Diagnosis of the powertrain systems for autonomous electric vehicles:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Wiesbaden
Springer Vieweg
[2022]
|
| Schriftenreihe: | Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext http://www.springer.com/ Inhaltsverzeichnis |
| Beschreibung: | XXXII, 120 Seiten Illustrationen, Diagramme |
| ISBN: | 9783658369910 3658369914 |
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| 245 | 1 | 0 | |a Diagnosis of the powertrain systems for autonomous electric vehicles |c Tunan Shen |
| 264 | 1 | |a Wiesbaden |b Springer Vieweg |c [2022] | |
| 300 | |a XXXII, 120 Seiten |b Illustrationen, Diagramme | ||
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| 490 | 0 | |a Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart | |
| 502 | |b Dissertation |c Universität Stuttgart |d 2021 | ||
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| 653 | |a Predictive Diagnosis | ||
| 653 | |a Powertrain system | ||
| 653 | |a Autonomous electric vehicle | ||
| 653 | |a Multi-stage diagnosis | ||
| 653 | |a Machine faults | ||
| 653 | |a Bearing faults | ||
| 653 | |a Data-based diagnosis | ||
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Datensatz im Suchindex
| _version_ | 1804183564280922112 |
|---|---|
| adam_text | Inhaltsverzeichnis
Preface u eesesennersseessennsnnnnunonnessssnsnsnnnnannnnn
Figures eenneeeenecenneneneenostenserssensssstsannnsnenn
Tables Kensnuentententtnen sen tnenentstssnsesststensesssssssonstsenssentannenn
Abbreviations eeueeeseeseeeenseeensensnnensnennnnsennossnnsnsneneennnennnnsnanenannenn XV
Symbols ereueeeneesesssensssssnunnnnsnsessnnsenssnnsnnsnenennsensesanensnnen nennen RK
Abstract aneeseensesseesnsenennennnennnnerenonnnsssssssssesssesssnsessonesnenanrsanennneene AXL
Kurzfassung 2 2442220Rn0enannnenennsesnenessnnsennsesssssnnesntserssssenunsannene XXI
1 Introduction 2susesseseesnenneeneenennennnnenensssnnnsnensnenssersnssnensunente J
1 1 Motivation ueessesseseeneeseeeeennesnsnenenensnnenennanseserssennontnenentonn 1
1 2 Objectives cnuenemeneeneseneseneeeeneneennnee nt 2
1 3 Contributions of the Thesis ueenenenneneetsesn 3
1 4 Organization of the Thesis nennen 3
2 Fault Analysis nee 5
2 1 Battery ee 5
kunst 6
2 2 Inverter nennen 3
2 3 Electric Machine een o
2 4 Gearbox een ent iO
2 5 Scope of the Thesis
wae 13
3 Background and State of the Art i
3 1 Fault Diagnostic Methods NT 13
311 Signal-based Fault Detection Methods 14
312 Model-based Fault Detection Methods -vvrerrsee 15
313 Data-based Fault Detection Methods ve 16
3 2 Signal Processing Techniques or ae 16
32 1 Time Domain Features TEN I
322 Statistical Features u 7
323 Frequency Domain Features 7
324 Envelope Analysis
3 2 5 Time Frequency Domain Features ceeerneneeessreneneneeenn 18
3 3 Machine Learning Algorithms :::eseseceeseeeeseeeerteessesens 2
331 Decision Tree
332 Anomaly Detection eseosessesessasssesseneeenensennersenennnnn 22
333 Artificial Neural Network cccsseeseceseesensceessnasavens 23
Diagnosis of Electrical Faults in Electric Machines 0 27
4 1 Related Works and Current Challenges -02ers02000n0000n 27
4 2 Contributions of the Thesis :ccseceseecceeeeeseaeeeeesveeusanes 29
4 3 Analytical Modeling of Faults ccccscseececenaneseeeeeseeeeas 30
431 Analytical Modeling of a Healthy PMSM essences 30
432 Analytical Modeling of PMSM with TSC 006 32
433 Analytical Modeling of PMSM with PSC cceeeeunanan 32
