may be developed by application of smart actuators equipped with sufficient set of sensors (e.g. set-point signal, pressure in servo-motor chamber, positioner.
APPLICATION OF INFORMATION SYSTEM THEORY FOR ACTUATORS DIAGNOSIS
Jan M.
Warsaw University of Technology, Faculty of Mechatronics Institute of Automatic Control and Robotics, Chodkiewicza 8, 02-525 Warszawa, Poland
Abstract: For actuator assembly consisting of: control valve, pneumatic linear servomotor and positioner the set of possible faults was defined. Fault related algorithms are given. For description of the faults-symptoms relations the information system theory was applied. The fault identification algorithms basing on the information system rules are given. Copyright © 2000 IFAC. Keywords: actuators, positioning systems, fault detection, fault isolation, information systems
1. INTRODUCTION Technological processes may be generally described in terms of controlling the energy and mass flows. For acting on this flows the control final elements are commonly used. Faults of final control elements (control valves, servo-motors, positioners) are appearing relatively often in the industrial practice. The final control elements are installed mainly in the technological nodes working in the harsh environment: high temperature, humidity, pollution, chemical solvents, etc. Control valves are controlling often aggressive, high temperature and high pressure media flows. This has the crucial influence on the predicted life of those devices. Harsh environmental and hard internal working conditions are causing final control elements' faults. Faulty devices or decreased actuators’ performance are leading in most cases to long term technological process disturbances and even in some cases are reasons of bringing the installation to standstill. Moreover, final control elements faults may influence the final product quality, that directly leads to economical losses. For fault prevention or prediction, the on-line diagnostics of final control elements should be applied. Continuously or periodically performed diagnosis of actuators leads to lowering the maintenance costs. The common industrial maintenance practice is the
periodical inspection of all devices independently of its real technical state. The devices are dismounted from installation and then tested on special inspection stands. In most cases this is not necessary, particularly if the device standing is sufficient enough. The repeated mounting of the dismounted device is even more complicated and costly than dismounting. The pipes' terminals must be for example once again axially adjusted. The introduction of remote on-line diagnostic of actuators may bring down the periodical inspection costs by 50-70%. In such a case, the inspections and repairing of the actuator are undertaken only if necessary. In the recent 20 years there were developed a numerous of process fault detection and isolation methods. The problem of actuator diagnosing was also considered. For fault detection and isolation many different approaches were used, for example: • parity equation (Massoumnia and Van der Velde, 1988; Mediavilla et. al. ,1997) • unknown input observer (Phatak and Viswanadham, 1988) • extended Kalman filter (Oehler R. et al., 1997) • signal analysis (Deibert R., 1994) • fuzzy logic ( ! "##$% The analysis of possible faults of assembly: control valve, pneumatic servo-motor was studied by
(Koj, 1998). There were also developed intelligent positioners supporting auto diagnostic functions (Isermann and Raab, 1993; Yang and Clarke,1997; Bayart and Staroswiecki, 1991; & '()*+,-. /-0
1 /23. (4 566789 :;+ 0+)'< ='>*3*'- '? 3;+ 0*/@-'>3*)
tasks in the complex systems and the concept of intelligent actuators providing diagnostic features 1 were also presented in papers (& '()*+,-. /-0 /23. (4 1997; Bouras and Staroswiecki, 1998).
