research article safety assessment for electrical motor ...induction motor drive demux scope stator...

Research ArticleSafety Assessment for Electrical Motor Drive System Based onSOM Neural Network

Linghui Meng Peizhen Wang Zhigang Liu Ruichang Qiu Lei Wang and Chunmei Xu

School of Electrical Engineering Beijing Jiaotong University Beijing Engineering Research Center of Electric Rail TransportationBeijing 100044 China

Correspondence should be addressed to Linghui Meng 13810476488163com

Received 19 December 2015 Accepted 16 February 2016

Academic Editor Wen Chen

Copyright copy 2016 Linghui Meng et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

With the development of the urban rail train safety and reliability have become more and more important In this paper the faultdegree and health degree of the system are put forward based on the analysis of electric motor drive systemrsquos control principleWiththe self-organizing neural networkrsquos advantage of competitive learning and unsupervised clustering the systemrsquos health clusteringand safety identification are worked out With the switch devicesrsquo faults data obtained from the dSPACE simulation platform thehealth assessment algorithm is verified And the results show that the algorithm can achieve the systemrsquos fault diagnosis and healthassessment which has a point in the health assessment and maintenance for the train

1 Introduction

Urban rail train traction drive system is the key subsystemof the train which is the guarantee for the trainrsquos safe andsmooth running However urban rail trainrsquos motor drivesystem is a multivariable nonlinear strong coupling complexsystem Its failure frequency and failure mode are intricatemutual coupling interference which seriously affected thesafety and reliability of the trainHowever the present processof fault identification and intervention ldquobackwardnessrdquo deter-mines its inevitable failure which is limited for improving thesafety So the traditional urban rail train motor drive systemneeds online real-time fast health assessment and safetyassessment Safety control measures also should be taken intime to ensure the trainrsquos safe running

Reference [1] introduced the idea of themachine learningand artificial intelligence to fault diagnosis for themotor drivesystem which is effective for the intelligent algorithm to solvethe complex systemrsquos fault diagnosis [2] used the entropyweight multi-information algorithm to complete a compre-hensive health assessment for the high-speed catenary whichplays a positive role in health monitoring and safety earlywarning for the catenary [3 4] applied the SOM neuralnetwork to the fault diagnosis and health evaluation forthe steam turbine and the forest system respectively which

embodied the SOM network algorithmrsquos unique advantage offault diagnosis and health evaluation

References [5ndash8] used the traditional methods of thephysical aging damage mechanism of the system level forthe damage assessment and reliability modeling of the trainwhich achieved the reliability assessment and safety prog-nosis for the train References [9ndash16] used model-based anddifferent intelligent methods such as the neural network andfuzzy logic to research on the diagnosis of power convertersIn this paper based on the above literatures the controlprinciple of trainrsquos motor drive system is analyzed to getthe health characteristics factor Then the dada is obtainedfrom the dSPACE fault simulation platform At last theself-organizing feature map network intelligent algorithm isrealized byMATLAB2011b to complete the health assessmentand the results showed that the health degree and the safetycan be calculated and assessed accurately which can providea positive guiding role in safety warning andmaintenance forthe train

2 The Analysis of System Health Parameters

As can be seen from Figure 1 urban rail trainrsquos motor drivesystem mainly includes the traction inverter and tractionmotor which is controlled by the space vector modulation

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2016 Article ID 2358142 8 pageshttpdxdoiorg10115520162358142

2 Mathematical Problems in Engineering

MDC+

Control

InverterInductionmachine

Switchingpulses

Currentsensing

Speed and fluxcommands

minus

T1

T2

T3

T4

T5

T6

Figure 1 AC motor drive system

Vectorcontrol Inverter

Three-phasemotor

Field currentTorque current

Figure 2 Vector control system

algorithmThe basic principle of vector control is to measureand control the stator current vector of the induction motorand control field current and torque current of the inductionmotor respectively according to the principle of magneticfield orientationwhich is described in Figure 2 so as to realizethe induction motor torque control

Traction inverter converts the DC voltage required by thetraction system to variable voltage and variable frequencythree-phase AC power supply for three-phase inductionmotor So the inverterrsquos output voltage and current waveformquality directly affect the performance of motor drive systemand also reflect the health status of the motor drive system Inaddition three-phase motorrsquos output torque and speed alsodirectly reflect the traction motorrsquos traction ability whichalso indirectly reflects the health status of the motor drivesystem So the three-phase output voltage three-phase outputcurrent output torque and speed are identified as the healthvariables to analyze the health characteristics of motor drivesystem

3 Health Characteristic Parameters Extraction

31 Data Preprocessing Lu Murphey et al [1] presented amodel-based fault diagnostics system which used the three-phase voltages and currents as feature signal for detectingand locating multiple classes of faults in an electric driveand achieved good results Bowen [17] and Diallo et al [18]researched on the fault diagnosis of inverterrsquos open circuitwith the three-phase currents and voltages Based on theliterature above when the system is in a stable state the healthstatus of motor drive system can be reflected by three-phasevoltage three-phase current and torque and speed whichare respectively 119881

119886119899 119881119887119899 119881119888119899 119868119886 119868119887 119868119888 119879119890 and 119878 of which

119881119886119899 119881119887119899 and 119881

119888119899represent the root mean square value of

three-phase voltage respectively 119868119886 119868119887 and 119868

119888represent the

root mean square value of three-phase current respectivelyand 119879 and 119878 represent the average torque and speed respec-tively

Suppose the number of sampling voltages 119881119894119899(119894 =

119886 119887 119888) in time 119905 is 119873 and the samples are 119881119894119899

1

119881119894119899

2

sdot sdot sdot 119881119894119899

119873respectively then

119881119894119899=radic(119881119894119899

1

)2

+ (119881119894119899

2

)2

+ sdot sdot sdot + (119881119894119899

119873

)2

119873

(1)

