Aşırı Öğrenme Makinası Yöntemi ile Sürekli Mıknatıslı Senkron Motorun Moment Verileri Kullanılarak Sargı İzolasyonu Arızasının Tespiti

Year 2019, Volume: 9 Issue: 2, 7 - 15, 06.02.2020

Ferhat Çıra

Abstract

Elektrik makinalarının arızalarının erken tespitinin yapılmaması, felaketle sonuçlanabilecek arızalara neden olduğu bilinmektedir. Endüstride en çok kullanılan motorların asenkron motorlar olması sebebiyle durum izleme ve arıza tespiti bu makinalar üzerine yoğunlaşmıştır. Oysa hassas hız ve konum kontrolü gerektiren uygulamalarda sürekli mıknatıslı motorların (SMSM) kullanılmaya başlanmasıyla birçok araştırmacı bu motorların arıza tespiti çalışmaları yaygınlaştı. SMSM’lerin hassas hız ve konum kontrolü kabiliyetleri tamamen sağlıklı çalışmalarına bağlıdır. En küçük bir arıza sonucu bu hassasiyet kaybolabileceğinden bu tür motorlarda arızanın erkenden tespit ve teşhis edilebilmesi çok önemlidir. Bu çalışmada SMSM’lerde sıkça meydana gelen stator izolasyon arızasının erken evrede arıza tespiti için bir boyutlu yerel ikili desenler (1b-YİÖ) tabanlı öznitelik çıkarım yöntemi kullanılmıştır. Bu amaçla sağlıklı ve farklı kısa devre arıza oranlarına sahip SMSM’lerdenlabview programı tabanlı veri toplama kartı ile moment verileri alınmıştır. Sağlıklı ve arızalı motorlardan alınan moment işaretlerine1b-YİÖ uygulanmış ve histogramları elde edilmiştir. Elde edilen histogramlar ile sağlıklı ve arızalı motorların öznitelikleri oluşturularak aşırı öğrenme makinesi (AÖM) yöntemi ile işaretler sınıflandırılmıştır. Arızanın tespitinin erken evrede yapılabilmesi için önerilen bu yaklaşım ile oldukça büyük bir başarı sağlandığı görülmüştür. Bu amaçla üretilen farklı arıza şiddetine sahip motorların farklı hız ve yüklenme koşulları altında yapılan deneyler ile yöntemin başarısı doğrulanmıştır. Böylece daha önce literatürde olmayan bir yöntem ile SMSM’nin stator izolasyon arızasının tespiti yüksek güvenirlikli ve başarıyla yapılmıştır. 

Keywords

Tek boyutlu İkili örüntü yöntemi, Öznitelik çıkarma, Arıza tespiti, Sınıflandırma

Detection of Winding Insulation Fault by Using Moment Data of Permanent Magnet Synchronous Motor with Extreme Learning Machine Method

Abstract

Failure in earlydetection of faults of electricalmachines is knowntocausecatastrophicmalfunction. As inductionmotorsaremostcommonmotors in industry, conditionmonitoringandfaultdetectionareconcentrated on thesemachines. However, withtheuse of permanentmagnetmotors (PMSM) in applicationsrequiringprecisespeedandpositioncontrol, manyresearchershavestudiedthefaultdetection of motors. Theprecisespeedandpositioncontrolcapabilities of thePMSMstotallydepend on theirhealthyoperation. Since thisprecision can be lostduetotheslightestfailure, it is veryimportanttodetectanddiagnosethefaultearly on suchmotors. Inthisstudy, a featureextractionmethodbased on onedimensionallocalbinarypattern (1D-LBP) methodwhich is a distinctivemethod, has beenusedforfeatureextraction. It has beenproposedforfaultdetection of earlystage stator insulationfaultoccurringfrequently in PMSMs.
Inthisstudy, torquedatawereobtainedfromPMSMswithhealthyanddifferentshortcircuitfaultrates. 1b-YİÖwasappliedtothesedata, andthehistograms of torquesignalswereobtained. Healthyandfaultymotorscould be classified at highsuccessratesapplyingone of theextremelearningmachine (ELM) technique, tohistograms. Using thesefeatures, ELM method is usedtoclassifythesignals. It has beenobservedthatgreatsuccess has beenachievedwiththisapproach in ordertodetectthefault in an earlystage. Thesuccess of themethod has beenconfirmedbyexperimentsperformedunderdifferentspeedandloadingconditions of motorswithdifferentfaultseverities. 

Keywords

PermanentMagnetSynchronous Machine, LocalBinaryPattern, Texture Analysis

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