ABSTRACT
The data-fusion detection and isolation (DFFDI) process becomes more potentially challenging if the faulty component of the system causes partial loss of data. In this chapter, we present an iterative approach to DFFDI that is capable of recovering the model and detecting the fault pertaining to that particular cause of the model loss. The method developed is an expectation-maximization (EM) based on forward-backward Kalman filtering. We test the method on a rotational drivebased electro-hydraulic system using various fault scenarios. It is established that the method developed retrieves the critical information about presence or absence of a fault from partial data-model with minimum time-delay, and provides accurate unfolding-in-time of the finer details of the fault, thereby completing the picture of fault detection and an estimation of the system under test. This in turn is completed by the fault diagnostic model for fault isolation. The experimental results obtained indicate that the method developed is capable of correctly identifying various faults, and then estimating the lost information.
