Abstract
This paper presents the implementation of a novel multi-class diagnostic technique for the detection and identification of faults based on an approach called logical analysis of data (LAD). LAD is a data mining, artificial intelligence approach that is based on pattern recognition. In the context of condition based maintenance (CBM), historical data containing condition indices and the state of the machine are the inputs to LAD. After training and testing phases, LAD generates patterns that characterize the faulty states according to the type of fault, and differentiate between these states and the normal state. These patterns are found by solving a mixed 0–1 integer linear programming problem. They are then used to detect and to identify a future unknown state of equipment. The diagnostic technique has already been tested on several known machine learning datasets. The results proved that the performance of this technique is comparable to other conventional approaches, such as neural network and support vector machine, with the added advantage of the clear interpretability of the generated patterns, which are rules characterizing the faults’ types. To demonstrate its merit in fault diagnosis, the technique is used in the detection and identification of faults in power transformers using dissolved gas analysis data. The paper reaches the conclusion that the multi-class LAD based fault detection and identification is a promising diagnostic approach in CBM.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbasion, S., Rafsanjani, A., Farshidianfar, A., & Irani, N. (2007). Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mechanical Systems and Signal Processing, 21, 2933–2945.
Abramson, S., Alexe, G., Hammer, P., & Kohn, J. (2005). A computational approach to predicting cell growth on polymeric biomaterials. Journal of Biomedical Materials Research Part A, 73, 116–124.
Alexe, G., Alexe, S., Bonates, T., & Kogan, A. (2007). Logical analysis of data-the vision of Peter L. Hammer. Annals of Mathematics and Artificial Intelligence, 49, 265–312.
Alexe, G., Alexe, S., Axelrod, D., Bonates, T., Lozina, I., Reiss, M., et al. (2006). Breast cancer prognosis by combinatorial analysis of gene expression data. Breast Cancer Research, 8, R41.
Alexe, G., Alexe, S., Axelrod, D., Hammer, P., & Weissmann, D. (2005). Logical analysis of diffuse large B-cell lymphomas. Artificial Intelligence in Medicine, 34, 235–267.
Alexe, G., Alexe, S., Liotta, L., Petricoin, E., Reiss, M., & Hammer, P. (2004). Ovarian cancer detection by logical analysis of proteomic data. Proteomics, 4, 766–783.
Almuallim, H., & Dietterich, T. (1994). Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69, 279–305.
Bennane, A., & Yacout, S. (2012). LAD-CBM; new data processing tool for diagnosis and prognosis in condition-based maintenance. Journal of Intelligent Manufacturing, 23, 265–275.
Berkelaar, M., Eikland, K., & Notebaert, P. (2004), Lp solve, open source (mixed-integer) linear programming system. In (GNU LGPL (Lesser General Public Licence) Version 5.5.
Boros, E., Crama, Y., Hammer, P., Ibaraki, T., Kogan, A., & Makino, K. (2009). Logical analysis of data: Classification with justification. Rutcor Research Report, RRR 5-2009.
Boros, E., Hammer, P., Ibaraki, T., Kogan, A., Mayoraz, E., & Muchnik, I. (2000). An implementation of logical analysis of data. IEEE Transactions on Knowledge and Data Engineering, 12, 292–306.
Christian, K., Mureithi, N., Lakis, A., & Thomas, M. (2007). On the use of time synchronous averaging, independent component analysis and support vector machines for bearing fault diagnosis. In First international conference on industrial risk engineering, Montreal
Duval, M., & DePablo, A. (2001). Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases. IEEE Electrical Insulation Magazine, 17, 31–41.
Frank, A., & Asuncion, A. (2010). UCI machine learning repository. http://archive.ics.uci.edu/ml.
Hand, D. J. (1999). Statistics and data mining: Intersecting disciplines. SICKDD Explorations, 1(1), 16–19.
Hammer, P., Kogan, A., Simeone, B., & Szedmák, S. (2004). Pareto-optimal patterns in logical analysis of data. Discrete Applied Mathematics, 144, 79–102.
