Bayesian networks, which may be compiled to arithmetic circuits in the interest of speed and predictability, provide a probabilistic method for system fault diagnosis. Currently, there is a limitation in arithmetic circuits in that they can only represent discrete random variables, while important fault types such as drift and offset faults are continuous and induce continuous sensor data.

Anomaly detection is the identification of abnormal system behavior, in which a model of normality is constructed, with deviations from the model identified as “abnormal”. Complex high-integrity systems typically operate normally for the majority of their service lives, and so examples of abnormal data may be rare in comparison to the amount of available normal data. Anomaly detection is particularly suited for Intelligent Fault diagnosis of such systems since it allows previously-unseen or poorly understood modes of failure to be correctly identified.

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic schemes (both model-based and data-driven) that attempt to forecast machinery health by constructing health propagation models for the underlying systems. In particular, algorithms that use the data-driven approach learn models directly from the data, rather than using a hand-built model based on human expertise.

This paper addresses the problem of system design for diagnosability. Specifically, it focuses on design of built-in self-tests (BISTs) for subsystems based on electronic control units ECUs). The BISTs play a major role in diagnosis of the systems and in particular in determining if the failure is in the ECU or externally in the sensors, detectors, or actuators. The design of BISTs involves a tradeoff between the diagnostic benefit gained by the presence of a BIST versus cost of providing it in the system.