Data-Driven Fault Diagnosis of Shaft Furnace Roasting Processes Using Reconstruction and Reconstruction-Based Contribution Approaches

Transcription

1 Preprnts of the 19th World Congress he Internatonal Federaton of Automatc Control Data-Drven Fault Dagnoss of Shaft Furnace Roastng Processes Usng Reconstructon and Reconstructon-Based Contrbuton Approaches Xnglong Lu*, Qang Lu*, anyou Cha* and S. Joe Qn*, ** * State Key Laboratory of Synthetcal Automaton for Process Industres, Northeastern Unversty Shenyang, Chna (e-mal: tycha@mal.neu.edu.cn). ** Dept. of Chemcal Engneerng and Materals Scence, Unversty of Southern Calforna Los Angeles, CA 989 USA and he Chnese Unversty of Hong Kong, Shenzhen 1 Longxang Blvd., Longgang, Shenzhen, Chna(e-mal: sqn@usc.edu). Abstract: he process faults of shaft furnace roastng processes, e.g. fre-emttng, flame-out, underreducton, and over-reducton are undesrable for stable operaton of the processes. he processes share multple complextes such as mult-varate and strong correlatons, whch make t dffcult to dagnose the faults usng model-based or knowledge-based methods. In ths paper, a data-drven fault dagnoss method for shaft furnace roastng processes s presented based on reconstructon and reconstructon-based contrbuton. he proposed method explots hstorcal faulty data to derve fault drectons to dentfy ongong faults wth the help of addtonal explanaton from contrbuton plots. A case study on a smulaton system of shaft furnace roastng processes llustrates the effectveness of the proposed method. 1. INRODUCION Shaft furnace roastng processes are mportant chemcal reducton processes that transform weakly magnetc ore nto strongly magnetc one n a hgh temperature condton. Stable operaton of the processes s essental to safety and product qualty. But process faults usually occur when there s a msmatched acton whch cannot meet frequent changes of magntude, class, and ngredents of the raw ore. ypcal process faults are fre-emttng, flame-out, under-reducton and over-reducton. Whle fre-emttng and flame-out would brng up hazard of equpment damage, under-reducton and over-reducton would degrade ore concentrate to have dssatsfactory products. It s necessary to detect and dagnose the process faults n tme. he operatng condtons of shaft furnace roastng processes change frequently wth complex mechansm. On one hand, t s dffcult to model the processes precsely (Wu et al., 6). On the other hand, process faults are more complex than those caused by sensor or actor falure. Consequently modelbased fault dagnoss methods (Zhou & Hu, 9) are not advsable for dagnoss of shaft furnace roastng processes. o solve ths problem, researchers proposed knowledgebased fault dagnoss methods wth artfcal ntellgence for shaft furnace roastng processes. Yan et al. (8) proposed an ntellgent fault predcton system for shaft furnace wth case-based reasonng technque by matchng an ongong fault wth hstorcal ones n a fault case to get a dagnoss result n the form of probablty. Wu et al. (6) and Cha et al. (7) proposed ntellgent fault dagnoss systems wth rulebased reasonng technque separately, whch used process varable observatons to reason by rule and then drew dagnoss conclusons. But the methods mentoned above suffer from certan lmtatons. Frstly dagnoss conclusons are only categorzed by results rather than causes. Secondly the accuracy of dagnoss s not relably guaranteed. Furthermore, wth a large number of varables n the process, the correlaton among varables s qute complcated because of process couplng and closed loop feedback. It requres extensve pror knowledge and tedous work to establsh rules for a fault case, whch means consderable cost. Data-drven fault dagnoss methods have advantages of no need of process model, dmenson reducton, easy vsualzaton, and ease of use and mantenance. Usng multvarate statstcal analyss, statstcal process montorng (SPM) has found wde applcatons n many ndustral processes, ncludng chemcals, polymers, and