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Maintenance planning in rail transportation
Maintenance planning in rail transportation Robert
G E N S E R
()sterrewl'a~t'he 13wldesbahnetr. G<,neraldlrektion. Stabsstelle Infi~rmatik und i~ybernctik. A.I090 Wren. Au.~trta
tSl`ler outlining tile maintenance problem for railway systems a survey is given of the pfesenA state of maintenance planning in rail transportation, The differences between an engineering =lnd a mathematical point of view are stressed. In Ihe particular case of maintenance planning l,or traction unit~ a planning system is described, The application of a l,uzzy set metht~.l is discussed.
I. Introduction A railway administration has the opporturlity to undertake operational planning and the planning of its workshops simultaneously, considering the overall objective!, of the railway system itself. The data needed for optimal maintenance planning are available inside the enterprise. These data are only restricted by technical and economic limitations, This is quite different from common repairshops. Even if the breakdown of a private car could be predicted, private repairshops would not know when the customer would arrive with his car, The arrivals of cars for repair can only be forecast by analysing past events. But railways can use information on the behaviouf of equipment to formulate suitable maintenance plans to control throughput in railway workshops, because they control the workshops as well as the operational use of equipment. A single private workshop need only consider its internal organisation, with goals such as ensuring: - the shortest time is spent on a job in the workshop. an accurate delivery date, the optimal use of a',ailab!e capacity, and the financial allocation, '~,bich is needed for materials, spare parts, etc., should be as late as possible.
North.Holland Publishing Company
European Journal of Operational Row;arch I I (1982135-41
But in the case of railways the gc,al of optimal workshop control has to be: - the smallest investment in, and commitmenl of. workshop a n d operational equipment, like rolling stock etc. The optimal strategy will be therefore: - to have an information system with htghly efficient data processing Io maximise the reliability of the railway system, - to use a suitable maintenance plan. and - to analyse deviations from the pk~n witlt the Igurpose of improving the s'~stem. The emphasis on reliability is not ~n[y because of the safety and reliability regulatmc~n~, which have to be obeyed by railways. Very high reliability standards most be achieved by railways, hut reliability is also in the Long run their most economic strategy [161. Tile objectives o1' a railway system, acuordhlg Lo I 17J are: governmental and regional policy. safety and reliability. - efficiency, quality of service. econonly, and serving the social system. The railway system has also to work during a genertd state of crisis. It has to be borne in mind that a railway system • covers large areas, - consists of highly technical elements, needs much investment in dedicaled equipment. - has long service life and long depreciatin~l lime. and - ,can influence the stability or objective, of the hypersy~tem "state'. Therefore the systems implemented have to be robust. Large scale job flow control by computers is much more sensitive than c~,mputer aided phmning systems, An aatomated on-line control for workshops just smooths over bad states. The real trouble spots, for example an unsuitable organization, will not be investigated, Man only produces data and cannot recognize the effect. He feels he is being controlled by a machine. This is not al ~111 stimulating for him. But tobusmess demands that 'unknown' eanl~t)t
0 3 7 7 - 2 2 1 7 / 8 2 / 0 0 0 0 - 0 0 0 0 / $ 0 2 . 7 5 ~ 1982 North-Holland
R. G't*n,~er/ Af¢lttttena,l¢¢,pht~tninx m ratl Irlln.~p~lrtatlon be declared 'certain'. It would be a mistake to transform fuzzy information to deterministic valties, which cannot be adapted to real events as the process develops. The time available for phmning shoulLI be used as far as possible for evaluating the feedback front the real world. Uncertainty shoe~ld be reduced step by step, This demand is caused by technical, economic, and human limitations. It is a belief, which can be found in many organizations. hut it is an error, to assume that complex planning can be done by single step final decisions. Ev~,:ry stage of planning needs a freedom of decisi~ns dependent upon the pkmning horizon. M;tchines can handle vast amounts of information tiuickly and reliably, but man is more fle:dble. The best solution will be to have a system in tvhich the nlachine relieves Inafl of routine work, but where m:ul can make decisions which have regard to real liie In safety related systems it is not so important to reach the theoretical optimum a!, frequently as possible as to be fail-safe any time. It is obvious that increas'~ng the categories of ob.icclive~to be considered and extending tbc range of planning also increases the complexity of methods and the effort of implcrneiltation. This is the reason that only advanced railway administrations are using the technology available to any great extent, see for example [ 15,21,25,32,37.511. in many production plants and workshops job scheduling and capacity planning are earl'led out by computers with well-,known sot'tware package. Of course these are also used in railways in track construction, see for example [23.2,~.]. or in subsystenls for maintenance as described in [35,66.64,25.63,18]. But for general application the railways had to Ioo!.. for new appn~aches to solve their problems.
