Data Mining-based Fault Diagnosis And Energy Consumption Pattern Identification For Refrigeration And Air Conditioning Systems

As for the fault detection and diagnosis(FDD)researches on refrigeration and air conditioning systems,there exist some major problems,i.e.,the under-utilization of operating data,the lack of energy consumption patterns recognition models and the shortage of inadequate interpretation for data-driven fault diagnosis results,etc.Therefore,this study attempts to overcome these limitations by proposing an integrated framework for fault detection and diagnosis and energy consumption patterns recognition for refrigeration and air conditioning systems based on data mining(DM)techniques.We selected some refrigeration and air conditioning systems like,screw chillers,centrifugal chillers and variable refrigerant flow(VRF)systems as main research objects to validate the data mining-based framework.After collecting the sensor fault data,thermdynamic fault data and energy consumption data,we conducted studies such as,sensor fault detection and diagnosis,thermdynamic fault detection and fault severity levels prediction,power consumption patterns identification and association rules mining,which as a result can ensure the system operating reliably and cost effectively.Firstly,based on expert knowledge and fault experimental results in the literature,critical decoupling features for multiple faults and their statistics characteristics were summarized and prepared for developing the integrated framework for fault detection and diagnosis and energy consumption patterns recognition.The entire framework for refrigeration and air-conditioning systems were established using two different DM-based models,supervise and unsupervised learning models.According to the different targets,two sub frameworks,FDD and ESA were developed to improve the FDD performance,identify power consumption patterns and fault energy saving energy consumption analysis,respectively.Secondly,fault diagnosis is essentially to classify the fault types and predict the fault severity levels.The supervized learning data mining algorithms are most suitable for solving the classification and prediction problems.In accordance with the data mining-based sub-framework for fault detection and diagnosis,we proposed a sensor fault detection and diagnosis model for screw chiller systems using the supervised one-class classification algorithm,support vector data description(SVDD).This model uses a distance-based D-statistic plot to detect sensor faults and a new distance variation-based DV-contribution plot diagnose the sensor faults.Four typical sensor faults including fixed bias,drifting,precision degradation and complete failure with different fault severity levels were introduced into the temperature and water flow rate sensors,respectively.The fault data were used to assess the fault detection sensitivity and fault diagnosis accurucay of the SVDD-based model.Also,the effects on FDD results were considered and evaluated in terms of fault types,severity levels and the statistical property of training data.Thirdly,in order to detect seven typical thermodynamic faults in the centrifugal chiller system,this study proposed a novel principle component analysis-residual-support vector data description(PCA-R-SVDD)method based on the the data mining-based sub-framework for fault detection and diagnosis.Centrifugal chiller experimental data from the ASHRAE Research Project 1043(RP-1043)was used to evaluate the proposed model.The sensitivity for fault detection was analyzed on three aspects,monitoring statistic,data distribution and fault detection correct ratio.Instead of principle component subspace(PCs),the proposed model constructed the SVDD model in the residual subspace(Rs).The SVDD based distance based monitoring statistic was used for fault detection.The proposed method showed significant improvements of fault detection correct ratio comparing with the traditional methods due to the better fault data distribution and tighter monitoring statistic.For six common faults of the seven,at least 50%of the fault data can be correctly detected even at the least severe fault level.Forthly,this study further performed a to verify the commonality of the DM-based FDD framework between different refrigeration and air conditioning systems,this study proposed a Pearson-SVR-based method to improve the prediction performance.Traditional virtual refrigerant charge(VRC)sensor models perform well at undercharge situations but produce large prediction errors at overcharge situations.When the refrigerant charge level(RCL)is over 90%,the Person correlation coefficient data-based method was introduced to select the additional input variables to modify the VRC models.Results reveal that the overall prediction errors for SVR based modified VRC model(SVR-VRC)is 5.53%in the range of 63.64%-130%RCL.The SVR-VRC model improves the VRC models and extends the application in the VRF system when only the system self-provided sensor measurements are used.Finally,the tasks for ESA are open-ended.The unsupervized learning DM algorithm is just suitable for exploratory data analysis.Based on unsupervised ESA framework,an integrated clustering and association rules mining analysis(ICAA)method was put forward for VRF refrigerant charge fault energy consumption analysis.The agglomerative hierarchical clustering method was selected using the Dunn index.It was employed to divide the original data set and identify the operation mode of energy consumption pattern for VFR system.The Aprior algorithm was seletectd for association rules analysis.The abnormal operating data and interesting energy consumption patterns were obtained by comparing the useful rules hidden in various data clusters,thus improve the data utilization ratio.Overall,the proposed data mining-based framework for fault diagnosis and energy consumption pattern recognition application and its validation results have been published in some international journals.The framework has been proven to be quite effective on improving the data utilization ratio,the fault detection ratio and the diagnostic accuracy.Moreover,it has extened the application of data-driven models from pure fault diagnosis to energy consumption pattern recoginition.It is a promising perspective and worthy of further investigation.