the Remaining Useful Life of Wind Turbine Generator Bearings Based on Domain Adaptation
by: Miao Tian, et al.
Published: (2023-11-01)

The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network
by: Xuejun Liu, et al.
Published: (2022-06-01)

Remaining Useful Life Prediction of Bearings Based on Convolution Attention Mechanism and Temporal Convolution Network
by: Haitao Wang, et al.
Published: (2023-01-01)

Fault Diagnosis of Rolling Bearings of Different Working Conditions Based on Multi-Feature Spatial Domain Adaptation
by: Tao Wen, et al.
Published: (2021-01-01)

Remaining Life Prediction of Bearings Based on Improved IF-SCINet
by: Jing Zhang, et al.
Published: (2024-01-01)

Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm
by: Xiao Li, et al.
Published: (2022-08-01)

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-Scale Attention Residual Network
by: Lin Song, et al.
Published: (2023-05-01)

Remaining Useful Life Estimation of Spindle Bearing Based on Bearing Load Calculation and Off-Line Condition Monitoring
by: Jiri Sova, et al.
Published: (2023-05-01)

Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN
by: Yaping Wang, et al.
Published: (2022-06-01)

Research on Predicting Remain Useful Life of Rolling Bearing Based on Parallel Deep Residual Network
by: Xingang Wang, et al.
Published: (2022-04-01)

Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved Particle Filter Algorithm
by: Tianyu Zhang, et al.
Published: (2023-01-01)

Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression
by: Qiaoping Tian, et al.
Published: (2021-05-01)

Rolling Bearing Remaining Useful Life Prognosis Method based on Improved CHSMM
by: Zhu Shuo, et al.
Published: (2018-01-01)

A Method for Predicting the Remaining Life of Rolling Bearings Based on Multi-Scale Feature Extraction and Attention Mechanism
by: Changhong Jiang, et al.
Published: (2022-11-01)

Prediction of Remaining Service Life of Rolling Bearings Based on Convolutional and Bidirectional Long- and Short-Term Memory Neural Networks
by: Zhidan Zhong, et al.
Published: (2022-07-01)

Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning
by: Xiaochuan Li, et al.
Published: (2019-07-01)

Incipient Fault Feature Enhancement of Rolling Bearings Based on CEEMDAN and MCKD
by: Ling Zhao, et al.
Published: (2023-05-01)

A Multi-Featured Factor Analysis and Dynamic Window Rectification Method for Remaining Useful Life Prognosis of Rolling Bearings
by: Cheng Peng, et al.
Published: (2023-11-01)

A New Fault Diagnosis of Rolling Bearing Based on Markov Transition Field and CNN
by: Mengjiao Wang, et al.
Published: (2022-05-01)

Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation
by: Jianghua Ge, et al.
Published: (2019-11-01)

Visualized Stacked Denoising Auto-Encoder Model for Extracting and Evaluating the State Features of Rolling Bearings
by: Qing Zhang, et al.
Published: (2022-09-01)

WEAK FAULT FEATURE EXTRACTION OF ROLLING BEARING BASED ON PARAMETER OPTIMIZED MOMEDA AND CEEMDAN
by: HAN XueFei, et al.
Published: (2021-01-01)

A New Method of Bearing Remaining Useful Life Based on Life Evolution and SE-ConvLSTM Neural Network
by: Shuai Yang, et al.
Published: (2022-08-01)

Fault Feature Extraction for Rolling Bearing based on LMD Energy Entropy
by: Xu Le, et al.
Published: (2019-01-01)

Bearing Fault Diagnosis Method Based on Attention Mechanism and Multi-Channel Feature Fusion
by: Hongfeng Gao, et al.
Published: (2024-01-01)

Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network
by: Xiong Zhang, et al.
Published: (2023-04-01)

Application of Time Domain Index and Kurtosis analysis Method in the Fault Diagnosis of Rolling Bearing
by: Guo Qingfeng, et al.
Published: (2016-01-01)

Transfer Prediction Method of Bearing Remaining Useful Life Based on Deep Feature Evaluation under Different Working Conditions
by: Yongzhi Liu, et al.
Published: (2023-10-01)

Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology
by: Zhengkun Xue, et al.
Published: (2023-10-01)

Early Prediction of Remaining Useful Life for Rolling Bearings Based on Envelope Spectral Indicator and Bayesian Filter
by: Haobin Wen, et al.
Published: (2024-01-01)

A mixture distributions analysis based feature selection approach for bearing remaining useful life estimation
by: Fei Huang, et al.
Published: (2023-10-01)

Deep Spatiotemporal Convolutional-Neural-Network-Based Remaining Useful Life Estimation of Bearings
by: Xu Wang, et al.
Published: (2021-06-01)

Remaining Useful Life Prediction of Rolling Bearings Based on ECA-CAE and Autoformer
by: Jianhua Zhong, et al.
Published: (2024-01-01)

A Two-Stage Transfer Regression Convolutional Neural Network for Bearing Remaining Useful Life Prediction
by: Xianling Li, et al.
Published: (2022-05-01)