Acoustical source separation and identification using principal component analysis and correlation analysis

Acoustical signals from mechanical systems reveal the operational status of mechanical components, which can be used for machinery condition monitoring and fault diagnosis. However, it is very difficult to extract or identify the acoustical source features as the measured acoustical signals are mixed signals of all the sources. Therefore, this paper studies on the source separation and identification of acoustical signals using principal component analysis and correlation analysis. The effectiveness of the presented method is validated through a numerical case study and an experimental study on a test bed with shell structures. This study can provide pure acoustical source information of mechanical systems, and benefit for machinery condition monitoring and fault diagnosis.