A Comparative Study of Bayes Classifiers for Blade Fault Diagnosis in Wind Turbines through Vibration Signals
Renewable energy sources are considered much in energy fields because of the contemporary energy calamities. Among the important alternatives being considered, wind energy is a durable competitor because of its dependability due to the development of the innovations, comparative cost effectiveness a...
Saved in:
Published in | Structural durability & health monitoring Vol. 11; no. 1; p. 69 |
---|---|
Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Forsyth
Tech Science Press
01.01.2017
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Renewable energy sources are considered much in energy fields because of the contemporary energy calamities. Among the important alternatives being considered, wind energy is a durable competitor because of its dependability due to the development of the innovations, comparative cost effectiveness and great framework. To yield wind energy more proficiently, the structure of wind turbines has turned out to be substantially bigger, creating conservation and renovation works troublesome. Due to various ecological conditions, wind turbine blades are subjected to vibration and it leads to failure. If the failure is not diagnosed early, it will lead to catastrophic damage to the framework. In order to increase safety observations, to reduce down time, to bring down the recurrence of unexpected breakdowns and related enormous maintenance, logistic expenditures and to contribute steady power generation, the wind turbine blade must be monitored now and then to assure that they are in good condition. In this paper, a three bladed wind turbine was preferred and using vibration source, the condition of a wind turbine blade is examined. The faults like blade crack, erosion, hub-blade loose connection, pitch angle twist and blade bend faults were considered and these faults are classified using Bayes Net (BN), Discriminative Multinomial Naïve Bayes (DMNB), Naïve Bayes (NB), Simple Naïve Bayes (SNB), and Updateable Naïve Bayes (UNB) classifiers. These classifiers are compared and better classifier is suggested for condition monitoring of wind turbine blades. |
---|---|
AbstractList | Renewable energy sources are considered much in energy fields because of the contemporary energy calamities. Among the important alternatives being considered, wind energy is a durable competitor because of its dependability due to the development of the innovations, comparative cost effectiveness and great framework. To yield wind energy more proficiently, the structure of wind turbines has turned out to be substantially bigger, creating conservation and renovation works troublesome. Due to various ecological conditions, wind turbine blades are subjected to vibration and it leads to failure. If the failure is not diagnosed early, it will lead to catastrophic damage to the framework. In order to increase safety observations, to reduce down time, to bring down the recurrence of unexpected breakdowns and related enormous maintenance, logistic expenditures and to contribute steady power generation, the wind turbine blade must be monitored now and then to assure that they are in good condition. In this paper, a three bladed wind turbine was preferred and using vibration source, the condition of a wind turbine blade is examined. The faults like blade crack, erosion, hub-blade loose connection, pitch angle twist and blade bend faults were considered and these faults are classified using Bayes Net (BN), Discriminative Multinomial Naïve Bayes (DMNB), Naïve Bayes (NB), Simple Naïve Bayes (SNB), and Updateable Naïve Bayes (UNB) classifiers. These classifiers are compared and better classifier is suggested for condition monitoring of wind turbine blades. |
Author | Sugumaran, V Joshuva, A |
Author_xml | – sequence: 1 givenname: A surname: Joshuva fullname: Joshuva, A – sequence: 2 givenname: V surname: Sugumaran fullname: Sugumaran, V |
BookMark | eNqNj01PAjEURRsDiSD-AHcvcc3YDwS6lFHiHqJLUjKdmUdm-rCvNeHfOyTGtatzF-fe5E7FKFDwQjwoWRi7kk9ctX2hpVoVUulCLu2NmChr5Fxbq0Z_eW1uxZT5JOViqc3zRPQvUFJ_dtEl_PawS7m6ANWwcRfPUHaOGWv0kaGmCJvOVR62LncJXtE1gRgZMMAnhgr2OR4xDLXURspNCx94vO5SgB02wXU8E-N6gL__5Z143L7ty_f5OdJX9pwOJ8rxah70cEsvtDZr8z_rB1W9UhA |
ContentType | Journal Article |
Copyright | 2017. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2017. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU D1I DWQXO HCIFZ KB. L6V M7S PDBOC PIMPY PQEST PQQKQ PQUKI PRINS PTHSS |