434 Analytical Modeling of PMSM with UWR 0 33
435 Analytical Modeling of PMSM with Sensor Faults 3
4 4 Analysis of the Behavior of a PMSM in Different Conditions 34
4 5 Feature Engineering cccccccccsscsccseseeeeessseccessteneserece 38
451 Data Normalization ccccccsscceceessseceessccecseusecs 38
452 Feature Extraction c:cccccccssccsccscesecessecessseensesseee 39
4 6 Diagnostic Concept cccscccscssesecceesesssessscessesesvesecoseeecs 4]
4 7 Physical Model based Diagnostic Model 42
4 8 Self Condition Monitoring (SCM) Diagnostic Model 48
4 9 Fleet Data-based Fault Diagnostic Model nn 52
4 10 Multi-stage Diagnostic Concept ccscscccsseseeccecececececcecescee 56
ALL Conclusion scsccsssssessssssssssescsssstcatsrsasssssceveeeeeseccecececec 65
Diagnosis of Mechanical Faults in Electric Machines 67
5 1 Fault Mechanisms of Bearing oo cccceeeccsessceccesceeceseees 67
5 2 Related Works ssscsssssesecsesseecssseeessttetesseessc 69
5 3 Current Challenges and Contribution of the Thesis 72
531 Current Challenges 00 ccccccessessecesececescccscesees 72
532 Contributions of the Thesis scannen 72
3 4 Data Set Description uu0 00022222002000 73
En 7
551 De-noising
Inhaltsverzeichnis Ix
552 Feature Extraction cccsscccseceesseceeeseeeoustennereeses 716
553 Evaluation of Features cccssseccsseeceseeeceneeeeesseees 90
5 6 Validation with Other cAxles cccccesceescceseessenesceeeeeeeeees 92
5 7 Diagnostic Concept cnnnensssenensnasnennnnnensensnesnnnsnenennensnsnenen 97
5 8 Conclusion ccccecceecesceeeeceecsueeeescavceneesesesaeseosenseegasens 101
6 Conclusion and Outlook ccccccccescecseceneeeseeeeeneecesesoeeenees 103
Bibliography 2000 cccccecccsccesesseneeeecesnseneeeesnsneeseeessaeesesensaeaeees 107
|
| adam_txt |
Inhaltsverzeichnis
Preface u eesesennersseessennsnnnnunonnessssnsnsnnnnannnnn
Figures eenneeeenecenneneneenostenserssensssstsannnsnenn
Tables Kensnuentententtnen sen tnenentstssnsesststensesssssssonstsenssentannenn
Abbreviations eeueeeseeseeeenseeensensnnensnennnnsennossnnsnsneneennnennnnsnanenannenn XV
Symbols ereueeeneesesssensssssnunnnnsnsessnnsenssnnsnnsnenennsensesanensnnen nennen RK
Abstract aneeseensesseesnsenennennnennnnerenonnnsssssssssesssesssnsessonesnenanrsanennneene AXL
Kurzfassung 2 2442220Rn0enannnenennsesnenessnnsennsesssssnnesntserssssenunsannene XXI
1 Introduction 2susesseseesnenneeneenennennnnenensssnnnsnensnenssersnssnensunente J
1 1 Motivation ueessesseseeneeseeeeennesnsnenenensnnenennanseserssennontnenentonn 1
1 2 Objectives cnuenemeneeneseneseneeeeneneennnee nt 2
1 3 Contributions of the Thesis ueenenenneneetsesn 3
1 4 Organization of the Thesis nennen 3
2 Fault Analysis nee 5
2 1 Battery ee 5
kunst 6
2 2 Inverter nennen 3
2 3 Electric Machine een o
2 4 Gearbox een ent iO
2 5 Scope of the Thesis
wae 13
3 Background and State of the Art i
3 1 Fault Diagnostic Methods NT 13
311 Signal-based Fault Detection Methods 14
312 Model-based Fault Detection Methods -vvrerrsee 15
313 Data-based Fault Detection Methods ve 16
3 2 Signal Processing Techniques or ae 16
32 1 Time Domain Features TEN I
322 Statistical Features u 7
323 Frequency Domain Features 7
324 Envelope Analysis
3 2 5 Time Frequency Domain Features ceeerneneeessreneneneeenn 18
3 3 Machine Learning Algorithms :::eseseceeseeeeseeeerteessesens 2
331 Decision Tree
332 Anomaly Detection eseosessesessasssesseneeenensennersenennnnn 22
333 Artificial Neural Network cccsseeseceseesensceessnasavens 23
