Abstract
Fault diagnosis is crucial for the operation of energy systems such as nuclear plants, and heavily relies on various types of sensors for temperature, pressure, concentration, etc. Due to the redundancy of sensors in each energy system, the sensor selection scheme can deeply influence the diagnostic efficiency. In this paper, a Boolean network (BN) with its linear representation is proposed for describing the fault propagation among sensors. Both the sufficient condition of fault detectability and that of fault discriminability are given. Then, a sensor selection method for fault detection and discrimination is proposed. Finally, the theoretic result is applied to realize the diagnosis oriented sensor selection for a nuclear steam supply system based on a modular high temperature gas-cooled reactor (MHTGR). The computation and simulation results verify the correctness of the theoretical results.
Highlights
Process behavior is inferred by using sensors measuring the important variables in processes such as those of nuclear and fossil thermal plants
The mix integer linear programming (MILP) approach has been applied to the sensor selection problem of the fault diagnosis for integral pressurized water reactors and helical coil steam generators [8,9]
The Boolean network (BN) model and its linear representation for process fault propagation given by Theorem 1, the sufficient conditions for fault detectability and discriminability given by Theorem 2, and the sensor selection method proposed in Remark 6 are applied to realize a fault diagnosis-oriented sensor selection of a nuclear steam supply system (NSSS) based on a modular high temperature gas-cooled reactor (MHTGR)
Summary
Process behavior is inferred by using sensors measuring the important variables in processes such as those of nuclear and fossil thermal plants. Directed graph (DG) is one such qualitative model that can be used to infer the fault propagation or cause-effect behavior in a process system. The MILP approach has been applied to the sensor selection problem of the fault diagnosis for integral pressurized water reactors (iPWRs) and helical coil steam generators [8,9]. By regarding both the sensors and faults in a process as the nodes in a BN, and by further regarding the cause effect behaviors as the directed edges, the BN is utilized as a qualitatively. The sensor selection problem for fault diagnosis is solved by analyzing the steady state-space structure of this BN model, and the sufficient conditions for both fault detection and fault discrimination are proposed. High Temperature Gas-cooled Reactor (MHTGR)-based nuclear steam supplying system, and the corresponding computation and simulation results show the feasibility of this new approach
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