Fault detection and isolation of electrical induction motors via LPV fault observers: A case study

Abstract

An application of a recently proposed fault detection and isolation (FDI) design methodology for linear parameter varying (LPV) systems is presented which concerns the robust detection of stator windings faults of an electrical induction motor regulated by a speed controller. On the basis of a detailed nonlinear mathematical model of the motor, it is shown how, based on a judicious convex interpolation of a family of linearized models, a quasi-linear parameter varying (quasi-LPV) approximation capable to catch most of the nonlinearities of the model can be achieved and can directly be used for synthesizing LPV-FDI observers. The design methodology consists in solving a multi-objective convex linear matrix inequalities optimization problem where disturbance rejection and fault sensitivity are traded-off in suitable frequency regions. The resulting diagnostic observer is gain-scheduled and uses a set of motor variables, assumed measurable online, as a scheduling vector. The effectiveness of the LPV-FDI framework is illustrated in a final numerical example. LPV FDI SCHEMES FOR INDUCTION MOTORS 631Notice that in the quasi-LPV approach, each scheduling term is allowed to be a suitable function of the state and input, that is i .t / D i .x.t /; u.t //. The term 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 Time [s] J r1 (t) J th1 =0.2514 J r1,LPV (t) J r1,LIN (t) 0 1 2 3 4 5 6 7 8 9 10 0 0.1 0.2 0.3 0.4 Time [s] J r2 (t) J th2 =0.056 J r2,LPV (t) J r2,LIN (t) 0 1 2 3 4 5 6 7 8 9 10 0 0.1 0.2 0.3 0.4 Time [s] J r3 (t) J th3 =0.02512 J r3,LPV (t) J r3,LIN (t) Figure 10. First Experiment: rotor angular speed sensor fault (f 1 .t/). 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 Time [s] J r1 (t) J th1 =0.2514 J r1,LPV (t) J r1,LIN (t) 0 1 2 3 4 5 6 7 8 9 10 0 0.1 0.2 0.3 0.4 Time [s] J r2 (t) J th2 =0.056 J r2,LPV (t) J r2,LIN (t) 0 1 2 3 4 5 6 7 8 9 10 0 0.1 0.2 0.3 0.4 Time [s] J r3 (t) J th3 =0.02512 J r3,LPV (t) J r3,LIN (t)

Input-output based fault estimation for linear systems with partially dynamic uncertainty and actuator faults

“…In this section, for the two types of actuator faults acting on the system, respectively, the corresponding robust fault-tolerant controllers are to be designed based on the online estimates. Firstly, based on the online estimate O f , which asymptotically converges to the additive actuator fault f , the robust controller for the normal system is to be redesigned so that the closed-loop system is asymptotically stable when the faults occur in the system (1).…”

“…It is assumed that the robust FTC policy for the first synchronous generator has been given as follows: Then when the actuator fault occurs in the system, the robust FTC can be designed as u 1f D u 1 O f 1 , where O f 1 is the online estimate of the additive actuator fault f 1 , which is shown in Figure 5. It can be seen that the estimate can better depict the size and shape of the additive actuator fault.…”

“…Over the past decades, fault detection and isolation and fault-tolerant control (FTC) have been active fields of research with the increasing demand for higher safety and reliability standards [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. As a result of unexpected model uncertainties, time delays, disturbances, perturbations, and noises may occur in the fault systems, it is quite difficult to obtain the accurate size of the fault from a fault detection and isolation scheme only in practical engineering [15,16].…”

A data-based fault detection scheme for the stratospheric airship control system

“…The FD results are achieved from the comparison between the actual system output and the mathematical system output. Some main methods of the model-based approach such as observer-based method, [10][11][12][13] , Kalman filter method, 14 and parameter estimation method 15 are main model-based FD approaches have been applied for some systems.…”

Adaptive PID actuator fault tolerant control of single-link flexible manipulator

“…On the other hand, the active approach designs dynamical control strategies for fault compensation. Primarily, FDI (Ahmed et al, 2013; Arogeti et al, 2012; Casavola and Gagliardi, 2015; Chen and Wang, 2014; Du and Mhaskar, 2014; Loukil et al, 2013; Roth et al, 2012; Simani et al, 2014; Sobhani and Poshtan, 2012; Zhang et al, 2011, 2014) and adaptive techniques (Hashemi et al, 2015; Kamali et al, 2014; Li and Yang, 2012; Ma et al, 2014; Sui et al, 2014; Tahoun, 2015; Ye and Yang, 2008; Zhang and Chen, 2009; Zhang et al, 2011) are two typical approaches for fault compensation in active FTC that have been used in the literature. FDI-based designs identify the system fault information and uses it to reconfigure the controller structures.…”