Air Compressor Fault Diagnosis Algorithm Using Voiceprint Feature Fusion - Feng, Guoliang; Wang, Fumin; Yu, Tianming; Xu, Ce (IAENG international journal of computer science, 2024)
As an important piece of equipment in industrial production, the health of an air compressor directly impacts the success of production. Therefore, researching fault diagnosis methods for air compressors is of significant importance in improving the continuity, reliability, and safety of production. Traditional fault diagnosis methods, however, struggle to obtain accurate fault features. The measurement of feature distribution differences between various working conditions lacks sufficient domain adaptability, making it challenging to achieve high recognition accuracy. Additionally, the operation of air compressors generates background noise, which can introduce interference and impact the accuracy in fault detection. In order to overcome these limitations, a fault diagnosis method for air compressors based on feature fusion is proposed. Firstly, the Mel-frequency cepstral coefficients (MFCC) features and wavelet transform features of the air compressor are extracted separately. Then, late fusion is applied at the decision level to combine confidence scores and predicted bounding boxes. The best network model is determined based on evaluation metrics to complete the classification. Based on the experimental results analysis, the feature fusion method demonstrated superior recognition performance.