TWO DIAGNOSTIC MODELS FOR PLC CONTROLLED FLEXIBLE MANUFACTURING SYSTEMS. W. HU*, A. G. STARR* and A. Y. T. LEUNG*

Transcription

1 TWO DIAGNOSTIC MODELS FOR PLC CONTROLLED FLEXIBLE MANUFACTURING SYSTEMS W. HU*, A. G. STARR* and A. Y. T. LEUNG* * Manchester School of Engneerng, The Unversty of Manchester, Manchester M13 9PL, UK To whom correspondence should be addressed Abstract: The control of flexble manufacturng systems (FMS s) s generally charactersed by logcal and sequental functons under the auspces of a programmable logc controller (PLC). Operatonal faults assocated wth control processes are often confusng to mantenance personnel at workshop level. Ths has resulted n the development of automatc dagnoss technques. In ths paper two generc dagnostc models based on the logcal functon chart and sequental control process of the PLC are developed. Wth the two complementary models, the major operatonal faults of PLC controlled FMS s can be dagnosed. Applcaton of the models to a typcal FMS s presented. Keywords: Dscrete dagnoss, PLC, FMS, Logcal dagnoss, Sequental dagnoss 1. INTRODUCTION Programmable Logc Controllers (PLC s) are used by most Flexble Manufacturng Systems (FMS s) as ther process controllers because they are adaptable, modular, user-frendly and acqured at low cost. For a sophstcated FMS, however, the PLC s capablty n fault detecton s lmted because of ts nflexble programmng system [1]. The PLC s also a relatvely smple devce, and has no nherent archtecture for fault dagnoss other than of ts 1

2 own functons and hardware. Therefore, t s necessary to develop effectve methods or models to assst the mantenance personnel n dagnostcs. Lke that of an ordnary FMS, the faults of a PLC controlled FMS usually nclude structural faults, operatonal faults, condton faults or abnormaltes, and NC faults. Among these faults, operatonal faults occur most often (about 70%). These manly appear through the I/O of the PLC n a FMS [2]. Here operatonal faults refer to those assocated wth PLC controlled actons [3]. Typcally, when a fault occurs, about 80% of downtme s spent locatng ts source and only about 20% s spent on the repar [4]. Thus, the dagnoss of operatonal faults s a major problem for mantenance personnel at workshop level. The operaton of a FMS s a complex process wth multple operatng condtons. At any tme, several actuators may act smultaneously. When one acton s out of synchronsaton, not only the next acton but also actons of other components wll be affected [5]. Hence, n usng the I/O dsplay of the PLC to dagnose a fault, mantenance personnel are requred to be very famlar wth the transformaton relatonshp between the operatng process and the sgnals from the machne n order to locate the fault accurately. In other words, to dagnose operatonal faults, the use of smple and drect I/O dsplay from the PLC s serously nsuffcent. Varous fault dagnoss methods and models for manufacturng systems have been reported n recent years. Kuo modelled the fault dagnoss behavour of a FMS usng the Coloured Tmed Petr Net (CTPN). The models depend on the measured data from the nspecton machnes and the sensor data from the devces [6]. Toguyen proposed two reasonng mechansms for the mplementaton of an on-lne dagnostc system for FMS s,.e., temporal reasonng and hypothetcal reasonng. These mechansms are based on a dstrbuted processng of symptoms that enables the problems of real-tme constrants to be solved [7]. Ye developed a hybrd ntellgent system that ntegrated neural networks wth a procedural decson-makng 2

