Research on bearing fault identification of pedal mechanical system based on abnormal audio signal
In order to ensure the sound quality of the piano,the health of the pedal mechanical system bearing is very important.Detection and identification of potential failures can play a significant role in maintaining performance.To meet this requirement,a set of abnormal audio signal processing and fault diagnosis model is built.Features are extracted from audio signals using wavelet analysis techniques and combined with machine learning methods to identify bearing failures.The experimental data set is collected by the sys-tem and verified in the proposed model.The results show that the model has the ability to accurately identify abnormal audio signals,with a judgment accuracy of 90.00％and a judgment accuracy of 93.25％for fault classification.Both indicators exceed the perform-ance level of the traditional comparison model.The conclusion verifies the validity and high efficiency of the proposed model in the bearing fault diagnosis of piano pedal mechanical system,which provides a strong technical support for the fault judgment of similar musical instruments.
万方数据
维普