Heathcote, M. (2007). The J & P transformer book: A practical technology of the power transformer. Amsterdam: Elsevier.
Hu, W., Starr, A., Zhou, Z., & Leung, A. (2001). An intelligent integrated system scheme for machine tool diagnostics. The International Journal of Advanced Manufacturing Technology, 18, 836–841.
Jamaludin, N., Mba, D., & Bannister, R. (2001). Condition monitoring of slow-speed rolling element bearings using stress waves. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 215, 245–271.
Jardine, A., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20, 1483–1510.
Li, Z., Wu, Z., He, Y., & Fulei, C. (2005). Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery. Mechanical Systems and Signal Processing, 19, 329–339.
Lim, T., Loh, W., & Shih, Y. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40, 203–228.
Lv, G., Cheng, H., Zhai, H., & Dong, L. (2005). Fault diagnosis of power transformer based on multi-layer SVM classifier. Electric Power Systems Research, 75, 9–15.
Ma, J., & Li, J. (1995). Detection of localised defects in rolling element bearings via composite hypothesis test. Mechanical Systems and Signal Processing, 9, 63–75.
Mayoraz, E., & Moreira, M. (1996). On the decomposition of polychotomies into dichotomies. Internal report, University of Rutgers, RUTCOR
Moreira, L. (2000). The use of Boolean concepts in general classification contexts. Lausanne: École Polytechnique Fédérale De Lausanne.
Mortada, M., Carroll, T., & Yacout, S. (2012). Rogue components: Their effect and control using logical analysis of data. Journal of Intelligent Manufacturing, 23(2), 289–302.
Mortada, M. (2010). Applicability and interpretability of logical analysis of data in condition based maintenance. Doctoral Thesis. École Polytechnique de Montréal.
Ocak, H., Loparo, K. (2001). A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals. In IEEE international conference on acoustic speech signal processing citeseer, pp. 3141–3144.
Quinlan, J. (1993). C4. 5: Programs for machine learning. San Mateo: Morgan Kaufmann.
Ryoo, H., & Jang, I. (2009). MILP approach to pattern generation in logical analysis of data. Discrete Applied Mathematics, 157, 749–761.
Saitta, S., Raphael, B., & Smith, I. (2005). Data mining techniques for improving the reliability of system identification. Advanced Engineering Informatics, 19, 289–298.
Salamanca, D., & Yacout, S. (2007). Condition based maintenance with logical analysis of data. In 7e Congrès International de genie industriel, Quebec.
Saxena, A., & Saad, A. (2004). Fault diagnosis in rotating mechanical systems using self-organizing maps. Artificial Neural Networks in Engineering (ANNIE04).
Spoerre, J. (1997). Application of the cascade correlation algorithm (CCA) to bearing fault classification problems. Computers in Industry, 32, 295–304.
Staszewski, W., Worden, K., & Tomlinson, G. (1997). Time-frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition. Mechanical Systems and Signal Processing, 11, 673–692.
Subrahmanyam, M., & Sujatha, C. (1997). Using neural networks for the diagnosis of localized defects in ball bearings. Tribology International, 30, 739–752.
Widodo, A., Yang, B., & Han, T. (2007). Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Systems with Applications, 32, 299–312.
Wu, S., & Chow, T. (2004). Induction machine fault detection using SOM-based RBF neural networks. IEEE Transactions on Industrial Electronics, 51, 183–194.
Xu, Y., & Ge, M. (2004). Hidden Markov model-based process monitoring system. Journal of Intelligent Manufacturing, 15, 337–350.
Yacout, S. (2010). Fault detection and diagnosis for condition based maintenance using logical analysis of data. In The 40th international conference on computers and industrial engineering.
Yam, R., Tse, P., Li, L., & Tu, P. (2001). Intelligent predictive decision support system for condition-based maintenance. The International Journal of Advanced Manufacturing Technology, 17, 383–391.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mortada, MA., Yacout, S. & Lakis, A. Fault diagnosis in power transformers using multi-class logical analysis of data. J Intell Manuf 25, 1429–1439 (2014). https://doi.org/10.1007/s10845-013-0750-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-013-0750-1