This paper proposes a novel subspace approach towards direct identification of a residual model for fault detection and isolation (FDI) in a system with non-uniformly sampled multirate (NUSM) data without any knowledge of the system. From the identified residual model, an optimal primary residual vector (PRV) is generated for fault detection. Furthermore, by transforming the PRV into a set of structured residual vectors, fault isolation is performed. The proposed algorithms have been applied to an experimental pilot plant with NUSM data for sensor FDI, where different types of faults are successfully detected and isolated, fully validating the practicality and utility of the developed theory.

According to one industrial engineer, “reconciliation of mass balances to monitor chemical tank inventories is a struggle even at the best of times”. This paper is concerned with the design of an omine and online fault detection and diagnosis monitoring system for the caustic tank inventory process at a pulp and paper company. Even with limited instrument redundancy, the omine monitoring analysis was able to correctly detect and diagnose sensor calibration errors. The scheme is now undergoing online implementation tests

This paper proposes a novel scheme of sensor/actuator fault detection and isolation (FDI) for multivariate dynamic systems in the presence of process uncertainties, including model-plant-mismatch and process disturbances. Given an estimated model that can be biased from the true one, the primary residual vector (PRV), for detecting faults in output sensors, can be made completely insensitive to process uncertainties under certain conditions. For detecting faults in actuators, the PRV can be made almost insensitive to the process uncertainties. Numerical and experimental examples justify the effectiveness of the proposed scheme, where comparison with an existing robust FDI scheme is conducted.

This paper proposes a novel scheme for the generation of primary residual vector (PRV) for sensor or actuator fault detection and isolation (FDI) in multivariate dynamic systems. The PRV, which is used for fault detection purpose, is designed to be insensitive to process uncertainties, including model–plant mismatch (MPM) and process disturbances. To generate the PRV, we do not need a precise system model. Instead, all we need is an estimate of the system model, which may be biased from the true model. Under the condition that the number of process uncertainties is less than the number of outputs, the generated PRV can be made perfectly insensitive to process uncertainties. Even when this condition does not hold, the most important elements in the process uncertainties can still be decorrelated from the PRV. A numerical example to demonstrate the theory is given. The newly proposed approach is compared with existing robust FDI schemes, e.g., the Chow–Willsky scheme.