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Data Science and Digital Business pp 301–316Cite as

False Alarms Management by Data Science

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Abstract

Due to the development of control system technology over the last years, the number of sensors has increased dramatically and the configuration of alarms in control systems has become easier. It leads to a large number of alarms and increased operator workload. Industrial plants are currently underperforming due to alarm flood, which can cause minor, or even catastrophic, incidents. The businesses are demanding data science to avoid this, it is necessary to use process and alarm data. The industrial plants must understand the entire process and they count on the experience of the operator. It has been considered that collaborative research between academic world and industry should be undertaken to prevent flooding of alarms, both in normal and transitory conditions. New guidelines, standards and scientific/academic research should be developed. Nowadays new statistical, analytical and mathematical tools are being implemented for alarm detection, and the role of the operator must also be taken into account for correct alarm flood resolution. It will lead to a future with safer and more cost-effective industrial systems.

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References

  1. ANSI. (2009). ISA-18.2-2009 management of alarm systems for the process industries. Durham, NC, USA: International Society of Automation.

    Google Scholar 

  2. Jiménez, A. A., Gómez Muñoz, C. Q., & García Márquez, F. P. (2018). Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliability Engineering and System Safety.

    Google Scholar 

  3. Munoz, J. C., Márquez, F. G., & Papaelias, M. (2013). Railroad inspection based on ACFM employing a non-uniform b-spline approach. Mechanical Systems and Signal Processing, 40, 605–617.

    Article  Google Scholar 

  4. EEMUA. (2007). Alarm systems: A guide to design, management and procurement. Engineering Equipment and Materials Users Association.

    Google Scholar 

  5. Hollifield, B. R., & Habibi, E. (2010). Alarm management: A comprehensive guide: Practical and proven methods to optimize the performance of alarm management systems. ISA.

    Google Scholar 

  6. Rothenberg, D. H. (2009). Alarm management for process control: A best-practice guide for design, implementation, and use of industrial alarm systems. Momentum Press.

    Google Scholar 

  7. Walker, B., Smith, K. D., & Kekich, M. D. (2003). Limiting shift-work fatigue in process control. Chemical Engineering Progress, 99, 54–57.

    Google Scholar 

  8. Gómez Muñoz, C. Q., Arcos Jimenez, A., García Marquez, F. P., Kogia, M., Cheng, L., Mohimi, A., & Papaelias, M. (2017). Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers. Structural Health Monitoring. https://doi.org/10.1177/1475921717734501.

    Article  Google Scholar 

  9. Gómez Muñoz, C. Q., García Marquez, F. P., Lev, B., & Arcos, A. (2017). New pipe notch detection and location method for short distances employing ultrasonic guided waves. Acta Acustica United with Acustica, 103, 772–781.

    Article  Google Scholar 

  10. de la Hermosa González, R. R., García Márquez, F. P., & Dimlaye, V. (2015). Maintenance management of wind turbines structures via MFCS and wavelet transforms. Renewable and Sustainable Energy Reviews, 48, 472-482.

    Article  Google Scholar 

  11. Hu, W., Al-Dabbagh, A. W., Chen, T., & Shah, S. L. (2016). Process discovery of operator actions in response to univariate alarms. IFAC-PapersOnLine, 49, 1026–1031.

    Article  Google Scholar 

  12. Severson, K., Chaiwatanodom, P., & Braatz, R. D. (2016). Perspectives on process monitoring of industrial systems. Annual Reviews in Control, 42, 190–200.

    Article  Google Scholar 

  13. Marquez, F. G. (2006). An approach to remote condition monitoring systems management.

    Google Scholar 

  14. Arcos Jiménez, A., Gómez Muñoz, C. Q., & García Márquez, F. P. (2017). Machine learning for wind turbine blades maintenance management. Energies, 11, 13.

    Article  Google Scholar 

  15. García Márquez, F. P., Muñoz, G., Quiterio, C., Papelias, M., & Arcos Jiménez, A. (2015). A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers.

    Google Scholar 

  16. Roberts, C., Márquez, F., & Tobias, A. (2010). A pragmatic approach to the condition monitoring of hydraulic level crossing barriers. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 224, 605–610.

    Article  Google Scholar 

  17. Izadi, I., Shah, S. L., Shook, D. S., & Chen, T. (2009). An introduction to alarm analysis and design. IFAC Proceedings Volumes, 42, 645–650.

    Article  Google Scholar 

  18. Landgrebe, T. C., & Duin, R. P. (2008). Efficient multiclass roc approximation by decomposition via confusion matrix perturbation analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 810–822.

    Article  Google Scholar 

  19. Wang, J., Yang, F., Chen, T., & Shah, S. L. (2016). An overview of industrial alarm systems: Main causes for alarm overloading, research status, and open problems. IEEE Transactions on Automation Science and Engineering, 13, 1045–1061.

    Article  Google Scholar 

  20. Kondaveeti, S. R., Izadi, I., Shah, S. L., Shook, D. S., Kadali, R., & Chen, T. (2013). Quantification of alarm chatter based on run length distributions. Chemical Engineering Research and Design, 91, 2550–2558.