mcroelectroncs manufacturng (Qn, 3). o take advantage of the convenence of data-drven methods, a data-drven reconstructon and reconstructon-based contrbuton (RBC) method s therefore appled to the fault dagnoss of shaft furnace roastng processes. Wth fault drectons derved from hstorcal faulty data, t can reconstruct faults to dentfy fault types. In addton, major contrbutng fault varables are sngled out. he objectve of ths paper s to present an applcaton of data-drven fault dagnoss method for an mportant ndustral process,.e., the shaft furnace roastng process. he paper s organzed as follows. Secton provdes descrptons of the shaft furnace roastng process and the process faults. Secton 3 descrbes the reconstructon and RBC method. Followng that, a case study on a smulaton system of shaft furnace roastng processes s presented n Secton 4. Secton dscusses conclusons and further work.. SHAF FURNACE ROASING PROCESS AND PROCESS FAULS Copyrght 14 IFAC 8897

2 .1 Descrpton of Shaft Furnace Roastng Processes he shaft furnace roastng processes consst of ore feedng, ore preheatng, heatng, reducton, and coolng & dscharge phases, as shown n Fg. 1. Square funnel Hematte ore Preheatng zone Heatng zone Reducton zone Watersealed pool Reductve gas Belt-Conveyer Ore-store slot Conbuston chamber Ejecton roller Fg. 1. Illustraton of shaft furnace roastng processes he raw ores of Fe O 3 mxed wth reductve gas turn to ore concentrate n the reducton zone after beng heated n the preheatng zone and the heatng zone. he followng reductons take place when a proper temperature range n the reducton zone s realzed. 3Fe O +CO Fe O +CO Fe O +H Fe O +H O If the reductons are not completely performed, the output of reacton s a blend of strongly magnetc ore Fe 3 O 4 and weakly magnetc one Fe O 3. And when reducton overreacts n a hgher temperature, followng reductons would take place. However resultant FeO s also weakly magnetc. Fe O +CO 3FeO+CO 3 4 Fe O +H 3FeO+H O 3 4 Accordng to some basc knowledge, the process varables that correlate wth faulty operatng stuatons and product qualty nclude the followng categores of varables: ) pressures of the heatng gas, heatng ar, and reductve gas; ) negatve pressure nsde the furnace; ) temperatures of the combuston zone, preheatng zone, and exhaust gas; v) flow rates of the heatng gas, heatng ar, and reductve gas, and v) dschargng tme. In a normal operatng stuaton, control loops can balance the relatonshp between temperature of the combuston chamber, flow rate of the heatng ar and dschargng tme to perform the desred reducton, but also to guarantee normal ranges of negatve pressure nsde the furnace, temperature of the exhaust gas, and so on. But because of correlatons between process varables, a normal stuaton s at rsk of process faults even though varables under closed loop are able to follow desred trajectores. he process faults would alter the correlatons between varables. hs property provdes an opton to use data-drven SPM methods to montor the processes. (1) (). Descrpton of Process Faults Msmatched set ponts n control loops would not only degrade ore concentrate, but also adversely lead to process faults. he process faults of shaft furnace roastng processes nclude the followng four man knds. a) Fre-emttng: fre emts out of the combuston chamber; b) Flame-out: flame reaches above the top of the furnace; c) Under-reducton: raw ores are pushed out of the furnace before fully reduced; d) Over-reducton: ore concentrate s over reduced before comng out of the furnace. In a real ndustral settng, dagnoss of these faults s mostly based on the operator s observaton and experence. It could hardly meet the need of process faults dagnoss. Process faults rarely have well-understood mechansm or patterns of emergence. And the boundary between normal and faulty stuatons s blurry. Settng up rules for dagnoss requres much process knowledge and tral-and-error rule adjustments. Wth so many varables, t s complcated to develop a set of rules to descrbe varous faults. When a fault occurs, the correlaton of varables would be broken. he mpact on process varables dffers from fault to fault. hus a vector or subspace can be extracted as the drecton of every fault, whch makes t possble to dentfy faults (Valle et al., 1). A data-drven dagnoss method for shaft furnace roastng processes s proposed n the next secton. 