Automatic Modeling and Fault Diagnosis of Timed Concurrent Discrete Event Systems : Automatische Modellierung und Fehlerdiagnose Zeitlicher Nebenläufiger Ereignisdiskreter Systeme.

Annotation

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Schneider, Stefan
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin : Logos Verlag Berlin, 2015.
Temas:
Discrete-time systems.
Systèmes échantillonnés.
Discrete-time systems
Acceso en línea:Texto completo

MARC

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040 |a EBLCP  |b eng  |e pn  |c EBLCP  |d YDX  |d MERUC  |d OCLCQ  |d LOA  |d OCLCO  |d VT2  |d K6U  |d OCLCF  |d OCLCO  |d OCLCQ  |d OCLCO  |d OCLCL 
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049 |a UAMI 
100 1 |a Schneider, Stefan. 
245 1 0 |a Automatic Modeling and Fault Diagnosis of Timed Concurrent Discrete Event Systems :  |b Automatische Modellierung und Fehlerdiagnose Zeitlicher Nebenläufiger Ereignisdiskreter Systeme. 
260 |a Berlin :  |b Logos Verlag Berlin,  |c 2015. 
300 |a 1 online resource (198 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
588 0 |a Print version record. 
505 0 |a Intro; 1 Introduction; 1.1 Motivation; 1.2 Discrete Event Systems (DES); 1.3 Bosch Mechatronics System (BMS); 1.4 Contribution; 1.5 Organization; 2 Automatic Modeling and Fault Diagnosis Challenges; 2.1 Fault Diagnosis of DES; 2.1.1 Terms; 2.1.2 Concepts; 2.2 Model-based Fault Diagnosis Challenges; 2.3 Modeling Challenges; 3 Identification of Timed Distributed DES Models; 3.1 Preliminaries; 3.2 Timed Modeling; 3.2.1 Internal and External Behavior; 3.2.2 Timed Model and Languages; 3.3 Identification of Timed Models; 3.3.1 Time Identification Approach; 3.3.2 Timed Identification Algorithm. 
505 8 |a 3.3.3 Precision and Completeness Properties3.3.4 Identification Parameters; 3.4 Timed Distributed Modeling; 3.5 Identification of Timed Distributed Models; 3.5.1 Timed Distributed Identification Approach; 3.5.2 Precision and Completeness Properties; 3.5.3 Discussion on Shared I/Os; 3.6 Identification of Timed Distributed BMS Models; 3.6.1 Data Collection; 3.6.2 Timed Distributed Identification; 4 Partitioning of DES Models; 4.1 Preliminaries; 4.2 Causal Partitioning; 4.2.1 Distance and Causality; 4.2.2 Causal Partitioning Algorithm; 4.3 Optimal Partitioning; 4.3.1 Optimization Approach. 
505 8 |a 4.3.2 Optimal Partitioning Algorithm4.4 Partition Synthesis; 4.5 Partitioning of BMS Models; 4.5.1 Causal Partitioning; 4.5.2 Optimal Partitioning; 4.5.3 Partition Synthesis; 5 Fault Detection and Isolation using Timed Distributed DES Models; 5.1 Preliminaries; 5.2 Evaluation; 5.3 Timed Fault Detection; 5.4 Timed Fault Isolation; 5.4.1 Preliminaries; 5.4.2 Deadlock Behavior; 5.4.3 Early and Late Behavior; 5.5 Extension to Timed Distributed Models; 5.5.1 Overview; 5.5.2 Distributed Evaluation; 5.5.3 Distributed Fault Detection and Isolation; 5.6 Fault Detection and Isolation of the BMS. 
505 8 |a 5.6.1 Online Fault Diagnosis Implementation5.6.2 Fault Scenarios; 5.6.3 Model Validation; 6 Related Works -- Analysis and Comparison; 6.1 Modeling of Timed DES; 6.1.1 Language Model; 6.1.2 Automaton Model; 6.1.3 Petri Net Model; 6.1.4 Discussion; 6.2 Identification of DES Models; 6.2.1 Preliminaries; 6.2.2 Identification of Logical Models; 6.2.3 Identification of Timed Models; 6.2.4 Discussion; 6.3 Automatic Modeling of Concurrent DES; 6.3.1 Preliminaries; 6.3.2 Probabilistic Data Mining; 6.3.3 Model Optimization; 6.3.4 Discussion; 6.4 Model-based Fault Diagnosis of DES; 6.4.1 Preliminaries. 
505 8 |a 6.4.2 Fault Diagnosis using Logical Models6.4.3 Fault Diagnosis using Timed Models; 6.4.4 Discussion; 7 Conclusion; 7.1 Summary; 7.2 Further Work; 8 Kurzfassung in deutscher Sprache (extended summary in German). 
520 8 |a Annotation  |b The productive operation of machines and facilities is of great economic importance for industrial companies. In order to achieve high productivity, unscheduled production downtimes induced by faults need to be minimized. In this work, an approach for modelbased fault diagnosis of timed concurrent Discrete Event Systems is proposed that can contribute to this aim. The models are automatically determined by timed identification and partitioning. These approaches allow for efficient modeling of large and complex industrial systems with concurrent behavior requiring only little system knowledge. The work explains the theoretical and practical aspects of the presented approaches and gives a detailed evaluation based on a laboratory manufacturing system. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Discrete-time systems. 
650 6 |a Systèmes échantillonnés. 
650 7 |a Discrete-time systems  |2 fast 
758 |i has work:  |a Automatic modeling and fault diagnosis of timed concurrent discrete event systems (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCFvBcftdVdXHKCqjCdY9Dq  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Schneider, Stefan.  |t Automatic Modeling and Fault Diagnosis of Timed Concurrent Discrete Event Systems : Automatische Modellierung und Fehlerdiagnose Zeitlicher Nebenläufiger Ereignisdiskreter Systeme.  |d Berlin : Logos Verlag Berlin, ©2015  |z 9783832539818 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5247101  |z Texto completo 
938 |a EBL - Ebook Library  |b EBLB  |n EBL5247101 
938 |a YBP Library Services  |b YANK  |n 15138932 
994 |a 92  |b IZTAP 

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