2. Maintenance planning
Maintenance comprises: inspection, servicing, overhaul. replacement, modification, and repair. The effects of disrupting high-speed heavy traffic train operation are far-reaehingt therefore the prevention of faults is of the greatest intpor-
tahoe. Preventiee maintenance is well developed as a result of long experience, as shown in [14.30.34], and is also required by regulations, see for example 15.331. The need for better productive use of capital investment and reduction of manpower has forced inspection periods to be extended. This is possible without a fall in reliability standards if fixed time periods are replaced by inspection perio~ls, depending on the working time of facilities or running distance of rolling stock, see [22,59]. But also intensive fault and equipment data analysis can result in longer inspection intervals as shown in [1.50,58,59,371. It was found that more li'~quent dismantling for inspection can reduce equipment reliability. Strict review of inspection data and the analy,~ib, of important equipment functions allow ~\~r the mainlenance of snme components lifter breakdown. Muinterance.free components arc ,ncreasingly used. With facilities for complex data processing, steps can be taken to introduce t'orrt,ttitle maintenance, see [29.4J. But if the design of equipment is not adapted to this strategy then preventive maintenance results in better availability than corrective maintenance [Iq]. Maintenance planning for fixed installations. see [4.23.29,19,20.38]. is easier and less sensitive than workshop planning for vehicles. Thi~ is not only due to mobility, but is also caused by tile compactness of this construction, being packed with many different functional and very expel~,,~ive pieces of equipment, which need much attention in the workshop, and the necessity to take the vehicle out of use as a whole for a relatively long time. It is not possible only to decrease performance during overhaul as is practicable in the case of track. Railways have a great incentive to ensure that at least traction units are called in for mainlenance according to the mileage reached during operation. This is due to the investment, which is idle during maintenance, and because of high costs in the workshop, compared to wagons. But the runningdistance reached during operation depend,~ on traction unit allocation. Also the complexity o~'job flow scheduling in the workshop itself makes manuul planning very difficult, Without the help of computers long-term planning can be done only in a very simplified and non-informative way.
It (h.n.~er/ Ma#ttemmo, planning m t~lil tran~portat¢on 3. C o m p u t e r aided planning system for w o r k s h o p s
As mentioned already, the greatest effects can be achieved by improving the maintenance of vehicles done in workshops, Because of the conditions found in a railway system, the solution has to be a planning system with information feedback. An automatic on-line control is only suitable at lower levels and for subsystems, as for example in workshops in the case of numerically-controlled machines, automatic test facilities, etc.. Figure 1 gives an outline of a planning system for railway workshops. The structure contains time-dependent and functional decomposition for the purpose of a step by step implementation. The steps are not only needed in case of limited personnel capacity, but they are also used for gaining a learning feedback during implementation, Well defined interfaces inside the system and structured and well documentczd programming should allow adaptation to future developments.
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Fig, I, Computer aided planning sy~lenl for workshops,
In the long range planning stage, simulation method~ or operations research approach*.:s are used for non-routine special investigation~, llke evaluating the effects of improving workshop capacity, or determining optimal intervals for inspection, as shown in [27.46.12.7.49.20,38,41, 42,43.45], ete,, Simulation model,'; are described in I I 1,13.54,56,65t. Steinbock [541 has modelled the repair of traclion motors by considering every facility in the workshop, using the simulalion hm. guage GPSS for investigating bottlenecks and I\~r evaluating the ,:ffects of changes in capacity. In [541 the probabdistic behaviour of arrival rz~tcs is also investigated in detail. Generally failure rates correspond to a Weihull distribution for early fadures and normal operation interval especially lot fatigue and wearing out. see 17,8,38,./.0A6.49.53.62]. The e:q:~onentlal distribution is appropriate Io normal operati~m inlerval and overload or human errors. The n~r. real distribution can be used for late failures and corrosion and wearing out. As far as possible Markovian models are used. see 113281. The feedback of inlk~rmation is given by the system for opt'rutional data analrsis, More and more. such data are obtained b~ diagnostic facilities, sec for example 12.3.31.37,39A4,47.52, 53.55,57.60.61 l, which arc set up: on the vehicle, - at the depot or along the line. or . in the repair shop. The extension of automation in train operatitnl has led to an increase in the use of on-line diagnosis. Tl~e impact of micro..computers permits data to be acquired at the source of any trouble and opens up quite new possibilities for informatkm processing, see [57,61]. It can be said that the development of diagnostic systems is just beginning. Methods Ik~r data analysis include regression. correlation, filtering, see [9] for an example, idemification, a survey is giwm in [26]. and pattern recognition techniques us shown by Pau ~48J, a:, well as algorithms for testing stali~tlcal hypotheses and reliability evaluations. :¢c for example I 16.6,7.8.40,62], etc.. The information gained by an operational dat:t analysis system is used as an inptil to the planning system for decision making, fi)r inlplenlemirL~aclion, or for control purposes.
