2. Industrial solutions It is quite difficult to get On-line data for FDD in industrial applications, because there safety is on first place. T hat m

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1 Fault Detection and Diagnosis Methods in HVAC Building Autom ation S y stem s using I ndustr ial S olutions S toj an P er š in, Bor is T ov or nik Laboratory for process automation U niv ersity of M aribor, F acul ty of E l ectrical E ng ineering and C omputer S cience S metanov a 1 7, M aribor, S l ov enia Abstract Fault Detection and Diagnosis systems offers enhanced availability and reduced risk of safety haz ards w hen comp onent failure and other unex p ected events occur in a controlled p lant. For O n- line FDD an ap p rop riate method an O n- line data are req uired. I t is q uite difficult to get O n- line data for FDD in industrial ap p lications and solution, using O P C is suggested. T op - dow n and bottom- up ap p roaches to diagnostic reasoning of w hole system w ere rep resented and tw o new ap p roaches w ere suggested. S olution 1 using q ualitative data from similar subsystems w as p rop osed and S olution 2 using reference subsystem w ere p rop osed. 1. Introduction Modern plants are large scale, highly complex, and operate with a large number of variables. Processes are becoming more heavily instrumented, resulting in large q uantities of data becoming available for use in detecting and diagnosing faults. T he design of such systems req uires trade- off between several competing req uirements, of which the most important are: accuracy, resolution, robustness, sensitivity, diagnostic stability and reliability. T here are many approaches to F ault D etection and D iagnosis ( F D D ) or F ault D etection and I solation ( F D I ) available today. T heir general structure consists of the three maj or stages: symptom generation, symptom evaluation and fault diagnosis as is shown on F igure 1. Pa t t ern recog n i t i on a p p roa ch es I n t eg ra t i on of q u a n t i t a t i v e a n d q u a l i t a t i v e k n ow l ed g e - st a t i st i ca l m et h od s - n eu ra l n et w ork s S y m p t om s S y m p t om s R ea son i n g Process ( R esi d u a l s) ( R esi d u a l s) ( B ool ea n g en era t i on ev a l u a t i on F u z z y, T B M ) m e asur e m e n ts sy m p to m s e v aluate d sy m p to m s faults Q u a n t i t a t i v e a p p roa ch es: m od el - b a sed p a ra m et er est i m a t i on m od el - b a sed ob serv er m od el - b a sed p a ri t y sp a ce a p p roa ch si g n a l - b a sed m et h od s Q u a li t a t i v e a p p roa ch es: ru l e- b a sed m et h od s con si st en cy ch eck i n g ca u sa l rel a t i on sh i p s q u a l i t a t i v e si m u l a t i on Process R ecov ery F igure 1 : G eneral structure of F D D T he F D D method is in laboratory environment relative easy to implement using one of the commercial mathematical tools ( i.e. Matlab) and one of the special data acq uisition boards for measurements. S o, for O n- line F D D an appropriate method an O n- line data are req uired.