3 algorthm to hypothess-test cycles of fault dagnoss n advanced manufacturng systems [8]. McFarlane proposed a generc methodology for self-dagnoss wthn a holonc manufacturng system, n whch faults and problems are analysed as a standard functon of the system [9]. Grault explored an approach for a model-based dagnoss of dscrete event dynamc systems such as manufacturng plants. The prncple of ths approach s to use lnear logc n order to elaborate adductve reasonng above a Petr net based deep model of the behavour of the manufacturng system [10]. These methods, models or systems have been mplemented n the laboratory and some of them have been appled to practcal manufacturng systems. However, no dagnoss method, model or system has been reported to drectly depend on the nternal sgnals n the controller, n partcular n a PLC controlled FMS. Ths should dagnose the fault by modellng both the PLC operaton and the human way of thnkng n the dagnostc process. Operatonal faults occur most often n a PLC controlled FMS and are the major cause of down tme. Therefore, research on dagnoss models for PLC controlled FMS s s necessary. The objectve of the development of the models reported here s to address operatonal fault dagnoss of PLC controlled FMS s. They provde effectve methods for mantenance personnel at workshop level to dentfy, classfy, and correct operatonal faults occurrng n producton. Such models wll also help to rase the effcency and accuracy of dagnoss usng PLC data. The models make full use of the powerful I/O capacty of the PLC and varous control sgnals avalable. The followng two dagnoss models are put forward to meet ths need. A logcal dagnoss model based on the PLC logcal functon chart (LFC). The fault s sought accordng to the logcal transformaton relatonshp between the sgnals from the PLC. Ths s nherent n the LFC, and dentfes the faulty sgnal status. 3

4 A sequental dagnoss model based on the PLC sequental control process (SCP). Ths method of dagnoss traces the executon of the sequental control commands and dentfes the step n whch the fault occurs, so as to locate the unt. 2. PLC CONTROL IN A FMS A FMS s a complex manufacturng system. It s normally composed of a tool system, several automated producton systems, a materal transportaton system, several load/unload statons, a computer control and management system, and an nterface. All these components are nterdependent, nterrelated and nteractve. They carry out dfferent tasks respectvely so as to complete the functons of the entre system. The layout of an exstng FFS FMS n Zhengzhou Textle Machnery Plant s shown n Fgure 1. The control system, machne and process data contan the most mportant nformaton because they are closely related to operatonal fault dagnoss. In a FMS, the PLC drectly montors and controls the logcal and sequental operaton of the entre system, whch s usually modelled for the purpose of dagnoss. The hardware of PLC control s normally n two or three levels. For the FFS FMS, t s as shown n Fgure 2. The system level controller,.e. the man computer, comples the part programs nto object code. It s also responsble for management, control, reports and tables, and recordng hstorcal data. The md-level PLC dstrbutes the data from the man computer to cell level controllers, and co-ordnates the operaton of all PLC s at the cell level. The cell level controllers, n ths case the PFZ1500 PLC, KBNG85 PLC and AGV PLC, perform the I/O functons of the entre system. A PLC typcally executes the followng algorthm [3,11]: For (ever) { Read all PLC nputs; Evaluate the PLC program; Set all PLC outputs; } 4

5 The PLC controls the FMS operaton accordng to ts user program, whch s embedded n the controller. The user program defnes a seres of operatons n the FMS, ncludng logcal and sequental operatons. When a fault occurs, the current status of all sensors or actuators s saved as an array of nput or flag sgnals n the PLC memory. Therefore, the program s the bass of the dagnoss models for operatonal faults n a PLC controlled manufacturng system. 3. LOGICAL DIAGNOSIS MODEL When dagnosng a PLC controlled FMS, mantenance personnel often focus on the LFC of the unts that compose the machne, and trace the faulty output along t [4]. Ths method of dagnoss s based upon the concept of the machne as a transformer of power, nformaton and materal: the effect of a faulty unt s propagated wth the LFC [12]. Therefore the LFCbased dagnoss model whch we call a logcal dagnoss model wll be effectve for locatng FMS faults. 3.1 Constructon of the logcal dagnoss model In the logcal dagnoss model, all varables assocated wth the LFC are descrbed n a bnary form. These bnary varables nclude all the sgnals n the PLC, such as nput sgnals, output sgnals, flag sgnals, tmer sgnals, counter sgnals, and data sgnals. The logcal dagnoss model s constructed from these varables n accordance wth the LFC. The detaled algorthm s as follows. We assume that S(x) s the state functon of an operatng state sgnal of the machne. S(x) = 1 means that the operaton assocated wth S(x) s on, whle S(x) = 0 means that the operaton assocated wth S(x) s off. {s k } denotes the combnaton of several PLC sgnals connected by 5