The number of sampled currents 119868119894(119894 = 119886 119887 119888) in time 119905 is

119872 and the samples are 119868119894

1

119868119894

2

sdot sdot sdot 119868119894

119872 respectively then

119868119894=radic(119868119894

1

)2

+ (119868119894

2

)2

+ sdot sdot sdot + (119868119894

119872

)2

119872

(2)

The number of types of sampled torque 119879119890in time 119905 is 119875

and the samples are 119879119890

1

119879119890

2



119879119890=119879119890

1

+ 119879119890

2

+ sdot sdot sdot + 119879119890

119875

119875 (3)

The number of sampled speeds 119878 in time 119905 is 119876 and thesamples are 1198781 1198782 sdot sdot sdot 119878119876 respectively then

119878 =1198781

+ 1198782

+ sdot sdot sdot + 119878119876

119876 (4)

32 Health Characteristics Factor Calculation Suppose thatthe variablesrsquo rating standard values of the system in steadystate are 119881

119886119899 119881119887119899 119881119888119899 119868119886 119868119887 119868119888 119879119890 and 119878 respectively and

the values different between the actual state and standardstate represent the health status of the system The greaterthe deviation is the worse the system health is To thecontrary the smaller the deviation is and the better thesystem health is Therefore the health characteristics factorcan be described as

1199091=

10038161003816100381610038161003816100381610038161003816

119881119886119899minus 119881119886119899

lowast

119881119886119899

lowast

10038161003816100381610038161003816100381610038161003816

1199092=

10038161003816100381610038161003816100381610038161003816

119881119887119899minus 119881119887119899

lowast

119881119887119899

lowast

10038161003816100381610038161003816100381610038161003816

1199093=

10038161003816100381610038161003816100381610038161003816

119881119888119899minus 119881119888119899

lowast

119881119888119899

lowast

10038161003816100381610038161003816100381610038161003816

1199094=

10038161003816100381610038161003816100381610038161003816

119868119886minus 119868119886

lowast

119868119886

lowast

10038161003816100381610038161003816100381610038161003816

1199095=

10038161003816100381610038161003816100381610038161003816

119868119887minus 119868119887

lowast

119868119887

lowast

10038161003816100381610038161003816100381610038161003816

1199096=

10038161003816100381610038161003816100381610038161003816

119868119888minus 119868119888

lowast

119868119888

lowast

10038161003816100381610038161003816100381610038161003816

1199097=

10038161003816100381610038161003816100381610038161003816

119879119890minus 119879119890

lowast

119879119890

lowast

10038161003816100381610038161003816100381610038161003816

1199098=

10038161003816100381610038161003816100381610038161003816

119878 minus 119878lowast

119878lowast

10038161003816100381610038161003816100381610038161003816

(5)

Mathematical Problems in Engineering 3

4 Health Assessment Based onSelf-Organizing Feature Map Network

41 The Principle of Self-Organizing Feature Map NetworkSelf-organizing feature map (SOM) model [19] is a kindof competitive neural network which introduces the self-organizing characteristics and is the same as competitiveneural network by using unsupervised learning style Thedifference is that the self-organizing map network can notonly learn the distribution of input samples but also identifythe topology of the input vector Classifications are performedby multiple neurons interop with each other Figure 3 isthe networkrsquos structure diagram The self-organizing mapnetwork contains two layers which are the input layer and theoutput layer the input and output neurons are linked togetherby weight at the same time neighboring neurons are alsolinked by weight vector The transfer function of the outputneurons is mainly the linear function so the output value isthe sum of the linear weighted input value Suppose that thenumber of input neurons is119898 the output neuron is 119899 weightis 119908119894119895 and the output value of the output neurons 119884

119895will be

119884119895= 119891(sum

119894

119909119894119908119894119895) (6)

Self-organizing feature map algorithm is a kind of clus-tering method without teachers which can map any inputmode into one- two- or multidimensional discrete graphicsin the output layer and still keep its structure unchangedThe learning process can be described as follows for each ofthe networks input health characteristics it just adjusts partsof the weights which make the weight vectors more closeor far from the input vector And the adjustment process iscalled the competitive learning As the continuous learningthe weight vectors in the input space are separated and forma mode which represents the input space respectively whichrealizes the clustering of the health characteristics and healthlevel

42 The Network Learning and Health Assessment Figure 4is the flow chart of for the network learning and systemhealth assessmentThe healthier the system is the smaller thedeviation value is Therefore the fault degree of the clusteringneuronsrsquo output is lower and the health degree is higherThe following is to explain how the clustering parameters areselected and the training steps of the network respectively

421The Selection of Clustering Parameters Parameters hereare mainly the number of categories When we use the self-organizing featuremapnetwork to cluster the structure of thecompetition layer is set as 3times 3 and the clustering category ofthe number is 9 As for meticulous category the system healthwill be divided into many health levels that do not have muchpoint While it is not enough detailed if only it is dividedinto two categories As the input vector is 8 dimensions sothe networkrsquos input layer contains eight neuron nodes andthe competitive layer contains nine neuron nodes After thetraining each input vector belongs to a competitive layer

Output layer

Input layer

x1 x2 x3

Figure 3 SOM neural network

Health variablessamples

Feature extraction

Test samples

Networktraining Network test

Fault degree

Health degree

Figure 4The flow chart of network learning and health assessment

node And the fault degree119863 of networkrsquos output ranges from01 to 09 01 means the system is in normal or safe statewhile 09 means that the system has a major failure and thenumber between them means that the system is in a state ofdegradation failure The higher the fault degree is the moreserious the system failure isThus the system health degree119867is defined as follows

