rotating machinery predictive maintenance through expert system · 2019. 8. 1. · analysis...

International Journal of Rotating Machinery2000, Vol. 6, No. 5, pp. 363-373Reprints available directly from the publisherPhotocopying permitted by license only

(C) 2000 OPA (Overseas Publishers Association) N.V.Published by license under

the Gordon and Breach SciencePublishers imprint.

Printed in Malaysia.

Rotating Machinery Predictive MaintenanceExpert System

Through

M. SARATH KUMAR and B.S. PRABHU*

Department of Applied Mechanics, Machine Dynamics Laboratory, Indian Institute of Technology, Madras 600 036, India

(Received 24 November 1998;In final form 19 February 1999)

Modern rotating machines such as turbomachines, either produce or absorb huge amount ofpower. Some of the common applications are: steam turbine-generator and gas turbine-compressor-generator trains produce power and machines, such as pumps, centrifugalcompressors, motors, generators, machine tool spindles, etc., are being used in industrialapplications. Condition-based maintenance of rotating machinery is a common practicewhere the machine’s condition is monitored constantly, so that timely maintenance can bedone. Since modern machines are complex and the amount of data to be interpreted is huge,we need precise and fast methods in order to arrive at the best recommendations to preventcatastrophic failure and to prolong the life of the equipment. In the present work usingvibration characteristics of a rotor-bearing system, the condition of a rotating machinery(electrical rotor) is predicted using an off-line expert system. The analysis of the problem iscarried out in an Object Oriented Programming (OOP) framework using the finite elementmethod. The expert system which is also developed in an OOP paradigm gives the type of themalfunctions, suggestions and recommendations. The system is implemented in C++.

Keywords: Condition monitoring, Vibration, Finite element method, Expert system,Object oriented programming, Rotating machinery

1. INTRODUCTION

Condition monitoring programs offer an innova-tive means for modern rotating equipment toimplement and schedule predictive maintenance,as opposed to relying on conventional preventivemaintenance techniques. Vibration, wear andtemperature are the three important conditionmonitoring techniques to predict the health of therotating machinery. Sohre (1972) has discussed the

analysis and prediction of the reliability of turbo-machinery installations and influence of variouscomponents on overall reliability. He has alsopresented, the prediction and identification offaults in the turbomachinery. Renwick (1984) haspresented a condition monitoring program forplant maintenance and diagnosis of heavymachinery problems in which he discussed theimportance of machinery dynamics in the designof a rotating machinery. Zimmer et al. (1986) have

*Corresponding author. Tel." (044)235 1365, ext. 8180. Telex: TECHMAS 041-8926 IITM IN. Fax: (044) 2350509.E-mail: [email protected].

363

364 M. SARATH KUMAR AND B.S. PRABHU

reviewed predictive maintenance programs for therotating machinery using vibration signature anal-ysis. Recently, the literature has given emphasis onexpert systems for condition-based maintenance.An expert system is an Artificial Intelligence systemcreated to solve problems in a particular domain.Hill et al. (1988) have presented the expert systemfor health monitoring of a rotating machinery.They discussed the expert system components forcondition monitoring and design considerations.Lewis et al. (1989) have described a rule-basedexpert system for fault identification and predictivemaintenance of turbomachinery using vibrationoriented diagnosis. Sakthivel and Kalyanaraman(1993) have presented a knowledge based expertsystems (KBES) approach to an engineering data.Rao (1993) discussed the various aspects of condi-tion monitoring based on the vibration signatureanalysis and development of an off-line expertsystem for fault diagnosis in rotating machines.Sarath Kumar and Prabhu (1997) presented a rule-based off-line expert system for condition moni-toring of a rotating machinery. Bettig and Han(1998) have discussed the usage of rotordynamicmodels in predictive maintenance, in which vari-ables characterizing the state of deteriorationmechanism are trended to determine the rate ofdeterioration to predict the machine life or themaintenance period (the time period between majorover haul shutdowns).