2. ON-LINE AND OFF-LINE DIAGNOSIS OF ACTUATORS Diagnostics of actuators is performed either in on-line or off-line mode. On-line diagnostics is suitable for rapid detection of faults and signalling faults to supervisory systems. Diagnostic tests realised in this mode must not disturb the process. Thus, the diagnostic tests must to be ground principally on the available process values. In some cases the generation of low amplitude test sequences for on-line diagnosis is taken into account if the suspected process disturbances are negligible. In a case of classical actuators the number of available signals to be analysed for diagnostics purposes is very limited, what particularly make difficulties in fault isolation. The on-line diagnostics may be developed by application of smart actuators equipped with sufficient set of sensors (e.g. set-point signal, pressure in servo-motor chamber, positioner temperature, piston rod displacement, end position switches, etc.). Basing on such a set of signals and eventually on the medium flow signal, not only detection, but also fault isolation is possible. In the industrial practice the on-line diagnostic systems are not widely available. A commonly applied technique is the alarms’ signalling system integrated with acquisition and visualisation software. This technique although, does not ensure the quickly enough fault detection of final control elements. They are signalled only some alarm states as a result of fault occurrence. The sequences of alarms are difficult for unique interpretation by the human operator. A sequence of suddenly appearing huge number of alarms involves the phenomena of "operator information overload". Stress situations caused by non-standard alarm states disturbs deeply human decision making process. The operator, could even in such situation, generate additional faults or undertake faulty decisions. It is clear, that to overcome such dangerous situations diagnostic system that is capable of generating diagnosis sufficiently quick and precise enough must be developed. The implementation of the on-line diagnostics in modern actuators may be extended by applying more advanced algorithms compared to contemporary
available (e.g. tests of consistency of an set-point and process value of piston rod or overcoming the limits of piston cycles or piston total way). Forecasts shows that the spectrum of diagnostic functions of intelligent actuators will expand. This paper is related to this topic. Off-line diagnosis is performed by appropriate technical services. This kind of diagnosis is mainly performed periodically by the process standstill or being repaired. In off-line diagnosis the tests signal values applied are only limited by device technical specifications. Both closed and open loop investigations may be performed. Off-line diagnosis may be also done for verification of faults detected by applying on-line diagnosis.
3. THE SET OF FAULTS OF THE ASSEMBLY CONTROL VALVE- SERVO-MOTOR POSITIONER Very popular solution for many industrial applications is the final control assembly consisting of: control valve, diaphragm-spring pneumatic servo motor and positioner. Fig. 1. shows the diagram of assembly. There are taken into account following realistic assumptions that for diagnostic purposes they are available following signal values: U positioner setpoint signal, I - control current of electro-pneumatic transducer, P - pressure controlling pneumatic servo-motor, X – servo-motor piston rod displacement, F - media volume flow rate. In the actuator, faults could appear in: control valve, servo-motor, electro-pneumatic transducer, XT and PT transducers or microprocessor control unit. The auto diagnostic functions of the microprocessor control unit allows detection of its internal faults. This is the reason why control unit faults are not further considerated. There are distinguished following fault subsets: Control valve faults f1 - valve clogging f2 - valve or valve seat sedimentation f3 - valve or valve seat erosion f4 - increasing of valve or bushing friction (including stick-slip effects) f5 - external leakage (leaky bushing , covers, terminals) f6 - internal leakage (valve tightness) f7 - medium evaporation or critical flow Pneumatic servo-motor faults f8 - twisted servo-motor's piston rod f9 - servo-motor's housing or terminals tightness f10 - servo-motor's diaphragm perforation f11 - servo-motor's spring fault Positioner faults f12 - electro-pneumatic transducer fault (E/P) f13 - rod displacement sensor fault (DT) f14 - pressure sensor fault (PT)
Positioner P
E/P
I
CPU
MODEM
Ud
ACQ
D/A
Ua
A PT
X
DT
FT F
V V1
V3
V2
Fig.1. Diagram of the control valve-pneumatic servo-motor positioner assembly. Notations: A - pneumatic spring-diaphragm servo-motor V - control valve, V1,V2,V3- bypass valves CPU - positioner central processing unit ACQ - data acquisition unit, MODEM - system for digital communication D/A - digital-to-analogue converter General faults/external faults f15 - positioner supply pressure drop f16 - increase of pressure on valve inlet or pressure drop on valve output f17 - pressure drop on valve inlet or increase of pressure on valve output f18 - fully or partly opened bypass valves f19 - flow rate sensor fault (FT)
4. FAULT DETECTION ALGORITHMS For fault diagnosis of the final control element the U, I, P, X, F signals could be used. However the flow rate signal F in many practical cases is not available because of relative high cost of industrial flow meter instrumentation. So, two variants of diagnostics will be considered: first taking into account that the flow rate signal is not available and second assuming availability of signal F. The algorithms applied for actuator fault detection could be divided into two following groups:
Ud Ua E/P DT PT FT I P F
- digital communication link - analogue communication link - electro-pneumatic transducer - displacement transducer - pressure transducer - volume flow rate transducer - control current of E/P transducer - output pressure of the E/P transducer - volume flow rate signal
a) Algorithms, based on the relations between the signals. This relations could be expressed in analytical terms, look-up tables, or as neural or fuzzy models. The fault symptoms are detected on the grounds of the estimation of the difference between the real and modelled signals (residual generation). Assuming availability of the U, I, P, X, F signals the following residual set could be generated.
