Deep Learning based End-to-End Rolling Bearing Fault Diagnosis
| Autor: | Li Yongjie, Liu Xueliang, Wenqiushi Sun, Bohua Qiu, Muheng Wei |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Network architecture
Bearing (mechanical) Computer science business.industry Deep learning 020206 networking & telecommunications Feature selection 02 engineering and technology Machine learning computer.software_genre Fault (power engineering) Convolutional neural network law.invention law 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data pre-processing business computer Network model |
| Zdroj: | 2019 Prognostics and System Health Management Conference (PHM-Qingdao). |
| DOI: | 10.1109/phm-qingdao46334.2019.8942956 |
| Popis: | Rolling bearings play an important part in rotating machinery. As they work in complex conditions, faults will occur sometimes. Therefore, it is necessary to detect the faults early. Traditional bearing fault diagnosis methods are often based on mechanism analysis and feature selection, and the process is relatively complicated. Deep learning methods, however, have the ability to extract and select features automatically, which greatly reduces the workload. In recent years, deep learning-based methods have been successfully used in many fields, such as computer vision, voice recognition, medical diagnosis. In this paper, the end-to-end fault methods based on deep learning are proposed. The Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network and One-Dimensional Convolutional Neural Network (1D CNN) are used to build the deep learning network architecture respectively. A methodology is proposed for rolling bearing fault diagnosis, including data preprocessing, network modeling, training, validation and testing. Test bench data is used for fault diagnosis and the results show that deep learning based end-to-end methods are effective for the fault diagnosis of rolling bearings and that the model based on 1D CNN has the best performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|