In finite element modeling of rotor-bearingsystems, Gmfir and Rodrigues (1991) proposed aC-compatible linearly tapered shaft element tomodel the rotor system. This element consists offour degrees of freedom per nodal point (i.e., twolateral displacements and two total cross-sectionalrotations), which includes the effects oftranslationalinertia, rotary inertia, gyroscopic moments, internalviscous and hysteretic damping, shear deformationand mass eccentricity. Singh et al. (1977) have madeparametric studies on the performance character-istics (load-carrying capacity, oil flow, attitudeangle, stiffness coefficients) of a hydrostatic oiljournal bearing using the finite element method.Birembaut and Peigney (1980) predicted the

dynamic characteristics of rotor supported on hy-drodynamic bearings using FEM and validated thetheoretically obtained results with the experimentalresults. Lund (1987) has reviewed the concept ofcalculating the spring and damping coefficientsofjournal bearings.When a rotor is operated beyond a certain rota-

tional speed, a very high and unstable vibration com-ponent with a fractional frequency ofthe rotor speedwill be developed. This rotor speed is referred to asthe instability threshold. Lund (1965) presented atheoretical analysis for predicting stability of a sym-metrical, flexible rotor supported in journal bear-ings. Rao (1983) and Muszynska (1988) presentedinstability criteria of the rotor supported on journalbearings. Majumdaret al. (1988) investigated the sta-bility ofa rigid rotor in oiljournal bearings includingthe effect of elastic distortion in the bearing liner.The method of implementation whereby pro-

grams are organized as a cooperative collections ofobjects, in which each object represents an instanceofsome class is called Object Oriented Programming(OOP). In rotor dynamic analysis a physical object isa constituent of the rotor, representing its geome-trical shape, material, etc. and also the linkages withother rotating components. The main features ofOOP are data encapsulation, operator overloadingand inheritance. Each object encapsulates (hides) itsdata attributes from other objects and only displaysits behavior, which enhances the portability ofthe application codes. Operator overloading refersto multiple usage of the same function name, withdiffering argument characteristics. A class may haveseveral derived classes that may inherit some of itsattributes and behavior from the parent class. Thisenables efficient and reusability of codes (e.g.,coupling of the shaft or bearing may inherit char-acters of the class shaft). In OOP, both data andsubroutines are linked intrinsically which leads toclear thinking of program design, so that the errorsare minimized. Allocation of less storage space, lesscomputational time, allowing programs to besubstantially altered, without the need to changeexisting code, are the advantages of the OOPcompared to FORTRAN language. Forde et al.

PREDICTIVE MAINTENANCE 365

(1990) have discussed the implementation of anobject-oriented program for the numerical analysisof two-dimensional solid and structural mechanics,emphasizing on knowledge-based expert system.Zeglinski et al. (1994) have discussed the conceptsof OOP for the finite element method using C++language in a generalized matrix library and showedthat the efficiency, flexibility and maintainabilityof the C+/ program is superior to a comparableversion written in a non-OOP language, such asFORTRAN. Bettig et al. (1997) described theconcepts of OOP for predictive maintenance of arotating machinery, in which trend variables (e.g.bearing rotordynamic coefficients) were trended topredict the machine life/maintenance period using afinite element model in a graphical framework.An expert system for condition monitoring pur-

poses consists of three key components. They are(i) knowledge base (ii) inference engine and (iii) database. The architecture of the expert system is shownin Appendix A. The system comprises hierarchicallevels of generic rules (surface knowledge) andgeneric analytical simulation models (deep models).For a rotating machinery some surface knowledge isrequired about vibration, bearings, lubricant, seals,coupling etc. coupled to a much deeper knowledgeofvibration analysis (i.e., critical speeds, resonance,responses, stability, etc.), bearing analysis (static anddynamic characteristics), lubricant flow analysis,seal analysis, crack initiation/propagation etc. Inorder to predict the type of the malfunction exactly,the detailed models knowledge is used. In the presentstudy the deep knowledge regarding vibrationanalysis and bearing analysis is used to predict themalfunctions in rotating machinery. Knowledgeof the machine is stored in the form of IF-THENrules. Therefore rule has the following form

IF (antecedentAND (antecedent2)AND (antecedentn)THEN (consequentAND (consequent2)AND (consequentm)