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3.1. M a i m e t ~ a n c e p l a n n i n g a),stem
The maintenance planning system is intended for long range scheduling and i~'~r deciding the desired operational use of vehicle~,, considering the most important workshop capacities, as pointed out on Fig. 2, The final decision is made by man, If the rostering of traction units is not solved in a computer aided manner, as indicated on Fig. 2, then the internally deterministic value for the run. ning-distance desired is made fuzzy by the ranges: 'much more. 'more'. "same', 'less'. and 'much less". for defining the desired allocation of units. The range is evaluated by considering the allowable inaxinlunl and minimum runnlng-distance of the class OI' vehicle at the depot to which it is attached and the running-distance R t, reached in the past month, see Fig. 3. In practice, a man can deal with inforl'nation expressed, for example, as "more'. But if he were to be told just the value "21375 kin" he would be forced to make time consuming ealculalions.
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I'-U;t~y data arise in a decision process if at one stage of this process not everything is yet known. or it is ,lot desired to pin data down because sometimes the objectives influencing the deci,qon are changing with the increase of information as the process develops. Fuzzy sets allow a firm commitment to be postponed until the last decision stage. If fuzzy sets are Iransformed into probabilities then t:le decision has already been made. In lhe case of fuzziness one can expect something between some boundaries, for example, but nothing more, There is no probability distribution for it, only the possibility of the event occurring between the boundaries. Because of the built-in feedback, the actual value reached by the vehicle updates the original plan. as shown in [10]. Step by step the deciston range is reduced. The aim is to keep the planned dates for delivery of vehicles to the workshop as far as possible constant. Highest priority is given to externally fixed dates and dates not far ahead. A heuristic method is use~ to try to smoolh capacity load in the workshops or at least to keep the load below limits. On the other hand the operation
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job is available, Because of the co~llplex conditions and flexibility needed at ruilway workshops a list processing procedure (sneh approach can be ft )nlld in 136D is more adequate than a muthemalical approach. The w o r k s h o p capa('i O, data models time loading of the workshop. ('omplex conditiuns can he attached not only to the network elements, hUl also to eupacities, like truck o r ~;hunting conditions. etc.. An exerpt of the loading code. used by tile Austrian Federal P,aihvays, can be ~ccn in Table I. In short range phlntting only man is involved. Therefore tile experience of nlan cuu I'R2 LlSetl, which is also stimulating for hint to find gooA solutions inside the medium range solution grid. ()nly in the case of deviations, which cannot be snloothcd in the short time range, does tile job
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tBl,mk) Ranisbelastungsarl; IlehtMun~ durch Itt'd;lrl'Sltl~lllk~c As can be seen in Fig. 1. medium range workshop planning consists of the Ik)llowing functional systems: single network plannh~g, - gathering of work,~:hop capacity data. and job scheduling. Rostering and inventory c o n t r o l are functional[~ dedicated systems, as well as ol!+er subsystems like budgeting, personnel system, etc., For job scheduling the results of maintenance planning are used as input data, which call up the single networks needed, which have been evaluated once btfforehand. Taking into consideration the capacity data. u solution is looked for inside the given limits. By using the maintenance planning information the range of variation is much reduced, which makes the solution process much easier. Man has the opportunity to make the final decision, The planning horizon can be more than one year. but three months is used for planning during the year, The results are also tran:~mitted to inventory control A feedback to job scheduling is given if the targets cannot be achieved. The single n e t w o r k s correspond to the theoretically shortest duration of the job in the workshop on the assumption that all capacity needed for this
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R, Gt'nser / Muinwnanee planning in rud tran~partatilm scheduling of the m e d i u m range have to be called up. If m a i n t e n a n c e of e q u i p m e n t is d o n e in workshops at fixed t i m e intervals, as v, a s usually d o n e fc, r w a g o n s w h e n there was no m o d e r n w a g o n i n f o r m a t i o n s y s t e m available, then the m e t h o d s are more simplified.
