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Fault detection for hydraulic pump based on chaotic parallel RBF network

Author(s): Lu Chen | Ma Ning | Wang Zhipeng

Journal: EURASIP Journal on Advances in Signal Processing
ISSN 1687-6172

Volume: 2011;
Issue: 1;
Start page: 49;
Date: 2011;
Original page

Keywords: Fault detection | Chaotic parallel radial basis function (CPRBF) | Hydraulic pump | Residual error generator | Time series prediction

Abstract In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network) is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of CPRBF is a weighted sum of all RBF subnets. It was first trained using the dataset from normal state without fault, and then a residual error generator was designed to detect failures based on the trained CPRBF network. Then, failure detection can be achieved by the analysis of the residual error. Finally, two case studies are introduced to compare the proposed CPRBF network with traditional RBF networks, in terms of prediction and detection accuracy.
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