Performance assessment of hydraulic servo system based on bi-step neural network and autoregressive model

In recent years, condition monitoring and fault diagnosis of hydraulic servo systems has attracted increasing attention. However, few studies have focused on the performance assessment of these systems. This study proposes a performance assessment method based on a bi-step neural network and an autoregressive model for a hydraulic servo system; the performance is quantized by the performance confidence value (CV). First, a fault observer based on a radial basis function (RBF) neural network is designed to estimate the output of the system and calculate the residual error. Second, the corresponding adaptive threshold is generated by using another RBF neural network during system operation. Third, the difference value between the coefficients of the autoregressive model for the generated residual error and the adaptive threshold is obtained, and the Mahalanobis distance (MD) between the most recent difference (unknown conditions) and the constructed Mahalanobis space by using samples under normal conditions is calculated. Then, the condition of the system can be determined by normalizing the MD into a CV. The proposed method was further validated for three types of faults, and data were obtained using a simulation model. The experimental analysis results show that the performance of hydraulic servo systems can be assessed effectively by the proposed method.

[1]  Jianbo Yu,et al.  Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models , 2011 .

[2]  Sarangapani Jagannathan,et al.  Mahalanobis Taguchi System (MTS) as a Prognostics Tool for Rolling Element Bearing Failures , 2010 .

[3]  S. Poyhonen,et al.  Signal processing of vibrations for condition monitoring of an induction motor , 2004, First International Symposium on Control, Communications and Signal Processing, 2004..

[4]  F. Ronchi,et al.  Control and performance evaluation of a clutch servo system with hydraulic actuation , 2004 .

[5]  Jay Lee,et al.  Watchdog Agent - an infotronics-based prognostics approach for product performance degradation assessment and prediction , 2003, Adv. Eng. Informatics.

[6]  Yang Yu,et al.  A fault diagnosis approach for roller bearings based on EMD method and AR model , 2006 .

[7]  Emanuel Parzen,et al.  Autoregressive Spectral Estimation. , 1983 .

[8]  Mohieddine Jelali,et al.  An overview of control performance assessment technology and industrial applications , 2006 .

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

[10]  Shaoping Wang,et al.  An adaptive threshold based on support vector machine for fault diagnosis , 2009, 2009 8th International Conference on Reliability, Maintainability and Safety.

[11]  Cui Lingli,et al.  Application of Multiwavelet Adaptive Threshold Denoising in the Fault Diagnosis of Gearbox , 2011, 2011 Third International Conference on Measuring Technology and Mechatronics Automation.

[12]  Nariman Sepehri,et al.  Nonlinear observer-based fault detection technique for electro-hydraulic servo-positioning systems , 2005 .

[13]  Seung Ho Doo,et al.  Fast time-frequency domain reflectometry based on the AR coefficient estimation of a chirp signal , 2009, 2009 American Control Conference.

[14]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[15]  Jay Lee,et al.  A novel method for machine performance degradation assessment based on fixed cycle features test , 2009 .

[16]  Li Jun,et al.  An adaptive threshold segmentation method based on BP neural network for paper defect detection , 2011, 2011 IEEE 2nd International Conference on Software Engineering and Service Science.

[17]  Bin Jiang,et al.  Observer-based fault diagnosis for a class of nonlinear systems , 2004, Proceedings of the 2004 American Control Conference.

[18]  Lei Guo,et al.  Robust bearing performance degradation assessment method based on improved wavelet packet–support vector data description , 2009 .

[19]  Xiangyu He,et al.  Fault diagnosis approach of hydraulic system using FARX model , 2011 .

[20]  Viliam Makis,et al.  Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov–Smirnov test , 2009 .