An alternative data-driven fault detection scheme for dynamic processes with deterministic disturbances

This paper proposes an alternative fault detection (FD) scheme, in which the so-called residual signals are generated by means of a projection of process input data. This is the major difference to the existing model-based and data-driven FD schemes, where residual generator is realized based on the process input and output relationship/dynamics. Moreover, this way of residual generation avoids the parameter identification procedure and also allows us to address deterministic disturbances (unknown inputs), which be paid often less attention by data-driven FD methods. In this fashion, the FD issue reduces to detect change of a random matrix. Since it is difficult to directly measure this change, so the trace of a matrix is adopted as the evaluation function. Furthermore, the threshold can be set by considering the boundedness of disturbance. The effectiveness of the proposed method is verified by a simulation study on an inverted pendulum system.