A Hybrid Failure Diagnosis and Prediction using Natural Language-based Process Map and Rule-based Expert System

  • Dohyeong Kim Department of Computer Science and Engineering, Kyung Hee University
  • Yingru Lin School of Engineering and ICT, University of Tasmania
  • Sungyoung Lee Department of Computer Science and Engineering, Kyung Hee University
  • Byeong Ho Kang School of Engineering and ICT, University of Tasmania
  • Soyeon Caren Han School of Information Technologies, University of Sydney


Preventive maintenance is required in large scale industries to facilitate highly efficient performance. The efficiency of production can be maximized by preventing the failure of facilities in advance. Typically, regular maintenance is conducted manually in which case, it is hard to prevent repeated failures. Also, since measures to prevent failure depend on proactive problem-solving by the facility expert, they have limitations when the expert is absent, or any error in diagnosis is made by an unskilled expert. In many cases, an alarm system is used to aid manual facility diagnosis and early detection. However, it is not efficient in practice, since it is designed to simply collect information and is activated even with small problems. In this paper, we designed and developed an automated preventive maintenance system using experts’ experience in detecting failure, determining the cause, and predicting future system failure. There are two main functions in order to acquire and analyze domain expertise. First, we proposed the network-based process map that can extract the expert’s knowledge of the written failure report. Secondly, we designed and implemented an incremental learning rule-based expert system with alarm data and failure case. The evaluation results shows that the combination of two main functions works better than another failure diagnosis and prediction frameworks.


[1] Ahmed, K.; Izadi, I.; Chen, T.; Joe, D.; Burton, T. (2013); Similarity analysis of industrial alarm flood data, IEEE Transactions on Automation Science and Engineering, 10(2), 452- 457, 2013.

[2] Chen, B.; Matthews, P. C.; Tavner, P. J. (2015); Automated on-line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition, IET Renewable Power Generation, 9(5), 503-513, 2015.

[3] Cheng, Y.; Izadi, I.; Chen, T. (2013); Optimal alarm signal processing: Filter design and performance analysis, IEEE Transactions on Automation Science and Engineering, 10(2), 446-451, 2013.

[4] Foong, O.; Sulaiman, S.; Rambli, D. R. B. A.; Abdullah, N. (2009); ALAP: Alarm prioritization system for oil refinery, Proc. of the World Congress on Engineering and Computer Science, 2, 2009.

[5] Izadi, I.; Shah, S. L.; Shook, D. S.; Kondaveeti, S. R.; Chen, T. (2009); A framework for optimal design of alarm systems, IFAC Proceedings Volumes, 42(8), 651-656, 2009.

[6] Ju, Z.; Wang, J.; Zhu, F. (2011); Named entity recognition from biomedical text using SVM; Bioinformatics and Biomedical Engineering,(iCBBE) 2011 5th International Conference on, 1-4, 2011.

[7] Kang, B. H.; Kim, Y. S.; Chen, Z.; Kim, T. (2013); Detecting significant alarms using outlier detection algorithms, Interdisciplinary Research Theory and Technology (IRRT 2013) 1-8, 2013.

[8] Langone, R.; Alzate, C.; Bey-Temsamani, A.; Suykens, J. A. (2014); Alarm prediction in industrial machines using autoregressive LS-SVM models, Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on, 359-364, 2014.

[9] Liu, Y.; Jiang, J. (2008); Fault diagnosis and prediction of hybrid system based on particle filter algorithm, Automation and Logistics, 2008. ICAL 2008. IEEE International Conference on, 1491-1495, 2008.

[10] Mohapatra, H.; Jain, S.; Chakrabarti, S. (2013); Joint Bootstrapping of Corpus Annotations and Entity Types, EMNLP, 436-446, 2013.

[11] Morwal, S.; Jahan, N.; Chopra, D. (2012); Named entity recognition using hidden Markov model (HMM), International Journal on Natural Language Computing (IJNLC), 1(4), 15-23, 2012.

[12] Orair, G. H.; Teixeira, C. H.; Meira Jr, W.; Wang, Y.; Parthasarathy, S. (2010); Distancebased outlier detection: consolidation and renewed bearing, Proceedings of the VLDB Endowment, 3(1-2), 1469-1480, 2010.

[13] Santos, I.; Nieves, J.; Bringas, P. G. (2010); Enhancing fault prediction on automatic foundry processes, World Automation Congress (WAC), 1-6, 2010.

[14] Sawsaa, A.; Lu, J. (2011); Extracting information science concepts based on jape regular expression, WORLDCOMP'11The 2011 World Congress in Computer Science, Computer Engineering, and Applied Computing, 18-21, 2011.

[15] Zhao, W.; Bai, X.; Wang, W.; Ding, J. (2005); A novel alarm processing and fault diagnosis expert system based on BNF rules, Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES, 1-6, 2005.

[16] Zhu, J.; Shu, Y.; Zhao, J.; Yang, F. (2014); A dynamic alarm management strategy for chemical process transitions, Journal of Loss Prevention in the Process industries, 30, 207- 218., 2014
How to Cite
KIM, Dohyeong et al. A Hybrid Failure Diagnosis and Prediction using Natural Language-based Process Map and Rule-based Expert System. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 2, p. 175-191, apr. 2018. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3189>. Date accessed: 14 july 2020. doi: https://doi.org/10.15837/ijccc.2018.2.3189.


expert’s knowledge, preventive maintenance, failure prediction, alarm management, knowledge reuse