Device fault diagnosis apparatus and system

A technology for equipment failure and diagnostic devices, applied in lighting and heating equipment, refrigeration safety arrangements, refrigerators, etc., can solve the problems of slow convergence speed and local minimum, achieve the effect of optimizing network structure and improving diagnostic performance

Pending Publication Date: 2018-08-07
UNIV OF SHANGHAI FOR SCI & TECH
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However, the traditional BP neural network has defects such as slow convergence spee

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  • Device fault diagnosis apparatus and system
  • Device fault diagnosis apparatus and system

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[0027] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the following embodiments will specifically elaborate on the fault diagnosis method of the present invention based on the particle swarm algorithm optimization BP neural network model in conjunction with the accompanying drawings.

[0028] In the embodiment, the equipment fault diagnosis system 100 is used for fault diagnosis of chillers.

[0029] figure 1 It is a structural block diagram of the equipment fault diagnosis system in the embodiment of the present invention.

[0030] like figure 1 As shown, the equipment fault diagnosis system 100 includes a data collection device 10 , an equipment fault diagnosis device 20 and a communication network 40 .

[0031] Wherein, the equipment fault diagnosis device 20 stores the data collected by the data collection device 10 .

[0032] The data acquisition device 10 and the equipment fault diagnosi...

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Abstract

The invention relates to a device fault diagnosis apparatus and system. The device fault diagnosis apparatus comprises a management storage unit, a BP fault diagnostic device construction unit, a particle dimension determination unit, a particle initialization unit, a PSO-BP fault diagnostic device construction unit, a first diagnosis result generation unit, a particle position updating unit, a preliminary diagnosis model construction unit, an optimal particle selection unit, a final diagnosis model construction unit, and a fault diagnosis result generation unit. The BP neural network model fault diagnosis method is optimized by using a particle swarm algorithm; and on the basis of the traditional BP neural network, the particle swarm optimization (PSO) algorithm is introduced to change the original error iterative calculation mode. The global search capability of the particle swarm optimization algorithm is combined with the local fast search capability of the BP neural network to avoid a phenomenon of trapping into local optimization by the result. Therefore, the PSO-based optimized BP neural network model is established and the network structure is optimized; and the weight andthreshold of the BP neural network are optimized by the PSO, so that the diagnostic performance is improved.

Description

technical field [0001] The invention relates to an equipment fault diagnosis device and system, in particular to a chiller fault diagnosis device and system. Background technique [0002] Refrigeration systems are more and more widely used in various fields of production and life. Its structure is relatively complex and the degree of automation is high. Various failures will inevitably occur during operation. Operation with faults will cause the COP of the system to drop, causing unhealthy , Uncomfortable working or living environment, resulting in waste of resources, loss of products, and damage to equipment. Studies have shown that failures can increase the energy consumption of refrigeration systems by up to 30%. Regular maintenance will result in over-maintenance or under-maintenance, and a lot of human and material resources are used in the fault diagnosis of refrigeration systems, but the results are not satisfactory. From "Artificial Intelligence (Artificial Intelli...

Claims

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Application Information

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IPC IPC(8): G06N3/08F25B49/00
CPCG06N3/084F25B49/00
Inventor 韩华崔晓钰徐玲范雨强武浩
Owner UNIV OF SHANGHAI FOR SCI & TECH
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