Singular spectral analysis of ill-known signals and its application to predictive maintenance of windmills with SCADA records

Abstract

A generalization of the singular spectral analysis (SSA) technique to ill-defined data is introduced in this paper. The proposed algorithm achieves tight estimates of the energy of irregular or aperiodic oscillations from records of interval or fuzzy-valued signals. Fuzzy signals are given a possibilistic interpretation as families of nested confidence intervals. In this context, some types of Supervisory Control And Data Analysis (SCADA) records, where the minimum, mean and maximum values of the signal between two scans are logged, are regarded as fuzzy constrains of the values of the sampled signal. The generalized SSA of these records produces a set of interval-valued or fuzzy coefficients, that bound the spectral transform of the SCADA data. Furthermore, these bounds are compared to the expected energy of AR(1) red noise, and the irrelevant components are discarded. This comparison is accomplished using statistical tests for low quality data, that are in turn consistent with the possibilistic interpretation of a fuzzy signal mentioned before. Generalized SSA has been applied to solve a real world problem, with SCADA data taken from 40 turbines in a Spanish wind farm. It was found that certain oscillations in the pressure at the hydraulic circuit of the tip brakes are correlated to long term damages in the windmill gear, showing that this new technique is useful as a failure indicator in the predictive maintenance of windmills.