Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application

•Streaming data from a Swedish industrial hydraulic drive system (Bosch Rexroth).•Comparing a knowledge-based (FTA) to a data-driven method (PCA), performance is similar.•The two methods produce acceptable results, thus the developed models are verified.•Both methods generate queries fast enough to...

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Veröffentlicht in:	Computers in industry Jg. 65; H. 8; S. 1126 - 1135
Hauptverfasser:	Alzghoul, Ahmad, Backe, Björn, Löfstrand, Magnus, Byström, Arne, Liljedahl, Bengt
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier B.V 2014 Elsevier Elsevier Sequoia S.A
Schlagworte:	Applied sciences Centre - The Faste Laboratory Centrumbildning - Fastelaboratoriet Comparative analysis Computer Aided Design Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Data processing. List processing. Character string processing Data stream management system Data stream mining Data-driven Datavetenskap Datorstödd maskinkonstruktion Drives Exact sciences and technology Fault detection Fault diagnosis Hydraulics Industrial metrology. Testing Information systems Knowledge-based Mechanical engineering. Machine design Memory organisation. Data processing Methods Principal components analysis Product development Product quality Software Speed variators, torque converters. Hydraulic drives and controls, pneumatic drives and controls, fluids and components, hydraulic motors, pneumatic motors Studies Knowledge-based Product development Data-driven Fault detection Data stream mining Data stream management system Measurement Database query Fault tree Information extraction Data driven modelling Data mining Quality assurance Virtual memory Monitoring Availability Streaming Data analysis Data processing Knowledge base Tree structure Mechanical system Drive mechanism Causality Hydraulic drive Shared memory Fault diagnostic Principal component analysis Defect detection
ISSN:	0166-3615, 1872-6194, 1872-6194
Online-Zugang:	Volltext
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