6 a logcal AND whch s wrtten as n model expressons, and ( {s k }) s the state of {s k }. Then we have { s } s s (1) k 1 2 s k In practce, the logcal expresson of S(x) s gven by a sgnal decomposton accordng to the LFC. Therefore, f we defne j ({s k }) as the -th term of S(x) at the j-th level, then the result of the decomposton of S(x) at the frst level s S ( x) 11({ sk1}) 12 ({ sk 2}) 1 ({ sk}) (2) where 1 ({s k }) s a factor that makes S(x)=1. It may be a PLC sgnal or the combnaton of several sgnals connected by logcal AND. Except for the terms expressed by nput sgnals or flag sgnals that cannot or need not be decomposed, other terms usually can be decomposed further accordng to the LFC. The decomposton of these terms s also the decomposton of S(x) at the second level,.e. 1 ({ s k}) 21({ sk1}) 22 ({ sk 2}) 2 ({ sk}) (3) The decomposton can proceed level by level n the same form tll the n-th level s reached where all the terms are expressed by nput sgnals or non-decomposable flag sgnals,.e. ( n1) ({ s k}) n1({ sk1}) n2 ({ sk 2}) n ({ sk}) (4) So far, substtutng the decomposton expresson at every level nto that at ts hgher level, we have ({ s n ( n1) ({ s 2 ({ s 1 k }) ({ s k k }) }) k }) ( n1) ({ s 1 S( x) ( n2) ({ s k ({ s }) k }) k }) (5) In the end we get a non-decomposable and mnmsed logcal expresson of S(x),.e. 6

7 S ( x) 1({ sk}) 2 ({ sk}) ({ sk}) (6) where ({s k }) s also a factor that makes S(x) = 1 and s composed of nput sgnals or nondecomposable flag sgnals, ( {s k }). Now, let F(x) be the fault state functon of the machne. F(x) = 1 means that a fault has occurred, whle F(x) = 0 means that there s no fault. If F(x) equals S(x), all the fault terms that make S(x) = 1 can be determned, whch are expressed as f 1 ({s k1 }), f 2 ({s k2 }),, respectvely. These terms are fnally used to construct a logcal fault state expresson of the machne, that s F ( x) f1({ sk1}) f 2 ({ sk 2}) f ({ sk}) (7) If F(x) equals the nverse state of S(x), the frst step wll be to extract the logcal expresson of the nverse of S(x). Each term of the expresson s a combned pattern of causes of the machne fault,.e. ({ sk}) f1({ sk1}) f 2({ sk }) S x) f ({ s }) (8) ( 2 Thus, the logcal expresson at the faulty state of the machne s obtaned as follows. F ( x) S( x) f ({ s k }) (9) 3.2 Example The FFS FMS uses a SIEMENS U Seres PLC. The SIEMENS U Seres PLC has sgnals as shown below: k E A M T C D nputs outputs flags tmers counters data 7

8 Each tem n the logcal expressons above s a sngle sgnal or the combnaton of several sgnals va a logcal AND. In a LFC, the logcal relatonshps between sgnals are expressed as n Fgure 3. Fgure 4 s a LFC descrbng the start condtons of the NC (Numercal Control) system n a FFS FMS whch s nstalled at Zhengzhou Textle Machnery Plant n Chna. In Fgure 1, M132.4 s the flag sgnal ndcatng the start condton of the NC system. If we defne Xm.n as the nverse state of Xm.n, accordng to the logcal dagnoss model, M132.4 can be decomposed as follows. M134.2 M132.0 M132.3 N1 M21.2 M22.2 M23..2 M129.3 E7.1 E7.3 E7.5 M132.0 A9.6 A18.4 E19.0 E23.1 M144.1 E20.6 N1 E30.5 N2 N2 M133.2 E30.2 where + denotes logcal OR. After beng smplfed, S( x) M132.4 A9.6 A18.4 E19.0 E23.1 M144.1 E20.6 M132.3 (E30.5 M133.2 E30.2) M21.2 M22.2 M23.2 M129.3 E7.1 E7.3 E7.5 The frst possble fault s that the NC start condtons are not satsfed, n whch case the state functon S(x) = M132.4 = 0. From the above expresson, the logcal expresson at a faulty state,.e. F(x), can be obtaned as F( x) A9.6 A18.4 E19.0 E23.1 M144.1 E20.6 M132.3 E30.5 M133.2 E30.5 E32.2 M21.2 M22.2 M23.2 M129.3 E7.1 E7.3 E7.5 The components related to the terms that make F(x) = 1, are the potental fault locatons. 3.3 Dagnostc procedure usng the logcal model In the logcal dagnoss model each term of the logcal expresson at a fault state represents a possble combned sgnal pattern of causes. The next step s to analyse all the possble 8

