Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network

By Jun Zhu, Nan Chen, Weiwen Peng. IEEE transactions on industrial electronics (1982. Print), 2019

597 citations recorded by the source corpus.

Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper, we present a new deep feature learning method for RUL estimation approach through time frequency representation (TFR) and multiscale convolutional neural network (MSCNN). TFR can reveal nonstationary property of a bearing degradation signal effectively. After acquiring time-series degradation signals, we get TFRs, which contain plenty of useful information using wavelet transform. Owing to high dimensionality, the size of these TFRs is reduced by bilinear interpolation, which are further regarded as inputs for deep learning models. Here, we introduce an MSCNN model structure, which keeps the global and local information synchronously compared to a traditional convolutional neural network (CNN). The salient features, which contribute for RUL estimation, can be learned automatically by MSCNN. The effectiveness of the presented method is validated by the experiment data. Compared to traditional data-driven and different CNN-based feature extraction methods, the proposed method shows enhanced performance in the prediction accuracy.

Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/9c38986568db937527c3a6c8d847c317136eac74

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. Zhu Mao
2. Meng Ma
3. Ming Zhao
4. Jing Lin
5. Kaixuan Liang
6. Jinyang Jiao
7. Rui Zhao
8. Ruqiang Yan

In Safety, Risk, Reliability and Quality

1. Cheng-Geng Huang
2. Zhu Mao
3. Hongzhong Huang
4. Meng Ma
5. Yanfeng Li

Citations in the corpus

• Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction (2021, methodology): “Zhu et al. [22] presented a deep feature learning method for RUL prediction through time–frequency representation and multiscale convolutional neural network.”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “i et al. [168] used the STFT to process raw vibration signals to obtain the time-frequency domain information. Then a deep CNN was built to extract multi-scale features for RUL estimation. Zhu et al. [169] used the WT to acquire the time-frequency representation and then trained a multi-scale CNN to”
• Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, background): “Zhu et al. [24] proposed a multiscale convolutional neural network (MSCNN) to predict the RUL [24].”
• A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions (2019, methodology): “[21] utilized a multiscale convolutional neural network (MSCNN) for bearing RUL prediction.”