The antecedents to n are known as premises,which are given facts, and the consequents to rnare known as conclusions, which are deduced facts.The inference engine analyses the base and actualdata in the data base and detects the discrepanciesby using rules. The data base stores base values(from data books, reference manuals, past experi-ence of personnel and experts in particular field)and actual values (experimentally measuredvalues, analytically calculated values etc.). The rulecompiler converts the rules about a machine con-dition to a form expected by the inference engine.The report generator, creates a consolidated reportabout the health of the machine based on infer-ences. The system gives the type of malfunction,cause of the problem, recommendations and sug-gestions regarding the machine. Very little literatureis available which relates to combining theoreticalrotor dynamic analysis with expert system for pre-dicting the malfunctions in a rotating machinery. Inthe present work a theoretical model to predict thedynamic characteristics of the rotor-bearing systemusing the finite element method and a rule-basedoff-line expert system, for fault diagnosis of arotating machinery, are presented and discussed inan object oriented framework.

2. ANALYSIS

A true object oriented solution to an engineeringproblem arises from a thorough analysis of theproblem domain and the development of anapplication that emulates the existence of objects.A rotor analysis using the finite element methodconsists of four different classes of objects. Theyare: shaft, material, matrices and eigenvalue. Classshaft consists of details of the number of elements,number of nodes, type of element, type of shapefunction, speed of rotor, number of point massesetc. Figure corresponds to the C/+ program in anOOP paradigm and it illustrates how the class shaftdata has been accessed through member functionsin the main program without passing the datadirectly through functions.


class shaft{//class declarationprotected:int ne, nn, npt;//No, of elements, nodes, point

massesfloat len, dia, speed;//shaft length, diameter, speedchar eletyp[10], shafunc[ 10];//element type, shape

functionpublic:void input );//functions than can access the

private or protected datavoid printshaft );

//class method definitions followvoid shaft :: inputl ( ){cout npt >> speed;for (int i= 1; > diG;

cout > shafunc;nn ne + 1;//In beam element}void shaft :: printshaft {cout


class bearing: public shaft {private:float theta, z, **p,*h, L, U, eta; //eta =-r/float c, eps, W,Q,F; //eps efloat mu,trise, **K,**C; //**K,**C-stiffness& damping coefficients

public://functions to calculate steady state &dynamic, characteristics.

void calcsteady(float**p,float*h);void calcdyna(float **K,float **C);};

FIGURE 2 Inherited bearing class.

matrix, gyroscopic matrix, damping matrix andunbalance force matrix, etc.). Class eigenvalue isused for determining the critical speeds ofthe rotor.The different classes of objects for the expert systemare Class input, Class rules and Class inference.Class input consists of details of actual and basevalues of machine. Class rules consists of theknowledge base rules of the rotating machinery,and Class inference for details of interpreting thedata. The information transfer from one class toanother class was achieved through messages.

2.1. Bearing Modeling

A schematic diagram ofjournal bearing is shown inFig. 3. The two-dimensional Reynolds equation gov-erning the flow ofthe lubricant in the clearance spaceofjournal bearing under steady-state loading is

R2 00 - + z -z 200"By using the variation principles of calculus theabove equation can be expressed in pressurefunctional asIao=o 24R2 ++ p d0 dz. (2)

The fluid film has been divided into triangularelements as in the reference Booker and Huebner

XY

wFIGURE 3 Journal bearing.

(1972). Applying Reynolds boundary conditionsp---0 at 0=0 and at z=0, L; p=(Op/O0)=O at

The load carrying capacity, leakage of lubricantand frictional force are calculated by integrating theEqs. (3)-(5) which are given in Cameron (1966).

w-[ fI pOdO(0+ cos dz)7r+c L 2 I 1/2+(/oo /oo l sinOdOdz) (3)

Q--ZR dO+

L Uh/ dz,

27r h ( z)dl(4)

f0L

F- 7-dA

Coefficient of friction (#) F/W, (6)

Temperature rise AtFU

JmC where rn (pQ).

(7)

368

Bearing

M. SARATH KUMAR AND B.S. PRABHU

2585mm

200mm

620

FIGURE 4 Electrical rotor.

Dynamic characteristics: Stiffness and dampingcoefficients of the journal bearing are calculatedusing the perturbation analysis presented by Lund(1987).