2 2. Industrial solutions It is quite difficult to get On-line data for FDD in industrial applications, because there safety is on first place. T hat m eans also use of industrial solutions, none hom e-m ade products, standards and docum entation. A plant m ay easily hav e thousands of I/ O points connected to P L C s and S C A DA system s. E v ery data point has to be v alidated and docum ented, that m eans the connection betw een ev ery single I/ O point, ev ery P L C and ev ery S C A DA has to be v alidated, docum ented, and tagged. E v ery line of code becom es part of the proj ect database. If an additional data acquisition system for FDD system is added that m eans going back and redocum enting things, v alidate connections etc and first of all to stop the process. T he possibility for av oiding this procedure is» nothing to change«using already tak en data for S C A DA needs. T his is difficult because the source code of the protocol is usually not av ailable. One of the new industrial solutions for connection P L C to the S C A DA system is OP C, w hich can be also used for FDD-On-line connection in industrial system s ( Figure 2 ). Pr o c e s s s e r v e r S CA D A S CA D A c l i e n t M a t l a b M a t l a b S i m u l i n k i n t e r f a c e Figure 2 : G etting On-line data using OP C 4. F D D in H V A C B uilding A utom ation S y ste m s M odern buildings are being designed w ith increasingly sophisticated energy m anagem ent and control system s ( E M C S ), often using industrial solutions. E M C S hav e seem ingly lim itless capabilities for m onitoring and controlling the conditions in buildings. N onetheless, building heating, v entilating and airconditioning ( H V A C ) equipm ent routinely fails to satisfy perform ance ex pectations env isioned at design, so a FDD system could be useful in such a system s. H ow does this happen? T here are a num ber of ex planations. First, H V A C equipm ent is typically instrum ented w ith the m inim um num ber of sensors sufficient to im plem ent basic local-loop and superv isory control strategies. L ack of sensor inform ation is a significant barrier to assessing the operation of the equipm ent. A second ex planation is that the data that is collected ov erw helm s building operators because there is little effort to consolidate the inform ation into a clear and coherent picture of equipm ent status. T rend data from today s E M C S are useful, but only w hen analyz ed by a hum an, and this is not a costeffectiv e w ay to continuously m onitor system operation. A third ex planation is that building operators m ay ov erlook sym ptom s of a failure because they m ay not fully understand the control strategies im plem ented. A related ex planation is that lack of understanding of sophisticated control strategies leads to m anual ov errides that m ay tem porarily allev iate a problem, but m ay lead to unintended and undetected operating problem s in the future. U ndoubtedly other ex planations ex ist; how ev er, there is little argum ent that there is v ast room for im prov em ent in the w ay buildings are m onitored. G iv en this set of barriers, w hat can be done to im prov e the perform ance of H V A C equipm ent? W ebster's N ew C ollegiate Dictionary defines diagnosis as it is used in this contex t as follow s: T oday's E M C S lack the tools necessary to 1 ) detect that problems (often referred to as faults) exist, and 2 ) assist building operators in diag nosing the problems that arise.

3 Having the capability to quickly diagnose operational problems in HVAC equipment means that equipment w ill operate as intended a higher percentage of the total run time. S ome of the benef its of properly operating HVAC equipment are listed below : improved occupant comf ort and health improved energy ef f iciency longer equipment lif e reduced maintenance costs reduced unscheduled equipment dow n time F igure 3 provides a representation of the hierarchical structure of HVAC systems and subsystems in buildings and show s tw o approaches commonly used f or diagnostic reasoning. T he f irst approach, termed the top- dow n approach, uses perf ormance measures f rom higher levels of the building/ system/ controller hierarchy to reason about possible low er- level causes of degradations to those higher level measures. F or instance, w hole building energy use is one high level measure that provides usef ul inf ormation about the perf ormance of a building. I f building energy use ex ceeds its ex pected value by an amount considered to be signif icant, top- dow n reasoning w ould be used to navigate dow n through the hierarchy and isolate the most probable ex planations f or the ex cess energy use. T he second approach, termed the bottom- up approach, uses perf ormance measures at low er levels of the hierarchy to isolate problems and then propagates that problem up through the hierarchy to determine its impact on building perf ormance. I f the impact w ere considered to be large or potentially large, correcting the problem w ould be given a high priority. I f the impact is considered to be small, the decision may be to do nothing at this time. P erf ormance measures at intermediate levels can be used in a top- dow n approach to isolate f aults at low er levels, and also in a bottom- up approach to determine the impact of the f ault at the building level. F igure 3 : T op- dow n and bottom- up approaches to diagnostic reasoning. I n general, there is a f ew data f or usef ully model based F D D in HVAC systems. F D D can be applied if getting additional data f rom rest of the system. T hose additional data can be either qualitative or quantitative. I n f igure 4 is show n a possible solution to acquiring additional qualitative data f rom other subsystems. O ne of the data driven methods can be used f or observing. I n reasoning there is a combination of quantitative data f rom observed subsystem and qualitative data f rom other systems. I n f igure 5 a combination of tw o comparative subsystems is used, w here a ref erence subsystem is built. A ref erence subsystem has to be equipped w ith additional elements.

