Fault diagnosis of rotating machinery through visualisation of sound signals

As a method for diagnosing faults in rotating machinery, attention is being focused on changes in the sound signals generated by bearings. This provides the advantage of making it easier to set up sensors, since sound signals can be measured at a location some distance from the housing of the bearing. However, the signal-to-noise ratio is low compared with the vibration acceleration, which makes it difficult to identify any characteristic difference between the sound signals generated by normal and faulty bearings. This report describes a symmetrised dot pattern (SDP) method, which visualises sound signals in a diagrammatic representation. Using SDP to visualize sound signals measured for fans, it was possible to distinguish differences between normal and faulty bearings. Moreover, through the analysis of sound signals in the time-frequency domain and wavelet analysis, the signal component indicative of a fault was identified. When sound signals were modified by removing the above component, SDP with the modified faulty signal resembled the nun-faulty case. (C) 2000 Academic Press.