Recurrent neural network based sensor fault detection and isolation for nonlinear systems: Application in PWR.
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| Title: | Recurrent neural network based sensor fault detection and isolation for nonlinear systems: Application in PWR. |
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| Authors: | Kumar, Swetha R1 (AUTHOR) srk.ice@psgtech.ac.in, Devakumar, Jayaprasanth1 (AUTHOR) djp.ice@psgtech.ac.in |
| Source: | Progress in Nuclear Energy. Sep2023, Vol. 163, pN.PAG-N.PAG. 1p. |
| Subjects: | Recurrent neural networks, Sensor networks, Pressurized water reactors, Nonlinear systems, Nuclear models |
| Abstract: | Real-time monitoring of sensors is crucial in attaining consistent process performance with strict safety and eco-friendly measures. This article presents a fault detection and isolation (FDI) technique that diagnoses sensor faults regardless of the utilized model structure. A Recurrent Neural Network (RNN) is utilized to estimate state variables with the inputs and outputs of the process. RNNs build predictive models of the process. Then, a bank of residuals of state variables is obtained such that each residual is responsive to a subsection of faults and unresponsive to others. Consequently, an exclusive fault signature is attained for each fault case. One of the merits of the proposed procedure is that it does not necessitate the fault history of the process or first principle models contrasting to other existing outcomes in the literature. The efficacy of the proposed FDI methodology is studied on a highly nonlinear pressurized water nuclear reactor model. • This article presents a fault detection and isolation (FDI) technique that diagnoses sensor faults regardless of the utilized model structure. • A Recurrent Neural Network (RNN) with 10 hidden neurons is utilized to estimate state variables with the inputs and outputs of the process. • The effectiveness of the designed recurrent neural network model for fault detection and isolation is studied under five different sensor faults scenarios: drift, erratic, hard-over, spike and stuck. • Upon analyzing the residuals, a structure matrix is formed based on the fault signature. It was noted that the sensor fault in the estimated state variables of RNN leaves a fault signature of '1' in its corresponding residual only, whereas the sensor fault on the input variable of RNN leaves a fault signature of '1' in all other state residuals except its corresponding residual [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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