Stator Fault Detection in Induction Machines by Parameter Estimation Using Adaptive Kalman Filter

This paper presents a parametric low differential order model, suitable for mathematically analysis for Induction Machines with faulty stator. An adaptive Kalman filter is proposed for recursively estimating the states and parameters of continuous–time model with discrete measurements for fault detection ends. Typical motor faults as interturn short circuit and increased winding resistance are taken into account. The models are validated against winding function induction motor modeling which is well known in machine modeling field. The validation shows very good agreement between proposed method simulations and winding function method, for short-turn stator fault detection

Adaptive passive sensor selection for maneuvering target localization and tracking using a multisensor surveillance system

This paper investigates maneuvering-target tracking problem based on a multisensor system and interacting multiple model (IMM). The estimation is performed by a novel particle filter (PF) with a capability to deal with the state-dependent noises and interference of the sensors’ coverage environment. An adaptive sensor selection algorithm, where some sensors are selected in each stage based on the signal-to-interference pulse noise ratio (SINR) and participate in the state estimation, is proposed. To deal with the effect of interference, we focus on designing and implementing the sensor selection algorithm, where a multisensor system with nonuniform arrays is derived by solving a convex optimization problem. On this basis, a nonuniform array of sensors is selected in each time interval aiming at maximizing the SINR of the received information from the undercoverage area. This would allow tracking in practical environments experiences interference. This method also is able to reduce the tracking error rate