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Mechanical resonance detection of turntable based on motor current data
Published:2024-08-30 author:FENG Ruizhe, MA Yaqiong, YAN Binbin. Browse: 651 Check PDF documents

Mechanical resonance detection of turntable based on motor current data


FENG Ruizhe1, MA Yaqiong2, YAN Binbin1

(1.School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China; 2.AVIC Beijing Changcheng Aeronautical 
Measurement and Control Technology Research Institute, Beijing 100176, China)


Abstract: As a key testing equipment in the aerospace field, turntables may experience resonance during testing, causing interruptions and unnecessary losses in the testing process. Traditional turntable resonance detection method has problems such as dependence on engineering experience, susceptibility to control signal interference, and insufficient accuracy, resulting in false alarms and missed alarms. Aiming at the problems of traditional turntable resonance detection method, a mechanical resonance detection method for turntables based on motor current data was proposed. Firstly, the operating data of a three-axis vertical turntable was analyzed, and the amplitude frequency characteristics of the turntable operating data were studied. The motor current signal with less control signal interference and more significant resonance characteristics was selected as the detection object. Then, a turntable resonance detection method based on convolutional neural network (CNN) was constructed, and the CNN structure and parameters were designed, followed by network training and learning. The network structure and parameters were optimized through experiments to achieve better recognition results. Finally, resonance detection experiments were conducted using data from the turntable operating in different working modes, and the results were compared with support vector machine, long shortterm memory neural network and gated recurrent neural network. The research results show that the accuracy of this method in detecting turntable resonance reaches 99.988%, which is better than other comparative methods, proving that this method is suitable for mechanical resonance detection of turntables. This method can be extended to the resonance detection of other equipment with motor.

Key words:  mechanical vibration; vibration test; operating data of threeaxis vertical turntable; motor current; convolutional neural networks (CNN); frequency domain characteristics of convolution kernel
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