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Equipment health state detection method for improving chicken flock optimization RBF neural network

A health state and neural network technology, applied in neural learning methods, biological neural network models, neural architectures, etc., to achieve the effect of improving accuracy and performance

Pending Publication Date: 2019-08-13
LIAONING UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Since traditional methods cannot meet the actual needs, the flexible use of artificial intelligence technology has become an important research direction for researchers in all walks of life.

Method used

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  • Equipment health state detection method for improving chicken flock optimization RBF neural network
  • Equipment health state detection method for improving chicken flock optimization RBF neural network
  • Equipment health state detection method for improving chicken flock optimization RBF neural network

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Embodiment 1

[0108] One, the theoretical basis of the program of the present invention:

[0109] 1. Chicken flock algorithm

[0110] Chinese scholar Meng Xianbing proposed a biologically inspired algorithm Chicken Swarm Optimization (CSO) for single-objective optimization in 2014. In the chicken flock algorithm, each chicken particle represents a potential optimization method solution. Each type of chicken has its own movement path and moves according to the corresponding movement rules. Different types of chicken particles have different motion rules, and under a specific hierarchy, different chicken particles have a competitive relationship. Compared with the traditional intelligent algorithm, the chicken swarm algorithm has faster convergence speed and higher convergence accuracy.

[0111] CSO idealizes chicken behavior using the following rules:

[0112] (1) In the whole flock, the identities of chickens are divided according to their fitness: a dominant rooster (best fitness value...

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Abstract

The invention relates to an equipment health state detection method for improving a chicken flock optimization RBF neural network. The basic steps are as follows: (1) signal sampling; (2) noise reduction processing; (3) feature extraction; (4) data normalization processing; and (5) detection of the health state of equipment. According to the improved chicken flock optimization RBF neural network model provided by the invention, an optimal center vector is found by using an intelligent optimization method, so that the performance of an RBF network is improved. In order to solve the problem thatthe chicken flock algorithm is easy to fall into local optimum to a certain extent, an improved chicken flock is combined with a chaotic search strategy to optimize an initial population, and meanwhile, a part of cock particles are replaced with chicks with high use degree values through growth operation, so that the problem of falling into local optimum is solved as much as possible.

Description

technical field [0001] The invention relates to a method for detecting the health state of equipment, in particular to a method for detecting the health state of equipment by improving the chicken flock optimization RBF neural network. Background technique [0002] With the development of modern industry, my country's industrial equipment is gradually combined with emerging technologies to become more intelligent. After long-term operation of large-scale industrial equipment, friction between parts, mutual extrusion and collision between equipment, and corrosion of equipment parts by chemical raw materials lead to gradual wear, corrosion and breakage of equipment, resulting in a series of equipment failures and ultimately causing huge economic losses. If it is possible to grasp the wear and tear of parts at any time, accurately locate and detect the wear position and degree of wear, and timely repair and replace the worn out equipment failure parts before the parts break dow...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/00G06N3/04G06N3/08
CPCG06N3/08G06N3/006G06N3/044G06N3/045G06F18/2411G06F18/214
Inventor 张利肖雪冬郭炜儒王青松张皓博王彦捷
Owner LIAONING UNIVERSITY
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