r1 = P − Pˆ ( I ) r = X − Xˆ ( P)
(1)
r3 = F − Fˆ ( X ) r = X − Xˆ ( I )
(3)
r5 = F − Fˆ ( P) r6 = F − Fˆ ( I ) r = I − Iˆ(U , X )
(5)
r8 = P − Pˆ (U , X ) r = X − Xˆ (U )
(8)
2
4
7
9
r10 = F − Fˆ (U )
(2)
(4)
(6) (7)
(9) (10)
b) Algorithms based on relatively simple heuristic relations between the measured data. They allowed detection of fault symptoms on the grounds of the signals and its derivatives conformity in the particular terminal states. Below, the examples of heuristic tests are given for normal closed control valve (increase of pressure P signal should open the valve). Index "o" denotes the nominal signal value assigned to state of starting valve opening; index "c" denotes nominal signal value assigned to state of valve closing; the indices "s" and "e" are denoting the start and end signal values after impulse disturbance on input. I = Io and P < Po - ∆Po
(11)
I = Io and P > Po + ∆Po
(12)
I = Ic and P < Pc - ∆Pc
(13)
I = Ic and P > Pc+ ∆Pc
(14)
X = Xc and F > 0 + ∆F
(15)
∆P > ∆Pmin and ∆X =0
(16)
P↑ and X¬↑ or P↓ and X¬↓
(17)
(18) In table 1 the set of heuristic tests is given Table 1. Heuristic tests of assembly: servo-motor, positioner, valve.
(17) (18)
Fault too low opening pressure too high opening pressure too low locking pressure too high locking pressure leakage of closed valve no piston displacement by pressure change discrepancy of pressure and displacement trends increase of system histeresis
(20)
Fault identification is performed based on the knowledge of the fault-diagnostic signals relations. This relations are defined by an expert on the grounds of his experience. The relation faults-diagnostic signals may be also described in terms of information system (Pawlak, 1991). Adaptation of such a system for diagnostic purposes was done by A BCDEFGHI J KLLMN OHPFQ RSF name of Fault Isolation System (FIS).
FIS = 〈 F , S ,V , R 〉
(21)
where: F- set of faults (objects), S- set of diagnostic signals (attributes), V- set of all diagnostic signals values defined as: (22) V = Vj
s j ∈S
R: F × S → r (V )
(23)
R-function defined on the Cartesian set F x S assigning to every pair of: fault-diagnostic signal , value or values possible by particular fault:
r ( f k , s j ) =V kj= {v ji ∈ V j } ⊂ V j
S ( f k ) = { V kj : j = 1,2,..., J
5. RELATIONS FAULTS-SYMPTOMS AS AN INFORMATION SYSTEM In the assembly: control valve servo-motor positioner a set of 19 primary faults (see section 3) was defined.
}
}
(24)
Thus FIS can be interpreted as a matrix defining the reference values of the diagnostic signals (symptoms) for particular faults. It is important, that for every diagnostic signal may exist an individual set of reference values. Though for example, the multiplevalued residuum evaluation is possible. Signature of k-th fault is defined by the set of possible diagnostic signals related to this fault:
This types of tests may be applied for any combination of measured signals.