    Article  Google Scholar 

  21. Naghoosi, E., Izadi, I., & Chen, T. (2011). Estimation of alarm chattering. Journal of Process Control, 21, 1243–1249.

    Article  Google Scholar 

  22. Hu, W., Chen, T., Shah, S. L., & Hollender, M. (2017). Cause and effect analysis for decision support in alarm floods. IFAC-PapersOnLine, 50, 13940–13945.

    Article  Google Scholar 

  23. IEC. (2014). IEC 62682 management of alarm systems for the process industries. International Electrotechnical Commission (IEC).

    Google Scholar 

  24. Timms, C. (2009). Hazards equal trips or alarms or both. Process Safety and Environmental Protection, 87, 3–13.

    Article  Google Scholar 

  25. 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, 452–457.

    Article  Google Scholar 

  26. Hu, W., Wang, J., & Chen, T. (2015). Fast sequence alignment for comparing industrial alarm floods∗. IFAC-PapersOnLine, 48, 647–652.

    Article  Google Scholar 

  27. Rodrigo, V., Chioua, M., Hagglund, T., & Hollender, M. (2016). Causal analysis for alarm flood reduction. IFAC-PapersOnLine, 49, 723–728.

    Article  Google Scholar 

  28. Cheng, Y., Izadi, I., & Chen, T. (2013). Pattern matching of alarm flood sequences by a modified smith–waterman algorithm. Chemical Engineering Research and Design, 91, 1085–1094.

    Article  Google Scholar 

  29. García Márquez, F. P., Chacón Muñoz, J. M., & Tobias, A. M. (2015). B-spline approach for failure detection and diagnosis on railway point mechanisms case study. Quality Engineering, 27, 177–185.

    Article  Google Scholar 

  30. García Márquez, F. P., Pliego Marugán, A., Pinar Pérez, J. M., Hillmansen, S., & Papaelias, M. (2017). Optimal dynamic analysis of electrical/electronic components in wind turbines. Energies, 10, 1111.

    Article  Google Scholar 

  31. García Márquez, F. P., Pedregal, D. J., & Roberts, C. (2015). New methods for the condition monitoring of level crossings. International Journal of Systems Science, 46, 878–884.

    Article  Google Scholar 

  32. García Márquez, F. P., & Chacón Muñoz, J. M. (2012). A pattern recognition and data analysis method for maintenance management. International Journal of Systems Science, 43, 1014–1028.

    Article  Google Scholar 

  33. de la Hermosa Gonzalez, R. R., García Márquez, F. P., Dimlaye, V., & Ruiz-Hernández, D. (2014). Pattern recognition by wavelet transforms using macro fibre composites transducers. Mechanical Systems and Signal Processing, 48, 339–350.

    Article  Google Scholar 

  34. Lai, S., & Chen, T. (2017). A method for pattern mining in multiple alarm flood sequences. Chemical Engineering Research and Design, 117, 831–839.

    Article  Google Scholar 

  35. Hu, W., Chen, T., & Shah, S. L. (2018). Detection of frequent alarm patterns in industrial alarm floods using itemset mining methods. IEEE Transactions on Industrial Electronics.

    Google Scholar 

  36. Guo, C., Hu, W., Lai, S., Yang, F., & Chen, T. (2017). An accelerated alignment method for analyzing time sequences of industrial alarm floods. Journal of Process Control, 57, 102–115.

    Article  Google Scholar 

  37. Stauffer, T., Sands, N., & Dunn, D. (2010). Alarm management and ISA-18–a journey, not a destination. In: Texas A&M Instrumentation Symposium.

    Google Scholar 

  38. Ávila, S., & Pessoa, F. (2015). Proposition of review in EEMUA 201 and ISO standard 11064 based on cultural aspects in labor team, lng case. Procedia Manufacturing, 3, 6101–6108.

    Article  Google Scholar 

  39. Kondaveeti, S. R., Izadi, I., Shah, S. L., Black, T., & Chen, T. (2012). Graphical tools for routine assessment of industrial alarm systems. Computers and Chemical Engineering, 46, 39–47.

    Article  Google Scholar 

  40. Kondaveeti, S. R., Izadi, I., Shah, S. L., & Black, T. (2010). Graphical representation of industrial alarm data. IFAC Proceedings Volumes, 43, 181–186.

    Article  Google Scholar 

  41. EEMUA. (1999). Alarm systems: A guide to design, management and procurement. Engineering Equipment and Materials Users Association London.

    Google Scholar 

  42. Yang, F., Shah, S. L., Xiao, D., & Chen, T. (2012). Improved correlation analysis and visualization of industrial alarm data. ISA Transactions, 51, 499–506.

    Article  Google Scholar 

  43. Su, J., Guo, C., Zang, H., Yang, F., Huang, D., Gao, X., et al. (2018). A multi-setpoint delay-timer alarming strategy for industrial alarm monitoring. Journal of Loss Prevention in the Process Industries, 54, 1–9.

