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Sensor data analysis for equipment monitoring

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Abstract

Sensors play a key role in modern industrial plant operations. Nevertheless, the information they provide is still underused. Extracting information from the raw data generated by the sensors is a complicated task, and it is usually used to help the operator react to undesired events, other than preventing them. This paper presents SDAEM (Sensor Data Analysis for Equipment Monitoring), an oil process plant monitoring model that covers three main goals: mining the sensor time series data to understand plant operation status and predict failures, interpreting correlated data from different sensors to verify sensors interdependence, and adjusting equipments working set points that leads to a more stable plant operation and avoids an excessive number of alarms. In addition, as time series data generated by sensors grow at an extremely fast rate, SDAEM uses parallel processing to provide real-time feedback. We have applied our model to monitor a process plant of a Brazilian offshore platform. Initial results were promising since some undesired events were recognized and operators adopted the tool to assist them finding good set points for the oil processing equipments.

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References

  1. Agogino A, Srinivas S (1998) Multiple sensor expert system for diagnostic reasoning, monitoring and control of mechanical systems. Mech Syst Signal Process 2(2): 165–185

    Article  Google Scholar 

  2. Agrawal R, Lin K-I, Sawhney HS, Shim K (1995) Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: Proceedings of the 21th international conference on very large data bases. pp 490–501

  3. Anstey J, Peters D, Dawson C (2005) Discovering novelty in time series data. In: Proceedings of the 15th annual newfoundland electrical and computer engineering conference

  4. Ayres J, Gehrke J, Yiu T, Flannick J (2002) Sequential pattern mining using a bitmap representation. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining. pp 429–435. ACM Press, New york

  5. Berndt DJ, Clifford J (1996) Finding patterns in time series: a dynamic programming approach. In: Proceedings of advances in knowledge discovery and data mining. pp 229–248

  6. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(3): 1–58

    Article  Google Scholar 

  7. Cheng J, Ke Y, Ng W (2008) A survey on algorithms for mining frequent itemsets over data streams. Knowl Inf Syst 16(1): 1–27

    Article  MathSciNet  Google Scholar 

  8. Dang XH, Ng W-K, Ong K-L (2008) Online mining of frequent sets in data streams with error guarantee. Knowl Inf Syst 16(2): 245–258

    Article  Google Scholar 

  9. Dash S, Venkatasubramanian V (2000) Challenges in the industrial applications of fault diagnostic systems. Comput Chem Eng 24(2–7): 785–791

    Article  Google Scholar 

  10. Das G, Lin K, Mannila H, Renganathan G, Smyth P (1998) Rule discovery from time series. In: Proceedings of fourth international conference on knowledge discovery and data mining. pp 16–22

  11. Eichner JF, Kantelhardt JW, Bunde A, Havlin S (2006) Extreme value statistics in records with long-term persistence. Phys Rev E 73(1): 180–192

    Article  Google Scholar 

  12. Fan H, Zaiane OR, Foss A, Wu J (2009) Resolution-based outlier factor: detecting the top-n most outlying data points in engineering data. Knowl Inf Syst 19(1): 31–51

    Article  Google Scholar 

  13. Fox MS, Lowenfeld S, Kleinosky P (1983) Techniques for sensor-based diagnosis. In: Proceedings of the eighth international joint conference on artificial intelligence

  14. Galhardo CEC, Penna TJP, de Menezes MA, Soares PPS (2009) Detrended fluctuation analysis of a systolic blood pressure control loop. New J Phys 11(10): 103005

    Article  Google Scholar 

  15. Giordana A, Saitta L, Bergadano F, Brancadori F, De Marchi D (1993) Enigma: a system that learns diagnostic knowledge. IEEE Trans Knowl Data Eng 5(1): 15–28

    Article  Google Scholar 

  16. Goebel K, Wood B, Agogino A, Jain P (1994) Comparing a neural-fuzzy scheme with a probabilistic neural network for applications to monitoring and diagnostics in manufacturing systems. In: Spring symposium series of AAAI. pp 45–50

  17. Honda R, Konishi O (2001) Temporal rule discovery for time series satellite images and integration with rdb. In: PKDD 01: proceedings of the 5th European conference on principles of data mining and knowledge discovery. pp 204–215

  18. Lian X, Chen L (2008) Efficient similarity search over future stream time series. IEEE Trans Knowl Data Eng 20(1): 40–54

    Article  Google Scholar 

  19. Liu X, Lin Z, Wang H (2008) Novel online methods for time series segmentation. IEEE Trans Knowl Data Eng 20(12): 1616–1626