F = { f k : k = 1,2,..., K
S = { s j : j= 1,2,..., J
FIS system is defined as the following quadruplet:
{Is=I;I:=Is+∆I;Ie :=I -∆I; Ie=I }∧ Xs - Xe >H+∆H
Equation (11) (12) (13) (14) (15) (16)
Tests outputs are treated as the diagnostic signals. Negative tests outputs are treated as fault symptoms. In that case the set of investigated diagnostic signals may be by described as follows:
(19)
}
(25)
The diagnostic analysis of final control element was done, using tests described in section 4. Minimal set of tests ensuring the same isolability as the set of all tests is given in table 2. In table 3 the FIS was given for assembly: control valve, servo-motor, positioner under assumption of the set of tests given in the table 2. Table 3 is the reduced form of information system, taking into account all possible tests. For the detection algorithms d1..d4 given in table 2 the tri-state residuals’ values evaluation approach was assumed.
Table 2. Minimal set of tests for actuator diagnosis. D d1
Test algorithms − 1 s1 = 0 +1
if r1 ≤ − K1 if r1 ∈ ( − K1 , K1 ) ; r1 = P − Pˆ ( I ) if r1 ≥ K1
d2
if r2 ≤ − K 2 − 1 s 2 = 0 if r2 ∈ ( − K 2 , K 2 ) ; r2 = X − Xˆ ( P) +1 if r2 ≥ K 2
d3
if r3 ≤ − K 3 − 1 s3 = 0 if r3 ∈ ( − K 3 , K 3 ) ; r3 = F − Fˆ ( X ) +1 if r3 ≥ K 3 if r4 ≤ − K 4 − 1 s 4 = 0 if r4 ∈ ( − K 4 , K 4 ) ; r4 = X − Xˆ ( I ) +1 if r4 ≥ K 4
d4
s4 = d5 d6
have at least for one diagnostic signal, the disjoint subsets of values. For analysed FIS this condition is fulfilled only for following faults (elementary blocks {f1}, {f10}, {f14}). Let us see, that if it is satisfied the condition that the signature of the one fault (elementary block) is subset of the signature of the other fault (elementary block), the isolability of this fault depends on the diagnostic signals achieved. For example the signature of fault S(f11)={0,+1,0,+1,0,0} is not contradictory with signature of faults f4, f8 but can be treated as its particular case (subset). If therefore will appear the diagnostic signal equal with signature S(f11), so the diagnosis should point out for the possibility of faults: f4, f8 or f11 . But if one of the symptoms s2 or s4 will be equal to -1, there is possible only fault f4 or f8. Fault signatures define the diagnostic inference rules. For instance, state of full system ability is related to the following rule:
1 if r4 ≤ K 4 ; r4 = X − Xˆ ( I )
1 if ∆P ≥ ∆PMIN and ∆X = 0 s5 = in opposite case 0 1 if X = X Z and F 〉 0 + δ s6 = in opposite case 0
If (s1 = 0) and (s2 = 0) and (s3 = 0) and (s4 = 0) and (s5 = 0) and (s6 = 0) then system able (26) For fault f1 the following rule is true:
The symptoms values generated by algorithms d5 and d6 are binary evaluated.
If (s1 =0) and (s2 = -1 or +1) and (s3 =0) and (s4 = (27) -1 or +1) and (s5 =1) and (s6 =0) then f1
6. FAULT ISOLATION
The general diagnosis form, in information system, by assumption of single faults, could be defined as follows:
The faults having the same signatures (identical FIS columns) are not isolable. In the analysed FIS there exist the following not isolable subsets (so called elementary blocks) {f3,f6}, {f4,f8}, {f5,f7,f17}, {f9,f15}, {f16,f18}.
