In this paper, a dynamic neural network (DNN) based on robust identification scheme is presented to determine compressor surge point accurately using sensor fault detection (FD). The main innovation of this paper is to present different and complementary technique for surge suppressing studies within sensor FD. The proposed method aims to utilize the embedded analytical redundancies for sensor FD, even in the presence of uncertainty in the compressor and sensor noise. The robust dynamic neural network is developed to learn the input–output map of the compressor for residual generation and the required data is obtained from the compressor Moore–Greitzer simulated model. Generally, the main drawback of DNN method is the lack of systematic law for selecting of initial Hurwitz matrix. Therefore, the subspace identification method is proposed for selecting this matrix. A number of simulation studies are carried out to demonstrate the advantages, capabilities, and performance of our proposed FD scheme and a worthwhile direction for future research is also presented.
Robust Fault Detection to Determine Compressor Surge Point Via Dynamic Neural Network-Based Subspace Identification Technique
Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received November 27, 2013; final manuscript received January 26, 2014; published online February 28, 2014. Editor: David Wisler.
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Mahdi Alavinia, S., Ali Sadrnia, M., Javad Khosrowjerdi, M., and Mehdi Fateh, M. (February 28, 2014). "Robust Fault Detection to Determine Compressor Surge Point Via Dynamic Neural Network-Based Subspace Identification Technique." ASME. J. Eng. Gas Turbines Power. August 2014; 136(8): 082602. https://doi.org/10.1115/1.4026610
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