119867 = (1 + 120576 minus 119863) times 100 (7)

Among them 119863 is the systemrsquos damage degree which isalso the fault degree119867 is the systemrsquos health degree and 120576 isthe correction coefficient of the system health and generallyranges from 0 to 01

422 Network Training SOM networkrsquos training steps [19]are as follows


PowerPC 603e

Digital IO Increncoder

TMS320F240 DSP

4ch 12-bitADCs

DS1104 PPC

DS1104

CP1104

PCI

PHS

IPMU M

FBSALM SVMN

RTWRTI

MATLABSimulink

ControlDesk

UDC

Ia Ib

SVM1 times 3-phase

sim

I

I

Figure 5 System health degradation simulation platform based on dSPACE

Set the Variable As the input sample vector is 119909 =

[1199091 1199092 119909

8] each sample is eight-dimensional vector

120596119894(119896) = [120596

1198941(119896) 1205961198942(119896) 120596

119894119899(119896)] is the weight vector

between each of input nodes and output neurons

Initialization Small random values are used as initializedweights then the input vector and weights are normalized asfollows

1199091015840

=119909

119909

1205961015840

119894(119896) =

120596119894(119896)

1003817100381710038171003817120596119894 (119896)1003817100381710038171003817

(8)

Network Input Samples do dot product with the weightvector and the maximum of the output neurons will winthe competition As the sample and weight vectors havebeen normalized so the minimum Euclidean distance can beworked out by calculating the maximum dot product

119863 = 119909 minus 120596 (9)

The neurons who get the minimum Euclidean distancewill win as the winning neuron

Update the Weights For the neurons on the winning neurontopological neighborhoodKohonen rule is applied to update

120596 (119896 + 1) = 120596 (119896) + 120578 (119909 minus 120596 (119896)) (10)

Different distance functions can be used to determine theneighborhood the commonly used Euclidean distance (dist)is the Manhattan distance (mandist) and so forth

Update the learning rate 120578 and the topological neighbor-hood and normalize the learned weights Learning rates andthe neighborhood sizes are adjusted according to the stage ofsorting and adjustment

Determine If Convergence Determine whether the numberof iterations reaches the maximum if it did not reach themaximum number of iterations then go to the third step orend the algorithm

5 Simulation Experiments andResults Analysis

51 Simulation Model and Test The motor drive fault sim-ulation experiment is conducted by the dSPACE real-timesimulation platform [17] The platform mainly consists ofthe computer dSPACE software system dSPACE hardwarecontrol board DS1104 PPC dSPACE hardware control panelCP1104 voltage and current sensors signal detection unitand an intelligent power module (IPM) The vector controlsystem structure of the induction motor set up by dSPACEreal-time simulation platform is shown in Figure 5

Figure 5 shows the architecture of the real-time dSPACEsimulation systemWith the dSPACE software controlmodelthe motor vector control model can be quickly convertedinto code and downloaded to the DS1104 hardware controlpanel with RTWRTI The control panel CP1104 convertedthe control and protection signal from TMS320F240DSP to astandard IPM signal for controlling the power switch At thesame time the voltage current speed and other signals areinput into the CP1104 hardware control panel via the signalconditioning board and then fed to the input of the model toforma closed loop control In this paper a different number of


Discrete

SP

A

B

C

A

B

C

Motor

Conv

Ctrl

Motor

Conv

Ctrl

Speed

Speed reference

Load torque

Field-oriented controlinduction motor drive

Demux Scope

Stator current

Rotor speed (rmin)

Electromagnetic torque

DC bus voltage

Ts = 2e minus 006 s

380V 50Hz

Tm

Wm

i_a

V_DC

Tem

Figure 6 Top-level block diagram of simulation model

Meas1

2

3

AA

B

C

B

C

++ g

A

B

C

Meas 2

2

MagC

GatesUY

MagC Ctrl

1

1Motor

4

K3

SP

Ctrl

Speed controller

FOC

Three-phase dioderectifier DC filter

Selector

Conv

Three-phase inverter

Measures Ratetransition

Induction machine

minus

minus

Nlowast

N

VL+

VLminusV+

Vminus

TaTbTc

TmTm

m

I_AB

Rads2Rpm

⟨Rotor speed (Wm)⟩

RTA

B

C

Wm

Wm

V_DC

Mta

Mtb

Mtc

Idlowast

Iqlowast

Idlowast

Iqlowast

Figure 7 Block diagram of RFOC model

Table 1 System health degradation state table

Number Condition1 Healthy2 119879

1OC

3 1198792OC

4 1198791 1198792OC

5 1198791 1198793OC

6 1198791 1198792 1198793OC

7 1198791 1198792 1198794OC

8 1198791 1198792 1198793 1198794OC

9 1198791 1198792 1198793 1198794 1198795OC

10 1198791 1198792 1198793 1198794 1198795 1198796OC

fault switching devices are triggered to simulate the systemrsquosdifferent fault degree And the switchrsquos open circuit faults aresimulated through the blockade of the pulses by dSPACEsoftware Table 1 is the system health degradation state table

Figure 6 is induction motor simulation model which iscontrolled by the rotor field-oriented vector The step lengthof simulationmodel is fixed in discrete simulation algorithmThe simulation step size is usually chosen as one percentof the switching cycle In this simulation the switchingfrequency is 5 kHZ and the simulation step 119879

119904is 2119890 minus 6 s

As shown in Figure 7 the simulation model includesseven modules of which the main circuit is a typical LCIstructure including a three-phase diode rectifier DC filterunit three-phase inverter measuring unit and the motormodel Three-phase 380V50Hz AC power runs through arectifier and a DC link filter and then from the three-phaseAC voltage inverter to the inductionmotorThe speed controlsystem includes a speed control module and a field-orientedcontrol module

