Fault Diagnosis Of Airborne Evaporative Cycle Refrigeration System Based On Intelligent Algorithm

The airborne evaporation cycle system has many advantages such as high cooling efficiency and fast cooling speed.It has been widely used in the refrigeration of aircraft electronic equipment cabins and kitchens.However,because the system contains a large number of components,it is difficult for the crew to accurately identify the type of fault and the specific location where it occurs in the event of a fault.Aiming at this problem,this paper constructs a fault diagnosis model based on intelligent algorithm for airborne evaporation cycle refrigeration system,and visualizes the model.The main research contents of this paper are:1.A test bench for the characteristics of the evaporative cycle refrigeration system was built.The common faults of the system were simulated and the fault data was obtained.The theoretical analysis of the fault data was carried out.2.For fault data,feature extraction is performed using principal component analysis.Principal component analysis is a multivariate statistical analysis method for dimensionality reduction and information extraction.The method can basically remove the correlation between data,and minimize the loss of data information,thereby reducing the calculation amount of the fault diagnosis model and improving the performance of the model.3.The fault diagnosis model based on support vector machine is constructed and the model is verified by the data of principal component analysis.Support vector machine plays an important role in pattern recognition,function fitting,fault diagnosis and other research directions,and is very efficient in solving local convergence problems,over-learning and under-learning problems.4.The ACO-SVM model is constructed,and the parameters(C,g)of the support vector machine model are optimized by the ant colony algorithm.Ant colony algorithm has positive feedback mechanism,good robustness,strong consistency and good global searchability.Using the ant colony algorithm to optimize the parameters of the support vector machine can greatly optimize the performance of the model and improve the diagnostic accuracy.5.Using the MATLAB GUI module to design a visual fault diagnosis software.The software can be used to train and test the fault diagnosis model,and to diagnose the system operation data.It can provide great convenience to the crew.