Innovation approach to detect the faults in multidimensional dynamic systems

Abstract

PurposeThe purpose of this paper is to design the fault detection algorithm for multidimensional dynamic systems using a new approach for checking the statistical characteristics of Kalman filter innovation sequence.Design/methodology/approachThe proposed approach is based on given statistics for the mathematical expectation of the spectral norm of the normalized innovation matrix of the Kalman filter.FindingsThe longitudinal dynamics of an aircraft as an example is considered, and detection of various sensor faults affecting the mean and variance of the innovation sequence is examined.Research limitations/implicationsA real‐time detection of sensor faults affecting the mean and variance of the innovation sequence, applied to the linearized aircraft longitudinal dynamics, is examined. The non‐linear longitudinal dynamics model of an aircraft is linearized. Faults affecting the covariances of the innovation sequence are not considered in the paper.Originality/valueThe proposed approach permits simultaneous real‐time checking of the expected value and the variance of the innovation sequence and does not need a priori information about statistical characteristics of this sequence in the failure case.