3. FAUL DIAGNOSIS OF SHAF FURNACE ROASING PROCESSES USING RECONSRUCION AND RBC APPROACHES 3.1 Fault Dagnoss Strategy of Shaft Furnace Roastng Processes he use of multvarate statstcs for SPM can yeld a latent varable model from data. Prncpal component analyss (PCA) s a basc projecton model n multvarate statstcs. he adopted method n ths paper s based on PCA and appled to a shaft furnace roastng process shown by Cha et al. (11). he strategy of fault dagnoss for shaft furnace roastng processes s shown n Fg.. A detaled descrpton for each module wll be dscussed as followng. PCA model: he model bult wth hstorcal normal data s the bass of the whole strategy. Fault relevant varables bult n the model should be specfed beforehand. Seven of varables are used ncludng temperature of the combuston zone, temperature of the exhaust gas, flow rate of the reductve gas, flow rate of the heatng gas, negatve pressure nsde the furnace, heat value of the heatng gas, and predctve magntude of magnetc tube recovery rate. 8898

3 Shaft Furnace Roastng Process Data Acqurement and Preprocess Real-me Data Hstorcal Normal Data Hstorcal Faulty Data Fault Detecton PCA Model Extracton of Fault Drectons Reconstructon Based Fault Identfcaton RBC Based Fault Dagnoss Fg.. Strategy of fault dagnoss shaft furnace roastng processes Analyss of Fault Reason Expert Knowledge Fault detecton: It s realzed by montorng real-tme data of shaft furnace roastng processes wth fault detecton ndces. he PCA model works as a dgtal template of the shaft furnace n ths module. Indces of statstc would show whether or not the process s faulty. Extracton of fault drectons: A dataset of fault drectons s derved from hstorcal faulty data of the same varables as used n PCA model. Each drecton corresponds to one fault wth certan cause. Seven knds of sensor falure and four knds of process faults are nvolved, ncludng fre-emttng, flame-out, under-reducton, and over-reducton. Reconstructon-based fault dentfcaton: It s based on explotaton of real-tme data and coordnaton between the PCA model and the dataset of fault drectons. Faults that have happened before can be dentfed. Fault dentfcaton ndces wll be dscussed n detals later. RBC-based fault dagnoss: RBC-based dagnoss can work wth PCA model to draw dagnoss results from real-tme data. Each one of contrbuton of varables to the fault s presented for further analyss. When a new fault that has never happened before occurs, reconstructon-based fault dagnoss would fal to dagnose t. After a new fault drecton s extracted, the fault can be augmented n the fault lbrary n case the new fault happens agan. Fault dentfcaton, fault varable dagnoss, and expert knowledge are combned for the root-cause dagnoss. Detals about the strategy are shown n Secton Fault Dagnoss Algorthms of Shaft Furnace Roastng Processes 3..1 Fault Detecton of Shaft Furnace Roastng Processes Based on Prncpal Component Analyss m Let x denote a sample vector of m sensors of the shaft furnace roastng processes. Assumng there are N samples of each sensor, a data matrx X N m s composed of N rows for N samples and m columns for m varables. X s scaled to zero mean and unt varance. hen the covarance matrx S of X can be decomposed by egendecomposton as S = PΛP + PΛP (3) ml where P m( ml) and P stand for loadng matrces of prncpal components and resdual components, l s the number of prncpal components, and dagonal matrces Λ and Λ respectvely contan egenvalues of the covarance matrx n descendng order. hen a new vector x can be decomposed nto two orthogonal subspaces as x xˆ x, where xˆ PP