Development and evaluation of a simple model-based automated Fault Detection and Diagnosis (FDD) method suitable for process faults of large chillers

Research into the development of fault detection and diagnosis (FDD) methods as applied to heating, ventilating, air-conditioning, and refrigerating equipment has been ongoing for over a decade, and several papers have been published. However, studies specifically related to large chillers have been few despite the importance of such equipment. The objective of this paper is to propose and illustrate a simple model-based FDD method for medium to large chillers that uses sensors available in most cooling plants, allows tuning of specific thresholds so as to attain the desired compromise between robustness and sensitivity, and has the potential to be automated and implemented online. Lacking actual field data of chillers operated under fault-free and faulty operation, the performance of the proposed FDD method is demonstrated with data generated from tests on a laboratory chiller in the framework of an earlier research study. This proposed FDD scheme is based on five important characteristic features (that have been identified from 15 variables evaluated) that allow six process faults to be identified (although two cannot be done uniquely). Since large chillers are not prepackaged as is unitary equipment, their design and assembly allow selecting different subsystems for a stipulated maximum cooling capacity. In such a case, selection of specific variables needed for FDD will be impacted by different faults, depending on several aspects unique to the chiller installation: the type of chiller load control (thermostatic expansion valve or inlet guide vane), specific choice of the relative heat exchanger size, and the load fraction at which the chiller is usually operated. Hence, it is likely that some customization would be needed to adapt the FDD thresholds and association rules for each chiller installation. Different options and challenges relevant to practical implementation of the proposed FDD method are discussed. BACKGROUND AND OBJECTIVES During the past several years, there has been growing interest in applying fault detection and diagnosis (FDD) methods, which are fairly mature in such engineering disciplines as nuclear, aeronautical, and automotive, to the HVAC&R industry. Indeed, several articles (for example, Moran [2006]) have identified FDD as one of the most urgent needs along with such measures as more stringent energy-efficiency standards, developing high-performance heating and cooling residential units, and improved training programs for operation and maintenance personnel. Braun (1999) pointed out that high reliability and safety are relatively less critical in building operations compared to operating cost and capital investment; therefore, FDD did not receive the same level of interest among the building researchers, owners, and operators. Although FDD has been an active area of research among the building and the HVAC&R community for several years, it is not widely deployed in the field. The primary reasons for slow adoption of FDD in buildings and the HVAC&R area are numerous, and the major ones are described below: • The cost-to-benefit ratio for an FDD implementation is relatively high, partly caused by the lack of extensive instrumentation in the building and HVAC&R systems and lack of data to quantify the benefits (Braun 1999). • Dexter and Ngo (2001) point out that the problems associated with identifying and isolating faults in HVAC systems are more severe than those that occur in most process control applications. The behavior of HVAC ©2007 ASHRAE. THIS PREPRINT MAY NOT BE DISTRIBUTED IN PAPER OR DIGITAL FORM IN WHOLE OR IN PART. IT IS FOR DISCUSSION PURPOSES ONLY AT THE 2007 ASHRAE ANNUAL MEETING. The archival version of this paper along with comments and author responses will be published in ASHRAE Transactions, Volume 113, Part 2. ASHRAE must receive written questions or comments regarding this paper by July 6, 2007, if they are to be included in Transactions. T. Agami Reddy is a professor in the Civil, Architectural and Environmental Engineering Department at Drexel University, Philadelphia, PA. plants and buildings is more difficult to predict due to lack of accurate mathematical models because most HVAC designs are unique and financial considerations restrict the amount of time and effort that can be put in deriving a model. • Detailed design information is seldom available, and measured data from an actual plant are often inadequate indicators of the overall behavior, since test signals cannot be injected during normal operation because of potential occupant discomfort, unacceptable energy penalties, and possibility of equipment damage (Haves et al. 1996). • Another problem is that many inputs to the FDD model cannot be measured accurately with commercial-grade sensors, and some measurements (such as flow rates) are often not even available. Finally, the issue of fault diagnosis can be problematic