2.2. Rotor-Bearing System Modeling

The electrical rotor-bearing system taken foranalysis is shown in Fig. 4. The schematic diagramof mathematical equivalent model is shown inFig. 5. The rotor has been descritized into 50beam elements. Equations of motion for a linearlydamped rotor-bearing system proposed by Gmtirand Rodrigues (1991 can be expressed in the matrixform as follows:

M0 + (G + r/1K)0 + (rllK + rl2C)q r (8)

where r represents the 4N dimensional nodalunbalance force vector. The matrices M and Kare symmetric, whereas G and C are skew-symmetric. Since the equations of motion arewritten in discrete form, the effects of thin rigiddisks and short journal bearings can be incorpo-rated directly into the formulation by addingadequate nodal contributions.

FIGURE 5 Mathematical equivalent model of rotor-bearingsystem.

developed in C++ in an object oriented paradigm.The formulated matrix equations are solved byGauss elimination algorithm to obtain the bearingpressures in the clearance space of the bearing.Hessenberg algorithm is used to obtain the eigenvalues of the rotor bearing system. The stabilitylimit of the rotor bearing system was obtained byplotting the values of growth factor versus rota-tional speed. The expert system code has been devel-oped in C++ in an object-oriented framework.Figure 6 shows the flow chart for rotor dynamicanalysis and its linkage to the expert system shell.

4. RESULTS AND DISCUSSION

3. SOLUTION PROCEDURE

The problem has been formulated by using thefinite element method. A computer code was

For a given rotor bearing system the programcalculates the static and dynamic characteristics ofthe journal bearing and critical speeds, unbalanceresponse, stability speed limit of the rotor. Thesevalues are taken as input to the actual value data


Solving Reynolds equationusing FEM

Calculation of static & dynamiccharacteristics of joumal bearing

Rotor -bearing system modellingusing FEM

Solving dynamic global matrix byHessenberg algorithm to get eigen values

Investigation of stability limitofrotor bearing system

To Expert system actualvalue data base

FIGURE 6 Flow chart showing rotor dynamic analysis andlinkage to expert system shell.

base. The rotor shown in Fig. 4 is a rigid rotorsupported on oil lubricated journal bearings. Totalmass of the rotor is 3500 kg. The bearing detailsused for the analysis are D--0.2m, L/D=0.675,f/- 0.025 Pa s, c 0.48, c 160 lam, N 1500 rpm.The steady state characteristics results of thejournal bearing obtained from the equations (3-7)are W-- 17.44 kN, Q-7.17 10-4m3/s, F= 251 N,#--0.014, At--5.18C. The coefficient of frictiondue to aerodynamic forces and unbalance whirlingis assumed to be negligible. When the journal isperturbed by a small displacement and a smallvelocity, the new journal forces can be expressed bya Taylor series expansion, from which one canestablish the stiffness and damping coefficients of

25.00

_20.00

(13

0 15.00

10.00

c

0 5.00

0.000.00 1ooo.oo ooo.oo ooo.oo ,,ooo.oo ooo.oo

Rotor Speed (RPM)

FIGURE 7 Rotational speed vs response.

the bearing. The values obtained are K- 1.88108, K12 6.83 x 107, K. 9.07 106, K22 2.36108 N/m, C]1-2.33105 C2- 2.10105C2]- 1.66 105, C22- 4.48 105N s/re.The static performance characteristic equations

(3)-(7) and dynamic characteristics have comparedwith the software package REYNOLDS developedby the second author for bearing modeling usingthe finite element method in FORTRAN language.The results obtained by using the above softwarepackage for the same input values are W=17.50 kN, Q- 7.25 x 10-4 m3/s, F-- 260 N,#-0.0148, At-3.13C, Kl--l.92x106.73 107, K21- 8.97 x 106, K22--2.48 x 108 N/m,C-2.13x105 C:-1.83105 C2-1.34x105, C22 4.24 105 N s/m. The former stiffnessand damping coefficients values are used in thecalculation of critical speeds, unbalance responseand the stability of the rotor bearing system. Theoperating speed of the rotor is 1500 rpm. The rotorsystem is stable and free from subsynchronousvibration at operating speed. The unbalance eccen-tricity of the rotor is taken as 0.01 mm. Figure 7shows the unbalance response ofthe rotor at the leftend bearing point. The peak of the curve corre-sponds to first critical speed of the rotor, whichis equal to 1780.2rpm. The critical speed of therotor is compared with the software packageROTODYNA developed by the second author forrotor-bearing modeling using the finite element


