The diagnosis and prevention of failures have allowed to evolve the maintenance strategies in the industries, improving the efficiency and optimizing the production stops. In the case of machining systems, timely fault diagnosis avoids products out of specification and/or extreme machines damage. Optimum machining depends of several parameters, including the spindle performance, within which the bearings system represents the mechanical component with the greatest likelihood of failure. From an exhaustive bibliographic review, the advances in the use of the Wavelet Transform (WT) for the analysis of mechanical vibrations of spindle bearings are presented. A fault detection method is proposed, which automatically detects the frequency range where most information of the faults are located and separates them from other frequencies associated with noise. Early results validated with experimental data are promising.
Garzón, C. V., Moncayo, G. B., Alcantara, D. H., & Morales-Menendez, R. (2018). Fault Detection in Spindles using Wavelets - State of the Art ⁎. IFAC Proceedings Volumes (IFAC-PapersOnline), 51(1), 450. https://doi.org/10.1016/j.ifacol.2018.05.075