Real-Time Model-Based Fault Detection and Diagnosis for Alternators and Induction Motors

Real-Time Model-Based Fault Detection andDiagnosis for Alternators and Induction Motors

Daniel F. Leite; Michel B. Hell; Patricia H. Diez; Bemardo S. L. Gariglio; Lucas 0. Nascimento; Pyramo Costa Jr.Graduate Program in Electrical Engineering, Pontifical Catholic University of Minas Gerais

Av. Dom Jose Gaspar, 500, Predio 3, 30535-610, Belo Horizonte, MG, BRAZIL

Abstract-This paper describes a real-time model-based fault Estimation Module (aPE), which includes the algorithms:detection and diagnosis software. The Electric Machines Diag- Recursive Least-Squares (RLS) and Extended Kalman Filternosis System (EMDS) covers field winding shorted-turns fault in (EKF); Dynamic Models Module (oaDM) for faults simulation;alternators and stator windings shorted-turns fault in inductionmotors. The EMDS has a modular architecture. The modules Computational Intelligence Module (ad), which includes:include: acquisition and data treatment; well-known parameters MLP and SOM Neural Networks and the FCM technique forestimation algorithms, such as Recursive Least Squares (RLS) patterns recognition. These modules working together generateand Extended Kalman Filter (EKF); dynamic models for faults the health state of various electric machines working in parallelsimulation; faults detection and identification tools, such as through routing. The system modules are trigged as soon asM.L.P. and S.O.M. neural networks and Fuzzy C-Means (FCM)technique. The modules working together detect possible faulty changes in the state variables occur. A great performanceconditions of various machines working in parallel through of the DBMS is essential to a fast, safe and efficient datarouting. A fast, safe and efficient data manipulation requires a manipulation.great DataBase Managing System (DBMS) performance. In our In the following section, the modules, sub-modules andexperiment, the EMDS real-time operation demonstrated that the the DBMS are presented. Individual results of the modulesproposed system could efficiently and effectively detect abnormal and some demo screens are shown in section III. Section IVconditions resulting in lower-cost maintenance for the company.

presents conclusions and considerations about the software.I. INTRODUCTION

II. MODULES OF THE SYSTEMCondition Monitoring and Fault Diagnosis of electric ma-

chines are of a great practical importance as they improve the The EMDS modular architecture is illustrated in fig. 1. Thequality and productivity of production lines and power gener- main environment was developed in C++ Builder Software,ating stations preventing machinery damage. In general, this and the LabView Software Is requested for data acquisdtion.procedure consists of two phases: extracting evidences from The DBMS manage both data of the main data base and data

. . ~~~~~~ofthe computational intelligence module's auxiliar data base.the sensors signals and recognizing possible faulty patterns ofp gths *viecs The computational intelligence module must have its ownthese evidences.

Many factors may cause system and measurement noise, knowledge base to generate faults hypotheses. The human-which makes the detection more complicated and harms its machine interface minimizes the need of deep knowledgeperformance. Efforts have been made to improve success- about the EMDS's algorithms and deals friendly with theful detections. Even a small improvement, either in data users. One can observe four main modules: aDAT, aPE, aDM,acquisition or in modeling of the diagnosis systems, may and aCI. These modules will be presented in the followinggenerate expressive financial profit. However, such process is subsections.complicated [1].

Faults in machines stator and rotor windings due to theinter-turn insulation breakdown are frequent. Many studies alabout their modeling, simulation and detection have beenreported in the literature [2] - [7]. Aiming at developinga specific real-time preventive maintenance software for theEnergetic Company of Minas Gerais, the presented EMDS Mod

was developed and is being improved.The EMDS includes faults in synchronous hidrogenerators

field winding and faults in the stator windings of auxiliarAiinduction motors. Such motors are also important to theenergy generating process performance. The system has amodular structure, where many algorithms frequently appliedFi.1ElcrcMhneFatsDgoisStmin literature works were implemented. These modules include:Acquisition and Data Treatment Module (agADT); Parameters

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A. Acquisition and Data Treatment Module Where, y - Prediction vector; T - Regression matrix; 0 -

