Descriptor Observers Design for Markov Jump Systems With Simultaneous Sensor and Actuator Faults

Yang, Hongyan; Yin, Shen

doi:10.1109/tac.2018.2879765

Cited by 75 publications

(43 citation statements)

References 28 publications

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“…The primitive inductance tensor (15) has been computed for the healthy motor using (16), and its first column is represented in Figure 6. Each one of the rest of the columns of (15) is equal to the previous one, with its elements rotated by one position.…”

Section: Primitive Inductance Tensor Of the Healthy Immentioning

confidence: 99%

“…In [13], an SMO that uses only input quantity information for on-line broken rotor bar detection is proposed, and in [14], a high-order SMO is designed for detecting inter-turn short circuit faults in IMs. Recent developments [15,16] in this field propose the use of a reduced-order SMO for fault estimation, which is able to simultaneously obtain the exact estimation of state, actuator faults, sensor faults, and extra disturbances. In [16], a novel approach is proposed without employing the equivalent output error injection technology, to overcome the problem of the traditional SMO in application to Markovian jump systems, and in [15], a new proposal is made for avoiding the sliding surface switching problem.…”

Section: Introductionmentioning

confidence: 99%

“…Recent developments [15,16] in this field propose the use of a reduced-order SMO for fault estimation, which is able to simultaneously obtain the exact estimation of state, actuator faults, sensor faults, and extra disturbances. In [16], a novel approach is proposed without employing the equivalent output error injection technology, to overcome the problem of the traditional SMO in application to Markovian jump systems, and in [15], a new proposal is made for avoiding the sliding surface switching problem. Another diagnostic technique is to run an electromechanical model of the machine [17][18][19][20] and compare the simulation outputs (currents and voltages) with the quantities measured at the machine terminals.…”

Section: Introductionmentioning

confidence: 99%

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Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

Martínez-Román

Puche-Panadero

Sapena-Bañó

et al. 2020

Sensors

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Induction machines (IMs) are critical components of many industrial processes, what justifies the use of condition-based maintenance (CBM) systems for detecting their faults at an early stage, in order to avoid costly breakdowns of production lines. The development of CBM systems for IMs relies on the use of fast models that can accurately simulate the machine in faulty conditions. In particular, IM models must be able to reproduce the characteristic harmonics that the IM faults impress in the spatial waves of the air gap magneto-motive force (MMF), due to the complex interactions between spatial and time harmonics. A common type of fault is the eccentricity of the rotor core, which provokes an unbalanced magnetic pull, and can lead to destructive rotor-stator rub. Models developed using the finite element method (FEM) can achieve the required accuracy, but their high computational costs hinder their use in online CBM systems. Analytical models are much faster, but they need an inductance matrix that takes into account the asymmetries generated by the eccentricity fault. Building the inductance matrix for eccentric IMs using traditional techniques, such as the winding function approach (WFA), is a highly complex task, because these functions depend on the combined effect of the winding layout and of the air gap asymmetry. In this paper, a novel method for the fast and simple computation of the inductance matrix for eccentric IMs is presented, which decouples the influence of the air gap asymmetry and of the winding configuration using two independent tensors. It is based on the construction of a primitive inductance tensor, which formulates the eccentricity fault using single conductors as the simplest reference frame; and a winding tensor that converts it into the inductance matrix of a particular machine, taking into account the configuration of the windings. The proposed approach applies routine procedures from tensor algebra for performing such transformation in a simple way. It is theoretically explained and experimentally validated with a commercial induction motor with a mixed eccentricity fault.

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Section: Primitive Inductance Tensor Of the Healthy Immentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

Martínez-Román

Puche-Panadero

Sapena-Bañó

et al. 2020

Sensors

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show abstract

“…To deal with this issue, a feasible alternative is to reconstruct the inaccessible state variables via a dynamic state observer. Much effort has been devoted to this area, and we refer the readers to [15][16][17][18]. Among them, several studies concentrate on the state reconstruction problem of interconnected systems.…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Decentralized Tracking Control with Preview Action for a Class of Nonlinear Interconnected Systems

2020

Complexity

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In this paper, for the first time, the observer-based decentralized output tracking control problem with preview action for a class of interconnected nonlinear systems is converted into a regulation problem for N augmented error subsystems composed of the tracking error dynamics, the difference equation of the state observer, and the available future reference trajectory dynamics associated with each individual subsystem. The developed innovative formulation of an observer-based decentralized preview tracking control scheme consists of the integral control action, the observer-based state feedback control action, and the preview action of the desired trajectory. The controller design feasibility conditions are formulated in terms of a linear matrix inequality (LMI) by using the Lyapunov function approach to ensure the existence of the suggested observer-based decentralized control strategy. Furthermore, both decentralized observer gain matrices and decentralized tracking controller gain matrices can be efficiently and simultaneously computed through a one-step LMI procedure. Stability analysis of the closed-loop augmented subsystem is carried out to illustrate that all tracking errors asymptotically converge toward zero. Finally, a numerical example is provided to demonstrate the effectiveness of the suggested control approach.

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“…Various model‐based detection and isolation schemes have been developed in the literature for industrial processes described by ordinary differential equations (ODEs), eg, sliding mode observer (SMO), adaptive observer, and high gain observer . Recently, Yang and Yin developed full‐order and reduced‐order descriptor SMO to solve the robustly simultaneous full state estimation and fault estimation problem for Markov jump systems in presence of unexpected sensor and actuator faults and unknown bounded external disturbances. Similarly, in the work of Li et al, simultaneous actuator and sensor fault estimations were completed for Markovian jump systems based on linear matrix inequality theory.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and estimation for a class of PIDE systems based on boundary observers

Yuan

Wang

et al. 2019

Intl J Robust & Nonlinear

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Summary In this paper, we propose a simultaneous state estimation and fault estimation approach for a class of first‐order hyperbolic partial integral differential equation systems. Specifically, we consider the multiplicative boundary actuator and sensor faults, ie, unknown fault parameters multiplying by the boundary input or boundary state (ie, output). As a consequence, two difficulties arise immediately: (1) simultaneous estimation of both plant state and faults is a nonlinear problem due to the multiplication between fault parameters and plant signals; (2) no prior information is available to determine the type (actuator or sensor) of faults. To overcome these difficulties, this paper develops adaptive fault parameter update laws and embeds the resulting laws into the plant state observer design. First, we propose new approaches to estimate actuator fault and sensor fault, respectively. Next, we develop a novel method to simultaneously estimate actuator and sensor faults. The proposed observer and update laws, designed using only one boundary measurement, ensure both state estimation and fault parameter estimation. By choosing appropriate Lyapunov functions, we prove that the estimates of state and fault parameters converge to an arbitrarily small neighborhood of their true values. Numerical simulations are used to demonstrate the effectiveness of the proposed estimation approaches.

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Descriptor Observers Design for Markov Jump Systems With Simultaneous Sensor and Actuator Faults

Cited by 75 publications

References 28 publications

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

Observer-Based Decentralized Tracking Control with Preview Action for a Class of Nonlinear Interconnected Systems

Fault detection and estimation for a class of PIDE systems based on boundary observers

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