A new bearing fault diagnosis method based on modified convolutional neural networks

By Jiangquan Zhang, Yi Sun, Liang Guo, Hongli Gao, Xin Hong, Hongliang Song. Chinese Journal of Aeronautics, 2020

298 citations recorded by the source corpus.

Abstract Fault diagnosis is vital in manufacturing system. However, the first step of the traditional fault diagnosis method is to process the signal, extract the features and then put the features into a selected classifier for classification. The process of feature extraction depends on the experimenters’ experience, and the classification rate of the shallow diagnostic model does not achieve satisfactory results. In view of these problems, this paper proposes a method of converting raw signals into two-dimensional images. This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process. And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network (CNN), which can automatically accomplish the process of the feature extraction and fault diagnosis. The effect of this method is verified by bearing data. The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed. The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/b84205cbaa5bec2b1f4fe0a07445a362df899996

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. Chuang Sun
2. Xuefeng Chen
3. Ruqiang Yan
4. Shibin Wang
5. Zhibin Zhao

In Mechanical Engineering

1. Shibin Wang
2. Chuang Sun
3. Ruqiang Yan
4. Xuefeng Chen
5. Zhibin Zhao

In Mechanics of Materials

1. Xuefeng Chen
2. Ruqiang Yan
3. Chuang Sun

Citations in the corpus

• Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, methodology): “In CNN models, for model improvement, different input types, such as time-frequency images [51], vibration spectrum images [52], infrared thermal images [53], and two-dimensional images [54], were used as the inputs of CNN models.”