9 combned patterns, untl an nput sgnal, or a non-decomposable flag sgnal, s found whch causes the fault. Consequently the fault s located. The detaled dagnostc procedure s as follows: Compare the faulty state of S(x) wth the current state of sgnals n PLC: f they are the same, then t s concluded that a fault has occurred; Establsh a logcal equaton about the faulty state,.e. F ( x) f ({ s k }) 1 (10) Substtute the actual state values of sgnals n the PLC nto the above equaton and calculate f any term f ({s k }) = 1; Acqure the combned pattern correspondng to the term f ({s k }) = 1. The pattern shows the exact cause of the fault. For comprehensve coverage of fault states we must make sure that the combned patterns n the logcal expresson cover all the possble fault causes, and are ndependent to each other. 4. SEQUENTIAL DIAGNOSIS MODEL The LFC-based logcal dagnoss model s a statc model and cannot descrbe the dynamc change of the operatng state of a FMS. However, many processes n a FMS, such as gear and tool exchange, are controlled sequentally. In other words, some actons must be done after some others have taken place. If the frst acton doesn t fnsh, then the second wll not start. There are many logcal relatonshps defnng chans and prohbton [13]. The sequental control s performed by a seres of sequental commands. These sequental commands lead to a dynamc change of the machne operatng state. For the dagnoss of a sequentally controlled FMS, the LFC-based logcal dagnoss model s obvously not sutable. Thus a SCP-based dagnoss model whch we call a sequental dagnoss model, s ntroduced. The 9

10 prncple of ths model s dentcal to the sequence of state changes nduced by the nput commands, so t s effectve for locatng the faults of sequentally controlled machnes. The sequentally controlled machne usually conssts of a number of functonal unts, and each command actvates a specfed unt. Therefore, f we trace the executons of the sequence of commands, and dentfy the step n the control sequence where a fault occurs, we can locate the faulty unt. In general, faults of the sequentally controlled FMS or ts processes are classfed nto three categores: control sgnal fault, sensor sgnal fault and actuatng mechansm fault [14]. For the frst two, we assume stuck-at-1 and stuck-at-0 faults, and for the latter, we assume sezures and mechancal open faults. Here we do not deal wth faults of the PLC tself. 4.1 Constructon of the sequental dagnoss model The sequental control of a PLC controlled FMS s usually performed by a sequencer. Sequental control conssts of a seres of acton steps. Each step contans a number of control commands and condtons on whch the next step can be started. The ntal condtons are used to trgger the frst step, whch depends on the specfc stuatons about desgn and applcaton. A SCP-based sequental dagnoss model conssts of a certan number of machne states and state changes n tme sequence. It descrbes the sequental changes of the machne operatng states. The acton n a step s not only related to the control commands n ths step, but also related to the condtons n the prevous step. The current step can only be started under the condton that the prevous step has fnshed and the current control commands have been receved. Whether a step s fnshed or not s decded accordng to ts step condtons. So, the sequental dagnoss model can be constructed as follows. 10