x s the projecton on prncpal component subspace and x PP x s the projecton on the resdual subspace. he number of components l s determned as Qn & Duna (). Wth fault detecton ndces defned, fault detecton can be performed on shaft furnace roastng processes. Qn (3) presented fve knds of fault detecton ndces. Among them, and Hotellng s statstcs are most typcal and common. he statstc defned n (4) measures the projecton of a sample vector on resdual subspace. x x PP x (4) he process s consdered normal f, where denotes the upper control lmt for wth a sgnfcance level. he Hotellng s statstc defned n () measures varatons n prncpal component subspace. -1 xpλ Px () he process s consdered normal f, where denotes the upper control lmt for wth a sgnfcance level. hese two control lmts are calculated as Alcala & Qn (9) n ths paper. 3.. Fault Dagnoss of Shaft Furnace Roastng Processes Based on Reconstructon When a process fault occurs, the frst step s to detect t. After that, t s necessary to dentfy the fault for further soluton. Wth hstorcal faulty data and causes avalable, reconstructon approach can be used to dagnose the faults that have happened before. he detectablty, reconstructablty, and solatablty of the faults are dscussed by Duna & Qn (1998a) and Duna & Qn (1998b). When a certan process fault occurs, samples of sensors n the roastng process need to be captured for further analyss. he same varables as n the PCA model are used here. Let Nm X denote the faulty data matrx of fault F, whch conssts of N rows of N samples and m columns of m varables. he work of Valle et al. (1) dscussed the relatonshp between projectons of fault drecton and faulty data on resdual subspace and then provded a method to extract fault drecton. We apply SVD on the resdual matrx of faulty data X. X U D V 8899

4 he fault drecton matrx can be chosen as U (6) o dentfy the ongong fault, t s requred to reconstruct sample vectors of the fault wth accessble fault drectons. he objectve of fault reconstructon s to estmate the normal values by elmnatng the effect of fault F. A reconstructed sample vector z along fault drecton Ξ s calculated n (7). z x f (7) where f s the estmaton of the magntude of fault along drecton Ξ. Fault reconstructon corrects the effect of a fault, whch means t can mnmze the fault detecton ndces of the faulty samples. he reconstructed along drecton Ξ,.e. becomes + f x x x ( I ) x (8) accordng to Qn (1). hen when a process fault takes place, fault detecton ndces would ncrease dramatcally. If ths fault s along the drecton Ξ, the reconstructon along that drecton would correct the effect of the fault n a proper way. So the reconstructed would drop to a relatvely normal level. Defne a fault dentfcaton ndex (9) If Ξ s the actual fault, η would be close to zero because reconstructon elmnates the effect of the ongong fault. hen the fault s dentfed Fault Dagnoss of Shaft Furnace Roastng Processes Based on Reconstructon-Based Contrbuton Contrbuton plots are well-known dagnostc tools. It s convenent to be used and requres no pror knowledge. Contrbuton plots of represent the sgnfcance of each varable of, separatng fault relevant varables from fault rrelevant ones. he process knowledge on shaft furnace roastng processes s necessary for more convncng explanatons of dagnostc concluson. If a sample vector x has an abnormal, every sngle varable has a contrbuton to t. An nvestgaton nto the varables should be carred on, especally nto the varables that have a sgnfcant contrbuton. A sgnfcant contrbuton ndcates whch varables are responsble for an nflated, whch means the largest several contrbutons are lkely potental cause of the fault. However the result could be nconclusve and lead to a msdagnoss because of the smearng effect, whch can be avoded by the reconstructonbased contrbuton. he reconstructon-based contrbuton of varable x to s used n ths work due to the advantage of RBC compared to regular contrbuton plots. It s defned as Alcala & Qn (9). where C PP. RBC x C ( C ) Cx () 1 4. CASE SUDY ON A SIMULAION SYSEM OF SHAF FURNACE ROASING PROCESSES In ths secton, we present a smulaton system of shaft furnace roastng processes creatng a fne envronment approxmate to a real shaft furnace feld to demonstrate the effectveness of the proposed method. he smulaton system hardware s composed of three parts, the smulated plant, PLC, and fault dagnoss module as shown n Fg. 3. OPC Communcaton Computer for Operatng Control and Fault Dagnoss Computer for Process Montorng DDZ-III Standard Sgnal Vrtual Instruments and Actors Ethernet Ethernet Rockwell PLC Cable Operatng Control and Fault Dagnoss Process Control Smulated Plant Computer for Plant Smulaton Fg. 3. Structure of the smulaton system of shaft furnace roastng processes he smulated plant s used for smulaton of the dynamcs of the shaft furnace. PLC acheves process control that a real shaft furnace needs. he fault dagnoss module works as a real-tme securty for the whole process. here are sensors of pressure, temperature, and flow rate to keep track of varables n the process. he PCA model contans seven fault relevant varables, whch are temperature of the combuston zone (CZ), temperature of the exhaust gas (EG), flow rate of the reductve gas (FRG), flow rate of the heatng gas (FHG), negatve pressure nsde the furnace (NPF), heat value of the heatng gas (HVG), and predctve magntude of magnetc tube recovery rate (MRR), as shown n able 1. As MRR cannot be collected n real tme, a predctve method s adopted from Cha et al. (11). able 1. Varables n the PCA model Varable No. Unt CZ 1 FRG m 3 /h MR 3 % EG 4 89

5 Fault Identfcaton Index (%) RBC square Fault Identfcaton Index (%) RBC square square NPF Kpa HVG 6 KJ/m 3 FHG 7 m 3 /h 1 8 normal Control Lmt normal square square Control lmt A PCA model s derved from normal data. And a fault drecton s bult usng hstorcal faulty data, whch conssts of seven sensor falures and four process faults, as shown n able. able. Seral number of faults Fault No. 1 st sensor falure 1 nd sensor falure 3 rd sensor falure 3 4 th sensor falure 4 th sensor falure 6 th sensor falure 6 7 th sensor falure 7 fre-emttng 8 flame-out 9 under-reducton over-reducton Fg. 4. Plots of and of normal data 3 3 Control Lmt Fault Identfcaton square square Control Lmt Reconstructon-Based Contrbuton Plots for In tradtonal method, every faulty sample corresponds to a fault concluson. o get an overall concluson of dagnoss, another fault dentfcaton ndex s defned as follows 6 4 N N (11) where N stands for the number of samples whch draw a concluson of th fault, and N stands for the total number of faulty samples. he samplng perod s 1 second n the experments. We use 6 samples under normal condtons to derve a PCA model for tests. he plots of and of normal data are shown n Fg. 4. False alarm rate of s 1.% and false alarm rate of s.17%. Fault dagnoss results for fre-emttng and under-reducton are shown n Fg. and Fg. 6 respectvely. Fg. llustrates that when fre-emttng occurs, and can both detect t mmedately. Both statstcs rse vsbly above ther control lmts. he fault dentfcaton ndexσ ndcates that % faulty samples can draw the correct dagnoss concluson. And RBC plots shows that the 1 st and 6 th varables,.e. CZ and HVG, are responsble for ths fault. Because RBCs of these two varables outwegh the others markedly. Fg. 6 can be explaned n the same way. % faulty samples lead to the correct dagnoss concluson of under-reducton and the nd varable s responsble for ths fault because of ts RBC outweghng the others sgnfcantly Fault No. Fg.. Dagnoss result for fre-emttng 3 Control Lmt Fault Identfcaton Fault No Varable No. square square Control Lmt Reconstructon-Based Contrbuton Plots for Varable No. Fg. 6. Dagnoss result for under-reducton 891

