Neural Network Predictionof Baseline Values for Centrifugal ChillerFault Detection and DiagnosisbyPaul RiemerJune 20, 2000Semester ProjectIntroduction to Artificial Neural Networks and Fuzzy SystemsECE/CS/ME 539Prof. Y.H. HuIntroductionChillersProblem DescriptionResultsConclusions and Future DirectionsNeural Network Prediction of Baseline Values for Centrifugal Chiller Fault Detection and Diagnosis byPaul RiemerJune 20, 2000Semester ProjectIntroduction to Artificial Neural Networks and Fuzzy SystemsECE/CS/ME 539Prof. Y.H. Hu2Introduction One of the most interesting research areas of neural networks, expert systems, and fuzzy logic is in application to building conditioning systems often referred to as the heating, ventilating, and air conditioning (HVAC) industry, in terms of both control and fault detection and diagnosis (FDD). My graduate research is in this vein and hence, the area of application for this semester project.Specifically, I am refining and expanding FDD methodologies for centrifugal chillers as a continuation of Ian McIntosh’s Ph.D. thesis project. The goal is to be able to identify faulty behavior to the extent of knowing which components are not performingoptimally using currently monitored quantities. Air conditioning systems are prime candidates for advanced control and FDD techniques for several reasons including: the number of systems in operation, their large operating hours, their high energy demand and usage, the health and comfort of the building occupants at stake, and their actual and possible environmental effects (energy usage and refrigerant issues).Chillers As the workhorse of large-scale commercial air conditioning, a chiller cools waterto be piped around the building to air handling units (ahu’s). In the ahu’s, the air temperature and humidity is decreased as it passes over the coiled water pipes. Chillers can be designed to utilize one of several mechanisms but the vast majority in the US, use a vapor compression cycle. A centrifugal, vapor compression chiller is represented in Figure 1 below:The dotted line denotes the physical boundary of the chiller. The blue lines denote water and the maroon line, R-22 refrigerant. At state 1, the refrigerant is a low-pressurevapor. As it passes through the compressor it becomes high-pressure vapor (2), which 34ExpansionDeviceCentrifugalCompressor321Condenser(Shell and Tube HX)Evaporator(Shell and Tube HX)Cooling TowerAir Handling Units Figure 1: Vapor Compression Cyclethen becomes liquid (3) in the condenser as it rejects heat to the cooling tower water loop. Through the expansion device the liquid pressure drops (4) and then the refrigerant boils back to vapor (1) in the evaporator as it absorbs heat from the ahu water loop. The condenser and evaporator are both large shell and tube heat exchangers. Fault Detection and DiagnosisThe existence of faults is determined through the comparison of characteristic quantities (CQ’s) that represent the actual operation of the equipment with some baseline quantities that have been deemed to represent acceptable operation. With some additional knowledge (experiment, model, or other), the patterns of which CQ’s vary, and the amount and direction in which they vary, can be interpreted to identify faulty operating conditions and hopefully determine which components are involved. McIntosh utilized 11 CQ’s for centrifugal chillers, which are computed from 10 typically, monitored quantities (MQ’s) using a data reduction program in EES. Chillers are designed and operated to meet the cooling and dehumidification loads of a building.Obviously, these loads change significantly over a day and during the season and the equipment must respond. The operation of the chiller is also affected by the operation of the water pumps and the cooling tower. So because the operation of the chiller is incredible dynamic the acceptable CQ’s are not constant but vary with the conditions. The conditions that the chiller operates in can be defined in five quantities, which from here on will be referred to as the forcing inputs quantities. These five forcing inputs are a subset of the 10 MQ’s. The 11 baseline CQ’s are each actually unknown functions of these five forcing inputs. (Technically, some are known, but I did not utilize that knowledge in this project.)All of these quantities are tabulated below with evaporator and condenser abbreviated as E. and C. respectively:Table 1: Quantity SummaryForcing Inputs(5)Monitored Quantities (10)Characteristic Quantities(11)CQ Abbr.C. Mass Flow Rate C. Mass Flow Rate E. Heat Transfer Rate QEVAPE. Mass Flow Rate E. Mass Flow Rate Chilled Water Temp Difference DTCHWE. Water Supply Temp E. Water Supply Temp E. Approach APPREVAPE. Water Return Temp E. Water Return Temp E. Conductance Area Product UAEVAPC. Water Supply Temp C. Water Supply Temp C. Heat Transfer Rate QCONDC. Saturation Temp C. Water Temp Difference DTCWE. Saturation Temp C. Approach APPRCONDCompressor Exit Temp C. Conductance Area Product UACONDC. Water Return Temp Compressor Isentropic Efficiency NISENPower Motor Efficiency NMOTORCoefficient of Performance COPProblem DescriptionThrough my research project, I have obtained an expansive data set on four “identical” centrifugal chillers. The data has the 10 measured quantities in one-minute intervals for an entire cooling season. My original intent for this course project was to choose one of the chillers and use a neural network to predict the 11 baseline CQ’s 4from the 5 forcing inputs for a cooling season. A portion of the data at the beginning of the cooling season would be treated as fault free or acceptable and used as the trainingand testing data sets. Prediction of the baseline CQ’s would be performed on the remainder of the cooling season, which would then be available for use in comparison to the actual CQ’s to perform FDD.However as I investigated further, I realized that an alternative approach would utilize a neural network to predicted the other 5 MQ’s from the 5 forcing inputs. This network configuration (Approach 1) would provide a set of 10 fault free monitored quantities, which could then be plugged through the EES data reduction code just like the actual monitored quantities. Thereby, we would have actual and baseline CQ’s. The prime advantage of this revised plan is that fewer outputs should lead to quicker convergence and higher accuracy of the network. This does
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