Device fault diagnosis apparatus and system

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...

AI Technical Summary

Problems solved by technology

However, the traditional BP neural network has defects such as slow convergence speed and easy to fall into local minimum in the fault diagnosis of centrifugal chillers.

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