11 We assume that C(t) s the combned state of all the step condtons n the t-th step. Snce each condton s normally a PLC sgnal, marked by c 1 (t), c 2 (t),, thus C ( t) c1 ( t) c2 ( t) c j ( t) (11) C(t) = 1 ndcates the step condtons are satsfed and the next step can be started. C(t) = 0 ndcates the condtons are not satsfed and the acton sequence cannot be carred out. Smlarly, the step condtons of the prevous step are expressed by C ( t 1) c1 ( t 1) c2 ( t 1) c j ( t 1) (12) Now we can let I(t) be the combned state of all the control commands n the t-th step. Every control command s also a PLC sgnal, marked by 1 (t), 2 (t),, thus j j I ( t) 1 ( t) 2 ( t) j ( t) (13) I(t) = 1 ndcates the commands are receved whle I(t) = 0 ndcates not receved. As mentoned above, we know that a fault at the t-th step s ether because the control commands are not receved, or because the commands are receved but the prevous acton has not fnshed. So, f we let F(t) be the state of the step, F(t) = 1 ndcates that the step s faulty. In the case where a fault exsts, t s possble that F( t) C( t 1) I ( t) (14) When F(t) = 1, C(t-1) = 1 and I(t) = 0 the prevous step has fnshed and current step started, but the control commands have not been receved. From the expresson j I t) ( t) ( t) ( t) ( t) 1 (15) ( j 1 2 j j j the exact command that s not receved can be found. It s also possble that F( t) I ( t) C( t) (16) 11

12 When F(t) = 1, I(t) = 1 and C(t) = 0 the current control commands have been receved, but the acton has not fnshed. Lke the frst case, the followng expresson determnes the exact condton whch s not satsfed, allowng fault locaton: C t) c ( t) c ( t) c ( t) c ( t) 1 (17) ( j 1 2 j j j 4.2 Example The SIEMENS U Seres PLC uses the programmng language STEP 5. A PLC program coded wth STEP 5 s dvded nto the followng blocks: OB PB SB FB DB organsaton block program block step block functon block data block The LFC n the logcal dagnoss model s usually programmed n the PB, whle the sequental control s carred out n the SB. Each SB contans a machne operaton command. Several SB s form a control sequence by lnkng together n a specfed order. The operaton of the tool-head exchange n the FFS FMS s shown here as an example to explan the sequental dagnoss model. The tool-head can be attached to the spndle so as to change the feed drecton of tools. The tool-head s exchanged frequently, and varous faults may occur n the process. The tool-head exchange sequence programmed n the SB s descrbed n Table 1, whch ncludes a seres of actons, from removng the old tool head to fttng a new one. Now let us consder the frst two steps, SB117 and SB118, and assume SB118 s the current step. The start condton of SB118 s that SB117 s fnshed. The programs of these two steps are shown as STEP 5 code n table 2. From SB117 we know that the start condtons of the current acton (SB118) are: 12

13 C ( t 1) M143.0 M143.1 M160.1 M Ths dentfes that: the nternal coolng ol s stopped (M143.0=1); the spndle blower s turned off (M143.1=1); step runnng s enabled (M160.1=1); automatc operaton of tool-head magazne s enabled (M165.0=1). From SB118 we can see that the control command of ths step s: I ( t) M227.4 The start condtons of the next step (SB119) are: C ( t) M159.0 M158.4 M165.2 M134.5 E18.4 E18.5 E Ths dentfes that: the mddle varables are cleared (M159.0=1); Z-axs s at ts reference poston (M158.4=0); the tool-head magazne has moved to ts rght poston (MM165.2=1); the spndle has been orented (M134.5=1); the protecton door for tool exchange manpulator s open (E18.4=1 and E18.5=0); the C-axs has returned to ts reference poston (E38.2=0). These condtons are also used to determne whether SB118 s fnshed. If C(t-1)=1 and I(t)=0, then the control command of step 2 s receved. When I(t)=1 and C(t)=0, the current step s not fnshed. From the expresson below we know that the components assocated wth the terms that make C(t)=1, are the potental fault locatons: C ( t) M159.0 M158.4 M165.2 M134.5 E18.4 E18.5 E Dagnostc procedure usng the sequental model 13

