Knowledge and Information Systems

, Volume 28, Issue 2, pp 333–364 | Cite as

Sensor data analysis for equipment monitoring

  • Ana Cristina B. Garcia
  • Cristiana BentesEmail author
  • Rafael Heitor C. de Melo
  • Bianca Zadrozny
  • Thadeu J. P. Penna
Regular Paper


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.


Time series analysis Equipment monitoring Data mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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): 165–185CrossRefGoogle Scholar
  2. 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 490–501Google Scholar
  3. 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 conferenceGoogle Scholar
  4. 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 yorkGoogle Scholar
  5. 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 229–248Google Scholar
  6. 6.
    Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(3): 1–58CrossRefGoogle Scholar
  7. 7.
    Cheng J, Ke Y, Ng W (2008) A survey on algorithms for mining frequent itemsets over data streams. Knowl Inf Syst 16(1): 1–27MathSciNetCrossRefGoogle Scholar
  8. 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): 245–258CrossRefGoogle Scholar
  9. 9.
    Dash S, Venkatasubramanian V (2000) Challenges in the industrial applications of fault diagnostic systems. Comput Chem Eng 24(2–7): 785–791CrossRefGoogle Scholar
  10. 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 16–22Google Scholar
  11. 11.
    Eichner JF, Kantelhardt JW, Bunde A, Havlin S (2006) Extreme value statistics in records with long-term persistence. Phys Rev E 73(1): 180–192CrossRefGoogle Scholar
  12. 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): 31–51CrossRefGoogle Scholar
  13. 13.
    Fox MS, Lowenfeld S, Kleinosky P (1983) Techniques for sensor-based diagnosis. In: Proceedings of the eighth international joint conference on artificial intelligenceGoogle Scholar
  14. 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): 103005CrossRefGoogle Scholar
  15. 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): 15–28CrossRefGoogle Scholar
  16. 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 45–50Google Scholar
  17. 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 204–215Google Scholar
  18. 18.
    Lian X, Chen L (2008) Efficient similarity search over future stream time series. IEEE Trans Knowl Data Eng 20(1): 40–54CrossRefGoogle Scholar
  19. 19.
    Liu X, Lin Z, Wang H (2008) Novel online methods for time series segmentation. IEEE Trans Knowl Data Eng 20(12): 1616–1626CrossRefGoogle Scholar
  20. 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): 1390–1400CrossRefGoogle Scholar
  21. 21.
    Ma KL (1995) Parallel volume ray-casting for unstructured-grid data on distributed-mmory architectures. In: IEEE parallel rendering symposium. pp 23–30Google Scholar
  22. 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 191–198Google Scholar
  23. 23.
    Papapetrou P, Kollios G, Sclaroff S, Gunopulos D (2009) Mining frequent arrangements of temporal intervals. Knowl Inf Syst 21(2): 133–171CrossRefGoogle Scholar
  24. 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): 1343–1346CrossRefGoogle Scholar
  25. 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): 180–192CrossRefGoogle Scholar
  26. 26.
    Podobnik B, Stanley HE (2008) Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Phys Rev Lett 100(8): 84102CrossRefGoogle Scholar
  27. 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 479–486Google Scholar
  28. 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: 662–667CrossRefGoogle Scholar
  29. 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): 137–143Google Scholar
  30. 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 974–979Google Scholar
  31. 31.
    Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423, 623–656Google Scholar
  32. 32.
    Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Extending database technology—EDBT. pp 3–17Google Scholar
  33. 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): 482–492CrossRefGoogle Scholar
  34. 34.
    Tatti N (2008) Maximum entropy based significance of itemsets. Knowl Inf Syst 17(1): 57–77CrossRefGoogle Scholar
  35. 35.
    Waterman DA (1986) A guide to expert systems. Addison-Wesley Publishing Company, ReadingGoogle Scholar
  36. 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 23–34Google Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Ana Cristina B. Garcia
    • 1
  • Cristiana Bentes
    • 2
    Email author
  • Rafael Heitor C. de Melo
    • 3
  • Bianca Zadrozny
    • 1
  • Thadeu J. P. Penna
    • 4
  1. 1.Computer Science InstituteFluminense Federal UniversityNiteróiBrazil
  2. 2.Department of Systems Engineering and Computer ScienceState University of Rio de JaneiroRio de JaneiroBrazil
  3. 3.Addlabs, Fluminense Federal UniversityNiteróiBrazil
  4. 4.National Institute of Science and Technology for Complex Systems, INCT-SCRio de JaneiroBrazil

Personalised recommendations