    Article  Google Scholar 

  20. Liu Y, Gopikrishnan P, Cizeau P, Meyer M, Peng CK, Stanley HE (1999) Statistical properties of the volatility of price fluctuations. Phys Rev E 60(2): 1390–1400

    Article  Google Scholar 

  21. Ma KL (1995) Parallel volume ray-casting for unstructured-grid data on distributed-mmory architectures. In: IEEE parallel rendering symposium. pp 23–30

  22. Oates T, Schmill MD, Cohen PR (1997) Parallel and distributed search for structure in multivariate time series. In: Proceedings of the ninth European conference on machine learning. pp 191–198

  23. Papapetrou P, Kollios G, Sclaroff S, Gunopulos D (2009) Mining frequent arrangements of temporal intervals. Knowl Inf Syst 21(2): 133–171

    Article  Google Scholar 

  24. Peng C-K, Mietus J, Hausdorff JM, Havlin S, Stanley HE, Goldberger AL (1993) Long-range anticorrelations and non-gaussian behavior of the heartbeat. Phys Rev Lett 70(9): 1343–1346

    Article  Google Scholar 

  25. Peng CK, Buldyrev SV, Goldberger AL, Havlin S, Mantegna RN, Simons M, Stanley HE (1995) Statistical properties of dna sequences. Phys A 221(1–3): 180–192

    Article  Google Scholar 

  26. Podobnik B, Stanley HE (2008) Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Phys Rev Lett 100(8): 84102

    Article  Google Scholar 

  27. Pradhan GN, Chattopadhyay R, Panchanathan S (2010) Processing body sensor data streams for continuous physiological monitoring. In: MIR ’10: proceedings of the international conference on multimedia information retrieval. pp 479–486

  28. Sarker B, Hirata T, Uehara K, Bhavsar V (2005) Mining association rules from multi-stream time series data on multiprocessor systems. Parallel Distrib Process Appl 3758/2005: 662–667

    Article  Google Scholar 

  29. Sarker B, Uehara K (2006) Efficient parallelism for mining sequential rules in time series data: a lattice based approach. IJCSNS Int J Comput Sci Netw Secur 6(7): 137–143

    Google Scholar 

  30. Shani G, Meek C, Gunawardana A (2009) Hierarchical probabilistic segmentation of discrete events. In: ICDM ’09: proceedings of the 2009 ninth IEEE international conference on data mining. pp 974–979

  31. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423, 623–656

    Google Scholar 

  32. Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Extending database technology—EDBT. pp 3–17

  33. Takeuchi J-I, Yamanishi K (2006) A unifying framework for detecting outliers and change points from time series. IEEE Trans Knowl Data Eng 18(4): 482–492

    Article  Google Scholar 

  34. Tatti N (2008) Maximum entropy based significance of itemsets. Knowl Inf Syst 17(1): 57–77

    Article  Google Scholar 

  35. Waterman DA (1986) A guide to expert systems. Addison-Wesley Publishing Company, Reading

    Google Scholar 

  36. Wu H, Salzberg B, Zhang D (2004) Online event-driven subsequence matching over financial data streams. In: SIGMOD ’04: proceedings of the 2004 ACM SIGMOD international conference on management of data. pp 23–34

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Authors and Affiliations

  1. Computer Science Institute, Fluminense Federal University, Rua Passo da Pátria 156, Niterói, RJ, 24210-240, Brazil

    Ana Cristina B. Garcia & Bianca Zadrozny

  2. Department of Systems Engineering and Computer Science, State University of Rio de Janeiro, Rua São Francisco Xavier 524, Rio de Janeiro, RJ, 20550-900, Brazil

    Cristiana Bentes

  3. Addlabs, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, Niterói, RJ, 24210-340, Brazil

    Rafael Heitor C. de Melo

  4. National Institute of Science and Technology for Complex Systems, INCT-SC, Rua Dr. Xavier Sigaud 150, Rio de Janeiro, RJ, 22290-180, Brazil

    Thadeu J. P. Penna

Authors
  1. Ana Cristina B. Garcia
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  2. Cristiana Bentes
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  3. Rafael Heitor C. de Melo
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  4. Bianca Zadrozny
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  5. Thadeu J. P. Penna
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Correspondence to Cristiana Bentes.

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Garcia, A.C.B., Bentes, C., de Melo, R.H.C. et al. Sensor data analysis for equipment monitoring. Knowl Inf Syst 28, 333–364 (2011). https://doi.org/10.1007/s10115-010-0365-1

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  • DOI: https://doi.org/10.1007/s10115-010-0365-1

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