method in FORTRAN. The calculated value offirst critical speed of the rotor using ROTODYNAis 1772.8 rpm. The results obtained through OOPprogram were compared with the two softwarepackages and found to be in good agreement. Theamplitude of vibration at critical speed is 24.0 lain.From the ISO 2372 vibration severity chart forstable operation of the rotor, the vibration displace-ment peak-to-peak at the bearing cap at 1500 cpm is10 l.tm, which is taken as the base value displace-ment ofthe rotor. These values can be directly fed tothe data base. The available knowledge is imple-mented in the form of a set of rules. Some of therules are shown in Appendix B, which will predictthe type of malfunction of the rotating machinerythrough the inference engine.The system uses data to match with the ante-

cedents of every rule. If any rule matches, then thatrule is said to be fired or triggered and the cor-responding inference is added to the report gene-rator. Suggestions and recommendations will bedisplayed by the system. For example, the actualvibration amplitude level at bearing point. atfrequency I*RPM is 24 lam, which exceeds the basevalue amplitude level by 14tm. The ruli R1 inAppendix B corresponds to a rotor unbalancemalfunction. In practice, rotors can never beperfectly balanced because of manufacturingerrors, such as inhomogeneties in material, manu-facturing tolerances and gain or loss of materialduring operation, which increases the vibrationalresponse of the rotor-bearing system. A state ofimbalance occurs when the center of mass of therotating system does not coincide with the center ofrotation. If the frequency of excitation coincideswith one of the natural frequencies of the systemand the unbalance response envelop is narrow (Inrules of Appendix B:--1 corresponds to true) andpredominantly in the radial plane, and amplitude ofvibration of journal at bearing point exceeds thebase value, then rule R1 is triggered. Since all theantecedents and consequents of the rule R1 satisfiesthe above input data, rule R1 gets triggered anddeduces that shaft unbalance is true. This newinference causes the rule R4 gets triggered, and

10.00

8.00

2o= d.dRotor Speed (rpr

-2.00

6.O0

4.00

2.00

0

L_9 o.oo

--4.00

FIGURE 8 Rotational speed vs growth factor.

deduces "shaft unbalance detected". Check foreccentricity and mass unbalance; if necessary,balancing is to be done, is the recommendationand suggestion given by the system from rule R4.

Fig. 8 shows growth factor versus speed of therotor, from which one can find that the thresholdspeed of the rotor is 3740.5rpm. If the rotorreaches this speed and the amplitude of vibrationexceeds the base value at bearing point 1, rule R3gets triggered and deduces that the rotor hasreached threshold speed. If 1X, 2X order frequen-cies are existing with 1X amplitude of vibrationgreater than 2X amplitude, the phase angle is equalto 180, the envelop is narrow, the dominant planeis radial, and the actual vibration amplitude levelsat bearing point at frequency I*RPM exceedstheir respective allowable percentage change ofbase value amplitude level, then rule R5 getstriggered, and deduces that shaft misalignmenthas occurred. This new inference causes the ruleR6 gets triggered, and deduces "misalignment oftheshaft is detected". Check alignment is the recom-mendation given by the system. If frequencies0.25*RPM, 0.33*RPM, 0.66*RPM, I*RPM exist inthe radial dominant plane with bearing lubricatingoil temperature exceeding base value temperature50C by 60.0% (i.e. 50 x 1.6=80C, At=30C)and bearing coefficient of friction exceeds 0.1, thenR15 gets triggered and deduces that the malfunc-tion is "light rotor rub in bearings". Similarly, thesystem uses data to match the compiled rules and


gives the type ofmalfunction, such as a rotor crack,looseness, gear box problem, etc. and necessaryaction should be taken. The expert system should berun each time to take into account any effect due tochanges in the parameters.

