High-resolution methods in vibratory analysis: application to ball bearing monitoring and production machine

摘要

This paper is concerned with the implementation of parametric spectrum analysis using a high-resolution technique for setting up a conditional maintenance program via vibration analysis on a forming press. To achieve this, the resolving power of signal-processing-based parametric techniques is illustrated using spectrum assessment computation. Processing of the experimental results enabled (i) various autoregressive (AR) spectrum analysis methods and especially Burg's algorithm to be tested and (ii) conventional spectrum analysis techniques such as the correlogram to be compared with parametric methods in terms of detection level as well as for mechanical component fault monitoring, especially ball bearing defects. Among various possible models, the AR model was retained along with Burg's algorithm and the Akaike information criterion. A detection and location methodology for faults likely to occur on rotating machinery was developed on the basis of the results that were obtained. The methodology, supplementing other analysis techniques, relies on the understanding of component spectrum behavior and various constraints, such as component access, spectral resolution of the industrial measuring device, and statistical properties of the power spectral density measurements of a random signal. The results show that parametric methods are particularly worthwhile in the early detection of component defects, especially when two characteristic frequencies are close to one another. However, the complexity of these techniques necessitates many precautions when they are implemented; consequently, they should not replace conventional methods, but supplement them. (C) 2001 Elsevier Science Ltd. All rights reserved. [References: 8]