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.