DGN = { f k : ∃ v j ∈ r ( f k , s j )}
(28)
sj
Thus, diagnosis detects this faults, which signatures contain the combination of obtained diagnostic signals values.
Isolable are only faults or elementary blocks, which signatures compared to signatures of all other faults,
Table 3. FIS for assembly: control valve- servo-motor-positioner FS f1
f2
f3
f4
f5
f6
f7
f8
f9
f10
f11
f12
f13
f14
0
s1
0
0
0
0
0
0
0
0
-1
-1
0
s2
+1 -1 0
-1
0
0
0
0
-1
+1
+1
-1
+1
-1
+1 -1 0
0
-1
+1 -1 0
+1 -1 0
0
0
0
0
-1
0
0
0
-1
+1
0
0
0
0
+1 -1 0
-1
0
+1 -1 0
0
s5
+1 -1 1
0
0
0
+1 -1 0
s6
0
0
1
0
0
1
0
0
0
0
0
0
s3 s4
+1 -1 +1 -1 +11 0 1 0 1
f15
f16
f17
f18
f19
+1- -1 1 +1 0 -1 0 0
0
0
0
0
0
0
0
0
+1
-1
+1
0
-1
0
0
0
+1 -1 0
0 1 0
0
0
0
0
0
0
0
0
0
0 1
Examples of diagnoses: a)
if (s1 = -1 or +1) and (s2 =-1 or +1) and (s3 = 0) and (s4 = 0) and (s5 =0 or 1) and (s6 = 0) then DGN =f14 (29)
b) if (s1 = 0) and (s2 = 0) and (s3 = 0) and (s4 = 0) and (s5 = 0 or 1) and (s6 = 0) then DGN = {f5 , f7 , f17 , f19 }; what means faults f5 or f7 or f17 or f19 (30) c) if (s1=0) and (s2=0) and (s3 = -1) and (s4=0) and (s5 =0 ) and (s6 =1) then DGN = f19 (31) Examples b) and c) show described above relation between the FIS fault isolability and the test results achieved (values of the diagnostic signals). For the signal values from example b) fault f19 is not isolable from the other three faults, but for signal values from example c) the same fault is isolable. This is characteristic feature of multiple-valued residuum evaluation. In the binary evaluation approach it is possible to define synonymously the subsets of isolable faults. Multiple-valued residuum evaluation, which is possible in FIS system, allows to achieve better isolability.
7. SUMMARY For the actuator diagnosis the description of relation faults-symptoms the information system was applied. The information system describes the fault inference rules (fault signatures). Information system gives more general description of the diagnostic relation then classical binary matrix approach. The main advantage of this theory is the possibility of bi- or tri-state evaluation of symptoms depending on the detection algorithm applied. This give an assumption to apply for the diagnostic system either simple heuristic algorithms or model based algorithms. Presented above diagnostic algorithm for final control elements is useful for application in the on-line diagnostics performed in supervisory control and diagnosing systems as well as in the intelligent positioning units. To be sufficiently effective, the availability of the majority of considered measurements must be ensured. If this is not the case, then isolation quality appropriately decreases.
ACKNOWLEDGEMENTS This work has been supported partially by EU FP5 grant DAMADICS entitled:” Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems”.
REFERENCES Bayart M. and M. Staroswiecki (1991). Smart Actuators for Distributed Intelligent Systems. In: IFAC Symposium, Distributed Intelligent Systems, Arlington, USA. Bouras A. and M. Staroswiecki (1998). Building Distributed Architecture's by the Interconnection of Intelligent Instruments. In: IFAC INCOM '98, Nancy, France. Deibert R. (1994). Model Based Fault Detection of Valves in Flow Control Loops. In: IFAC Symposium SAFEPROCESS '94, Espoo, Finland 1994, pp. 445-450. Isermann R. and U. Raab (1993). Intelligent Actuators- Ways to Autonomous Actuating Systems. Automatica 29(5). Koj J. (1998).The Fault Causes of Pneumatic Servo motor-Control Valve Assembly. In: III Polish National Conference on "Diagnostics of Industrial Processes". Jurata, Poland, pp. 415419 (in polish).
