Conditioner fault diagnosis method based on Bayesian optimization PCA-limit random tree

Abstract

The invention discloses an air conditioner fault diagnosis method based on a Bayesian optimization PCA-limit random tree. The air conditioner fault diagnosis method comprises the following steps: 1) acquiring operation data of an air conditioner under normal and different faults and normalizing the operation data; 2) carrying out dimensionality reduction on the normalized data through a PCA algorithm, and taking the normalized data as the input of an ExtraTree model; 3) establishing a limit random tree classification model, training and testing a classifier, and obtaining a PCA-limit random tree fault diagnosis model for an air conditioner; 4) utilizing a Bayesian optimization algorithm to optimize the feature number and the CART decision tree number of a PCA-extreme random tree fault diagnosis model after the PCA dimension reduction to obtain the optimal feature number and the optimal CART decision tree number after the dimension reduction; and 5) then, taking the calculated optimal PCA dimension-reduced feature quantity value and CART decision tree quantity value as parameters of a PCA-limit random tree model, training a sample to obtain a PCA-limit random tree fault diagnosis model, and then using the diagnosis model to diagnose real-time data.

Description

Technical field [0001] The present invention relates to the field of fault diagnosis of HVAC, which specifically involves a method of air conditioning fault diagnosis based on Bayesian optimized PCA-limit random tree. Background technique [0002] The current large-scale public building HVAC is often numerous components, complex structures, including cold heat source equipment, air treatment equipment, air conditioning wind systems, air conditioning water systems, and control and regulating devices. HVAC system is complex, there is a lot of subsystems and difficulty in data communication, which affects the coordinated management of overall equipment; the system itself has non-linear, structural complex and variable, multi-system parameters mutual coupling and other characteristics, resulting in difficult diagnosis difficulties . At the same time, the air conditioning fault is often exposed as the aging of the equipment electronic components or the clogging of various pipes is gra...

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Problems solved by technology

[0005] At present, there is no air-conditioning fault diagnosis method based on Bayesian optimized PCA-limit random tree proposed by the present invention in published literature and patents

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