FAULT DIAGNOSIS OF HYDRAULIC SERVO SYSTEM USING THE UNSCENTED KALMAN FILTER

Fault occurrence can be embodied by the physical parameter variations of the hydraulic servo system. Faults can, therefore, be diagnosed according to the model coefficient variations of the hydraulic servo system. This paper proposes an approach for fault diagnosis based on the unscented Kalman filter (UKF) with a mathematical model of the hydraulic servo system. The mathematical model is established using the dynamic equations of the hydraulic servo system. Based on the fault mechanism analysis results, several important system model parameters that can separately represent different faults in different components of the hydraulic servo system are chosen. Discrete state space equations are derived from the dynamic equations. The UKF algorithm is used to estimate the important system model parameters of the hydraulic servo system by utilizing the discretized state space model. According to the variations of these model parameters, the fault modes and locations of the hydraulic servo system can be diagnosed and isolated. Two types of faults, namely, abrupt fault in servovalve gain and slow wear fault in hydraulic cylinder piston, which cannot be directly detected from the system output, are introduced individually to the hydraulic servo system in this work. By comparing with the extended Kalman Filter, three different experimental cases are used to validate the effectiveness of the UKF for hydraulic servo system fault diagnosis.

[1]  Rui Liu,et al.  On the Limitations of Force Tracking Control for Hydraulic Servosystems , 1999 .

[2]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[3]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[4]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[5]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[6]  Nariman Sepehri,et al.  Hydraulic Actuator Leakage Fault Detection using Extended Kalman Filter , 2005 .

[7]  Liu Guan-ju.  Fault Diagnosis of the Hydraulic Servo System Based on LS-SVM Modeling and Predictio , 2006 .

[8]  Shaoping Wang,et al.  Fault Diagnosis in a Hydraulic Position Servo System Using RBF Neural Network , 2006 .

[9]  Hongmei Liu,et al.  A fault diagnosis method based on parametric estimation in hydraulic servo system , 2006, International Symposium on Instrumentation and Control Technology.

[10]  Rusheng Zhou.  CURRENT RESEARCH AND DEVELOPING TRENDS ON FAULT DIAGNOSIS OF HYDRAULIC SYSTEMS , 2006 .

[11]  Xing Wei.  Development of Electromechanical Actuators , 2007 .

[12]  Rolf Isermann,et al.  PERFORMANCE EVALUATION OF KALMAN FILTER FOR FAULT DETECTION OF HYDRAULIC ACTUATORS , 2007 .

[13]  Jianda Han,et al.  An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot , 2008 .

[14]  Xudong Wang,et al.  Fault detection, identification and estimation in the electro-hydraulic actuator system using EKF-based multiple-model estimation , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[15]  Bjarne A. Foss,et al.  Applying the unscented Kalman filter for nonlinear state estimation , 2008 .

[16]  Zhang Xue-zhuang.  Kalman Filter and its Engineering Application , 2008 .

[17]  Jiang Wei,et al.  The Application of EKF in State Estimation of the Hydraulic Circuit , 2009, 2009 International Forum on Computer Science-Technology and Applications.

[18]  Yang Shi,et al.  Discrete-time H2-optimal output tracking control for an experimental hydraulic positioning control system , 2009 .

[19]  Xiao Cui,et al.  Measurement of external leakage of hydraulic servo-motor based on robust extended kalman filter , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[20]  Zhang Jiang-bin.  Technique Research of Fault Diagnosis Based on Condition Inspector for Governor Hydraulic System , 2009 .

[21]  Clarence W. de Silva,et al.  Condition Monitoring in a Hydraulic System of an Industrial Machine Using Unscented Kalman Filter , 2010, Int. J. Inf. Acquis..

[22]  Farrokh Sassani,et al.  On-line fault diagnosis of hydraulic systems using Unscented Kalman Filter , 2010 .

[23]  Jubo Zhu,et al.  Unscented Kalman filtering in the additive noise case , 2010 .

[24]  Li Liang.  Research on Fault Diagnosis Approach of Hydraulic System Based on Observer and Neural Network , 2011 .

[25]  Jouni Mattila,et al.  Detection and Isolation of Leakage and Valve Faults in Hydraulic Systems in Varying Loading Conditions, Part 1: Global Sensitivity Analysis , 2011 .

[26]  Huajing Fang,et al.  Fault Detection for Nonlinear Systems with Unknown Input , 2013 .