Study On Fault Detection And Diagnosis Of Refrigeration System Based On Multivariate Statistical Analysis

It is necessary to carry out fault detection and diagnosis(FDD)study of refrigeration system.Because various failures of cooling system occurred frequently,not only can cause a lot of energy waste,and the equipment safety,reliability,economy will decline.In this paper,models are made with multivariate statistical analysis(mainly Kernel Independent Component Analysis and Multicolored Kernel Independent Component Analysis)and professional features.Then the models are used to do fault detection and diagnosis with the experimental data.The main works are summarized as follows:First,analyzing cooling system and the common failures to learn the theoretical connection between signs and failures,providing a theoretical basis for the later research.Five gradual of the most common refrigeration systems faults are selected to be simulated based on the survey results and the availability test rig.During the simulation,the required samples for the study are recorded and stored,and then the faults results are analyzed and validated by the thermodynamics theory.Secondly,according to the damage by the faults to refrigerant system and the actual demand for detection of them,this paper presents a fault detection strategy which is utilization of KICA to identify the principle analyze method to detection the initial gradual faults.Models of kernel independent component Analysis were established with experimental data.Then the models were used to do fault detection and diagnosis for both single failure(only one fault occurs)and concurrent failures(two or more simultaneous failures)of refrigeration system.The model is used for fault detection and diagnosis,and the simulation results verify the effectiveness of the proposed method.The faults detection strategy is validated by the measured experimental data,and shows satisfying results.At last,according to the wide range of experimental data and having nonlinear characteristic,this article proposed a multi-block kernel method independent component analysis which takes the advantage of KICA in handling non-Gaussian data,and distributes monitoring the characteristics of multi-block.In particular,definitions of nonlinear block contributions to SPE and T2 statistics are proposed in order to diagnose nonlinear faults.Models of kernel independent component analysis were established with experimental data.In this paper,the method is applied in the fault detection and diagnosis of the refrigeration system.The test result of proposed method shows that,Multi block method can detects the fault successfully,and improve the accuracy of fault detection.