Diagnosis of Electrical Faults in Electric Machines 0 27
4 1 Related Works and Current Challenges -02ers02000n0000n 27
4 2 Contributions of the Thesis :ccseceseecceeeeeseaeeeeesveeusanes 29
4 3 Analytical Modeling of Faults ccccscseececenaneseeeeeseeeeas 30
431 Analytical Modeling of a Healthy PMSM essences 30
432 Analytical Modeling of PMSM with TSC 006 32
433 Analytical Modeling of PMSM with PSC cceeeeunanan 32
434 Analytical Modeling of PMSM with UWR 0 33
435 Analytical Modeling of PMSM with Sensor Faults 3
4 4 Analysis of the Behavior of a PMSM in Different Conditions 34
4 5 Feature Engineering cccccccccsscsccseseeeeessseccessteneserece 38
451 Data Normalization ccccccsscceceessseceessccecseusecs 38
452 Feature Extraction c:cccccccssccsccscesecessecessseensesseee 39
4 6 Diagnostic Concept cccscccscssesecceesesssessscessesesvesecoseeecs 4]
4 7 Physical Model based Diagnostic Model 42
4 8 Self Condition Monitoring (SCM) Diagnostic Model 48
4 9 Fleet Data-based Fault Diagnostic Model nn 52
4 10 Multi-stage Diagnostic Concept ccscscccsseseeccecececececcecescee 56
ALL Conclusion scsccsssssessssssssssescsssstcatsrsasssssceveeeeeseccecececec 65
Diagnosis of Mechanical Faults in Electric Machines 67
5 1 Fault Mechanisms of Bearing oo cccceeeccsessceccesceeceseees 67
5 2 Related Works ssscsssssesecsesseecssseeessttetesseessc 69
5 3 Current Challenges and Contribution of the Thesis 72
531 Current Challenges 00 ccccccessessecesececescccscesees 72
532 Contributions of the Thesis scannen 72
3 4 Data Set Description uu0 00022222002000 73
En 7
551 De-noising
Inhaltsverzeichnis Ix
552 Feature Extraction cccsscccseceesseceeeseeeoustennereeses 716
553 Evaluation of Features cccssseccsseeceseeeceneeeeesseees 90
5 6 Validation with Other cAxles cccccesceescceseessenesceeeeeeeeees 92
5 7 Diagnostic Concept cnnnensssenensnasnennnnnensensnesnnnsnenennensnsnenen 97
5 8 Conclusion ccccecceecesceeeeceecsueeeescavceneesesesaeseosenseegasens 101
6 Conclusion and Outlook ccccccccescecseceneeeseeeeeneecesesoeeenees 103
Bibliography 2000 cccccecccsccesesseneeeecesnseneeeesnsneeseeessaeesesensaeaeees 107 |
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| author | Shen, Tunan 1988- |
| author_GND | (DE-588)1255079592 |
| author_facet | Shen, Tunan 1988- |
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| author_sort | Shen, Tunan 1988- |
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| bvnumber | BV047927551 |
| classification_rvk | ZO 4660 |
| ctrlnum | (OCoLC)1309520833 (DE-599)DNB1250658977 |
| discipline | Verkehr / Transport |
| discipline_str_mv | Verkehr / Transport |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV047927551 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:34:50Z |
| indexdate | 2024-07-10T09:25:27Z |
| institution | BVB |
| institution_GND | (DE-588)1043386068 |
| isbn | 9783658369910 3658369914 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033309050 |
| oclc_num | 1309520833 |
| open_access_boolean | |
| owner | DE-83 DE-29T |
| owner_facet | DE-83 DE-29T |
| physical | XXXII, 120 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Vieweg |
| record_format | marc |
| series2 | Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart |
