An investigation into the fault detection of machines based on acoustic array systems
Andrew D. Ball, D. J. Moore, Fengshou Gu, Iain D. J. Dupère, Jun Jiang, K. Liu · Research Explorer (The University of Manchester) · 2007
Acoustic condition monitoring (CM) has a number of significant merits such as remote measurement and rich information content. In recent years it has been gaining increasing attention because of the rapid development in sensing and processing methods for interference noise suppression and demands for accurate CM. In this paper, a new acoustic detection scheme is investigated based on array technology. The scheme uses only a small number of microphones (about 5), which is easy and realistic to implement in CM, compared with conventional array applications. The capabilities of different small array configurations are studied theoretically in terms of detection accuracy and potential diagnosis capabilities. Numerical simulations have shown that a 5-sensor tetrahedron array produces high accuracy for source identification and fault detection. Based on this array configuration, an acoustic CM system is developed and embedded with array technologies, advanced signal processing and pattern recognition. Experimental results show that this system has a great potential in detecting machine faults in an industrial environment.