Abstract
This paper discusses the fault diagnosis problem for a class of non-Gaussian stochastic processes with unknown fault. By using the spline function and T-S model simultaneously, the probability density function (PDF) control problem can be transformed into the control problem of T-S fuzzy weight dynamics. In this framework, an adaptive fuzzy filter based on output PDF is designed to estimate the size of system fault. Meanwhile, in order to solve the bounded problem of the fault, an adaptive projection algorithm is applied into adjust the estimated value for the fault. As a result, the satisfactory stability and fault diagnosis ability can be guaranteed by rigorous theoretical proof.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ding SX (2008) Model-based fault diagnosis techniques: design schemes, algorithms and tools. Springer, London
Zhang K, Jiang B, Shi P (2012) Fault estimation observer design for discrete-time Takagi-Sugeno fuzzy systems based on piecewise lyapunov functions. IEEE Trans Fuzzy Syst 20(1):192–200
Chen RH, Mingori DL, Speyer JL (2003) Optimal stochastic fault detection filter. Automatica 39:377–390
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cyber 15(1):116–132
Tong SC, Li YM, Shi P (2012) Observer-based adaptive fuzzy backstepping output feedback control of uncertain MIMO pure-feedback nonlinear systems. IEEE Trans Fuzzy Syst 20(4):771–785
Tong SC, Li YM (2011) Adaptive fuzzy output feedback tracking backstepping control of strict-feedback nonlinear systems with unknown dead zones. IEEE Trans Fuzzy Syst 20(1):168–180
Wang H (2000) Bounded dynamic stochastic systems: modeling and control. Springer, London
Guo L, Wang H (2010) Stochastic distribution control system design: a convex optimization approach. Springer, London
Xu CH, Gui WH, Yang CH, Zhu HQ, Lin YQ, Shi C (2013) Flotation process fault detection using output PDF of bubble size distribution. Miner Eng 26:5–12
Yi Y, Guo L, Wang H (2009) Constrained PI tracking control for output probability distributions based on two-step neural networks. IEEE Trans Circuits Syst I 56(7):1416–1426
Yi Y, Zheng WX, Guo L (2013) Improved results on statistic information control with a dynamic neural network identifier. IEEE Trans Circuits Syst II 60(11):816–820
Li T, Guo L (2009) Optimal fault-detection filtering for non-gaussian systems via output PDFs. IEEE Trans Syst Man Cyber A 39(2):476–481
Yao L, Qin J, Wang A (2013) Fault diagnosis and fault-tolerant control for non-gaussian non-linear stochastic systems using a rational square-root approximation model. IET Control Theory Appl 7(1):116–124
Acknowledgments
This paper is supported by the National Natural Science Foundation of China under Grants (61473249, 61174046, 61203195).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ye, Y., Yi, Y., Sun, X., Zhang, T. (2015). Fault Diagnosis with Adaptive Projection Algorithms for Complex Non-Gaussian Stochastic Distribution Systems. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46463-2_48
Download citation
DOI: https://doi.org/10.1007/978-3-662-46463-2_48
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46462-5
Online ISBN: 978-3-662-46463-2
eBook Packages: EngineeringEngineering (R0)