52 HealthDegradation Simulation andCalculation Figure 8is the motor drive systemrsquos fault simulation test platformDC voltage is about 513sim537V which is rectified from 380V


Table 2 Degradation data tables of system health state

Number Condition 1199091

1199092

1199093

1199094

1199095

1199096

1199097

1199098

1 Healthy 005 003 002 003 004 002 001 0022 119879

1OC 011 01 013 009 016 008 015 01

3 1198791 1198792OC 023 02 019 016 018 024 022 017

4 1198791 1198792 1198793OC 026 033 028 032 031 034 033 035

5 1198791 1198792 1198793 1198794OC 041 045 038 042 046 047 042 04

6 1198791 1198792 1198793 1198794 1198795OC 071 075 068 072 066 057 072 074

7 1198791 1198792 1198793 1198794 1198795 1198796OC 081 085 078 082 086 077 082 084

Table 3 System health assessment results

Number Condition Fault degree Health degree1 Healthy 01 902 119879

1OC 02 80

4 1198791 1198792OC 04 60

6 1198791 1198792 1198793OC 05 50

8 1198791 1198792 1198793 1198794OC 06 40

9 1198791 1198792 1198793 1198794 1198795OC 07 30

10 1198791 1198792 1198793 1198794 1198795 1198796OC 09 10

Figure 8 Motor drive system fault simulation test platform

three-phase AC voltage by the uncontrollable rectifier Thenthe DC voltage is transformed to three-phase AC by theIPM module and the inverter output power is 22 kW of theinductionmotor load From the dSPACE simulation platformwe can get eight state variables And they can be acquired andpreprocessed as the health degrees as in Table 2

Different switching devices faults are triggered to simulatedifferent fault degrees of motor drive system The healthdegraded data in Table 2 is input into the self-organizingfeature map network with MATLAB2011b The networkrsquosconnection weights weight distance and position are shownin Figures 9sim11 and the health assessment results are inTable 3 From the table we can see that with the increaseof the number of fault switching devices the system faultdegree increased and the health degree reduced What ismore when the number of faults is one the health degree isstill 80 percent which means the system can still operate in adegraded state When the number of faults is two the healthdegree is 60 percent which drops 20 percent compared with

0 1 2 3

0

05

1

15

2

25

minus05

minus1minus1

Figure 9 SOM neighbor connections

0 1 2 3

0

1

2

minus1minus1

Figure 10 SOM neighbor weight distances

one fault device But it is still above 50 percent which is higherthan three or more fault devices So we must take tolerancecontrol measures or maintenance action to keep the systemsafe before two fault devices as soon as possible in order toavoid property loss or casualties


minus1

minus08

minus06

minus04

minus02

0

02

04

06

08

1

Wei

ght 2

minus05 0 05 1minus1

Weight 1

Figure 11 SOM weight positions

6 Conclusions

This paper extracted the health variables of the motordrive system by analyzing the control principles and faultmechanism firstly Then they are preprocessed to get thehealth degreeWith the self-organizing featuremap networkrsquosunsupervised and autonomous learning characteristics thesystem fault is clustered and recognized quickly through thecompetition clustering The fault of the switching device istaken as example to validate the algorithm by the simulationexperiment and demonstration Finally the health degreeis put forward to complete the systemrsquos health assessmentwhich has an important guiding significance for railwaymotor drive systemrsquos safety assessment and maintenance

Of course due to the limited time and ability this paperjust put forward a preliminary health assessment scheme andalgorithm Later there is a need for research of the capac-itancersquos aging damage electrical insulation failure sensorfailure and also the analysis of different failure mode effecton the system in order to realize the online health assessmentand safety early warning for the trainrsquos safety reliability andstability

Competing Interests

The authors declare that they have no competing interests

Acknowledgments

The work was supported by the National Natural ScienceFoundation of High Speed Rail Joint Funds (U1134204)

References

[1] Y Lu Murphey M Abul Masrur Z-H Chen and B ZhangldquoModel-based fault diagnosis in electric drives using machinelearningrdquo IEEEASME Transactions onMechatronics vol 11 no3 pp 290ndash303 2006

[2] H-B Cheng Z-Y He H-T Hu X-Q Mu B Wang and Y-X Sun ldquoComprehensive evaluation of health status of high-speed railway catenaries based on entropy weightrdquo Journal ofthe China Railway Society vol 36 no 3 pp 19ndash24 2014

[3] P Zhi-Song W Qiong N Gui-Qiang and H Gu-Yu ldquoASOM-based of fault diagnosis for WANrdquo in Proceedings of theInternational Conference on Industrial and Information Systems(IIS rsquo09) pp 207ndash210 Haikou China April 2009

[4] M Shi C Zhao and Z Guo ldquoForest health assessment basedon self-organizing map neural networkrdquo Chinese Journal ofEcology vol 30 no 6 pp 1295ndash1303 2011

[5] L-H Meng Z-G Liu L-J Diao C-M Xu and L WangldquoEvaluation of reliability of urban rail train traction invertersystemrdquo Journal of the China Railway Society vol 36 no 9 pp34ndash38 2014

[6] H Wang Y Wang and C Xie ldquoReliability modeling andassigning for CRH2 electric multiple unitrdquo Journal of the ChinaRailway Society vol 31 no 5 pp 108ndash112 2009

[7] X Lu Z Liu and M Shen ldquoResearch on the damage modelof electrical locomotives traction subsystem based on the stressdamagerdquo Journal of Beijing Jiaotong University vol 33 no 6 pp13ndash16 2009

[8] MMolaei H Oraee andM Fotuhi-Firuzabad ldquoMarkovmodelof drive-motor systems for reliability calculationrdquo in Proceed-ings of the International Symposium on Industrial Electronics(ISIE rsquo06) pp 2286ndash2291 Quebec Canada July 2006

