Data-driven Residual Generation for Early Fault Detection with Limited Data

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Nov 3, 2020
Hamed Khorasgani Ahmed Farahat Chetan Gupta

Abstract

Traditionally, fault detection and isolation community have used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation. However, in many complex systems it is not feasible to develop highly accurate models for the systems and to keep the models updated during the system lifetime. Recently, data- driven solutions have received an immense attention in the industrial applications for several practical reasons. First, these methods do not require the initial investment and expertise for developing accurate models. Moreover, it is possible to update and retrain the diagnosers as the system or the environment change over time automatically. Finally, unlike the model-based methods it is straightforward to combine time series measurements such as pressure and voltage with other sources of information such as system operating hours to achieve a higher accuracy. In this paper, we extend the traditional model- based fault detection and isolation concepts such as residuals, and detectable and isolable faults to the data-driven domain. We then propose an algorithm to automatically generate residuals from the normal operating data. We compare the performance of our proposed approach with traditional model-based methods through a case study.

How to Cite

Khorasgani, H., Farahat, A., & Gupta, C. (2020). Data-driven Residual Generation for Early Fault Detection with Limited Data. Annual Conference of the PHM Society, 12(1), 9. https://doi.org/10.36001/phmconf.2020.v12i1.1162
Abstract 31 | PDF Downloads 51

##plugins.themes.bootstrap3.article.details##

Keywords

Fault Detection and Isolation, Data-driven Residual Generation, analytical redundancy relations

Section
Technical Papers