期刊
SENSORS
卷 22, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/s22072720
关键词
fault diagnosis; convolutional neural network; interfering signal; information fusion
资金
- Key R&D Programs of the Ministry of Science and Technology of China [2020YFB1712602]
This study proposes a new fault diagnosis model based on convolutional neural network, which can utilize the information contained in vibration signals effectively. The model achieves high accuracy in experiments and has positive implications for multiple inputs and information fusion.
In recent years, rotating machinery fault diagnosis methods based on convolutional neural network have achieved much success. However, in real industrial environments, interfering signals are unavoidable, which may reduce the accuracy of fault diagnosis seriously. Most of the current fault diagnosis methods are of single input type, which may lead to the information contained in the vibration signal not being fully utilized. In this study, theoretical analysis and comprehensive comparative experiments are completed to investigate the time domain input, frequency domain input, and two types of time-frequency domain input. Based on this, a new fault diagnosis model, named multi-stream convolutional neural network, is developed. The model takes the time domain, frequency domain, and time-frequency domain images as input, and it automatically fuses the information contained in different inputs. The proposed model is tested based on three public datasets. The experimental results suggested that the model achieved pretty high accuracy under noise and trend items without the help of signal separation algorithms. In addition, the positive implications of multiple inputs and information fusion are analyzed through the visualization of learned features.
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