4 Q u al it at iv e d at a f a ul t: f 1 subsystem 1 ( d at a d r iv en M et h od s) : p r o ba bi l i ty f o r f 1 E W M A C U S U M subsystem 2 P C A P L S - p a r ti a l l ea st sq ua r es Reasoning: f a ul t: f 2 p r o ba bi l i ty f o r f 2 N I P L S - n o n i ter a ti v e P L S B ool ean F u z z y subsystem 3 O b ser v ed Q u ant it at iv e d at a: p ar am et er est im at ion T B M f a ul t: f 3 p r o ba bi l i ty f o r f 3 st at e est im at ion anal y t ic al r ed u nd anc y subsystem n f a ul t: f m p r o ba bi l i ty f o r f m Figure 4: FDD can be applied assuring additional (qualitative) data from similar subsystems subsystem 1 A dditio nal eq uip m ent f o r r ef er enc e f a ul t: f 1 p r o ba bi l i ty f o r f 1 s ub s y s tem : s ens o r s, ac tuato r s, p r o c es s o r s subsystem 2 R eas o ning : f a ul t: f 2 p r o ba bi l i ty f o r f 2 B o o l ean F uz z y subsystem 3 O b s er ved Quantitative data: p ar am eter es tim atio n T B M f a ul t: f 3 p r o ba bi l i ty f o r f 3 s tate es tim atio n anal y tic al r edundanc y subsystem n f a ul t: f m p r o ba bi l i ty f o r f m Figure 3: FDD can be applied assuring additional (qualitative) data from reference subsystem 5. Conclusion T h e difference betw een real H V A C applications and laboratory test system is th at H V A C equipment is typically instrumented w ith th e minimum number of sensors sufficient to implement basic local- loop and supervisory control strategies. A single FDD system can be used for many pieces of equipment, w h ich improves th e cost- to- benefit ratio and th ereby allow s th e use of more ex pensive sensors. T h is is a logical initial deployment of th e FDD for th e H V A C industry. T h e integration of FDD meth ods into individual controllers w ould appear to be nex t step. T op- dow n and bottom- up approach es to diagnostic reasoning of w h ole system w ere represented and tw o new approach es w ere suggested. S olution 1 using qualitative data from similar subsystems w as proposed and S olution 2 using reference subsystem w as proposed. FDD in H V A C subsystem is possible if getting additional data. T h e problem h ow to get O n- line data is th e same as in industrial applications. For acquiring data w as suggested an industrial solution using O P C w h ere no additional data acquisition board is needed.

5 References [1] Sourabh Dash and Venkat Venkatasubramanian: Challenges in the Industrial applications of fault diagnostic sy stems, L aboratory for Intelligent P rocess Sy stems, P urdue U niv ersity, U SA, [2 ] J ames E. B raun: A utomated fault detection and diagnostics for the H VA C& R industry, H VA C& R R esearch, v ol. 5, no. 2, [3 ] Isermann R. : Integration of fault detection and diagnosis methods, IF A C Sy mposium SA F E P R O CE SS ' 9 4, E spoo, 2, pp [4 ] J ohn M. H ouse and G eorge E. K elly : A n ov erv iew of building diagniostics, Diagnostics for Commercial B uildings: from R esearch to P ractice, P acific E nergy Center, San F rancisco, [5 ] E v an R ussel et al: Data driv en methods for fault detection and diagnosis in chemical processes, Springer- Verlag L ondon,

OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9

OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9 OH BOY! O h Boy!, was or igin a lly cr eat ed in F r en ch an d was a m a jor s u cc ess on t h e Fr en ch st a ge f or young au di enc es. It h a s b een s een by ap pr ox i ma t ely 175,000 sp ect at