The Acquisition and Data Treatment Module, aADT, was Parameters vector.developed in LabView environment. The aADT serves the From (1) and algebra's considerations, 0 can be explicit asC++ main program as a client. When requested, it makes in (2). This expression is utilized in the batch identificationacquisitions of voltages, currents and speed. The number of process.machines connected to the system is limited by the number ofchannels of the acquisition board. The data are pre-processed, -=T'Y (2)the offset is removed and the magnitude correction factor is In recursive estimation, some relations are included in aapplied. Low pass filters minimize noises and salient poles loop. An instantaneous sample of the transducers is acquiredeffects. Other variables calculus like: active power, reactive for each loop of the program. Additional relations like (3)power, power factor, slip, are realized. Then, the processed for kalman gain calculus, (4) for 0 updating and (5) for thedata are displayed on the screen prior to being saved in a covariance matrix 'P' updating, are integrated.users pre-defined file. The amount of data points which aredisplayed on the graphics is also pre-defined by the user. Thedata are now in the correct format for main analysis. The Kk1 = (P+lTk+l)/(1 + Tk+lPk+lT+l) (3)LabView Software presents the data to the main program and 0k+1 = k + Kk+1(Vk-Tk0k); (4)the routine keeps running. Pk+1 = (Pk-KKkTkPk)/I (5)

B. DataBase Managing System 2) Extended Kalman Filter: The EKF for estimation of theThe DataBase Managing System, DBMS, consists in a mutual inductance and rotor resistance is another EMDS's tool.

collection of data registers saved systematically regarding: Voltages, currents and speed measurements are necessary formachines and related faults; power plants and codes; data- the estimation process. The algorithm is similar to the onesheets; users and accesses; machines parameters; data from proposed by Iwasaki, T. [9], therefore it is based on matricialacquisition; alarms; faults data and time; historic; among equations in stator fixed ao3 coordinates. The system noiseothers. Its structure is based on the entity-relationship model is assumed to be a zero-mean white Gaussian noise. Theproposed by Edgar F. Codd [8]. The DBMS realizes an covariance matrixes was empirically set in such a way thatautonomous management of the information accordingly with the EKF's results converge the aleatory initial parameters toa pre-established model adapted to a company. It manages its rated values.all the information contained in the data base and presents D. Machine Models Modulethe interface between the information and the system users, The Machine Models Module, aDM, consists of a faultfinal users or programmers. Resources are available to an user . .

.' '

depending on his associated permissions. The EMDS's data simulator ln virtual machInes. It presents the following sub-centralization brings clear benefits to the software such as: modules: Alternators and Induction Motors. The Alternatorsreduction of data's redundance; security improvement; better sub-module simulates field winding short-circuit and the In-organization and performance in terms of fast access and data duction Motors sub-module simulates stator's windings short-ineriy circuit.

1) Induction Motor: The induction motors dynamic modelC. Parameters Estimation Module was considered as in [10]. (6) represents the induction motor

Sometimes, data from blocked rotor and no-load tests are dynamic equation in state variables.utilized as parameters in machine simulations. However, dueto some machines can not be removed from the operation or [Iabcsr] = [L]1 [ - [[R] + [L]] [Iabcsrl] (6)due to these tests do not consider dynamics, the parametersidentification on-line techniques and the state-space formula- Where, [L] and [R] are the inductances and resistancestion becomes a necessity. Among the most usual identification matrixes respectively, [Vabcsr] and [Iabcsr] are, respectively, thetechniques are those which are based in RLS and EKF. stator's and rotor's voltages matrix and the stator's and rotor's

1) Least Squares: The Least Squares Method (LS) for currents matrix in abc coordinates. From (6), [Iabcsr] can beestimation of the machines equivalent impedance was imple- calculated by the Runge Kutta Algorithm (RKA). The EMDSmented in its two basic forms: batch estimation and recursive utilizes the fourth-order multi-variable RKA that presented aestimation. The recursive estimation is on-line applied, while good accuracy.the batch estimation is used by off-line algorithms. The batch The model is complemented with the electromagnetic torqueestimation is possible when the data vectors obtained from equation (7) and the speed equation (8).data acquisition or virtual mathematical models are availablein the main data base. P [T abc

In the LS, the system equation must be rewritten as in (1): Te = 2(iabcs)( [s]ac(7

y =T9H (1) rJT6JTJ (8)

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Where, Te is the electromagnetic torque; P the number ofpoles; Or the electrical angular displacement; [Li,] and 4ibcr ATe VoVsin()(15)the inductances and currents referred to the stator; T1 the load WmXstorque; and J the inertia. E. Computational Intelligence Module