[9] J-SWang S-X Li and J Gao ldquoSOMneural network fault diag-nosis method of polymerization kettle equipment optimizedby improved PSO algorithmrdquo The Scientific World Journal vol2014 Article ID 937680 12 pages 2014

[10] B Akin S Choi U Orguner and H A Toliyat ldquoA simple real-time fault signaturemonitoring tool formotor-drive-embeddedfault diagnosis systemsrdquo IEEE Transactions on Industrial Elec-tronics vol 58 no 5 pp 1990ndash2001 2011

[11] J M Bossio C H De Angelo G R Bossio and G O GarcıaldquoFault diagnosis on induction motors using Self-OrganizingMapsrdquo in Proceedings of the 9th IEEEIAS International Con-ference on Industry Applications (INDUSCON rsquo10) pp 1ndash6 SaoPaulo Brazil November 2010

[12] R L De Araujo Ribeiro C B Jacobina E R C Da Silva and AM N Lima ldquoFault detection of open-switch damage in voltage-fed PWM motor drive systemsrdquo IEEE Transactions on PowerElectronics vol 18 no 2 pp 587ndash593 2003

[13] F Filippetti G Franceschini C Tassoni and P Vas ldquoRecentdevelopments of induction motor drives fault diagnosis usingAI techniquesrdquo IEEE Transactions on Power Electronics vol 47no 5 pp 994ndash1004 2002

[14] S Khomfoi and L M Tolbert ldquoFault diagnosis and reconfigu-ration for multilevel inverter drive using AI-based techniquesrdquoIEEE Transactions on Industrial Electronics vol 54 no 6 pp2954ndash2968 2007

[15] Y L Murphey M A Masrur Z Chen and B Zhang ldquoModel-based fault diagnosis in electric drives using machine learningrdquoIEEEASME Transactions on Mechatronics vol 11 no 3 pp290ndash303 2006


[16] J O Estima and A J M Cardoso ldquoA new approach for real-time multiple open-circuit fault diagnosis in voltage-sourceinvertersrdquo IEEE Transactions on Industry Applications vol 47no 6 pp 2487ndash2494 2011

[17] C U Bowen ldquoSimulation study for inverter-fed motor drivesystem under fault conditionsrdquo Electric Machines and Controlvol 11 no 6 pp 578ndash583 2007

[18] D Diallo M E H Benbouzid D Hamad and X PierreldquoFault detection and diagnosis in an induction machine drivea pattern recognition approach based on concordia stator meancurrent vectorrdquo IEEE Transactions on Energy Conversion vol20 no 3 pp 512ndash519 2005

[19] C Delpha D Diallo E H B Mohamed and C MarchandldquoPattern recognition for diagnosis of inverter FED inductionmachine drive a step toward reliabilityrdquo in Proceedings of theIET Colloquium on Reliability of Electromagnetic Systems pp 1ndash5 Paris France May 2007

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Algebra

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Decision SciencesAdvances in

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


MDC+

Control

InverterInductionmachine

Switchingpulses

Currentsensing

Speed and fluxcommands

minus

T1

T2

T3

T4

T5

T6

Figure 1 AC motor drive system

Vectorcontrol Inverter

Three-phasemotor

Field currentTorque current

Figure 2 Vector control system

algorithmThe basic principle of vector control is to measureand control the stator current vector of the induction motorand control field current and torque current of the inductionmotor respectively according to the principle of magneticfield orientationwhich is described in Figure 2 so as to realizethe induction motor torque control

Traction inverter converts the DC voltage required by thetraction system to variable voltage and variable frequencythree-phase AC power supply for three-phase inductionmotor So the inverterrsquos output voltage and current waveformquality directly affect the performance of motor drive systemand also reflect the health status of the motor drive system Inaddition three-phase motorrsquos output torque and speed alsodirectly reflect the traction motorrsquos traction ability whichalso indirectly reflects the health status of the motor drivesystem So the three-phase output voltage three-phase outputcurrent output torque and speed are identified as the healthvariables to analyze the health characteristics of motor drivesystem

3 Health Characteristic Parameters Extraction

31 Data Preprocessing Lu Murphey et al [1] presented amodel-based fault diagnostics system which used the three-phase voltages and currents as feature signal for detectingand locating multiple classes of faults in an electric driveand achieved good results Bowen [17] and Diallo et al [18]researched on the fault diagnosis of inverterrsquos open circuitwith the three-phase currents and voltages Based on theliterature above when the system is in a stable state the healthstatus of motor drive system can be reflected by three-phasevoltage three-phase current and torque and speed whichare respectively 119881

119886119899 119881119887119899 119881119888119899 119868119886 119868119887 119868119888 119879119890 and 119878 of which

119881119886119899 119881119887119899 and 119881

119888119899represent the root mean square value of

three-phase voltage respectively 119868119886 119868119887 and 119868

119888represent the

root mean square value of three-phase current respectivelyand 119879 and 119878 represent the average torque and speed respec-tively

Suppose the number of sampling voltages 119881119894119899(119894 =

119886 119887 119888) in time 119905 is 119873 and the samples are 119881119894119899

1

119881119894119899

2

sdot sdot sdot 119881119894119899

119873respectively then

119881119894119899=radic(119881119894119899

1

)2

+ (119881119894119899

2

)2

+ sdot sdot sdot + (119881119894119899

119873

)2

119873

(1)

The number of sampled currents 119868119894(119894 = 119886 119887 119888) in time 119905 is

119872 and the samples are 119868119894

1

119868119894

2



119868119894=radic(119868119894

1

)2

+ (119868119894

2

)2

+ sdot sdot sdot + (119868119894

119872

)2

119872

(2)