5. CONCLUSIONS

In the present work the static and dynamiccharacteristics of journal bearing and the vibra-tion characteristics of a rotor-bearing system wereobtained using the finite element method in anOOP framework. Using detailed deep knowledgeof vibration analysis and bearing analysis one canpredict the condition of the machine accurately. Anoff-line expert system for condition monitoring of arotating machinery has been developed in an objectoriented programming frame work. An objectorientation is a promising paradigm for handlingcomplexities in the large scale software applica-tions. The development of a fault diagnostic systemfor rotating machinery requires a resolution of anumber of issues involving expert systems, analy-tical methods and user interaction.

NOMENCLATURE

English Symbols

A

C

C

EGGh

cross section area of rotor, m2

radial clearance of bearing, mcirculation or pseudo-gyroscopic matrixdamping coefficients of bearing,N s/m (i,j= to 2)

specific heat of oil, kCal/kg Kdiameter of the journal, mdistance between bearing center andjournal center, mYoung’s modulus, N/m2

gyroscopic matrixshear modulus, N/m2

film thickness [c (1 + e cos 0)], mcross sectional moment of inertia, kg m2

polar moment of inertia, kgm2

K

L

NNPPT1qQ

RAtUWWz1X

stiffness matrixstiffness coefficients of bearingN/m (i,j= to 2)

length of the bearing, mmass flow rate of the oil, kg/smass matrixnumber of gear teeth in meshingrotor speed, rev/minnumber of nodeshydrodynamic pressure, Palocation of bearinggeneralized displacements, mtotal leakage of oil through bearing, m3/snodal unbalance force vector, Nradius of the journal, mtemperature rise Cvelocity component in x direction, m/sweight of the rotor, Nload carrying capacity of bearing, Naxial coordinate of bearing, ml*rpm

Greek Symbols

7117]

711,7]2

#0

Pp

7-

film extent, radeccentricity ratio (e/c)viscous loss factor, sviscosity of oil, Pa sauxiliary damping factorscoefficient of frictioncircumferential coordinate, radmass density of the shaft, kg/m3

density of the oil (900), kg/mshear stress, Paangular velocity, rad/s

References

Bettig, B.P. and Han, R.P.S. (1997) An Object-oriented programmachine condition monitoring scheme for rotatingmachinery, Proc. of ASME Design Engineering TechnicalConference, Sacramento, California, pp. 1-13.

Bettig, B:P. and Han, R.P.S. (1998) Predictive maintenance usingthe rotordynamic model of a hydraulic turbine-generatorrotor, ASME Transactions, Journal of Vibration andAcoustics, 120, 441-448.

Birembaut, Y. and Peigney, J. (1980) Prediction of dynamicproperties of rotor supported by hydrodynamic bearingsusing the finite element method, computers and structures,12, 483-496.


Booker, J.F. and Huebner, K.H. (1972) Application of finiteelement methods to lubrication: an engineering approach,ASME Transactions, Journal of Lubrication Technology,313-323.

Cameron, A. (1966) The Principles of Lubrication, Longmans,London.

Forde, B.W.R., Foschi, R.O. and Stiemer, S.F. (1990) Object-oriented finite element analysis, Computers and Structures,34 (3), 355-374.

Gmfir, T.C. and Rodrigues, J.D. (1991) Shaft finite elements forrotor dynamics analysis, ASME Transactions, Journal ofVibration and Acoustics, 113, 482-493.

Hill, J.W. and Baines, N.C. (1988) Application of an expertsystem to rotating machinery, Proc. of I Mech. E, C307,449-454.

Lewis, D.W. and Sheth, P. (1989) ROMEX An expert systemtestbed for turbomachinery diagnostics, Proc. of 19 thTurbomachinery Symposium, pp. 135-148.

Lund, J.W. (1965) Stability ofan elastic rotor in journal bearingswith flexible, damped supports, ASME Transactions, JournalofApplied Mechanics, 911-920.

Lund, J.W. (1987) Review of the concept of dynamic coefficientfor fluid filmjournal bearings, ASME Transactions, Journal ofTribology, 109, 37-41.

Majumdar, B.C., Brewe, D.E. and Khonsari, M.M. (1988)Stability of a rigid rotor supported on flexible oil journalbearings, ASME Transactions, Journal of Tribology, 110,181-187.

Muszynska, A. (1988) Stability of whirl and whip in rotor-bearing systems, Journal of Sound and Vibration, 127, 49-64.