since several faults may have the same symptoms. The objective of this paper is to propose and demonstrate a simple model-based FDD method for medium-to-large chillers that uses available sensors, allows tuning of specific thresholds so as to attain the desired compromise between robustness (i.e., low false alarm rate) and sensitivity (i.e., low missed opportunity rate for detecting faulty operation), and has the potential to be automated and implemented online. Since proper field-monitored data from actual chillers under both fault-free and known faulty operation were unavailable, the performance of the proposed FDD method is demonstrated with labeled data from a laboratory chiller (Comstock and Braun 1999). Since large chillers are not prepackaged as is unitary equipment, their design and assembly allow selecting different subsystems for a stipulated maximum cooling capacity. In such a case, selection of specific variables likely to be impacted by different faults depends on several aspects unique to the chiller installation: the type of chiller load control (thermostatic expansion valve [TXV] or inlet guide vane), the specific choice of the relative heat exchanger (HX) size, and the load level at which the chiller is usually operated. Hence, some customization would be needed to adapt the FDD thresholds and associated rules for each chiller installation. This paper describes the FDD method proposed in a manner clear enough for it to be implemented and evaluated by facility energy managers, chiller manufacturers, control equipment companies, chiller service companies, and independent software developers. The scope of the proposed method is limited to process fault detection as against sensor faults (see, for example, Wang and Cui [2006]) or actuator faults or control loop or controller faults and relying on continuous thermal/pressure/electrical measurements as against one-time diagnostic measurements or other tests (such as vibration and electrical signature analysis, visual inspection checks, oil wear debris analysis, and surface and internal defect detection tests as described in Davies [1998]). Large chillers and chiller plants are a natural choice for FDD implementation. There are several published articles on FDD applied to HVAC&R equipment such as air handlers, unitary vapor-compression systems, and a few medium-tolarge chillers. The reader can refer to review articles by Comstock et al. (1999), Katipamula et al. (2001), Katipamula and Brambley (2005a, 2005b), and Reddy (2006). The focus of this paper is on medium-to-large chillers. Katipamula and Gaines (2003) analyzed published data and determined that central chillers and district cooling serve about 28% of the commercial building floor area and consume about 0.3 quads of primary energy per year. Assuming a power plant conversion efficiency of 33%, cooling equipment, they inferred, consumes about 30 TWh of electricity. If savings of 10% are attributable to FDD systems being in place, the net value of the energy savings would be $300M/yr at $0.10/kWh. Further, only centrifugal chillers are considered. This limits the size of chillers to sizes above 80 tons or so and excludes unitary equipment such as rooftop units. Medium-tolarge chillers come equipped with elaborate safety control mechanisms for critical/catastrophic faults. This paper is not targeted toward such faults or to the detection of hard faults (such as fan belt breakage or a burnt motor) but toward incipient faults that lead to energy waste and gradually damage equipment. Further, such medium-to-large chillers come equipped with numerous sensors, usually temperature, pressure, and electrical measurements, on individual subcomponents and loops, such as the condenser and evaporator loops, refrigerant loops, and cooling oil loops. Thus, any FDD method should explicitly make use of such data-rich environment for which component isolation methods (McIntosh et al. 2000; Jia and Reddy 2003; Wang and Cui 2006) seem particularly appropriate. On the other hand, calibrated simulation model approaches for FDD are best suited for systems where limited sensor data are available (such as those for unitary rooftop cooling equipment addressed by Rossi and Braun [1997], Brueker and Braun [1998a, 1998b], and Castro 2002). Finally, most of the FDD work to date in the HVAC&R area is limited to steady-state data. Notable exceptions are studies by Bruecker and Braun (1998a, 1998b) and Stylianou (1997), but these use such data only cursorily and in a manner lacking in rigor. This is logical given the relative infancy of such FDD tools in the HVAC&R industry. Figure 1 is our attempt at a broad classification of FDD methods. It is our opinion that a strict real-time online FDD method (which detects and flags faults within, say, minutes of their appearance) is both unwarranted and probably infeasible. Certain faults appear momentarily and disappear (such as equipment surging) and others may be caused by short-lived transients in the overall system. It would be more practical in the earl