In a condition of stator shorted-turns and considering that The Computational Intelligence Module, aCI, is constitutedthere are no changes in the motor's physical dimensions, the by the MLP and SOM neural networks previously trainedinductance can be given by (9) - (11) [11]. to the detection of field shorted-turns in alternators, and

stator shorted-turns in induction motors. The weight matrix,(Ik)2L (9)

activation functions and bias for each specific machine areL(1_k) = (1 - k)L (9) stored in the data base. The data are loaded when the machine

Lk k2L (10) is under evaluation. Another tool of the aCI is the FCMLk(1k) (1 - k)L (11) technique, the center of its clusters are also stored in the data

base. In on-line mode, only the tool that presents the bestWhere, k is the shorted-turns percentage, L(1 ) refers to performance for an individual machine will be considered in

the inductance of the winding's part without fault, Lk refers future evaluations.to the inductance of the faulty winding's part. (11) represents Once aCI detects a fault, the state of the real-time modelthe mutual inductance between the part of the winding without is sent to the diagnosis subsystem along with a descriptionfault and the other phases, including rotor's phases. of the detected symptom, so the subsystem begins its search

According with (9) - (11), the inductances matrix is rewrit- for potential faults. Once it has finished its search, it lists theten as a seven-dimensions matrix. Three lines and three faults and levels of faults in the output, ranked according tocolumns representing the part of the stator's phases without a predefined probability scale. aCI generates a report aboutfault, one line and one column representing the part of the machine condition. It relates the machine, health state,the faulty stator's phase, and three lines and three columns fault level, fault's localization, recommendations, life time,representing the rotor's phases. maximum time for maintenance. Thus, the right steps to the

At the same manner of the inductances matrix, in a faulty maintenance can be taken.condition, the resistances matrix is also rewritten considering Neural network training specific results and on-line perfor-(12) - (13). mance specific results of some machines will be presented in

the next section.R(1_k) = (1 - k)R (12) F Hardware Details

Rk = kR (13) The EMDS's installation requires a 90MB free space inthe hard disk. Besides that, a microcomputer with 1.8GHz

Where, R(1 ) is the resistance of the winding'spart with- processor and 512MB RAM is minimum requirement for theout fault, and Rk is the resistance of the faulty windings part software operation. The Windows 98 and Windows XP oper-The resistances matrix has seven-dimensions and is diagonal. ational systems support the EMDS's first version. A installed

2) Synchronous Generator: The synchronous generators printer is convenient to print reportsdynamic model connected to an infinite bus was considered. Along the time, a great amount of data is accumulated in(14) represents the synchronous generator dynamic equation. the data base. The DBMS exports the data to text files every

F F *] time the data base reaches 200MB. Thus, it is ideal that the[Iaf] [L] [[Vaf] + [[R] + [L]] [Iaf]] (14) hard disk has a minimum 400MB free space (installation and

Where, [L] and [R] are the inductances and resistances data accumulation).matrixes'espectivey, Vaf an [laf]are thearmature'sand Future expansions of the software resources will require

matrixes respctiely[Vf]ndJafartharatue's other components' presence and a higher performance com-field's voltages matrix and the armature's and field's currents puter. These expansions consist of: web via access and neuralmatrix. The voltage matrix is composed by the d.c. excitation networks retraining through on-line technical support.voltage and by the armature voltages displaced 1200 and guar-* ~~~~~TheEMDS's work depends on the LabView Software foranteed by the infinite bus. From (14), [laf] is also calculated sensors' data acquisitions. The LabView Software is dispens-by the fourth-order multi-variable RKA. able only when the power plant has a supervisory system.

In a condition of rotor shorted-turns, the inductances and Thus, the EMDS may read the available acquisition dataresistances change similarly as in (11)-( 12).Thes modlciscomplementedarby th electrcItorquevaria through the supervisory system. Current, voltage and speed

sensors for each power plant machine are also needed for theequation (15) as in [12]. Where, V0 is the no-load terminal full workvoltage; Vt is the terminal voltage on load; sin(A\d) =Ai forsytmsflwoksmall variations and d represents the load angle; WJm is the III. SIMULATION RESULTSsynchronous speed under steady-state conditions; and Xs is Preliminary results of parameters estimation are illustratedthe synchronous reactance. If the machine is connected to an in fig. 2. Both algorithms (LS and EKF) begin from parame-infinite bus, only d changes with load variations. ters aleatory values and after some milliseconds converge them