4. Conclusion It has to be r e c o g n i z e d that suitable solutions for such c o m p l e x problems, w h e r e even the self. organizing b e h a v i o u r of m a n has to be considered, c a n n o t be found by t e c h n o c r a t s a n d bureaucrats. It is a task w h i c h needs an e n g i n e e r i n g approach. Simple a l g o r i t h m s should be preh,'rred that give insight into the p r o b l e m and that p r e s e n t a collection of solutions rather than just o n e a b s t r a c t value, where m u c h i n f o r m a t i o n is lost. In nearly all railways w h i c h use a d v a n c e d technology, a tendency to fewer faults a n d a c c i d e n t s d u r i n g ope r a t i o n c a n be found. B u t new t e c h n o l o g y is just e n t e r i n g the field o f m a i n t e n a n c e in railways.
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R Genser/ Mamwnamephunhngm
[321 S. Kufio, Systems fi)r mainlemmce-vehieles. Rep,rt , f the IIASA Shmkan.~en ('¢mfi'rence (IIASA. Laxenbttrg. 19811. [331 D. Lecker lind K.-D. Kraus¢. Bestimmung des neuen Trieb fahrzeug-[ nstandbahungssystems, Schiem.t,,fahr=eugc 10(5) (1975) 158-160. [34] E, Lorenz and M. Deschner. Der Stand der ILJnterballung~wirl~baft ftir Wagen. Etsenbahntechnische Rutul~rhuu 25(4) ( 1976A 219-229. [35] O. Ll~bke. Die Anwendung der Netzplanlcchnlk bei der Pflege und Unterfialtung moderner elektrischer Trlebfahrzeuge in Bahnbetriebswerken. Eh.btrtsc~wBah,,len 42(3) (1971) 50-55. [36] H.L. Mack and L.D. Smith. Scheduling wilh list processing and problem oriented data structures. European J. Operational Rt,s. 2(3) 11978) 175-184. [37] B. Marklund. Unt~rhafiung yon Loknmofiven beate und morgen. ZEV.Glasers finn. 10312/3) (1978) 104 106. [38] H. Materne. Rationalislerung der Instandhahung v~m Eisenbahnsicberung.~anlagen durcb systemafisches Erfas~en und Auswerten yon Driftausf~llen. Die Etstvtl.ahntecbmk 27(4) (1979) 142- t45. 139] H, Matsuzawa. Aulomafion of car inspecfion and repair. Japanese Railway Engrg. 15(2) (1974) 5.9. 140] W.Q. Mceker and W. Nelson. We)bull percentile estimate~ and confidence limits from singly censored data 6y maximum llkellbood. IEEE "l'rans. Rehahthtr 2.';11111976) 2 0 24. [411 E. Menipaz. Cost opfimiz~ttlon of sonic stochastic maintenance policies. 1ELSE Trans. Rehabd~O' 28~2) 11979) 133137. 1421 H G . Miller and R. Braff. Impact of the freq0ency ~.f technician visits on facility failure r~te. IEEE Trans. Reh,* hlhO, 2o~4) (1977) 245-.247. [43} H. Mine and H. Kawai. An optimal inapection and re. placement poli¢~, IEISE Trans. Rehahiht~ 24(5) 11975) 305-309. [44] M. Mocblzuki. S'abway .:at inspection and regair management system. Japanese Railway Engrg. 171d) i 1978) 12-13. [45] T. Nakagawa. Optimum preventive maintenance policies for repairable system~, IE~E Trans. Rehabiho ~26(3) (1977) 168-173. [46] P. Naumann. Vereinfitchtes Verfahren ~ u r Ermit0ung koslenoptimaler Instandhaltu'lg~intetvalle. Signal und Schiene 2J(5) (1979) 235-237. 1471 E.A. Pachomow, Di~gno~tik tmd Prognose der zuverlassigen Arbeit der Die~elmotoren yon Dieseltriehfahrzeugen nach den Ergebnissen der /-)lanalyse. Z. OSShD 2013) 1197~) 10-12. [48] L.F. Pau. Failure Diagmxs~ and I'er/ormuvce Momtormg (Marcel Dekker. New York. 1981). [49] g. Peter. Die Be.~limmun 8 elner optimalen InMandbaltungsstratcgie auf Aherung~.basis rail Hil~'c nunterischer Melhoden. Wts.scaschafiliche Z tlochscbu& Verkehrsw~wen Drt~iden 77 (1977) 183-190. [501 H. Rath. Die ~k:hadensforschung in den Ausbesserungs~tken tier Deutschen Bunde~bahn. in: EL~ner.~ Ta~('ht./lhuth (Eisner. Frankfurt. 197"1 451-474.
