Abstract
Asset Management and maintenance is an area which is undergoing rapid change due to new budgetary and environmental pressures and rapid progression in the technologies applied. At the heart of this topic are the collection, management and use of data pertaining to the condition and maintenance of key assets. In this chapter we outline some of the technologies which have recently been developed and applied to the area of asset management.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baglee D, Knowles MJ (2009) Evidence that Maintenance has an Essential Role in Energy Saving. Project report, DEFRA funded Energy use in Food Refrigeration project
Baglee D, Knowles MJ (2010a) Modelling the properties of oil with various contaminants. The Seventh International Conference on Condition Monitoring and Machinery Failure Prevention Technologies
Baglee D, Knowles MJ (2010b) Maintenance strategy development within SMEs: the development of an integrated approach. Control and Cybernetics 39(1)
Baglee D, Knowles MJ (2010c) Condition monitoring in an on-ship environment. The Seventh International Conference on Condition Monitoring and Machinery Failure Prevention Technologies
Baldwin A, Lund S (2010) Latest Developments in Online Oil Condition Monitoring Sensors. The Seventh International Conference on Condition Monitoring and Machinery Failure Prevention Technologies
Bernieri A, D’Apuzzo M (1994) A Neural Network Approach for Identification and Fault Diagnosis on Dynamic Systems. IEEE Transactions On Instrumentation And Measurement 43(6)
Byington C, Brewer R, Mackos N, Argenna G (2010) Prognostic Solution for Real-Time Lubricant Quality Health. The Seventh International Conference on Condition Monitoring and Machinery Failure Prevention Technologies
Campos J, Jantunen E, Prakash O (2007) Modern Maintenance System Based on Web And Mobile Technologies,” Sixth IMA International Conference on Modelling in Industrial Maintenance and Reliability (MIMAR), The Lowry Centre, Salford Quays, Manchester, UK
Gerst M, Bunduchi R, Graham I (2005) Current issues in RFID standardisation, University of Edinburgh
Gorritaxetegi E, Arnaiz A, Belew (2007) Maine Oil Monitorization by Means of On-Line Sensors. Instrumentation Viewpoint, No 6
Hu QP, Xie M, Ng SH, Levitin G (2007) Robust recurrent neural network modeling for software fault detection and correction prediction. Reliability Engineering and System Safety 92:332–340
Isermann R (2005) Model-Based Fault Detection and Diagnosis - Status and Applications. Annual Reviews in Control 29(1):71-85
Khomfoi S, Tolbert LM (2007) Fault Diagnostic System for a Multilevel Inverter Using a Neural Network. IEEE Transactions On Power Electronics 22(3)
Lerner U, Parr R, Koller D, Biswas G (2000) Bayesian Fault Detection and Diagnosis in Dynamic Systems. Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), pp 531-537, Austin, Texas
Lewin PL (2005) Continuous On-line Condition Monitoring of HV Cable Systems. First UHVNet Colloquium on Condition Monitoring and Ageing of High Voltage Plant/Equipment, Cardiff University, Cardiff
Marwala T, Mahola U, Nelwamondo FV (2006) Hidden Markov Models and Gaussian Mixture Models for Bearing Fault Detection Using Fractals. 2006 International Joint Conference on Neural Networks, Canada
Mohamed EA, Abdelaziz AY, Mostafa AS (2005) A neural network-based scheme for fault diagnosis of power transformers. Electric Power Systems Research pp29–39
Mohammadi LB, Kullmann F, Holzki M, Sigloch S, Spiesen J, Tommingas T, Weismann P, Kimber G, Klotzbücher T (2010) A low cost mid-infrared sensor for on line contamination monitoring of lubricating oils in marine engines, SPIE Photonics Europe 2010, Brussels
Muller A, Crespo Marquez A, Iung B (2008) On the concept of e-maintenance: Review and current research. Reliability Engineering & System Safety 93:1165-1187
Murphey YL, Abul Masrur M, Chen Z, Zhang B (2006) Model-Based Fault Diagnosis in Electric Drives Using Machine Learning. IEEE/ASME Transactions On Mechatronics 11(3)
Sidhu TS, Singh H, Sachdev MS (1995) Design, Implementation and Testing of An Artificial Neural Network Based Fault Direction Discriminator for Protecting Transmission Lines. IEEE Transactions on Power Delivery 10(2)
Zhang J (2006) Improved on-line process fault diagnosis through information fusion in multiple neural networks. Computers and Chemical Engineering 30:558–571
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Baglee, D., Knowles, M., Yau, CY. (2012). Development of Techniques to Manage Asset Condition Using New Tools. In: Van der Lei, T., Herder, P., Wijnia, Y. (eds) Asset Management. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2724-3_9
Download citation
DOI: https://doi.org/10.1007/978-94-007-2724-3_9
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-2723-6
Online ISBN: 978-94-007-2724-3
eBook Packages: EngineeringEngineering (R0)