T UVWXYZ[\ ]^_ ^ ` abbbc^ d effZXWghXU[ Ui jkll\ mUnXW
Fault Isolation in a Three-Tank System. In: 14th World Congress IFAC, Beijing, China, P-7e05-1, pp. 73-78.
T UVWXYZ[\ ]^_ ^ ` abboc^ pkXhgqXZXh\ e[gZ\ rXr Ui s Uknt
Sets Theory for Fault Isolation. In: III Polish National Conference on "Diagnostic of Industrial Processes", Jurata, Poland pp. 55-60. (in polish)
T UVWXYZ[\ ]^_ ^ g[u _ ^v ^ w gxh\ V ` abbyc^ pz gxh
Positioner with Fuzzy Based Fault Diagnosis. In: IFAC Symposium SAFEPROCESS '97, Kingston Upon Hull, UK, pp. 603-608. Massoumia, M.A. and W.E. Van der Velde (1988). Generating Parity Relation for Detecting and Identifying Control System Component Failures. Journal of Guidance, Control and Dynamics, 11, 60-65. Mediavilla M., L.J. de Miguel, and P. Vega (1997). Isolation of Multiplicative Faults in the Industrial Actuator Benchmark. In: IFAC Symposium SAFEPROCESS '97, Kingston Upon Hull, UK, pp. 855-860. Oehler R., A. Schoenhoff and M. Schreiber(1997). On-line Model Based Fault Detection and Diagnosis for a Smart Aircraft Actuator. In: IFAC Symposium SAFEPROCESS '97, Kingston Upon Hull, UK, pp. 591-596. Pawlak Z. (1991). Rough Sets. Theoretical aspects of reasoning about data. Kluwer Academic Publishers. Phatak M. and N. Wiswanadham (1988). Actuator Fault Detection and Isolation in Linear Systems. Int. J. Sys. Sci., 19, N° 12, 2593-2603. Yang J.C. and D.W. Clarke (1997). The SelfValidating Actuator. In: IFAC Symposium SAFEPROCESS '97, Kingston Upon Hull, UK, pp. 579-584.
2.1 On- and off-line diagnostic of actuators Diagnostics of actuators is performed either in on- or off-line modes. On-line diagnostics is suitable for rapid detection of faults and signalling faults to supervisory systems. Diagnostic tests realised in this mode must not disturb the process. Thus, the diagnostic tests must be ground principally on the available process values. In some cases the generation of low amplitude test sequences for on-line diagnosis are taken into account if they influence negligible process disturbances. In the case of classical actuators the number of available signals to be analysed is very limited, what make difficulties by faults isolation. The on-line diagnostics may be developed by application of novel smart actuators equipped with relatively wide set of sensors (e.g. set-point signal, pressure in servo-motor chamber, positioner temperature, piston rod displacement, end position switches, etc.). Basing on such a set of signals and eventually on the medium flow signal not only detection but also fault isolation is possible. Contemporary the on-line diagnostics functions in modern actuators are limited for simple algorithms of fault detection and determining wearing state. For example the tests of: consistency of an set-point and process value of piston rod or overcoming the limits of piston cycles or piston total way is signalled. Forecasts shows that the spectrum of diagnostic functions in intelligent actuators will be expanding. This paper is devote to this topic. Off-line diagnostics is performed by appropriate technical services. This kind of diagnostics is mainly performed periodically by the process standstill or being repair. In off-line diagnostics the tests signal values applied are only limited by device technical specifications. Both closed and open loop investigations may be performed. Off-line diagnostics may be done also for for verification and accuracy after fault signalling by on-line diagnostics.
On line diagnostics of final control elements may be done either in remote mode, by the supervision diagnostic system or locally by the microprocessor controllers being part of the intelligent actuator
