A method of window motor detection based on psychological acousticquality and modulation frequency(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2018年02期
- Page:
- 112-119
- Research Field:
- 汽车与机械工程
- Publishing date:
Info
- Title:
- A method of window motor detection based on psychological acousticquality and modulation frequency
- Author(s):
- YI Zikui; TAN Jianping
- （State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410083, Hunan, China）
- Keywords:
- automobile engineering; window motor detection; loudness; fixed sharpness; subjective and objective evaluation; modulation frequency; optimization of BP neural network
- PACS:
- -
- DOI:
- -
- Abstract:
- In order to ensure the sound quality and performance of the window motor before leaving the factory, the method of window motor detection based on the psychoacoustic parameters and modulation frequency was put forward. Based on the traditional psychology objective model of, the loudness, roughness and sharpness of the sound samples of the normal motor and fault motor were calculated. The sharpness was calculated based on the modified model，and the correlation between the loudness, roughness, sharpness, corrected sharpness and subjective perception of the normal motor and fault motor was analyzed through subjective and objective evaluation experiments. The motor was divided into normal motor and abnormal noise motor by using the loudness and corrected sharpness as feature vectors. On this basis, the modulation frequency of physical parameters was added as the characteristic quantity to predict the fault type of the vehicle window motor in order to diagnose the fault type of the abnormal noise motor. Finally, the BP neural network classifier optimized by the additional momentum method was constructed to classify the motor, and the optimized neural network classifier was verified by experiments. The results show that there are obvious differences between the loudness and the corrected sharpness of the normal motor and fault motor. The loudness and corrected sharpness have good consistency with peoples subjective psychology, and the consistency coefficient is more than 0.8. The frequency of motor noise of carbon brushcommutator defect is 80 to 100 Hz, the motor noise of wormgear defect is 20 to 40 Hz, while the noise of normal motor is above 100 Hz. The modulation frequency can be used as the characteristic quantity to detect the fault type of motor. The classification accuracy of the optimized neural network classifier for vehicle window motor is more than 90%, and it has higher accuracy and less time consumption compared with the traditional BP neural network classifier. 5 tabs, 10 figs, 20 refs.
Last Update: 2018-04-03