Detection of Intermittent Fault for Discrete-Time Systems with Output Dead-Zone: A Variant Tobit Kalman Filtering Approach

This paper is concerned with the intermittent fault detection problem for a class of discrete-time linear systems with output dead-zone. Dead-zone phenomenon exists in many real practical systems due to the employment of low-cost commercial off-the-shelf sensors. Two Bernoulli random variables are utilized to model the dead-zone effect and a variant formation of Tobit Kalman filter is brought forward to generate a residual that can indicate the occurrence of an intermittent fault. A numerical example is presented to demonstrate the effectiveness and applicability of the proposed technique. The statistical performance of the technique is illustrated as well.

[1]  Donghua Zhou,et al.  Detection of intermittent faults for linear stochastic systems subject to time-varying parametric perturbations , 2016 .

[2]  P. Bickel,et al.  Obstacles to High-Dimensional Particle Filtering , 2008 .

[3]  Damiano Rotondo,et al.  An Interval NLPV Parity Equations Approach for Fault Detection and Isolation of a Wind Farm , 2015, IEEE Transactions on Industrial Electronics.

[4]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[5]  Alan S. Morris,et al.  A fuzzy expert system for fault detection in statistical process control of industrial processes , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[6]  Shaocheng Tong,et al.  Fuzzy Adaptive Actuator Failure Compensation Control of Uncertain Stochastic Nonlinear Systems With Unmodeled Dynamics , 2014, IEEE Transactions on Fuzzy Systems.

[7]  Donghua Zhou,et al.  Active Fault-Tolerant Control for an Internet-Based Networked Three-Tank System , 2016, IEEE Transactions on Control Systems Technology.

[8]  Zhiwei Gao,et al.  Unknown Input Observer-Based Robust Fault Estimation for Systems Corrupted by Partially Decoupled Disturbances , 2016, IEEE Transactions on Industrial Electronics.

[9]  Frank L. Lewis,et al.  Deadzone compensation in motion control systems using adaptive fuzzy logic control , 1997, Proceedings of International Conference on Robotics and Automation.

[10]  Zhou Dong,et al.  Review of Intermittent Fault Diagnosis Techniques for Dynamic Systems , 2014 .

[11]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .

[12]  Michael J. Piovoso,et al.  Nonlinear estimators for censored data: A comparison of the EKF, the UKF and the Tobit Kalman filter , 2015, 2015 American Control Conference (ACC).

[13]  Gang Tao,et al.  Adaptive Fault-Tolerant Control of Uncertain Nonlinear Large-Scale Systems With Unknown Dead Zone , 2016, IEEE Transactions on Cybernetics.

[14]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[15]  Yang Liu,et al.  Least-Squares Fault Detection and Diagnosis for Networked Sensing Systems Using A Direct State Estimation Approach , 2013, IEEE Transactions on Industrial Informatics.

[16]  Wei Xing Zheng,et al.  Generalized H2 fault detection for two-dimensional Markovian jump systems , 2012, Autom..

[17]  A. Doucet,et al.  Particle filtering for partially observed Gaussian state space models , 2002 .

[18]  Xiaofeng Wang,et al.  Networked Strong Tracking Filtering with Multiple Packet Dropouts: Algorithms and Applications , 2014, IEEE Transactions on Industrial Electronics.

[19]  Hongjiu Yang,et al.  Fault Detection for T-S Fuzzy Discrete Systems in Finite-Frequency Domain. , 2011, IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society.

[20]  Yanyan Hu,et al.  Intermittent Fault Detection for Uncertain Networked Systems , 2013 .

[21]  E. Bai,et al.  Convergence results for an adaptive dead zone inverse , 1998 .

[22]  D. Diallo,et al.  Fault detection and diagnosis in an induction Machine drive: a pattern recognition approach based on concordia stator mean current vector , 2005, IEEE Transactions on Energy Conversion.

[23]  J. Tobin Estimation of Relationships for Limited Dependent Variables , 1958 .

[24]  Bin Jiang,et al.  Fault-tolerant control for a class of non-linear systems with dead-zone , 2016, Int. J. Syst. Sci..

[25]  Antero Arkkio,et al.  A simplified scheme for induction motor condition monitoring , 2008 .

[26]  Frank L. Lewis,et al.  Deadzone compensation in motion control systems using neural networks , 2000, IEEE Trans. Autom. Control..

[27]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[28]  Muhammad Riaz,et al.  An improved PCA method with application to boiler leak detection. , 2005, ISA transactions.

[29]  Michael J. Piovoso,et al.  The Tobit Kalman Filter: An Estimator for Censored Measurements , 2016, IEEE Transactions on Control Systems Technology.

[30]  Ying-Hong Lin,et al.  An adaptive PMU based fault detection/location technique for transmission lines. I. Theory and algorithms , 2000 .

[31]  Vicenç Puig,et al.  Robust Fault Diagnosis of Nonlinear Systems Using Interval Constraint Satisfaction and Analytical Redundancy Relations , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[32]  Gang Tao,et al.  Adaptive Control of Plants with Unknown Dead-zones , 1992, 1992 American Control Conference.

[33]  Jie Liu Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection , 2012 .

[34]  Gang Tao,et al.  Discrete-time adaptive control of systems with unknown deadzones , 1995 .

[35]  Rolf Isermann,et al.  Fault diagnosis of machines via parameter estimation and knowledge processing - Tutorial paper , 1991, Autom..

[36]  Donghua Zhou,et al.  Detecting scalar intermittent faults in linear stochastic dynamic systems , 2015, Int. J. Syst. Sci..

[37]  Yongming Li,et al.  Observer-Based Adaptive Decentralized Fuzzy Fault-Tolerant Control of Nonlinear Large-Scale Systems With Actuator Failures , 2014, IEEE Transactions on Fuzzy Systems.