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[51[ G, Richlcr and G. Seifcrt, Aulomafsierl¢ Systeme der Leitung und dle Anwcndung dcr Recbcnlechnik hn Aushesberungswcsen der DR. Nchiem,nfahr:euge 23151 (1979) 253-.256. [52] R.E, Rivier, Alorhhme d'analyse en temp~ r~et tics lnesur~:s de I'&al g;=omelrique des voie~ de chemin de fer. Bull. Tei'lmiq~w de fa Sulase Romande lep ( 19721 I •9, [531 A. Semrau and H. BaIueb. Ver~olikomnlung dcr ()herbauerllahung durch Anweadung yon Metbodcn der tecllniscben Diagnosfik. Verkctm~,elnnuh,n, speehd p0b[ication: f:ahrweg und Tran.~pnrtgualuitl der EJsenlnlhnt,n 11978~ 104-.115. [5.1~ R. Steinbock. Simulatlonsnlodeil f/Jr die Eiektron.,lorcnreparatur. I)ipk)marbe0. Insfitul fur IJnlernebnlens* fi)r:,chung. 'Pechnische UniversiILit Wien 119781, 155j M. SugaL The present data processing itl JNR IJapaneseh SVMOtI.¢ attd C¢,,:lnpl f (Vo¢ol 16112) (1972) 901-907. [561 H. Sugivama eg aL, Optinluln control of repair process of passenger cars through simulation. Prot .vdl Sv,tpowl~m m; tht' UA¢ ¢~ (:Vbt'rnt'lit's i~n tbe RailwlO' (lapdl~ Railway ('ybernefics Associafion. Tokyo. 1972) [5"] "1'. Tanino el aL. Monitoring system of eleclrlcat railcar~ for Shinkanscn Lines. using a mlcrocompuler. Pro., 16m S~.mp~xwumon Ihc Use of ('~'berltetnw on the R..dwq~ (Japlm Raiiw~ty Cybernetics Association. Tokyo. 198111, 15~.1 M. Teufel el al.. Ein Jahr '['riebfahrzeug-Z~ff~hrun~ssystematik m0 Hifi'e der EDV in der Praxi~.. Deutsche Et.lLt'llbahmethnik 20181 (1972) 346-350. 15!)1 Sh. Tsubol. Infl~rmafion system for repairshnps tlf rolling st(~ck (Japanese). Japane.le Railway En~.mcer A.woc 1415t (1971) 7412-7416. [60] Z. Veber. Gegenw~rtiger Stand und Perspektive der demontagelosen Oiagnostik fur "rriebfahrzeuge bel dell ('SD. Z. OSShD 20(3) (1977) 13-16. 1611 H. Welsz. Erprobung und wirtscfiaftlicher Einsatz des Fehler~rfassungs- und -bewertungssystems BEFUND. Forschungsvorhaben TV 7529 de~ Bundcsmlnisteriums fill' Forschung und Tecbn
G E N S E R
()sterrewl'a~t'he 13wldesbahnetr. G<,neraldlrektion. Stabsstelle Infi~rmatik und i~ybernctik. A.I090 Wren. Au.~trta
tSl`ler outlining tile maintenance problem for railway systems a survey is given of the pfesenA state of maintenance planning in rail transportation, The differences between an engineering =lnd a mathematical point of view are stressed. In Ihe particular case of maintenance planning l,or traction unit~ a planning system is described, The application of a l,uzzy set metht~.l is discussed.
I. Introduction A railway administration has the opporturlity to undertake operational planning and the planning of its workshops simultaneously, considering the overall objective!, of the railway system itself. The data needed for optimal maintenance planning are available inside the enterprise. These data are only restricted by technical and economic limitations, This is quite different from common repairshops. Even if the breakdown of a private car could be predicted, private repairshops would not know when the customer would arrive with his car, The arrivals of cars for repair can only be forecast by analysing past events. But railways can use information on the behaviouf of equipment to formulate suitable maintenance plans to control throughput in railway workshops, because they control the workshops as well as the operational use of equipment. A single private workshop need only consider its internal organisation, with goals such as ensuring: - the shortest time is spent on a job in the workshop. an accurate delivery date, the optimal use of a',ailab!e capacity, and the financial allocation, '~,bich is needed for materials, spare parts, etc., should be as late as possible.
