A Fault Detection Method Based on Fully Symmetric Polytope Filtering

Abstract

The invention discloses a fault detection method based on fully symmetrical multi-cell filtering, which belongs to the field of fault diagnosis. The method includes establishing a system model of the wireless sensor network system according to the state vector of the wireless sensor network system; determining the observation vector according to the system model, and iteratively determining the fully symmetrical polytope at k time; predicting k+1 according to the fully symmetrical polytope at k time Estimation of time is a fully symmetric polytope; obtain the observation vector at time k+1, and calculate the consistent state set of the wireless sensor network at time k+1 according to the observation vector at time k+1; detect the consistent state set and k at time k+1 At +1 time, it is estimated whether the fully symmetrical polytopes have intersection; if no intersection is detected, it is determined that the wireless sensor network system has a fault at k+1 time; the problem of low real-time performance when the wireless sensor network system detects faults is solved; the detection is added The practicability of the method improves the efficiency and accuracy of fault detection.

Description

technical field [0001] The embodiments of the present invention relate to the field of fault diagnosis, and in particular to a fault detection method based on fully symmetrical multi-cell filtering. Background technique [0002] A wireless sensor network is a fully distributed system without a central node. Through random placement, many sensor nodes are deployed in predetermined areas. The sensor nodes placed in a predetermined area have sensors, data processing units and communication modules, and they form a network system self-organized through wireless connections. If the wireless sensor network fails, it may cause serious losses, so it is necessary to detect the fault of the wireless sensor network system in real time and accurately. [0003] Generally, faults can be detected by using the membership estimation method, which uses the state feasible set including the system model, measurement data, interference and noise boundaries to describe the real state of the sys...

AI Technical Summary

Problems solved by technology

In general, the state feasible set is an irregular convex polyhedron, which is difficult to describe with a mathematical model. Therefore, it is necessary to find a set that can approximately describe the state feasible set, such as ellipsoid, interval, parallel polyhedron, ordinary polyhedron, etc. However, ellipsoid Spheres, intervals, and parallel polyhedrons are less accurate and more conservative, and the dimensionality of ordinary polyhedrons will increase with the number of iterations, resulting in increased computational complexity

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