The number of types of sampled torque 119879119890in time 119905 is 119875

and the samples are 119879119890

1

119879119890

2



119879119890=119879119890

1

+ 119879119890

2

+ sdot sdot sdot + 119879119890

119875

119875 (3)

The number of sampled speeds 119878 in time 119905 is 119876 and thesamples are 1198781 1198782 sdot sdot sdot 119878119876 respectively then

119878 =1198781

+ 1198782

+ sdot sdot sdot + 119878119876

119876 (4)

32 Health Characteristics Factor Calculation Suppose thatthe variablesrsquo rating standard values of the system in steadystate are 119881

119886119899 119881119887119899 119881119888119899 119868119886 119868119887 119868119888 119879119890 and 119878 respectively and

the values different between the actual state and standardstate represent the health status of the system The greaterthe deviation is the worse the system health is To thecontrary the smaller the deviation is and the better thesystem health is Therefore the health characteristics factorcan be described as

1199091=

10038161003816100381610038161003816100381610038161003816

119881119886119899minus 119881119886119899

lowast

119881119886119899

lowast

10038161003816100381610038161003816100381610038161003816

1199092=

10038161003816100381610038161003816100381610038161003816

119881119887119899minus 119881119887119899

lowast

119881119887119899

lowast

10038161003816100381610038161003816100381610038161003816

1199093=

10038161003816100381610038161003816100381610038161003816

119881119888119899minus 119881119888119899

lowast

119881119888119899

lowast

10038161003816100381610038161003816100381610038161003816

1199094=

10038161003816100381610038161003816100381610038161003816

119868119886minus 119868119886

lowast

119868119886

lowast

10038161003816100381610038161003816100381610038161003816

1199095=

10038161003816100381610038161003816100381610038161003816

119868119887minus 119868119887

lowast

119868119887

lowast

10038161003816100381610038161003816100381610038161003816

1199096=

10038161003816100381610038161003816100381610038161003816

119868119888minus 119868119888

lowast

119868119888

lowast

10038161003816100381610038161003816100381610038161003816

1199097=

10038161003816100381610038161003816100381610038161003816

119879119890minus 119879119890

lowast

119879119890

lowast

10038161003816100381610038161003816100381610038161003816

1199098=

10038161003816100381610038161003816100381610038161003816

119878 minus 119878lowast

119878lowast

10038161003816100381610038161003816100381610038161003816

(5)




119895will be

119884119895= 119891(sum

119894

119909119894119908119894119895) (6)




Output layer

Input layer

x1 x2 x3



Feature extraction

Test samples


Fault degree

Health degree



119867 = (1 + 120576 minus 119863) times 100 (7)




PowerPC 603e


TMS320F240 DSP

4ch 12-bitADCs

DS1104 PPC

DS1104

CP1104

PCI

PHS

IPMU M

FBSALM SVMN

RTWRTI

MATLABSimulink

ControlDesk

UDC

Ia Ib

SVM1 times 3-phase

sim

I

I



[1199091 1199092 119909


120596119894(119896) = [120596

1198941(119896) 1205961198942(119896) 120596




1199091015840

=119909

119909

1205961015840

119894(119896) =

120596119894(119896)

1003817100381710038171003817120596119894 (119896)1003817100381710038171003817

(8)


119863 = 119909 minus 120596 (9)



120596 (119896 + 1) = 120596 (119896) + 120578 (119909 minus 120596 (119896)) (10)








Discrete

SP

A

B

C

A

B

C

Motor

Conv

Ctrl

Motor

Conv

Ctrl

Speed

Speed reference

Load torque


Demux Scope

Stator current

Rotor speed (rmin)


DC bus voltage

Ts = 2e minus 006 s

380V 50Hz

Tm

Wm

i_a

V_DC

Tem


Meas1

2

3

AA

B

C

B

C

++ g

A

B

C

Meas 2

2

MagC

GatesUY

MagC Ctrl

1

1Motor

4

K3

SP

Ctrl

Speed controller

FOC


Selector

Conv



Induction machine

minus

minus

Nlowast

N

VL+

VLminusV+

Vminus

TaTbTc

TmTm

m

I_AB

Rads2Rpm


RTA

B

C

Wm

Wm

V_DC

Mta

Mtb

Mtc

Idlowast

Iqlowast

Idlowast

Iqlowast




1OC

3 1198792OC

4 1198791 1198792OC

5 1198791 1198793OC

6 1198791 1198792 1198793OC

7 1198791 1198792 1198794OC

8 1198791 1198792 1198793 1198794OC

9 1198791 1198792 1198793 1198794 1198795OC

10 1198791 1198792 1198793 1198794 1198795 1198796OC



119904is 2119890 minus 6 s






1199092

1199093

1199094

1199095

1199096

1199097

1199098

1 Healthy 005 003 002 003 004 002 001 0022 119879

1OC 011 01 013 009 016 008 015 01

3 1198791 1198792OC 023 02 019 016 018 024 022 017

4 1198791 1198792 1198793OC 026 033 028 032 031 034 033 035

5 1198791 1198792 1198793 1198794OC 041 045 038 042 046 047 042 04

6 1198791 1198792 1198793 1198794 1198795OC 071 075 068 072 066 057 072 074

7 1198791 1198792 1198793 1198794 1198795 1198796OC 081 085 078 082 086 077 082 084



1OC 02 80

4 1198791 1198792OC 04 60

6 1198791 1198792 1198793OC 05 50

8 1198791 1198792 1198793 1198794OC 06 40

9 1198791 1198792 1198793 1198794 1198795OC 07 30

10 1198791 1198792 1198793 1198794 1198795 1198796OC 09 10




0 1 2 3

0

05

1

15

2

25

minus05

minus1minus1


0 1 2 3

0

1

2

minus1minus1




minus1

minus08

minus06

minus04

minus02

0

02

04

06

08

1

Wei

ght 2


Weight 1


6 Conclusions



Competing Interests


Acknowledgments


References




























Volume 2014




Journal of











Journal of


Function Spaces






Algebra












119895will be

119884119895= 119891(sum

119894

119909119894119908119894119895) (6)




