Abstract
The purpose of data mining is to find new knowledge from databases in which complexity or the amount of data has so far been prohibitively large for human observation alone. Self-Organizing Map (SOM) is a special type of Artificial Neural Networks (ANNs) used in clustering, visualization and abstraction. In modern process automation systems, it is possible to collect and store huge amounts of measurement data. In this paper, SOM is used successfully to discover the base models from the automation system. Strategies based on data mining techniques are further developed for efficient fault detection and diagnostics. A semi-supervised anomaly detection technique is used with classification rules based on standardized data and domain experts’ analysis to construct the condition monitoring system.
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
Similar content being viewed by others
References
Kohonen T (1995) Self-organizing maps. Series in information sciences, vol 30, 2dn edn. Springer, Heidelberg (1997)
Quinlan, JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco
Fakhr M, Elsayad AM (2012) Steel plates faults diagnosis with data mining models. J Comput Sci 8(4):506–514
Pang-Ning T, Michael S, Vipin K (2005) Introduction to data mining, p 769, 1st edn. May, 2005. ISBN-10: 0321321367 • ISBN-13: 9780321321367©2006. Addison-Wesley, Cloth. Published 05/02/2005
Alexandra MC (2009) Scoring functions for learning Bayesian networks. INESC-ID Tec. Rep. 54/2009 Apr 2009 IST, TULisbon/INESC-ID
Microsoft Decision Trees Algorithm technical references at http://msdn.microsoft.com/en-us/library/cc645868.aspx
Chawla NV (2003) C4. 5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure
Liu W, Chawla S, Cieslaky Nitesh DA, Chawlay V (2010) A robust decision tree algorithm for imbalanced data sets. In: the Proceedings of SIAM International Conference on Data Mining
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357
Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEBoost: improving prediction of the minority class in boosting. Lecture notes in computer science, pp 107–119
Cieslak DA, Chawla NV (2008) Learning decision trees for unbalanced data. In: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases-Part I, pp 241–256. Springer, Heidelberg
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chung, S., Chung, D. (2015). Fault Detection and Diagnostics Using Data Mining. In: Tse, P., Mathew, J., Wong, K., Lam, R., Ko, C. (eds) Engineering Asset Management - Systems, Professional Practices and Certification. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-09507-3_72
Download citation
DOI: https://doi.org/10.1007/978-3-319-09507-3_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09506-6
Online ISBN: 978-3-319-09507-3
eBook Packages: EngineeringEngineering (R0)