Fault Detection And Diagnosis Of Refrigeration Systems Based On Sequentially Integrated Methodology

Heating, ventilation, air-conditioning and refrigeration (HVAC&R) systems are becoming increasingly complex with various faults happening during operation. If not being fixed in time, system operation parameters will deviate even far from their original design value and accordingly, a series of negative effects will arise --- uncomfortable people indoor, low productivity efficiency, more system energy consumption, shorter equipment lifecycle and even worse atmospheric environment, etc. Fault detection and diagnosis (FDD) may help in timely finding and fixing faults, so as to improve system security, reliability and stability, prevent or avoid faults from happening and spreading. In order to improve accuracy & sensitivity and save computational time for the intelligent detection and diagnosis of typical individual faults and multi-simultaneous faults (MSF) for refrigeration systems, this study put forward a variety of sequentially integrated models, established FDD model evaluation guidelines based on confusion matrix, investigated all kind of possible fault indicative features.Firstly, refrigeration system and its typical soft faults were first theoretically analyzed with the relationship between symptom and faults, results and cause primarily understood. Intelligent methods for feature selection and extraction were studied for the purpose of finding better fault indicative features set by reducing or removing correlation between features, cutting redundancy, making the faults'appear'clearly and easy to be identified, shortening FDD time span and improving FDD accuracy simultaneously. Filter models based on mutual information (MI) and minimum- redundancy-maximum-relevance (mRMR), wrappers based on genetic algorithm (GA), linear discriminant analysis (LDA) and support vector machine (SVM), feature extraction model based on principal component analysis (PCA) were widely investigated and carefully applied to the historical normal and faulty data for a 90 tons centrifugal chiller. Fault indicative features sets were obtained and further studied in the later chapters to single out the best one.Secondly, for the seven typical individual faults in refrigeration systems, intelligent feature selection and extraction methods were sequentially integrated with SVM, a newly concerned machine learning method based on minimizing structural risk, to perform detection and diagnosis. FDD model evaluation system with correct rate (CR), hit rate (HR) and false alarm rate (FAR) as its core was established based on confusion matrix commonly used in pattern recognition field. CR is for the evaluation of the model's overall FDD performance for all samples; HR and FAR are guidelines for the evaluation of model's individual performance for each class, normal or each fault. What about the ratio of samples that are hit or correctly reported and what about those falsely alarmed. The results showed that SVM model was better than the famous decision tree (C4.5) in the FDD of refrigeration system, with test CR over 99%. The eight-feature subset selected by the GA-SVM wrapper from the original 64 features behaved much better than other subsets selected by other schemes, even in the FDD by C4.5 model. Investigation on the number of features for fault indication demonstrated that that no matter the selection of the original features or the extraction of the comprehensive features by PCA, the fewer the features, the less training and testing time consumed by the FDD model, but the performance would not be that consistent with the increasing or decreasing of the feature numbers. Excessively fewer features might cause lack of information and undermine the performance accordingly, while too many features would add excessive redundancy, cause interference for FDD and harm model's robustness. In fact, the features should better be at least equal to or more than the number of individual class including normal and all types of faults concerned. Only when the cumulative variance contribution rate was a little bit more than 95%, did the integrated model with feature extraction by PCA perform better than the SVM model without integration (64 features), but still, it could not surpass those models that integrated with feature selection schemes. Four faults such as condenser fouling, reduced condenser water flowrate, non-condensables and reduced evaporator water flowrate were easy to be detected, isolated and identified, even with the most slight level (level 1), whereas refrigerant leakage/undercharge or overcharge were the most difficult to be hit, especially when the SVM model without integration was employed, but GA-SVM model performed much better for these two faults.Thirdly, for the detection and diagnosis of MSF, put forward a sequentially integrated model that combined multi-label (ML) decoupling technique with SVM. Model performance was investigated for the MSF of the reduced condenser water and evaporator water flowrate simultaneously, both about 20% less than the rated. It was found that the integrated model had an excellent behavior in the FDD of MSF even when it was trained just by the normal and the individual faults instead of the MSF, especially when the eight fault indicative features previously stated were employed. Although the condenser water flowrate (FWC) and the valve position in evaporator water loop (VE) could independently indicate the individual faults (sub-faults) well, they were incapable of indicating MSF and must get assistance from other features to obtain a better performance. Moreover, designated component analysis (DCA), a multivariate statistical analysis method better than PCA in a sense, was adopted for the FDD of MSF in refrigeration systems. The method was effective as long as the prior knowledge and experience for the investigated systems were enough.At last, a dedicated fault detection and diagnosis test stand has been established by designing energy balance system and introducing fault simulation lines and components for and into an air-source heat pump of 16.8kW rated cooling capacity. Typical faults that could be simulated include refrigerant leakage/undercharge, overcharge, liquid line restriction, compressor valve leakage, reduced evaporator water flowrate, reduced condenser air flowrate, condenser fouling, thermal expansion valve over or less pre-tightened, etc. Experiments have been done for some types of the individual faults and two or three faults happening simultaneously. Variation of the critical parameters while faults happening was analyzed and possible cause or reasons were discussed. To the individual faults of liquid line restriction, condenser fouling, reduced evaporator flowrate and the combinations of two or three of them, the sequentially integrated models previously studied was applied, and four features selected from the original 44 features, including the environment relative humidity or temperature, the temperature difference between the inlet and outlet air of condenser, the temperature difference between the inlet and outlet water of evaporator, and the supply water temperature, were regarded as the best fault indicative features for the faults investigated and the test CR was as high as 99.58%.In general, the sequentially integrated models put forward in this study and the concept of fault indicative features for the intelligent FDD of refrigeration systems are effective, having a promising perspective and worthy of further investigation.(This research was supported by the Chinese National Natural Science Foundation under No. 50876059.)...