Results 1 to 10 of about 1,541,566 (221)

Adaptively Lightweight Spatiotemporal Information-Extraction-Operator-Based DL Method for Aero-Engine RUL Prediction [PDF]

Sensors, 2023
Accurate prediction of machine RUL plays a crucial role in reducing human casualties and economic losses, which is of significance. The ability to handle spatiotemporal information contributes to improving the prediction performance of machine RUL ...
Junren Shi, Jun Gao, Sheng Xiang
doaj   +3 more sources

A noval RUL prediction method for rolling bearing: TcLstmNet-CBAM. [PDF]

Sci Rep
Abstract Rolling bearings are pivotal components within rotating mechanical systems, and accurately predicting their remaining service life holds significant practical importance. This paper addresses issues prevalent in common deep learning methods for predicting remaining useful life (RUL), notably inadequate feature extraction and low ...
Liu Q, Dai Z, Lai H, Chen M, Huang H, Fu J, Hou M, Xu X, Wang G, Yan J.   +9 more
europepmc   +5 more sources

A novel gear RUL prediction method by diffusion model generation health index and attention guided multi-hierarchy LSTM [PDF]

Scientific Reports
Gears, as indispensable components of machinery, demand accurate prediction of their Remaining Useful Life (RUL). To enhance the utilization of ordered information within time series data and elevate RUL prediction precision, this study introduces the ...
Xinping Chen
doaj   +2 more sources

Research on Remaining Useful Life Prediction of Equipment Based on Digital Twins [PDF]

Sensors
Remaining Useful Life (RUL) prediction is a key factor in fault diagnosis, prediction, and health management (PHM) during equipment operation and service.
Jiaju Wu, Yuanlin Zhou, Xiaodong Wang, Chuan Chen, Yongqi Ma, Chunrui Zhang   +5 more
doaj   +2 more sources

A Prediction Method for the RUL of Equipment for Missing Data [PDF]

Complexity, 2021
We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short‐term memory (MSDCNN‐LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications.
Wenbai Chen, Chang Liu 0120, Weizhao Chen, Huixiang Liu, Qili Chen, Peiliang Wu   +5 more
openaire   +2 more sources

A Framework for Predicting Remaining Useful Life Curve of Rolling Bearings Under Defect Progression Based on Neural Network and Bayesian Method

IEEE Access, 2021
In order to improve Remaining Useful Life (RUL) prediction accuracy for rolling bearings under defect progressing, the robustness for individual differences and the fluctuation of vibration features are challenging issues.
Masashi Kitai, Takuji Kobayashi, Hiroki Fujiwara, Ryoji Tani, Masayuki Numao, Ken-Ichi Fukui   +5 more
doaj   +1 more source

Uncertainty-Controlled Remaining Useful Life Prediction of Bearings with a New Data-Augmentation Strategy

Applied Sciences, 2022
The remaining useful life (RUL) of bearings based on deep learning methods has been increasingly used. However, there are still two obstacles in deep learning RUL prediction: (1) the training process of the deep learning model requires enough data, but ...
Ran Wang, Fucheng Yan, Ruyu Shi, Liang Yu, Yingjun Deng   +4 more
doaj   +1 more source

Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning

Heliyon, 2022
RUL (remaining useful life) shapelets were recently developed to overcome the shortcomings of similarity-based RUL prediction methods, such as high sensitivity to parameters.
Gilseung Ahn, Min-Ki Jin, Seok-Beom Hwang, Sun Hur   +3 more
doaj   +1 more source

LSTM-Based Multi-Task Method for Remaining Useful Life Prediction under Corrupted Sensor Data

Machines, 2023
Data-driven remaining useful life (RUL) prediction plays a vital role in modern industries. However, unpredictable corruption may occur in the collected sensor data due to various disturbances in the real industrial conditions.
Kai Zhang, Ruonan Liu
doaj   +1 more source

XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries

Sensors, 2022
The instability and variable lifetime are the benefits of high efficiency and low-cost issues in lithium-ion batteries.An accurate equipment’s remaining useful life prediction is essential for successful requirement-based maintenance to improve ...
Sadiqa Jafari, Yung-Cheol Byun
doaj   +1 more source