Reducing false positives for residual-based on-line fault detection by means of filters

Abstract

Departing from a previously demonstrated Residual-Based Fault-Detection system, where the residual signals are statistically tracked by means of tolerance bands, we introduce the use of filters as an intermediate step in the online operation of the system. This step will provide smoother residual signals where the underlying trend of the signal is kept and the significant anomalies pointing to potential fault candidates are still not filtered out. The research was performed with well-known existing filters, used in other domains such as engineering, image processing or econometrics. We demonstrate how the better stability of the residual signals significantly boosts the performance of a residual-based fault detection framework, decreasing the false positives rates whereas also increasing the true positives rates.