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Data Mining Approaches for Intelligent Condition-based Maintenance: A Framework of Intelligent Fault Diagnosis and Prognosis System (IFDPS)
Norwegian University of Science and Technology, Faculty of Engineering Science and Technology, Department of Productions and Quality Engineering.
2014 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Condition-based Maintenance (CBM) is a maintenance policy that take maintenance action just when need arises with real-time condition monitoring. Intelligent CBM means a CBM system is capable of understanding and making maintenance decisions without human intervention. To achieve this objective, it is needed to detect current conditions of mechanical and electrical systems and predict the fault of the systems accurately. What’s more, the maintenance scheduling need to be optimized to reduce the maintenance cost and improve the reliability, availability and safety based on the results of fault detection and prediction.

Data mining is a computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The goal of the data mining is to extract useful information from a data set and transform it into an understandable structure for further use.

This thesis develops framework of Intelligent Fault Diagnosis and Prognosis System (IFDPS) for CBM based on Data Mining Techniques. It mainly includes two tasks: the one is to detect and predict the condition of the equipment and the other is to optimize maintenance scheduling accordingly. It contains several phases: sensor selection and its placement optimization, signal processing and feature extraction, fault diagnosis, fault prognosis and predictive maintenance scheduling optimization based on results of fault diagnosis and prognosis. This thesis applies different data mining techniques containing Artificial Neural Network such as Supervised Back-Propagation (SBP) and Self-Organizing Map (SOM), Swarm Intelligence such as Particle Swarm Optimization (PSO), Bee Colony Algorithm (BCA) and Ant Colony Optimization (ACO), and Association Rule (AI) in most of these phases.

The outcomes of the thesis can be applied in mechanical and electrical system in industries of manufacturing, wind and hydro power plants. 

Place, publisher, year, edition, pages
NTNU: NTNU-trykk , 2014. , 209 p.
Doctoral Theses at NTNU, ISSN 1503-8181 ; 2014:75
National Category
Production and maintenance engineering
URN: urn:nbn:no:ntnu:diva-25148ISBN: ISBN 978-82-326-0074-8 (printed version)ISBN: ISBN 978-82-326-0075-5 (electronic version)OAI: diva2:733073
Public defence
2014-06-10, 00:00 (English)
Available from: 2014-07-24 Created: 2014-07-07 Last updated: 2014-07-24Bibliographically approved

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