Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information

Measurement ◽  
2021 ◽  
Vol 170 ◽  
pp. 108718
Author(s):  
Zhuo Long ◽  
Xiaofei Zhang ◽  
Li Zhang ◽  
Guojun Qin ◽  
Shoudao Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Attention Mechanism ◽  
Sensor Information

scholarly journals Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23717-23725
Author(s):  
Jiaxing Wang ◽  
Dazhi Wang ◽  
Sihan Wang ◽  
Wenhui Li ◽  
Keling Song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
Sensor Information

General Fault Monitoring and Diagnosis Expert System Based on Fault Tree and Multi-Sensor Information

2011 ◽  
Vol 121-126 ◽  
pp. 4481-4485
Author(s):  
Ai Yu Zhang ◽  
Xiao Guang Zhao ◽  
Lei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Expert Knowledge ◽  
Sensor Failure ◽  
Fault Trees ◽  
Intelligent Algorithms ◽  
Monitoring And Diagnosis ◽  
Backward Reasoning ◽  
Fault Monitoring ◽  
Sensor Information

Due to the limited generality of traditional fault diagnosis expert system and its low accuracy of extracting failure symptoms, a general fault monitoring and diagnosis expert system has been built. For different devices, users can build fault trees in an interactive way and then the fault trees will be saved as expert knowledge. A variety of sensors are fixed to monitor the real-time condition of the device and intelligent algorithms such as wavelet transform and neural network are used to assist the extraction of failure symptoms. On the basis of integration of multi-sensor failure symptoms, the fault diagnosis is realized through forward and backward reasoning. The simulation diagnosis experiments of NC device have shown the effectiveness of the proposed method.

scholarly journals Application of Multi-sensor Information Fusion Technology on Fault Diagnosis of Electrical System

2017 ◽  
Vol 11 ◽  
pp. 05003
Author(s):  
Ling-Wen Meng ◽  
Ji-Pu Gao ◽  
Ming-Yong Xin ◽  
Jin-Mei Xiong ◽  
Guo Rui
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Fusion Technology ◽  
Electrical System ◽  
Sensor Information

Interpreting network knowledge with attention mechanism for bearing fault diagnosis

2020 ◽  
Vol 97 ◽  
pp. 106829
Author(s):  
Zhi-bo Yang ◽  
Jun-peng Zhang ◽  
Zhi-bin Zhao ◽  
Zhi Zhai ◽  
Xue-feng Chen
Keyword(s):  
Fault Diagnosis ◽  
Attention Mechanism ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

The Research of Intelligent Fault Diagnosis Model on Multi-Information Fusion

2010 ◽  
Vol 40-41 ◽  
pp. 637-642
Author(s):  
Xiao Hua Liu ◽  
Song Qing Li
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Large Scale ◽  
Strong Support ◽  
Intelligent Fault Diagnosis ◽  
The Hierarchical Structure ◽  
System Requirements ◽  
Diagnosis Model ◽  
Sensor Information ◽  
The Relationship

From the intelligent fault diagnosis system requirements, this article analyzes the relationship between the fault diagnosis and the multi-information fusion basing on the summing up the multi-sensor information fusion technology, and studies the hierarchical structure of multi-sensor information fusion system and the content of integration, and establishes an intelligent fault diagnosis model with the multi-information fusion, which provides strong support for large-scale equipments, system monitoring and fault diagnosis in production process.

scholarly journals Motor Fault Diagnosis Algorithm Based on Wavelet and Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yong Yan ◽  
Qiang Liu ◽  
Xiao qin Gao
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Attention Mechanism ◽  
Time Dimension ◽  
High Frequency Signal ◽  
Diagnosis Accuracy ◽  
Diagnosis Algorithm ◽  
Motor Vibration

In order to improve the maintenance efficiency of the motor and realize the real-time fault diagnosis function of the motor, a motor fault diagnosis algorithm based on wavelet and attention mechanism is proposed. Firstly, the motor vibration signal is decomposed by wavelet transform, and the high-frequency signal is denoised to improve the signal-to-noise ratio. Secondly, the frequency band and time dimension after wavelet decomposition are taken as input data, the convolution neural network is used to fuse the frequency band features of data, and the bidirectional gated loop unit is used to fuse the time series features. Then, the attention mechanism is used to adaptively integrate the features of different time points. Finally, motor fault diagnosis and prediction are realized by classifier recognition. Experimental results show that, compared with the existing deep learning fault diagnosis model, this method has higher diagnosis accuracy and can accurately diagnose the running state of the motor.

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