Bayesian network-based system measurement node optimization configuration method

Abstract

The invention relates to a Bayesian network-based system measurement node optimization configuration method. According to the method, a bayesian network model of a system is established. According tothebayesian network model, a mutual information matrix between a fault node and a measuring node is calculated. According to the mutual information matrix between the fault node and the measuring node, the contribution degree of the measuring point to the diagnosis of the fault node is calculated, and a comprehensive diagnosis capability index is determined. According to the contribution degree ofthe measuring point to the diagnosis of the fault node, the cost of the measuring point and the number limit of the measuring point, an optimization problem is described. Based on the improved discrete binary particle swarm algorithm, the optimization treatment is carried out. Finally, the optimized configuration result of the measuring pointis obtained. According to the invention, the optimization configuration problem of a sensor under the constraint condition is considered, so that the method is more suitable for practical engineering application. Meanwhile, the fault diagnosis capabilityand the measurement cost problem of the measuring point are considered at the same time, and the optimization treatment is carried out based on the improved optimization algorithm. As a result, an optimal measuring node configuration scheme of the system is found.

Description

technical field [0001] The invention relates to the field of complex system control and fault diagnosis, in particular to a Bayesian network-based system measurement node optimization configuration method. Background technique [0002] Fault diagnosis is the key to ensure the reliable operation of the system. Fault diagnosis is to detect the key variables of the system through the measurement points to obtain the fault information of the variables, and determine the fault symptoms of the system according to the fault information. When the system performs fault diagnosis, it is often required to use as few measuring points as possible to obtain as much fault information as possible to meet the maximum fault diagnosis capability, that is, the optimal configuration of measuring points is the key. For a large and complex system such as the spacecraft attitude control system, in order to ensure its long-term reliable operation, it is necessary to configure measuring points to obt...

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Problems solved by technology

[0003] Aiming at the deficiencies of the prior art, the present invention provides a Bayesian network-based system measurement node optimization configuration method, which solves the problem of diagnostic ability and cost of the measurement point configuration scheme when complex systems such as spacecraft are performing fault diagnosis

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