6 Fault Identfcaton Index (%) RBC square Fault dagnoss results for a new fault are shown n Fg. 7. A new fault means the fault drecton s unknown. Fg. 7 presents a confusng dagnoss. he fault s detected by and, but we have got an nconclusve result from fault dentfcaton ndex and RBC plots. 9% of faulty samples ndcate the fault s under-reducton whle 8% of them ndcate the fault s over-reducton. And RBC plots show the fault s not a typcal under-reducton fault because the major contrbutor ths tme s 6 th varable HVG, whch means ths fault does not share the same cause as under-reducton. When a new fault occurs, the actual cause should be analysed wth expert knowledge. And then the drecton should be extracted. hus database of drectons of faults could be supplemented n case that ths fault would happen agan. 4 3 Control Lmt Fault Identfcaton Fault No. Fg. 7. Dagnoss result for a new fault. CONCLUSIONS 3 square square Control Lmt Reconstructon-Based Contrbuton Plots for Varable No. In ths paper we appled a data-drven dagnoss method for fault dagnoss of shaft furnace roastng processes. he applcaton to the smulaton system of shaft furnace roastng process shows that reconstructon and RBC are effectve tools for process fault dagnoss of shaft furnaces. When there are much more varables to be montored n the process, multblock analyss proposed by Qn et al. (1) and Lu et al. (13) can be effectve to nterpret the contrbuton plots. Feedback control would make t dffcult to dentfy the faults because feedback control obscures the source of faults (McNabb & Qn, ). Further, operatng control n the shaft furnace system s qute complcated. Future work s to fnd a way to elmnate the feedback control effect n the closed loop system of shaft furnace for accurate dagnoss results. ACKNOWLEDGEMENS , 61484, 63), the Chna Postdoctoral Scence Foundaton funded project (13M414), the 111 Project of Mnstry of Educaton of Chna (B8), and the IAPI Fundamental Research Funds (13ZCX-1). REFERENCES Alcala, C. and Qn, S. (9). Reconstructon-based contrbuton for process montorng. Automatca, 4(7): Cha,., Dng, J. and Wu, F. (11). Hybrd ntellgent control for optmal operaton of shaft furnace roastng process. Control Engneerng Practce, 19(3): Cha,., Wu, F., Dng, J. and Su C. (7). Intellgent workstuaton fault dagnoss and fault-tolerant system for roastng process of shaft furnace. Proceedngs of the Insttuton of Mechancal Engneers, Part I: Journal of Systems and Control Engneerng, 1: Duna, R. and Qn, S. (1998a). Jont dagnoss of process and sensor faults usng prncpal component analyss. Control Engneerng Practce, 6: Duna, R. and Qn, S. (1998b). Subspace approach to multdmensonal fault dentfcaton and reconstructon. Process Systems Engneerng, 44(8): Lu, Q., Qn, S. and Cha,. (13). Decentralzed fault dagnoss of contnuous annealng processes based on multlevel PCA. IEEE ransactons on Automaton Scence and Engneerng, (3): McNabb, C. and Qn, S. (). Fault dagnoss n the feedback-nvarant subspace of closed-loop systems. Ind. Eng. Chem. Res., 44: Qn, S. (3). Statstcal Process montorng: bascs and beyond. Journal of Chemometrcs, 17: 48-. Qn, S. (1). Survey on data-drven ndustral process montorng and dagnoss. Annual Revews n Control, 36: 34. Qn, S. and Duna, R. (). Determnng the number of prncpal components for best reconstructon. Journal of Process Control, : 4-. Qn, S., Valle, S. and Povoso, M. (1). On unfyng multblock analyss wth applcaton to decentralzed process montorng. Journal of Chemometrcs, (9): Valle, S., Qn, S., Povoso, M., Bachmann, M. and Mandakoro, N. (1). Extractng fault subspaces for fault dentfcaton of a polyester flm process. Proceedngs of the Amercan Control Conference, 6: Wu, F., Dng, J., Yue, H. and Cha,. (6). Intellgent fault dagnoss system for roastng Process of Shaft Furnace. Journal of Nanjng Unversty of Aeronautcs & Astronautcs, 38: Yan, A., Wang, P. and Zeng, Y. (8). Intellgent fault predcton system of combuston process n shaft furnace. CIESC Journal, 9(7): Zhou, D. and Hu Y. (9). Fault dagnoss technques for dynamc systems. Acta Automatca Snca, 3(6): hs work was supported n part by the Natural Scence Foundaton of Chna (61347, 663, 61933, 89