| spelling | Shen, Tunan 1988- Verfasser (DE-588)1255079592 aut Diagnosis of the powertrain systems for autonomous electric vehicles Tunan Shen Wiesbaden Springer Vieweg [2022] XXXII, 120 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart Dissertation Universität Stuttgart 2021 Sicherheitskritisches System (DE-588)4767762-4 gnd rswk-swf Elektrofahrzeug (DE-588)4151795-7 gnd rswk-swf Autonomes Fahrzeug (DE-588)7714938-5 gnd rswk-swf Diagnosesystem (DE-588)4149458-1 gnd rswk-swf Fahrzeugantrieb (DE-588)4123177-6 gnd rswk-swf Triebstrang (DE-588)4431076-6 gnd rswk-swf Predictive Diagnosis Powertrain system Autonomous electric vehicle Multi-stage diagnosis Machine faults Bearing faults Data-based diagnosis (DE-588)4113937-9 Hochschulschrift gnd-content Autonomes Fahrzeug (DE-588)7714938-5 s Elektrofahrzeug (DE-588)4151795-7 s Triebstrang (DE-588)4431076-6 s DE-604 Fahrzeugantrieb (DE-588)4123177-6 s Sicherheitskritisches System (DE-588)4767762-4 s Diagnosesystem (DE-588)4149458-1 s Springer Fachmedien Wiesbaden (DE-588)1043386068 pbl Erscheint auch als Online-Ausgabe 978-3-658-36992-7 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=03d622b98b8a4524b848bdaada16a2f8&prov=M&dok_var=1&dok_ext=htm Inhaltstext X:MVB http://www.springer.com/ HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033309050&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20220130 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Shen, Tunan 1988- Diagnosis of the powertrain systems for autonomous electric vehicles Sicherheitskritisches System (DE-588)4767762-4 gnd Elektrofahrzeug (DE-588)4151795-7 gnd Autonomes Fahrzeug (DE-588)7714938-5 gnd Diagnosesystem (DE-588)4149458-1 gnd Fahrzeugantrieb (DE-588)4123177-6 gnd Triebstrang (DE-588)4431076-6 gnd |
| subject_GND | (DE-588)4767762-4 (DE-588)4151795-7 (DE-588)7714938-5 (DE-588)4149458-1 (DE-588)4123177-6 (DE-588)4431076-6 (DE-588)4113937-9 |
| title | Diagnosis of the powertrain systems for autonomous electric vehicles |
| title_auth | Diagnosis of the powertrain systems for autonomous electric vehicles |
| title_exact_search | Diagnosis of the powertrain systems for autonomous electric vehicles |
| title_exact_search_txtP | Diagnosis of the powertrain systems for autonomous electric vehicles |
| title_full | Diagnosis of the powertrain systems for autonomous electric vehicles Tunan Shen |
| title_fullStr | Diagnosis of the powertrain systems for autonomous electric vehicles Tunan Shen |
| title_full_unstemmed | Diagnosis of the powertrain systems for autonomous electric vehicles Tunan Shen |
| title_short | Diagnosis of the powertrain systems for autonomous electric vehicles |
| title_sort | diagnosis of the powertrain systems for autonomous electric vehicles |
| topic | Sicherheitskritisches System (DE-588)4767762-4 gnd Elektrofahrzeug (DE-588)4151795-7 gnd Autonomes Fahrzeug (DE-588)7714938-5 gnd Diagnosesystem (DE-588)4149458-1 gnd Fahrzeugantrieb (DE-588)4123177-6 gnd Triebstrang (DE-588)4431076-6 gnd |
| topic_facet | Sicherheitskritisches System Elektrofahrzeug Autonomes Fahrzeug Diagnosesystem Fahrzeugantrieb Triebstrang Hochschulschrift |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=03d622b98b8a4524b848bdaada16a2f8&prov=M&dok_var=1&dok_ext=htm http://www.springer.com/ http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033309050&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT shentunan diagnosisofthepowertrainsystemsforautonomouselectricvehicles AT springerfachmedienwiesbaden diagnosisofthepowertrainsystemsforautonomouselectricvehicles |