Fault Diagnosis for the Intermittent Fault in Gyroscopes: A Data-Driven Method

In this paper, a data-driven method is proposed to detect and isolate the intermittent fault in gyroscopes. A mathematical description for the intermittent fault is first proposed in a probabilistic framework. Based on this probabilistic model, we present a random generation algorithm to emulate the occurrence of the intermittent fault. Considering the cross-correlation and autocorrelation between the measurement data, this paper proposes a data-driven fault diagnosis method based on dynamic principal component analysis. In the simulation part, both additive intermittent fault and multiplicative intermittent fault scenarios are considered. Simulation results illustrate that the proposed method is able to detect and isolate the intermittent fault effectively.