Rao, J.S. (1983) Instability of rotor in fluid film bearings, ASMETransactions, Journal of Vibration, Acoustics, Stress andreliability in Design, 105, 274-279.

Rao, J.S. (1993) Future Perspective Condition Monitoring withSpecial Emphasis on Expert Systems, Frontiers of Tribologyand Condition Monitoring, Tata McGraw Hill, New Delhi,87-101.

Renwick, J.T. (1984) Condition monitoring using computerizedvibration signature analysis, IEEE Transactions on IndustryApplications, IA-20, 519-527.

Sakthivel, T.S. and Kalyanaraman, V. (1993) A knowledge basedexpert system for integrated engineering, Engineering withComputers, 9, 16.

Sarath Kumar, M. and Prabhu, B.S. (1997) Condition moni-toring of rotating machinery using expert systems, Proc. ofInternational Conference on Industrial Tribology, Calcutta,India, pp. 403--411.

Singh, D.V., Sinhasan, R. and Ghai, R.C. (1977) Performancecharacteristics of hydrostatic journal bearings with journalrotation by a finite element method, Wear, 44, 41-55.

Sohre, J.S. (1972) Turbomachinery analysis and protection,Proc. 1st Turbomachinery Symposium, pp. 1-9.

Zeglinski, G.W., Han, R.P.S. and Aitchison, P. (1994) Objectoriented matrix classes for use in a finite element code usingC+/, International Journal for Numerical Methods in Engi-neering, 37, 3921-3937.

Zimmer, S. and Bently, D.E. (1986) Predictive maintenanceprograms for rotating machinery using computerized vibra-tion monitoring systems, Proc. of 1st European Turboma-chinery Symposium, Brunel University, pp. 91-107.

APPENDIX A: THE ARCHITECTURE OF EXPERT SYSTEM

Report

Recommendationsfor Maintenance

ReportGenerator

Rules--RulecompilerInferenceEngine

KnowledgeBase

Data Base

Base value Values

Database fActual-valueDatabase

Values


APPENDIX B: RULES FOR ROTATINGMACHINERY

RI"IF FREQUENCY (I*RPM)AND NARROW BAND ENVELOPAND DOMINANT PLANE RADIALAND AMPLITUDE AT PTI >BASE_VALUE_AT_P 1.20)THEN SHAFT UNBALANCER2:IF FREQUENCY (I*RPM)AND AMPLITUDE AT PT1 >BASE_VALUE_AT_P 1.20)AND PHASE ANGLE 90THEN CRITICAL SPEEDR3:IF RPM 2.1*CRITICAL SPEEDAND AMPLITUDE AT PT1 >(BASE_VALUE_AT_PI* 1.20)THEN THRESHOLD SPEEDR4:IF SHAFT UNBALANCETHEN "SHAFT UNBALANCE DETECTED"AND "CHECK FOR ECCENTRICITY ANDMASS UNBALANCE"AND "IF NECESSARY BALANCING IS TO BEDONE"RS:IF FREQUENCY (1*RPM)IF FREQUENCY (2*RPM)AND 1X AMPLITUDE > 2X AMPLITUDEAND PHASE ANGLE- 180AND NARROW BAND ENVELOPAND DOMINANT PLANE RADIALAND AMPLITUDE AT PT1 >(BASE_VALUE_AT_P 1.20)THEN SHAFT MISALIGNMENT116:IF SHAFT MISALIGNMENTTHEN "MISALIGNMENT OF THE SHAFT ISDETECTED"AND "CHECK ALIGNMENT"117:IF FREQUENCY (0.48*RPM)IF FREQUENCY (I*RPM)AND 0.48X AMPLITUDE > 1X AMPLITUDEAND DOMINANT PLANE RADIALAND AMPLITUDE AT PT1 >(BASE_VALUE_AT_P1" 1.20)THEN ROTOR INSTABILITYR8:IF ROTOR INSTABILITYTHEN "ROTOR INSTABILITY DETECTED"AND "LOWER OR RAISE OIL VISCOSITY"

