Data-driven Residual Generation for Early Fault Detection with Limited Data
This work addresses fault detection for industrial systems with limited data, offering a practical alternative to model-based methods, though it appears incremental as it adapts existing concepts to a data-driven domain.
The paper tackles the challenge of fault detection in complex systems where accurate models are hard to develop and maintain, by extending model-based concepts like residuals to a data-driven approach and proposing an algorithm for automatic residual generation from normal operating data, with performance evaluated through a comparative case study.
Traditionally, fault detection and isolation community has 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 industries systems for several practical reasons. First, these methods do not require the initial investment and expertise for developing accurate models. Moreover, it is possible to automatically update and retrain the diagnosers as the system or the environment change over time. Finally, unlike the model-based methods it is straight forward 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 present the performance of our proposed approach through a comparative case study.