Fault prediction and diagnosis method adopting double intelligent algorithms and used for central air conditioner

Abstract

The invention discloses a fault prediction and diagnosis method adopting double intelligent algorithms and used for a central air conditioner. The fault prediction and diagnosis method adopting the double intelligent algorithms and used for the central air conditioner comprises the following steps: acquiring a real-time data set of running of the central air conditioner in the field; inputting thereal-time data set in the step 1 into a pre-established fault prediction and diagnosis classification neural network model for the central air conditioner and based on a simulated annealing algorithmand an extreme learning machine algorithm, so as to judge whether the real-time data set in the step 1 meets the minimum misclassification rate of fault prediction and diagnosis for the central air conditioner or not; and if the real-time data set in the step 2 meets the minimum misclassification rate of fault prediction and diagnosis for the central air conditioner, outputting a fault predictionand diagnosis classification result for the central air conditioner. The fault prediction and diagnosis method adopting the double intelligent algorithms and used for the central air conditioner hasfield feasibility, and is convenient to apply for engineering. Meanwhile, an extreme learning machine has high generalization ability and high computation efficiency. Therefore, the globally optimal solution for the problem of evaluating the minimum misclassification rate of the central air conditioner through the simulated annealing algorithm is much easily obtained, and the accuracy and precision of online deployment for fault prediction and diagnosis model classification for the central air conditioner are increased.

Description

Technical field: [0001] The invention relates to the technical field of fault prediction and diagnosis of central air-conditioning, and specifically refers to a method for predicting and diagnosing faults of central air-conditioning with dual intelligent algorithms based on a simulated annealing algorithm and an extreme learning machine algorithm. Background technique: [0002] The central air-conditioning system is not only an indispensable energy-consuming operation system in modern buildings, but also an important part of the building automation control system. With the improvement of people's quality of life requirements, the design of central air-conditioning system is becoming more and more complex. Central air-conditioning can not only provide cooling or heating, but also provide domestic hot water, bath water, constant temperature hot water for swimming pools, etc. If the central air conditioner breaks down, it will not only bring inconvenience to the operation of th...

AI Technical Summary

Problems solved by technology

The hill-climbing algorithm only receives the optimal solution during each iteration, and it is easy to fall into a local optimal solution

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