14 Under normal operatng condtons, the PLC controls the FMS accordng to a predefned sequence of actons. At the same tme, each step n the control sequence s montored by the watch dog tmer n the PLC. If the machne s normal, t wll operate sequentally accordng to the preset sequence. The occurrence of a fault may be ndcated by excessve tme spent at a control status durng an acton. When a sequental control fault s detected, dagnoss s carred out usng the sequental dagnoss model. At frst the current values of all the sgnals n the PLC wll be read. The start condtons of every step are analysed accordng to these values, n conjuncton wth the control sequence. By dong so, the step where a fault has occurred can be determned. Each control command and fault step condton s checked, untl the exact fault s located. 5. CONCLUSIONS The logcal dagnoss model descrbes the propagaton of the effects of faults through a machne system and ts components. The algorthm, n whch causes of the fault are dentfed wth the LFC, models the human approach to the dagnostc process. The LFC s a statc model and cannot represent the dynamc change of the machne operatng state. However, the sequental dagnoss model can descrbe such a seres of state changes, and can be used to dentfy the step n the operatng sequence where the fault occurs and the precse cause. The logcal dagnoss model and the sequental dagnoss model are complementary. Frst a faulty step n the control sequence s dentfed usng the sequental dagnoss model. Commands ssued n each step actvate unts of the machne. Further dagnostc procedures can be performed usng the logcal dagnoss model, whch corresponds to the unt. The logcal dagnoss model and sequental dagnoss model have been successfully used n an exstng automatc dagnoss system whch the author developed for a FFS FMS at 14

15 Zhengzhou Textle Machnery Plant n Chna. The system has been runnng for over a year at the tme of wrtng. The results show that the drect benefts le n the followng areas. An approxmate 80% ncrease n the effcency and accuracy of dagnosng a fault has led to a reducton n down tme. The requrement on techncal mantenance personnel s greatly reduced. Operators are able to handle most problems wth the help of the automatc dagnoss system. The cost of tranng mantenance personnel has been reduced. The machne avalablty has been mproved, hence the effcency of the FMS has been enhanced. The dagnoss models were developed for a SIEMENS U Seres PLC-controlled FMS. They can also be appled drectly to other manufacturng systems controlled by the same seres PLC. The control mechansms of PLC s (logcal and sequental) are genercally smlar even f they have dfferent programmng languages and methods. The models are theoretcally sutable for other PLC controlled manufacturng systems. The models have been ntegrated nto a dstrbuted fault dagnoss system for 21 automated producton lnes n a large desel engne manufacturng factory n Chna. The modellng of a complex manufacturng system s always labour ntensve. Fault dagnoss s not usually carred out n real tme. In addton, the sgnals from the PLC are lmted and there s no dagnostc knowledge avalable for more accurate dagnoss. The future work should tackle the followng problems: The desgn and mplementaton of a new generaton of knowledge-based dagnoss tools that can contnuously acqure data from the PLC, dentfy possble faults, search for ther causes and suggest correctve actons. The establshment of an automatc modellng mechansm that can automatcally reproduce the functonal and operatonal logc embedded n the control logc of a PLC. 15

16 The defnton of an embedded dagnoss system approach whch wll ntegrate the models n the manufacturng system controllers (CNC/PLC), so that faults can be dagnosed n real tme. REFERENCES [1] Lee, J., 1995, Modern computer-aded mantenance of manufacturng equpment and systems: revew and perspectve, Computers Ind. Eng., 28(4), [2] Hu, W., 1995, Research on a Qualty-Control-Based Fault Dagnoss System n a Flexble Manufacturng Envronment. Wuhan: Ph.D. Dssertaton, Huazhong Unversty of Scence and Technology [3] Jarvs, J. and Jarvs, D Lfe Cycle Support for PLC Controlled Manufacturng Systems, Software Engneerng for Manufacturng Systems, ed. A. Storr and D. Jarvs, Chapman & Hall, London, pp [4] Kegg, R.L., 1984, On-lne machne and process dagnostcs, Annals of the CIRP, 32(2), [5] L, M., 1993, Dagnostc technques for PC faults n automatc assembly lne, Industral Control Computer, 2, [6] Kuo, C.H. and Huang, H.P., 1997, Colored tmed Petr net based statstcal process control and fault dagnoss to flexble manufacturng systems. Proceedngs of IEEE Internatonal Conference on Robotcs and Automaton, 4, pp [7] Toguyen, A.K.A, Craye, E., Gentna, J.C., 1996, Framework to desgn a dstrbuted dagnoss n FMS. Proceedngs of the IEEE Internatonal Conference on System, Man and Cybernetcs, 4, pp

