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Description
BACKGROUND
In the last 15 years, the development of robust automated fault detection and diagnosis (FDD) methods applicable to HVAC&R equipment has been an area of active research, and several papers have been written on the issue (Comstock et al. 1999; Katipamula et al. 2001; Katipamula and Brambley 2005a, 2005b). Despite their importance in terms of cost and energy use, large chillers have been the focus of relatively few studies. An ASHRAE-funded research report by Reddy (2006) summarizes, with respect to FDD, the various published studies of unitary cooling equipment and chillers in terms of author, year of publication, type and size of equipment, number of faults studied, and type of action performed during both fault-detection and diagnoses stages. Practitioners had identified an urgent need to develop a general testing methodology against which different FDD methods and tools could be evaluated. The ultimate objective was to develop a test standard for the industry akin to those available for testing the normal performance of different types of HVAC&R equipment and evaluating building energy analysis computer programs (ASHRAE 2001). A preliminary version of such an evaluation methodology has been proposed as ASHRAE-funded research project RP-1275 (Reddy 2006, 2007).
The suggested methodology involved developing analytical expressions for FDD evaluation cast as an objective function made up of two competing considerations: 1) cost associated with false alarms and 2) penalties associated with the onset of faults. Further, special effort was made to determine the types of penalties associated with various faults in chiller installations, such as energy increase, loss of cooling capacity, reduced life, etc. (Reddy 2007). After discussion with service personnel of a large chiller company, it was decided to limit the FDD evaluation to the energy penalty alone. It was also pointed out that, from a practical viewpoint, FDD evaluation should be based on two criteria:
1. The normalized fault detection index resulting in a normalized score or rank between and 1, where the basis of evaluation is with respect to an ideal detector with a score of unity and with no false alarms:
[[PHI].sub.Detect,s] = [[N.sub.F].summation over (f = 1)][[P.sub.f]*[DELTA][E.sub.f]*(1 - [F.sub.N,f])]/[[N.sub.F].summation over (f = 1)]([P.sub.f]*[DELTA][E.sub.f]) (1)
where
[F.sub.N,f] = false negative rate for fault f (i.e., missed-opportunity rate)
f = index for fault type
[N.sub.F] = total number of possible faults in the system
[P.sub.f] = probability of occurrence of fault type f
[[DELTA]E.sub.f] = extra electric power required to provide necessary cooling due to performance degradation as a result of fault type f
2. The combined fault detection and fault diagnosis index consisting of four different diagnosis outcomes (correct and unique, correct but non-unique, unable to diagnose, and incorrect diagnosis), all of which have different implications for time taken (i.e., cost) by the technician or the serviceman to diagnose the fault, make an evaluation, and choose an appropriate course of action:
[[PHI].sub.FDD] = [[N.sub.F].summation over (f = 1)][[P.sub.f]*[DELTA][E.sub.f]*([w.sub.cu]*[r.sub.cu,f] + [w.sub.cn]*[r.sub.cn,f] + [w.sub.ic]*[r.sub.ic,f] + [w.sub.ud]*[r.sub.ud,f])]/[[N.sub.F].summation over (f = 1)][[P.sub.f]*[DELTA][E.sub.f]] (2)
where
[r.sub.cu] = correct and unique diagnosis rate expressed as a fraction of the signaled faulty data
[r.sub.cn] = correct but non-unique diagnosis rate
[r.sub.ic] = incorrect diagnosis rate
[r.sub.ud] = unable to diagnose rate
[w.sub.cu] = weighting factor for correct and unique diagnosis rate (same for each fault type)
[w.sub.cn] = weighting factor for correct but non-unique diagnosis rate
[w.sub.ic] = weighting factor for incorrect diagnosis
[w.sub.ud] = weighting factor for unable to diagnose
Numerical values of various quantities appearing in the above expressions are discussed later in this paper.
OBJECTIVES AND SCOPE
The objectives of the research summarized in this paper were to review existing literature and propose four different chiller FDD methods--either adapting existing ones to chillers or proposing new ones if necessary--in order to evaluate them on the basis of the proposed FDD methodology and thereby identify those most promising. The scope of the proposed research was limited to process fault detection and did not include sensor faults, actuator faults, or control loop or controller faults (Wang and Cui 2006). Also, the FDD processes were to rely on continuous thermal, pressure, and electrical measurements as opposed to one-time diagnostic measurements or other tests such as vibration and electrical signature analysis, visual inspection, oil-wear debris analysis, or surface and internal defect detection tests (Davies 1998). The scope of this research was limited to FDD methods based on steady-state data, which are consistent with most of the FDD work to date in the HVAC&R area with the exception of a couple of studies (Bruecker and Braun 1998a, 1998b; Stylianou 1997) that use such transient data only cursorily and in a manner lacking rigor. Finally, only centrifugal chillers were considered. This limits the size of chillers to above around 80 tons (281 kW) and excludes unitary equipment such as rooftop units. Medium-to-large chillers come equipped with elaborate safety control mechanisms for critical/catastrophic faults. This research was not targeted at these faults or the detection of hard faults, such as fan-belt breakage or a burnt motor, but rather toward incipient faults, which lead to energy wastage and gradually damage equipment. Further, medium-to-large chillers come equipped with numerous sensors (usually temperature, pressure, and electrical measurements on individual sub-components) and contain distinct loops, such as the condenser and evaporator loops, refrigerant loops, and cooling oil loops. Thus, any FDD method should explicitly make use of such a 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 deemed best suited for systems where limited sensor data are available, such as unitary rooftop cooling equipment (Rossi and Braun 1997; Brueker and Braun 1998a, 1998b; Castro 2002).
DESCRIPTION OF CHILLER DATA SETS USED
The research supporting this paper makes use of the numerous experiments, under both fault-free and faulty conditions, performed within the framework of previous ASHRAE research project, RP-1043 (Comstock and Braun 1999). Specifically, experimental data on a 90-ton (316 KW) centrifugal water-cooled chiller were collected in which 1) a wide variety of chiller faults were studied--eight to be exact, but only six are considered here (Table 1), and 2) each fault was introduced at four levels of severity (10%-40% fault levels in increments of about 10%) denoted by SL1-SL4. Which physical quantities were altered and by how much in order to simulate the effect of the faults and their severity levels are also indicated in Table 1. Numerous sets of tests were performed under each of the eight different faults, introduced one at a time, under benchmark (or normal, fault-free, or baseline) conditions and under four different fault-severity conditions. Note that several replicate sets of tests had to be performed under fault-free conditions in order to re-establish the baseline each time a specific fault, previously introduced, had to be rectified prior to introducing another fault. Each experimental test set consists of 27 performance points obtained by varying the... |
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