    Article  Google Scholar 

  44. Jiménez, A. A., Gómez Muñoz, C. Q., García Marquez, F. P., & Zhang, L. (2017). Artificial intelligence for concentrated solar plant maintenance management. In: Proceedings of the Tenth International Conference on Management Science and Engineering Management, pp. 125–134. Springer.

    Google Scholar 

  45. 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, 651–656.

    Article  Google Scholar 

  46. Tan, W., Sun, Y., Azad, I. I., & Chen, T. (2017). Design of univariate alarm systems via rank order filters. Control Engineering Practice, 59, 55–63.

    Article  Google Scholar 

  47. García Márquez, F. P. (2010). A new method for maintenance management employing principal component analysis. Structural Durability and Health Monitoring, 6, 89–99.

    Google Scholar 

  48. Cheng, Y., Izadi, I., & Chen, T. (2013). Optimal alarm signal processing: Filter design and performance analysis. IEEE Transactions on Automation Science and Engineering, 10, 446–451.

    Article  Google Scholar 

  49. Xu, J., Wang, J., Izadi, I., & Chen, T. (2012). Performance assessment and design for univariate alarm systems based on FAR, MAR, and AAD. IEEE Transactions on Automation Science and Engineering, 9, 296–307.

    Article  Google Scholar 

  50. Zang, H., Yang, F., & Huang, D. (2015). Design and analysis of improved alarm delay-timers. IFAC-PapersOnLine, 48, 669–674.

    Article  Google Scholar 

  51. Adnan, N. A., Cheng, Y., Izadi, I., & Chen, T. (2013). Study of generalized delay-timers in alarm configuration. Journal of Process Control, 23, 382–395.

    Article  Google Scholar 

  52. Wang, J., & Chen, T. (2013). An online method for detection and reduction of chattering alarms due to oscillation. Computers and Chemical Engineering, 54, 140–150.

    Article  Google Scholar 

  53. Wang, J., & Chen, T. (2014). An online method to remove chattering and repeating alarms based on alarm durations and intervals. Computers and Chemical Engineering, 67, 43–52.

    Article  Google Scholar 

  54. Pliego Marugán, A., García Márquez, F. P., & Lev, B. (2017). Optimal decision-making via binary decision diagrams for investments under a risky environment. International Journal of Production Research, 55, 5271–5286.

    Article  Google Scholar 

  55. Peres, F. A. P., & Fogliatto, F. S. (2018). Variable selection methods in multivariate statistical process control: A systematic literature review. Computers and Industrial Engineering, 115, 603–619.

    Article  Google Scholar 

  56. Gómez Muñoz, C. Q., García Márquez, F. P., & Sánchez Tomás, J. M. (2016). Ice detection using thermal infrared radiometry on wind turbine blades. Measurement, 93, 157–163.

    Article  Google Scholar 

  57. Abraham, B., & Chuang, A. (1993). Expectation-maximization algorithms and the estimation of time series models in the presence of outliers. Journal of Time Series Analysis, 14, 221–234.

    Article  Google Scholar 

  58. Chen, T., & Sun, Y. (2009). Probabilistic contribution analysis for statistical process monitoring: A missing variable approach. Control Engineering Practice, 17, 469–477.

    Article  Google Scholar 

  59. Singhal, A., & Seborg, D. E. (2000). Dynamic data rectification using the expectation maximization algorithm. AIChE Journal, 46, 1556–1565.

    Article  Google Scholar 

  60. Chen, T. (2010). On reducing false alarms in multivariate statistical process control. Chemical Engineering Research and Design, 88, 430–436.

    Article  Google Scholar 

  61. García Márquez, F. P., & García-Pardo, I. P. (2010). Principal component analysis applied to filtered signals for maintenance management. Quality and Reliability Engineering International, 26, 523–527.

    Article  Google Scholar 

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Acknowledgements

The work reported herewith has been financially supported by the Spanish Ministerio de Economía y Competitividad, under the Research Grants RTC-2016-5694-3 and DPI2015-67264-P.

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Authors and Affiliations

  1. Ingenium Reseach Group, University of Castilla-La Mancha, Ciudad Real, Spain

    Ana María Peco Chacón & Fausto Pedro García Márquez

Authors
  1. Ana María Peco Chacón
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  2. Fausto Pedro García Márquez
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Correspondence to Ana María Peco Chacón .

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Editors and Affiliations

  1. ETSI Industriales de Ciudad Real, University of Castilla-La Mancha, Ciudad Real, Spain

    Fausto Pedro García Márquez

  2. LeBow College of Business, Drexel University, Philadelphia, PA, USA

    Benjamin Lev

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Peco Chacón, A.M., García Márquez, F.P. (2019). False Alarms Management by Data Science. In: García Márquez, F., Lev, B. (eds) Data Science and Digital Business. Springer, Cham. https://doi.org/10.1007/978-3-319-95651-0_15

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