Output layer

Input layer

x1 x2 x3



Feature extraction

Test samples


Fault degree

Health degree



119867 = (1 + 120576 minus 119863) times 100 (7)




PowerPC 603e


TMS320F240 DSP

4ch 12-bitADCs

DS1104 PPC

DS1104

CP1104

PCI

PHS

IPMU M

FBSALM SVMN

RTWRTI

MATLABSimulink

ControlDesk

UDC

Ia Ib

SVM1 times 3-phase

sim

I

I



[1199091 1199092 119909


120596119894(119896) = [120596

1198941(119896) 1205961198942(119896) 120596




1199091015840

=119909

119909

1205961015840

119894(119896) =

120596119894(119896)

1003817100381710038171003817120596119894 (119896)1003817100381710038171003817

(8)


119863 = 119909 minus 120596 (9)



120596 (119896 + 1) = 120596 (119896) + 120578 (119909 minus 120596 (119896)) (10)








Discrete

SP

A

B

C

A

B

C

Motor

Conv

Ctrl

Motor

Conv

Ctrl

Speed

Speed reference

Load torque


Demux Scope

Stator current

Rotor speed (rmin)


DC bus voltage

Ts = 2e minus 006 s

380V 50Hz

Tm

Wm

i_a

V_DC

Tem


Meas1

2

3

AA

B

C

B

C

++ g

A

B

C

Meas 2

2

MagC

GatesUY

MagC Ctrl

1

1Motor

4

K3

SP

Ctrl

Speed controller

FOC


Selector

Conv



Induction machine

minus

minus

Nlowast

N

VL+

VLminusV+

Vminus

TaTbTc

TmTm

m

I_AB

Rads2Rpm


RTA

B

C

Wm

Wm

V_DC

Mta

Mtb

Mtc

Idlowast

Iqlowast

Idlowast

Iqlowast




1OC

3 1198792OC

4 1198791 1198792OC

5 1198791 1198793OC

6 1198791 1198792 1198793OC

7 1198791 1198792 1198794OC

8 1198791 1198792 1198793 1198794OC

9 1198791 1198792 1198793 1198794 1198795OC

10 1198791 1198792 1198793 1198794 1198795 1198796OC



119904is 2119890 minus 6 s






1199092

1199093

1199094

1199095

1199096

1199097

1199098

1 Healthy 005 003 002 003 004 002 001 0022 119879

1OC 011 01 013 009 016 008 015 01

3 1198791 1198792OC 023 02 019 016 018 024 022 017

4 1198791 1198792 1198793OC 026 033 028 032 031 034 033 035

5 1198791 1198792 1198793 1198794OC 041 045 038 042 046 047 042 04

6 1198791 1198792 1198793 1198794 1198795OC 071 075 068 072 066 057 072 074

7 1198791 1198792 1198793 1198794 1198795 1198796OC 081 085 078 082 086 077 082 084



1OC 02 80

4 1198791 1198792OC 04 60

6 1198791 1198792 1198793OC 05 50

8 1198791 1198792 1198793 1198794OC 06 40

9 1198791 1198792 1198793 1198794 1198795OC 07 30

10 1198791 1198792 1198793 1198794 1198795 1198796OC 09 10




0 1 2 3

0

05

1

15

2

25

minus05

minus1minus1


0 1 2 3

0

1

2

minus1minus1




minus1

minus08

minus06

minus04

minus02

0

02

04

06

08

1

Wei

ght 2


Weight 1


6 Conclusions



Competing Interests


Acknowledgments


References




























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










PowerPC 603e


TMS320F240 DSP

4ch 12-bitADCs

DS1104 PPC

DS1104

CP1104

PCI

PHS

IPMU M

FBSALM SVMN

RTWRTI

MATLABSimulink

ControlDesk

UDC

Ia Ib

SVM1 times 3-phase

sim

I

I



[1199091 1199092 119909


120596119894(119896) = [120596

1198941(119896) 1205961198942(119896) 120596




1199091015840

=119909

119909

1205961015840

119894(119896) =

120596119894(119896)

1003817100381710038171003817120596119894 (119896)1003817100381710038171003817

(8)


119863 = 119909 minus 120596 (9)



120596 (119896 + 1) = 120596 (119896) + 120578 (119909 minus 120596 (119896)) (10)








Discrete

SP

A

B

C

A

B

C

Motor

Conv

Ctrl

Motor

Conv

Ctrl

Speed

Speed reference

Load torque


Demux Scope

Stator current

Rotor speed (rmin)