IF FREQUENCY (n*RPM)AND DISCRETE ENVELOP=

AND AMPLITUDE AT PT1 >(BASE_VALUE_AT_P 1.20)THEN GEAR BOX PROBLEMRI0:IF FREQUENCY (I*RPM)AND FREQUENCY (2*RPM)AND FREQUENCY (3*RPM)AND AMPLITUDE AT PT1 >(BASE_VALUE_AT_P 1.20)AND DECREASE IN CRITICAL SPEEDTHEN CRACK IN THE ROTORRll:IF FREQUENCY (1 *RPM)AND AMPLITUDE AT PT1 >BASE_VALUE_AT_P 1.20)AND SPLIT IN CRITICAL SPEEDANDONE CRITICAL SPEED INCREASES AND ANOTHER DECREASESTHEN GYROSCOPIC EFFECTRI2:IF GEAR BOX PROBLEMTHEN "PROBLEM WITH GEAR BOXDETECTED"AND "CHECK FOR RUN OUT ECCENTRICITYAND CHECK ALIGNMENT"R13:IF FREQUENCY (I*RPM)AND FREQUENCY (2*RPM)AND FREQUENCY (3*RPM)AND FREQUENCY (4*RPM)AND 2X AMPLITUDE > 1X AMPLITUDEAND AMPLITUDE AT PT1 >(BASE_VALUE_AT_P1" 1.20)THEN LOOSENESSR14:IF LOOSENESSTHEN "PROBLEM WITH LOOSENESS OFPARTS DETECTED"AND "CHECK FOR LOOSENESS OFBEARINGS, PEDESTAL, FOUNDATION ETC."AND "CHECK ALIGNMENT"R15:IF FREQUENCY (0.25*RPM)AND FREQUENCY (0.33*RPM)AND FREQUENCY (0.66*RPM)AND FREQUENCY (I*RPM)AND DOMINANT PLANE RADIAL:AND AMPLITUDE AT PT1 >(BASE_VALUE_AT_PI* 1.20)AND ACTUAL VALUE OIL TEMP AT PT1 >BASE_VALUE_OIL_TEMP_AT_Pl*l.60)AND COEF FRICTION > 0.1THEN ROTOR RUB.

EENNEERRGGYY MMAATTEERRIIAALLSSMaterials Science & Engineering for Energy Systems

Economic and environmental factors are creating ever greater pressures for theefficient generation, transmission and use of energy. Materials developments arecrucial to progress in all these areas: to innovation in design; to extending lifetimeand maintenance intervals; and to successful operation in more demandingenvironments. Drawing together the broad community with interests in theseareas, Energy Materials addresses materials needs in future energy generation,transmission, utilisation, conservation and storage. The journal covers thermalgeneration and gas turbines; renewable power (wind, wave, tidal, hydro, solar andgeothermal); fuel cells (low and high temperature); materials issues relevant tobiomass and biotechnology; nuclear power generation (fission and fusion);hydrogen generation and storage in the context of the ‘hydrogen economy’; andthe transmission and storage of the energy produced.

As well as publishing high-quality peer-reviewed research, Energy Materialspromotes discussion of issues common to all sectors, through commissionedreviews and commentaries. The journal includes coverage of energy economicsand policy, and broader social issues, since the political and legislative contextinfluence research and investment decisions.

SSUUBBSSCCRRIIPPTTIIOONN IINNFFOORRMMAATTIIOONNVolume 1 (2006), 4 issues per year Print ISSN: 1748-9237 Online ISSN: 1748-9245Individual rate: £76.00/US$141.00Institutional rate: £235.00/US$435.00Online-only institutional rate: £199.00/US$367.00For special IOM3 member rates please emailssuubbssccrriippttiioonnss@@mmaanneeyy..ccoo..uukk

EEDDIITTOORRSSDDrr FFuujjiioo AAbbeeNIMS, Japan

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DDrr RR VViisswwaannaatthhaann, EPRI, USA

FFoorr ffuurrtthheerr iinnffoorrmmaattiioonn pplleeaassee ccoonnttaacctt::Maney Publishing UKTel: +44 (0)113 249 7481 Fax: +44 (0)113 248 6983 Email: [email protected] Publishing North AmericaTel (toll free): 866 297 5154 Fax: 617 354 6875 Email: [email protected]

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CCAALLLL FFOORR PPAAPPEERRSSContributions to the journal should be submitted online athttp://ema.edmgr.com

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