North.Holland Publishing Company
European Journal of Operational Row;arch I I (1982135-41
But in the case of railways the gc,al of optimal workshop control has to be: - the smallest investment in, and commitmenl of. workshop a n d operational equipment, like rolling stock etc. The optimal strategy will be therefore: - to have an information system with htghly efficient data processing Io maximise the reliability of the railway system, - to use a suitable maintenance plan. and - to analyse deviations from the pk~n witlt the Igurpose of improving the s'~stem. The emphasis on reliability is not ~n[y because of the safety and reliability regulatmc~n~, which have to be obeyed by railways. Very high reliability standards most be achieved by railways, hut reliability is also in the Long run their most economic strategy [161. Tile objectives o1' a railway system, acuordhlg Lo I 17J are: governmental and regional policy. safety and reliability. - efficiency, quality of service. econonly, and serving the social system. The railway system has also to work during a genertd state of crisis. It has to be borne in mind that a railway system • covers large areas, - consists of highly technical elements, needs much investment in dedicaled equipment. - has long service life and long depreciatin~l lime. and - ,can influence the stability or objective, of the hypersy~tem "state'. Therefore the systems implemented have to be robust. Large scale job flow control by computers is much more sensitive than c~,mputer aided phmning systems, An aatomated on-line control for workshops just smooths over bad states. The real trouble spots, for example an unsuitable organization, will not be investigated, Man only produces data and cannot recognize the effect. He feels he is being controlled by a machine. This is not al ~111 stimulating for him. But tobusmess demands that 'unknown' eanl~t)t
0 3 7 7 - 2 2 1 7 / 8 2 / 0 0 0 0 - 0 0 0 0 / $ 0 2 . 7 5 ~ 1982 North-Holland
R. G't*n,~er/ Af¢lttttena,l¢¢,pht~tninx m ratl Irlln.~p~lrtatlon be declared 'certain'. It would be a mistake to transform fuzzy information to deterministic valties, which cannot be adapted to real events as the process develops. The time available for phmning shoulLI be used as far as possible for evaluating the feedback front the real world. Uncertainty shoe~ld be reduced step by step, This demand is caused by technical, economic, and human limitations. It is a belief, which can be found in many organizations. hut it is an error, to assume that complex planning can be done by single step final decisions. Ev~,:ry stage of planning needs a freedom of decisi~ns dependent upon the pkmning horizon. M;tchines can handle vast amounts of information tiuickly and reliably, but man is more fle:dble. The best solution will be to have a system in tvhich the nlachine relieves Inafl of routine work, but where m:ul can make decisions which have regard to real liie In safety related systems it is not so important to reach the theoretical optimum a!, frequently as possible as to be fail-safe any time. It is obvious that increas'~ng the categories of ob.icclive~to be considered and extending tbc range of planning also increases the complexity of methods and the effort of implcrneiltation. This is the reason that only advanced railway administrations are using the technology available to any great extent, see for example [ 15,21,25,32,37.511. in many production plants and workshops job scheduling and capacity planning are earl'led out by computers with well-,known sot'tware package. Of course these are also used in railways in track construction, see for example [23.2,~.]. or in subsystenls for maintenance as described in [35,66.64,25.63,18]. But for general application the railways had to Ioo!.. for new appn~aches to solve their problems.
