Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition

This paper presents a study on the application of sound pressure and vibration signals to detect the presence of defects in a rolling element bearing using a statistical analysis method. The well established statistical parameters such as the crest factor and the distribution of moments including kurtosis and skew are utilised in the study. In addition, other statistical parameters derived from the beta distribution function are also used. A comparison study on the performance of the different types of parameter used is also performed. The statistical analysis is used because of its simplicity and quick computation. A computer program has been developed using the C++ language to perform the calculations of all the statistical parameters required in this study. A statistical analysis method is most suitable with random signals where other signal analysis methods based on the assumptions of deterministic signals are not applicable. The effect of shaft speed on the performance of the statistical method is also studied. Results from the study show that the statistical parameters are affected by the shaft speed due to the sensitivity of the bearing housing components to a longitudinal vibration that excites the fixing ring which holds the test bearing in its position. Under ideal conditions, the statistical method can be used to identify the different types of defect present in the bearing. In addition, the results also reveal that there is no significant advantages in using the beta function parameters when compared to using kurtosis and the crest factor for detecting and identifying defects in rolling element bearings from both sound and vibration signals. (C) 1997 Elsevier Science Ltd.