DC bus voltage

Ts = 2e minus 006 s

380V 50Hz

Tm

Wm

i_a

V_DC

Tem


Meas1

2

3

AA

B

C

B

C

++ g

A

B

C

Meas 2

2

MagC

GatesUY

MagC Ctrl

1

1Motor

4

K3

SP

Ctrl

Speed controller

FOC


Selector

Conv



Induction machine

minus

minus

Nlowast

N

VL+

VLminusV+

Vminus

TaTbTc

TmTm

m

I_AB

Rads2Rpm


RTA

B

C

Wm

Wm

V_DC

Mta

Mtb

Mtc

Idlowast

Iqlowast

Idlowast

Iqlowast




1OC

3 1198792OC

4 1198791 1198792OC

5 1198791 1198793OC

6 1198791 1198792 1198793OC

7 1198791 1198792 1198794OC

8 1198791 1198792 1198793 1198794OC

9 1198791 1198792 1198793 1198794 1198795OC

10 1198791 1198792 1198793 1198794 1198795 1198796OC



119904is 2119890 minus 6 s






1199092

1199093

1199094

1199095

1199096

1199097

1199098

1 Healthy 005 003 002 003 004 002 001 0022 119879

1OC 011 01 013 009 016 008 015 01

3 1198791 1198792OC 023 02 019 016 018 024 022 017

4 1198791 1198792 1198793OC 026 033 028 032 031 034 033 035

5 1198791 1198792 1198793 1198794OC 041 045 038 042 046 047 042 04

6 1198791 1198792 1198793 1198794 1198795OC 071 075 068 072 066 057 072 074

7 1198791 1198792 1198793 1198794 1198795 1198796OC 081 085 078 082 086 077 082 084



1OC 02 80

4 1198791 1198792OC 04 60

6 1198791 1198792 1198793OC 05 50

8 1198791 1198792 1198793 1198794OC 06 40

9 1198791 1198792 1198793 1198794 1198795OC 07 30

10 1198791 1198792 1198793 1198794 1198795 1198796OC 09 10




0 1 2 3

0

05

1

15

2

25

minus05

minus1minus1


0 1 2 3

0

1

2

minus1minus1




minus1

minus08

minus06

minus04

minus02

0

02

04

06

08

1

Wei

ght 2


Weight 1


6 Conclusions



Competing Interests


Acknowledgments


References




























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Discrete

SP

A

B

C

A

B

C

Motor

Conv

Ctrl

Motor

Conv

Ctrl

Speed

Speed reference

Load torque


Demux Scope

Stator current

Rotor speed (rmin)


DC bus voltage

Ts = 2e minus 006 s

380V 50Hz

Tm

Wm

i_a

V_DC

Tem


Meas1

2

3

AA

B

C

B

C

++ g

A

B

C

Meas 2

2

MagC

GatesUY

MagC Ctrl

1

1Motor

4

K3

SP

Ctrl

Speed controller

FOC


Selector

Conv



Induction machine

minus

minus

Nlowast

N

VL+

VLminusV+

Vminus

TaTbTc

TmTm

m

I_AB

Rads2Rpm


RTA

B

C

Wm

Wm

V_DC

Mta

Mtb

Mtc

Idlowast

Iqlowast

Idlowast

Iqlowast




1OC

3 1198792OC

4 1198791 1198792OC

5 1198791 1198793OC

6 1198791 1198792 1198793OC

7 1198791 1198792 1198794OC

8 1198791 1198792 1198793 1198794OC

9 1198791 1198792 1198793 1198794 1198795OC

10 1198791 1198792 1198793 1198794 1198795 1198796OC



119904is 2119890 minus 6 s






1199092

1199093

1199094

1199095

1199096

1199097

1199098

1 Healthy 005 003 002 003 004 002 001 0022 119879

1OC 011 01 013 009 016 008 015 01

3 1198791 1198792OC 023 02 019 016 018 024 022 017

4 1198791 1198792 1198793OC 026 033 028 032 031 034 033 035

5 1198791 1198792 1198793 1198794OC 041 045 038 042 046 047 042 04

6 1198791 1198792 1198793 1198794 1198795OC 071 075 068 072 066 057 072 074

7 1198791 1198792 1198793 1198794 1198795 1198796OC 081 085 078 082 086 077 082 084



1OC 02 80

4 1198791 1198792OC 04 60

6 1198791 1198792 1198793OC 05 50

8 1198791 1198792 1198793 1198794OC 06 40

9 1198791 1198792 1198793 1198794 1198795OC 07 30

10 1198791 1198792 1198793 1198794 1198795 1198796OC 09 10




0 1 2 3

0

05

1

15

2

25

minus05

minus1minus1


0 1 2 3

0

1

2

minus1minus1




minus1

minus08

minus06

minus04

minus02

0

02

04

06

08

1

Wei

ght 2


Weight 1


6 Conclusions



Competing Interests


Acknowledgments


References




























Volume 2014




Journal of











Journal of


Function Spaces






Algebra












1199092

1199093

1199094

1199095

1199096

1199097

1199098

1 Healthy 005 003 002 003 004 002 001 0022 119879

1OC 011 01 013 009 016 008 015 01

3 1198791 1198792OC 023 02 019 016 018 024 022 017

4 1198791 1198792 1198793OC 026 033 028 032 031 034 033 035

5 1198791 1198792 1198793 1198794OC 041 045 038 042 046 047 042 04

6 1198791 1198792 1198793 1198794 1198795OC 071 075 068 072 066 057 072 074

7 1198791 1198792 1198793 1198794 1198795 1198796OC 081 085 078 082 086 077 082 084



1OC 02 80

4 1198791 1198792OC 04 60

6 1198791 1198792 1198793OC 05 50

8 1198791 1198792 1198793 1198794OC 06 40

9 1198791 1198792 1198793 1198794 1198795OC 07 30

10 1198791 1198792 1198793 1198794 1198795 1198796OC 09 10




0 1 2 3

0

05

1

15

2

25

minus05

minus1minus1


0 1 2 3

0

1

2

minus1minus1




minus1

minus08

minus06

minus04

minus02

0

02

04

06

08

1

Wei

ght 2


Weight 1


6 Conclusions



Competing Interests


Acknowledgments


References




























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










minus1

minus08

minus06

minus04

minus02

0

02

04

06

08

1

Wei

ght 2


Weight 1


6 Conclusions



Competing Interests


Acknowledgments


References




























Volume 2014




Journal of











Journal of


Function Spaces






Algebra





















Volume 2014




Journal of











Journal of


Function Spaces






Algebra









research article safety assessment for electrical motor ...induction motor drive demux scope stator...

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