17 [8] Ye, N. and Zhao, B., 1996, Hybrd ntellgent system for fault dagnoss of advanced manufacturng system. Internatonal Journal of Producton Research, 34, No.2 pp [9] McFarlane, D., Mareet, B, Elsley, G, Jarvs, D., 1995, Applcaton of holonc methodologes to problem dagnoss n a steel rod mll. Proceedngs of the IEEE Internatonal Conference on System, Man and Cybernetcs, 1, pp [10] Grault, F., Pradn-Chezalvel, B. Kunzle, L,A. and Valette, R., 1995, Lnear logc as a tool for reasonng on a Petr net model. IEEE Symposum on Emergng Technologes & Factory Automaton, 1, pp [11] Jarvs, J, and Jarvs, D., 1997, Desgn Recovery for PLC Controlled Manufacturng Systems, Preprnts of MM 97, Venna, pp [12] Alexander, S. M., Vada, C. M., Graham, J. H., 1993, Model for the dagnoss of CIM equpment, Computer-Elect. Eng., 2, pp [13] Zhu, S. J., Weng, Z., Deng, M. L., Zhou, Z. L., 1994, Programmable Control System - Prncple Applcaton - Mantenance, Tsnghua Unversty Press, Bejng [14] Takata, S. and Sata, T., 1986, Model referenced montorng and dagnoss - applcaton to the manufacturng system, Computers n Industry, 7, pp

18 Computer Control and Management System Interface Tool System Tool Magazne Tool-Head Magazne Tool Magazne PFZ1500 Flexble Manufacturng Cell KBNG85 Machnng Centre Automatcally Guded Vehcle (AGV) Load/Unload Staton 1 Load/Unload Staton 2 Load/Unload Staton 5 Fgure 1 FFS FMS System level Man Computer Md-level Md-PLC Cell level PFZ1500 PLC KBNG85 PLC AGV PLC Fgure 2 The three-level control of the FFS FMS 18

19 & >=1 a. AND b. OR a. NOT Fgure 3. Logcal Relatonshp between Sgnals A9.6 A18.4 E19.0 E23.1 M144.1 E20.6 & M132.0 M132.3 M132.2 E30.2 E30.5 & N2 >=1 M21.2 M22.2 M23.2 M129.3 E7.1 E7.3 E7.5 N1 & M132.4 Fgure 4. LFC of NC Start Condtons 19

20 Table 1. Operaton sequence of tool-head exchange Step SB Acton descrpton 1 SB117 Tool-head magazne moves to the poston of the old tool-head 2 SB118 Z-axs returns to the reference 3 SB119 Y-axs moves to the poston to exchange tool-head 4 SB121 Z-axs moves to the poston to exchange tool-head 5 SB122 Adapter looses and the old tool-head s put nto tool-head magazne 6 SB123 Z-axs returns to the reference 7 SB124 Tool-head magazne moves to the poston of new tool-head 8 SB125 Z-axs descends to the poston to pck the new tool-head 9 SB126 Adapter clamps the new tool-head 10 SB131 Z-axs returns to the reference 11 SB132 Y-axs returns to the machnng poston 12 SB133 Tool-head magazne returns to the reference 20

21 Table 2: Step programs SB117: SB118: 0000 := M :UN M :R M :R M :L DW :U M :T DW :S M :A DB :S M :O D :U M :O D :UN E :O D :S M :O D :U M :O D :UN M A :O D A :U M B :S M B :U M C :A DB C :U E D :L DW21 000D :UN E E :L KB63 000E :UN E F :!=F 000F BE 0010 :U M := M :U M :U M :U M :BE 21