Sensor Fault Detection and Identification in an Electro-pump System using Extended Kalman Filter

In this article, the issue of sensor fault detection and identification with sensory information is considered. This is due to the dependence of successful Fult Detection (FD) method on correct sensory measurements that suffer from various soft sensory faults such as bias, drift, scaling factor, and hard faults that can be detected independently. They are not detectable but can be combined with other sensors. To solve this issue, firstly, a state space model for pump subsystem was constructed using the electrical simulation method. Then, the sensory soft faults are modeled and amplified to electro-pump state space model.  Both system states and amplified sensory soft faults are then estimated using an Extended Kalman filter (EKF) in which nonlinear model of the induction motor is linearized around the estimated states. Information of current, angular velocity (encoder) and pressure sensors are  melted for this goal. The efficiency of the method is firstly evaluated through simulation and then experimental results are provided from our laboratory setup. Measured volume currents, flow, and pressure are compared with simulated signals, and results show that the proposed model is able to successfully describe the laboratory system with good precision. These results show that the model can describe the electro-pump dynamic with good precision.

Keywords:

Electro-Pump System , Fault Detection , Sensory Soft Fault , Extended Kalman Filtering