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Unsupervised Anomaly Detection for Conveyor Temperature SCADA Data

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Advances in Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO 2016)

Part of the book series: Applied Condition Monitoring ((ACM,volume 9))

Abstract

Belt conveyor system is a crucial element of ore transport process in underground copper ore mine. Damage of single belt conveyor might cause stopping of huge part of underground transport network, especially when failure concerns the main haulage conveyor line. For that reason it is important to use SCADA monitoring system. The symptom of damage can be found in increasing temperature measured within the system. Unfortunately, operating belt conveyors can be considered as time-varying system and direct decision making using temperature value is difficult. Long-term analysis of time series enables to learn how to recognize alarming moment. Thus the removal of failure can be scheduled so as to minimize the losses in production. In this paper the clustering method was applied to the long-term observations of the temperature in order to gearbox fault detection. Moreover, the breaks in the activity of belt conveyors (no operation) caused by holidays will be determined. The clustering algorithm identifies also the specific character of the work at the beginning and end of week.

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Acknowledgements

This work is supported by the Framework Programme for Research and Innovation Horizon 2020 under grant agreement no. 636834 (DISIRE—Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock).

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Authors and Affiliations

  1. KGHM CUPRUM Ltd, R&D, Sikorskiego 2-8, 53-659, Wrocław, Poland

    Jacek Wodecki, Paweł Stefaniak, Marta Polak & Radosław Zimroz

Authors
  1. Jacek Wodecki
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  2. Paweł Stefaniak
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  3. Marta Polak
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  4. Radosław Zimroz
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Corresponding author

Correspondence to Jacek Wodecki .

Editor information

Editors and Affiliations

  1. Faculty of Mechanical Engineering, Silesian University of Technology, Gliwice, Poland

    Anna Timofiejczuk

  2. National School of Engineers of Sfax, Sfax, Tunisia

    Fakher Chaari

  3. Institute of Mining Engineering, Wrocław University of Technology, Wrocław, Poland

    Radoslaw Zimroz

  4. Institute of Mining Engineering, Wrocław University of Technology, Wrocław, Poland

    Walter Bartelmus

  5. National School of Engineers of Sfax, Sfax, Tunisia

    Mohamed Haddar

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Wodecki, J., Stefaniak, P., Polak, M., Zimroz, R. (2018). Unsupervised Anomaly Detection for Conveyor Temperature SCADA Data. In: Timofiejczuk, A., Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2016. Applied Condition Monitoring, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-61927-9_34

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  • DOI: https://doi.org/10.1007/978-3-319-61927-9_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61926-2

  • Online ISBN: 978-3-319-61927-9

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