2. Maintenance planning
Maintenance comprises: inspection, servicing, overhaul. replacement, modification, and repair. The effects of disrupting high-speed heavy traffic train operation are far-reaehingt therefore the prevention of faults is of the greatest intpor-
tahoe. Preventiee maintenance is well developed as a result of long experience, as shown in [14.30.34], and is also required by regulations, see for example 15.331. The need for better productive use of capital investment and reduction of manpower has forced inspection periods to be extended. This is possible without a fall in reliability standards if fixed time periods are replaced by inspection perio~ls, depending on the working time of facilities or running distance of rolling stock, see [22,59]. But also intensive fault and equipment data analysis can result in longer inspection intervals as shown in [1.50,58,59,371. It was found that more li'~quent dismantling for inspection can reduce equipment reliability. Strict review of inspection data and the analy,~ib, of important equipment functions allow ~\~r the mainlenance of snme components lifter breakdown. Muinterance.free components arc ,ncreasingly used. With facilities for complex data processing, steps can be taken to introduce t'orrt,ttitle maintenance, see [29.4J. But if the design of equipment is not adapted to this strategy then preventive maintenance results in better availability than corrective maintenance [Iq]. Maintenance planning for fixed installations. see [4.23.29,19,20.38]. is easier and less sensitive than workshop planning for vehicles. Thi~ is not only due to mobility, but is also caused by tile compactness of this construction, being packed with many different functional and very expel~,,~ive pieces of equipment, which need much attention in the workshop, and the necessity to take the vehicle out of use as a whole for a relatively long time. It is not possible only to decrease performance during overhaul as is practicable in the case of track. Railways have a great incentive to ensure that at least traction units are called in for mainlenance according to the mileage reached during operation. This is due to the investment, which is idle during maintenance, and because of high costs in the workshop, compared to wagons. But the runningdistance reached during operation depend,~ on traction unit allocation. Also the complexity o~'job flow scheduling in the workshop itself makes manuul planning very difficult, Without the help of computers long-term planning can be done only in a very simplified and non-informative way.
It (h.n.~er/ Ma#ttemmo, planning m t~lil tran~portat¢on 3. C o m p u t e r aided planning system for w o r k s h o p s
As mentioned already, the greatest effects can be achieved by improving the maintenance of vehicles done in workshops, Because of the conditions found in a railway system, the solution has to be a planning system with information feedback. An automatic on-line control is only suitable at lower levels and for subsystems, as for example in workshops in the case of numerically-controlled machines, automatic test facilities, etc.. Figure 1 gives an outline of a planning system for railway workshops. The structure contains time-dependent and functional decomposition for the purpose of a step by step implementation. The steps are not only needed in case of limited personnel capacity, but they are also used for gaining a learning feedback during implementation, Well defined interfaces inside the system and structured and well documentczd programming should allow adaptation to future developments.
SIMULATION ,~J
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OP I~RAT IOhlA t~ DATA
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Fig, I, Computer aided planning sy~lenl for workshops,
In the long range planning stage, simulation method~ or operations research approach*.:s are used for non-routine special investigation~, llke evaluating the effects of improving workshop capacity, or determining optimal intervals for inspection, as shown in [27.46.12.7.49.20,38,41, 42,43.45], ete,, Simulation model,'; are described in I I 1,13.54,56,65t. Steinbock [541 has modelled the repair of traclion motors by considering every facility in the workshop, using the simulalion hm. guage GPSS for investigating bottlenecks and I\~r evaluating the ,:ffects of changes in capacity. In [541 the probabdistic behaviour of arrival rz~tcs is also investigated in detail. Generally failure rates correspond to a Weihull distribution for early fadures and normal operation interval especially lot fatigue and wearing out. see 17,8,38,./.0A6.49.53.62]. The e:q:~onentlal distribution is appropriate Io normal operati~m inlerval and overload or human errors. The n~r. real distribution can be used for late failures and corrosion and wearing out. As far as possible Markovian models are used. see 113281. The feedback of inlk~rmation is given by the system for opt'rutional data analrsis, More and more. such data are obtained b~ diagnostic facilities, sec for example 12.3.31.37,39A4,47.52, 53.55,57.60.61 l, which arc set up: on the vehicle, - at the depot or along the line. or . in the repair shop. The extension of automation in train operatitnl has led to an increase in the use of on-line diagnosis. Tl~e impact of micro..computers permits data to be acquired at the source of any trouble and opens up quite new possibilities for informatkm processing, see [57,61]. It can be said that the development of diagnostic systems is just beginning. Methods Ik~r data analysis include regression. correlation, filtering, see [9] for an example, idemification, a survey is giwm in [26]. and pattern recognition techniques us shown by Pau ~48J, a:, well as algorithms for testing stali~tlcal hypotheses and reliability evaluations. :¢c for example I 16.6,7.8.40,62], etc.. The information gained by an operational dat:t analysis system is used as an inptil to the planning system for decision making, fi)r inlplenlemirL~aclion, or for control purposes.
