Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model
Open Access
Abstract
Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. Estimating the RUL enables the assessment of health bearing, maintenance planning, and significant cost reduction, thereby fostering industrial development. Existing models rely on traditional feature engineering with feature changes because operating conditions pose a major challenge to the generalization of RUL prediction models. This study focuses on neural network-based feature engineering and the downstream prediction of the RUL, eliminating the need for specific prior knowledge and simplifying the development and maintenance of models. Initially, a convolutional neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation characteristics of the engineered features and predict the RUL through regression. Finally, the study examines the influence of operating conditions in the model and integrates domain adaptation to minimize differences in feature distribution, thereby enhancing the model’s generalizability for the RUL prediction.Keywords
Funding Information
- National Natural Science Foundation of China (12304513, 62172242, 2022J138)
- Natural Science Foundation of Ningbo Municipality (12304513, 62172242, 2022J138)
This publication has 40 references indexed in Scilit:
- An effective fault diagnosis approach for bearing using stacked de-noising auto-encoder with structure adaptive adjustmentMeasurement, 2023
- Prediction of time series using an analysis filter bank of LSTM unitsComputers & Industrial Engineering, 2021
- Recurrent convolutional neural network: A new framework for remaining useful life prediction of machineryNeurocomputing, 2020
- Reliability assessment of wind turbine bearing based on the degradation-Hidden-Markov modelRenewable Energy, 2019
- Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualizationMechanical Systems and Signal Processing, 2018
- NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data FusionIEEE Transactions on Industrial Electronics, 2017
- Remaining useful life prediction of aircraft engine based on degradation pattern learningReliability Engineering & System Safety, 2017
- Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark studyMechanical Systems and Signal Processing, 2015
- Another look at measures of forecast accuracyInternational Journal of Forecasting, 2006
- Gradient-based learning applied to document recognitionProceedings of the IEEE, 1998