Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction

By Xiang Li, Qian Ding. Reliability Engineering & System Safety, 2019

512 citations recorded by the source corpus.

Abstract Accurate evaluation of machine degradation during long-time operation is of great importance. With the rapid development of modern industries, physical model is becoming less capable of describing sophisticated systems, and data-driven approaches have been widely developed. This paper proposes a novel intelligent remaining useful life (RUL) prediction method based on deep learning. The time-frequency domain information is explored for prognostics, and multi-scale feature extraction is implemented using convolutional neural networks. Experiments on a popular rolling bearing dataset prepared from the PRONOSTIA platform are carried out to show the effectiveness of the proposed method, and its superiority is demonstrated by the comparisons with other approaches. In general, high accuracy on the RUL prediction is achieved, and the proposed method is promising for industrial applications.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/4068a0372eb1c5f4aaccad5c424a5552fb057902

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Ming Zhao
2. Jing Lin
3. Kaixuan Liang
4. Jinyang Jiao

In Mechanical Engineering

1. Ming Zhao
2. Jing Lin
3. Kaixuan Liang
4. Jinyang Jiao

Citations in the corpus

• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “ input. Mao et al. [167] employed the CNN to learn features from the marginal spectrum of Hilbert-Huang transform. After that, a LSTM network was constructed for RUL prediction of bearings. Li et al. [168] used the STFT to process raw vibration signals to obtain the time-frequency domain information”
• Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, unknown): “The literature in this area includes applications of ML to different engineering items, for example, Lion-ion batteries [63], railway tracks [64], turbine cutting tools [65], rolling bearings [66], and aircraft engine [67, 68].”