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The maintenance planning system is intended for long range scheduling and i~'~r deciding the desired operational use of vehicle~,, considering the most important workshop capacities, as pointed out on Fig. 2, The final decision is made by man, If the rostering of traction units is not solved in a computer aided manner, as indicated on Fig. 2, then the internally deterministic value for the run. ning-distance desired is made fuzzy by the ranges: 'much more. 'more'. "same', 'less'. and 'much less". for defining the desired allocation of units. The range is evaluated by considering the allowable inaxinlunl and minimum runnlng-distance of the class OI' vehicle at the depot to which it is attached and the running-distance R t, reached in the past month, see Fig. 3. In practice, a man can deal with inforl'nation expressed, for example, as "more'. But if he were to be told just the value "21375 kin" he would be forced to make time consuming ealculalions.
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I'-U;t~y data arise in a decision process if at one stage of this process not everything is yet known. or it is ,lot desired to pin data down because sometimes the objectives influencing the deci,qon are changing with the increase of information as the process develops. Fuzzy sets allow a firm commitment to be postponed until the last decision stage. If fuzzy sets are Iransformed into probabilities then t:le decision has already been made. In lhe case of fuzziness one can expect something between some boundaries, for example, but nothing more, There is no probability distribution for it, only the possibility of the event occurring between the boundaries. Because of the built-in feedback, the actual value reached by the vehicle updates the original plan. as shown in [10]. Step by step the deciston range is reduced. The aim is to keep the planned dates for delivery of vehicles to the workshop as far as possible constant. Highest priority is given to externally fixed dates and dates not far ahead. A heuristic method is use~ to try to smoolh capacity load in the workshops or at least to keep the load below limits. On the other hand the operation
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job is available, Because of the co~llplex conditions and flexibility needed at ruilway workshops a list processing procedure (sneh approach can be ft )nlld in 136D is more adequate than a muthemalical approach. The w o r k s h o p capa('i O, data models time loading of the workshop. ('omplex conditiuns can he attached not only to the network elements, hUl also to eupacities, like truck o r ~;hunting conditions. etc.. An exerpt of the loading code. used by tile Austrian Federal P,aihvays, can be ~ccn in Table I. In short range phlntting only man is involved. Therefore tile experience of nlan cuu I'R2 LlSetl, which is also stimulating for hint to find gooA solutions inside the medium range solution grid. ()nly in the case of deviations, which cannot be snloothcd in the short time range, does tile job
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tBl,mk) Ranisbelastungsarl; IlehtMun~ durch Itt'd;lrl'Sltl~lllk~c As can be seen in Fig. 1. medium range workshop planning consists of the Ik)llowing functional systems: single network plannh~g, - gathering of work,~:hop capacity data. and job scheduling. Rostering and inventory c o n t r o l are functional[~ dedicated systems, as well as ol!+er subsystems like budgeting, personnel system, etc., For job scheduling the results of maintenance planning are used as input data, which call up the single networks needed, which have been evaluated once btfforehand. Taking into consideration the capacity data. u solution is looked for inside the given limits. By using the maintenance planning information the range of variation is much reduced, which makes the solution process much easier. Man has the opportunity to make the final decision, The planning horizon can be more than one year. but three months is used for planning during the year, The results are also tran:~mitted to inventory control A feedback to job scheduling is given if the targets cannot be achieved. The single n e t w o r k s correspond to the theoretically shortest duration of the job in the workshop on the assumption that all capacity needed for this
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R, Gt'nser / Muinwnanee planning in rud tran~partatilm scheduling of the m e d i u m range have to be called up. If m a i n t e n a n c e of e q u i p m e n t is d o n e in workshops at fixed t i m e intervals, as v, a s usually d o n e fc, r w a g o n s w h e n there was no m o d e r n w a g o n i n f o r m a t i o n s y s t e m available, then the m e t h o d s are more simplified.
4. Conclusion It has to be r e c o g n i z e d that suitable solutions for such c o m p l e x problems, w h e r e even the self. organizing b e h a v i o u r of m a n has to be considered, c a n n o t be found by t e c h n o c r a t s a n d bureaucrats. It is a task w h i c h needs an e n g i n e e r i n g approach. Simple a l g o r i t h m s should be preh,'rred that give insight into the p r o b l e m and that p r e s e n t a collection of solutions rather than just o n e a b s t r a c t value, where m u c h i n f o r m a t i o n is lost. In nearly all railways w h i c h use a d v a n c e d technology, a tendency to fewer faults a n d a c c i d e n t s d u r i n g ope r a t i o n c a n be found. B u t new t e c h n o l o g y is just e n t e r i n g the field o f m a i n t e n a n c e in railways.
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