@inproceedings{2cf5278236f14bbdb6e80193c26b6725,
title = "A fault diagnosis method based on parametric estimation in hydraulic servo system",
abstract = "Due to the fault occurrence could commonly be considered as results of the physical parameters variation of the system and this variation usually is embodied by model coefficients variation of the system, faults can be detected and diagnosed according to the model parameter variation of the system. In this paper, a parametric estimation method, which is extended to extract features existing in input and output data of the monitored system, is employed to realize the FDD for a hydraulic servo system. An Auto-Regressive model with exogenous input (ARX) is selected to approximate the dynamic behavior of the system. Then according to the feature vector constructed by the coefficient of ARX model, faults are classified in feature space using RBF neural network to realize the fault localization. Experiments and simulations results indicate that the proposed method is effective in fault diagnosis for hydraulic servo system.",
keywords = "ARX model, Fault detection and diagnosis, Least square identification, Neural network, Parameter estimation",
author = "Hongmei Liu and Pingchao Ouyang and Shaoping Wang",
year = "2006",
doi = "10.1117/12.717595",
language = "英语",
isbn = "081946452X",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Sixth International Symposium on Instrumentation and Control Technology",
note = "Sitxh International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence ; Conference date: 13-10-2006 Through 15-10-2006",
}