DOI | 10.3970/sdhm.2017.012.069 |
DatabaseName | ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Materials Science Collection ProQuest Central SciTech Premium Collection Materials Science Database ProQuest Engineering Collection Engineering Database Materials Science Collection Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection |
DatabaseTitle | Publicly Available Content Database ProQuest Materials Science Collection Engineering Database Technology Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition Materials Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection Materials Science Database ProQuest One Academic Engineering Collection |
DatabaseTitleList | Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering |
EISSN | 1930-2991 |
GroupedDBID | 8FE 8FG AAFWJ ABJCF ABUWG AFKRA ALMA_UNASSIGNED_HOLDINGS AZQEC BENPR BGLVJ CCPQU D1I DWQXO EBS EJD HCIFZ KB. L6V M7S OK1 PDBOC PIMPY PQEST PQQKQ PQUKI PRINS PTHSS RTS |
ID | FETCH-proquest_journals_23972422383 |
IEDL.DBID | BENPR |
ISSN | 1930-2983 |
IngestDate | Thu Oct 10 16:20:19 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | true |
Issue | 1 |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-proquest_journals_23972422383 |
OpenAccessLink | https://www.proquest.com/docview/2397242238?pq-origsite=%requestingapplication% |
PQID | 2397242238 |
PQPubID | 4577404 |
ParticipantIDs | proquest_journals_2397242238 |
PublicationCentury | 2000 |
PublicationDate | 20170101 |
PublicationDateYYYYMMDD | 2017-01-01 |
PublicationDate_xml | – month: 01 year: 2017 text: 20170101 day: 01 |
PublicationDecade | 2010 |
PublicationPlace | Forsyth |
PublicationPlace_xml | – name: Forsyth |
PublicationTitle | Structural durability & health monitoring |
PublicationYear | 2017 |
Publisher | Tech Science Press |
Publisher_xml | – name: Tech Science Press |
SSID | ssj0046235 |
Score | 4.1059937 |
Snippet | Renewable energy sources are considered much in energy fields because of the contemporary energy calamities. Among the important alternatives being considered,... |
SourceID | proquest |
SourceType | Aggregation Database |
StartPage | 69 |
SubjectTerms | Alternative energy sources Catastrophic failure analysis Classifiers Comparative studies Condition monitoring Cost effectiveness Electric power generation Expenditures Fault diagnosis Faults Pitch (inclination) Renewable energy sources Turbine blades Turbines Vibration Wind power Wind turbines |
Title | A Comparative Study of Bayes Classifiers for Blade Fault Diagnosis in Wind Turbines through Vibration Signals |
URI | https://www.proquest.com/docview/2397242238 |
Volume | 11 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LT8JAEJ4IXPRgfMYHkkn0WmnL9nUyFKnERGIUlRtZ7FabYFFaDvx7Z-gSTEw4b7ppZnbnm9fOB3DlWL7pStc0hFLCEIlLd871hGHJmD4I_ETa_N75oe_2XsT90BnqhFuu2ypXNnFpqOPpO-fImzYBJ8EJIczN94_BrFFcXdUUGhWo2ZbgMm0t7PYfn1a2WBC4O2Vd2TTswG-VdU3aymzm8Se_RLe8a5PzgW7wzxovISbag13tG2K7VOY-bKnsAHb-TAw8hK82dtbzupG7ABc4TTCUC5XjkuEyTZjdGskZxXBCssBIzicF3pY9dWmOaYZvFInjYD4bc9M7aq4efOXQmRWFz-kHz1U-gsuoO-j0jNUfj_TRy0drQbWOoZpNM3UCOLaUVOSdKdtWIrBiigt90ZKel8RSkQ9zCvVNO51tXj6HbRZkmZmoQ7WYzdUFYXUxbkDFj-4aWi2_nVGaTA |
link.rule.ids | 315,786,790,12792,21416,27955,27956,33406,33777,43633,43838 |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LT8JAEJ4oHtSD8RkfqJPotdLHsm1PBtCKClxE5UYWutUmWJTCgX_vTFuCiQnnzW6ame1889r5AK6rlmdKJU1DaC0MEUn656QrDEuFtMH3ImXze-d2RzZfxVOv2isSbmnRVrmwiZmhDsdDzpFXbAJOghNCmNvvH4NZo7i6WlBorMOGcKTD99wLHhaWWBC0V_OqsmnYvufkVU06yKyk4Se_Q7fcG5OzgdL_Z4szgAl2YafwDLGWq3IP1nSyD9t_5gUewFcNG8tp3cg9gHMcR1hXc51ixm8ZR8xtjeSKYn1EksBAzUZTvMs76uIU4wTfKQ7H7mwy4JZ3LJh68I0DZ1YTvsQfPFX5EK6C-26jaSy-uF9cvLS_FJNzBKVknOhjwIGllSbfTNu2Fr4VUlToCUe5bhQqTR7MCZRXnXS6evkSNpvddqvfeuw8n8EWCzXPUZShNJ3M9Dmh9nRwkanmF7lvmu0 |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Comparative+Study+of+Bayes+Classifiers+for+Blade+Fault+Diagnosis+in+Wind+Turbines+through+Vibration+Signals&rft.jtitle=Structural+durability+%26+health+monitoring&rft.au=Joshuva%2C+A&rft.au=Sugumaran%2C+V&rft.date=2017-01-01&rft.pub=Tech+Science+Press&rft.issn=1930-2983&rft.eissn=1930-2991&rft.volume=11&rft.issue=1&rft.spage=69&rft_id=info:doi/10.3970%2Fsdhm.2017.012.069 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1930-2983&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1930-2983&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1930-2983&client=summon |