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to the parameters' rated value. From the estimations, other connected to up 0.4% precision sensors. This value covers theparameters can be empirically approached. precision range of the great majority of the sensors found in

The armature currents of an alternator connected to an the market.infinite bus, and the stator currents of a no-load inductionmotor, are illustrated in figs. 3(a) - 3(b) respectively. The field I CONCLUSIONSfault's evolution makes the alternator's armature's currents de- This paper presented an electric machines diagnosis sys-crease. The stator fault's evolution in phase A of an induction tem. The described system covers field winding shorted-turnsmotor makes the stator currents increase, specially in the faulty fault in alternators, and stator windings shorted-turns fault inphase. induction motors. The system has a modular structure where

Figure 4 shows EMDS's demo screens. Figure 4(a) shows several algorithms and well-known techniques of parametersan on-line diagnosis' demo screen. The system routes four estimation, faults simulation, modeling and artificial intelli-alternators. If a faulty condition is found, the system alerts gence were implemented. The modules were trigged, andthe user and some recommendations are presented. The back the system's performance was real-time evaluated. Variousscreen in fig. 4(a) shows, partially, a table with information machines in different power plants may be evaluated by theabout: the faulty machine; the power plant where the machine software in a routing scheme.is located; fault severity level; time and date of the faults The methodology adopted by the system for the faultsdetection. Besides that, the recommendations to the operator detection has succeeded. The system's real-time operationmay be easily accessed. demonstrated its efficiency to detect abnormal conditions, what

The front screen in fig. 4(a) shows, partially, the graphics effectively results in lower-cost maintenance.of the fault evolution of each of the four machines through ACKNOWLEDGMENTSthe time. The graphics' X axis present the abnormalities'verification dates. Its inferior and superior boundaries are The authors acknowledge CEMIG, the Energetic Companydefined by the user for a better visualization of the fault's of Minas Gerais, for fellowship 4020000053 and the Pontif-evolution. ical Catholic University of Minas Gerais (PUC-MG) for its

Figure 4(b) illustrates the FCM diagnosis subsystem for support.alternators. Data from acquisition or stored in the data base REFERENCESare loaded, thus the subsystem indicates the field winding [1] Tlusty, G.; "Manufacturing Processes and Equipment". New Jersey:condition. The front screen in fig. 4(b) shows, partially, a Prentice Hall, 2000.table relating inputs and their respective expected output. The [2] Fong, A. C. M.; Hui, S. C.; "An Intteligent online machinefault diagnosisoutput represents the fault level in the field of a synchronous system". Computing & Control Engineering Journal, IEE, October 2001.

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1-5 Oct. 1989. Page(s): 248 - 253. vol.1.Of analyzed vectors. [10] Krause, P.C.; Wasynczuk, 0.; Sudhoff, S.D.; "Analysis of ElectricThe FCM technique revealed to be the most efficient Machinery". New York: IEEE Press, NJ:1995. Pages: 133 - 159.

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chines". Second Edition, John Wiley & Sons, New York, 1979.

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TABLE ISYSTEM PERFORMANCE

Machine's code T Machine type |Technique |Network Structure (Il-l-l) |Training time (s) TTraining epochs |Measurement noise () |Performance(%

0 94.510.1 91.00.2 55.50.3 50.0

1 InductIion SL (7/20/20/3) 41596 30000 0.4 2.5

0 .5 29.0

0.6 197.0

1 12 .0

0 100.05

0 . 9t( 000.2 99.60.3 97.2

1 nuto FCM -72/03 1950 5000 0.4 92.80.5 860.0.6 659.801 135.00 990.4

0.2 90.00.3 80.5

2 Inductino FMLP (23/01 180 200000 0.4 64.90.5 42.00.6 32.61 185.10 91.0

0.1 87.010.2 83.00.3 79.05

2 Sycrnu SMP (12/30/30/1) 21600 150000 0.4 63.00.5 40.00.6 31.01 18.010 991.9

0. 1 997.80.2 96.60.3 793.8

2 Synchronou FCM -1 343/1260 15000 0.4 88.40.5 79.80.6 641.1

1 26.2

207~~~~~~~~~~~~~~~~~~~~~~~~~~~~~09.


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Real-Time Model-Based Fault Detection and Diagnosis for Alternators and Induction Motors