Statistical vibration analysis for predictive maintenance of machines working under large variation of speed and load

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Luisa F. Villa Aníbal Reñones Jose R. Perán Luis J. de Miguel

Abstract

Prognosis of defects for machines working under large variation of speed and load conditions is a topic still under development. Wind turbines are recent examples of such kind of machines that need reliable diagnosis methods. Vibration analysis can be of very limited use when the speed variation is too high. An effective angular resampling method can be very valuable as the first step of vibration signal processing but it is important to know what are the appropriate variables to be monitored. The authors present a statistical analysis method consisting of a linear model based on the parameters that characterize the system, in our case the variable speed and load, and the fault condition to which the system is subjected. With this method can be determined if the variable analyzed is significant, that is to say if are sensitive to these parameters and hence can detect the fault faster. The aim of implementing this method is to reduce the number of variables to be monitored, resulting in a savings not only in measuring equipment but also in times of processing and analyzing information. The results of vibration analysis of a test-bed working under large variation of speed and load are shown. Different tests with increasing level of defects are tried and the corresponding vibration is analyzed and modeled so an effective detection and prognosis can be done. Taking in to account such variation of speed and load for the vibration modeling can lead to a very sensitive detection of incipient defects.

How to Cite

F. Villa, L. ., Reñones, A. ., R. Perán, J. ., & J. de Miguel, L. (2011). Statistical vibration analysis for predictive maintenance of machines working under large variation of speed and load. Annual Conference of the PHM Society, 3(1). https://doi.org/10.36001/phmconf.2011.v3i1.2005
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Keywords

PHM

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Section
Technical Papers