期刊
IEEE SENSORS JOURNAL
卷 22, 期 3, 页码 2572-2581出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2021.3137992
关键词
Harmonic reducer; wavelet threshold denoising; CNN-LSTM; fault detection
资金
- Guangxi Key Laboratory of Automatic Detecting Technology and Instruments [YQ19205]
This paper proposes a method for identifying faults in harmonic reducers using the joint wavelet regional correlation threshold denoising algorithm and the convolutional neural network-long short term memory fault detection method, which significantly improves the accuracy of fault detection.
The harmonic reducer is a key component of the industrial robot. Its reliability has significant influence on the consecutive operation of the industrial robot. However, its failure rate is high due to the complicated internal structure and long-term working conditions of large load and high torque. To identify the fault type of the harmonic reducer under different working conditions, this article proposes the joint wavelet regional correlation threshold denoising (WRCTD) algorithm and convolutional neural network-long short term memory (CNN-LSTM) fault detectionmethod. Firstly, the WRCTD algorithm utilizes the regional correlation of the wavelet decomposition coefficients and the 3 sigma criterion to suppress noise in the raw sensor data. Then, the CNN-LSTM model could mine hidden features of the processed sensor data to identify fault correctly. To evaluate the proposed method, the vibration signals of the harmonic reducer under different working conditions are collected by establishing a test rig. Comparative experimental results show that the proposed WRCTD algorithm can significantly improve the accuracy of the fault detection method. The specific values for the CNN-LSTM method and the LSTM method are 9.5% and 12.2%, respectively. In addition, the proposed CNN-LSTM method has better performance than traditional methods. It achieves the fault detection over 94.0%, which is 1.5%, 2.0%, 2.0%, 1.9%, and, 1.9% higher than the highest accuracy values of CNN, LSTM, and SVM methods in five experiments.
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