Deep residual learning-based fault diagnosis method for rotating machinery.

By Wei Zhang, Xiang Li, Qian Ding. ISA transactions, 2019

367 citations recorded by the source corpus.

Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/fb2251dd9273df65006fb40e97fe81b7a73400a0

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. Yong Qin
2. Ming Zhao
3. Jing Lin
4. Huan Wang
5. Kaixuan Liang
6. Dandan Peng
7. Zhiliang Liu
8. Jinyang Jiao

In Mechanics of Materials

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): “resented to artificially create additional valid samples for model training. The result showed that their method can achieve high diagnosis accuracy with small original training dataset. Zhang et al. [143] used raw vibration signals as the model input to train a deep ResNet for bearing fault diagnos”
• Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis (2020, methodology): “…[26] based on the 2DCNN, deep convolutional neural networks with wide first-layer kernels (WDCNN) [17], residual convolution neural network (ResCNN) [23] based on the 1DCNN, gated recurrent unit (GRU), recurrent neural network (RNN), stacked autoen-coder (SAE), and the five-layer back propagation…”