Advertisement

The Automatic Method of Technical Condition Change Detection for LHD Machines - Engine Coolant Temperature Analysis

  • Paweł StefaniakEmail author
  • Paweł Śliwiński
  • Paula Poczynek
  • Agnieszka Wyłomańska
  • Radosław Zimroz
Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 15)

Abstract

In the paper the long-term temperature data from LHD (load, haul, dump) machine from underground copper ore mine are analyzed. The main problem is to detect the moment when the temperature increases due to change of condition. Usually in condition monitoring system the problem is solved by selection of fixed threshold and observation if the temperature data exceeds this limit value. However this approach seems to be insufficient for the real data that are influenced by various factors related to harsh operating conditions in underground mine. In case of change of technical condition, events of exceeded temperature do not occur locally in time but affect the statistical properties of the temperature data for longer period of time. The key task could be defined as identification so called structural break point in raw signal based on statistical analysis in longer time window. In this paper a new method for detection of the structural break point of temperature data from LHD machine based on regime variance approach is presented. The data are investigated here as signals with two regimes behavior (good/bad condition). We select the most suitable critical point in order to separate different regimes. The introduced methodology is fully automatic and is based on simple statistics of the temperature signal.

Keywords

Loading-haulage-dumping machines Temperature data Condition monitoring Statistical analysis Structural break point detection 

References

  1. 1.
    Cutifani M, Quinn B, Gurgenci H (1996) Increased equipment reliability, safety and availability without necessarily increasing the cost of maintenance. In: Mining technology conference, Freemantle, WA, pp 10–1Google Scholar
  2. 2.
    Gustafson A, Schunnesson H, Galar D, Kumar U (2013) The influence of the operating environment on manual and automated load-haul-dump machines: a fault tree analysis. Int J Mining Reclam Environ 27(2):75–87CrossRefGoogle Scholar
  3. 3.
    Kumar U (1990) Reliability analysis of load-haul-dump machines. Ph.D. thesis, Luleå tekniska universitetGoogle Scholar
  4. 4.
    Stefaniak P, Zimroz R, Obuchowski J, Sliwinski P, Andrzejewski M (2015) An effectiveness indicator for a mining loader based on the pressure signal measured at a bucket’s hydraulic cylinder. Procedia Earth Planet Sci 15:797–805CrossRefGoogle Scholar
  5. 5.
    Zimroz R, Wodecki J, Król R, Andrzejewski M, Sliwinski P, Stefaniak P (2014) Self-propelled mining machine monitoring system-data validation, processing and analysis. In: Drebenstedt C, Singhal R (eds) Mine planning and equipment selection. Springer, Heidelberg, pp 1285–1294CrossRefGoogle Scholar
  6. 6.
    Gustafson A, Lipsett M, Schunnesson H, Galar D, Kumar U (2014) Development of a Markov model for production performance optimisation. Application for semi-automatic and manual LHD machines in underground mines. Int J Mining Reclam Environ 28(5):342–355CrossRefGoogle Scholar
  7. 7.
    Gustafson A, Schunnesson H, Galar D, Kumar U (2013) Production and maintenance performance analysis: manual versus semi-automatic LHDs. J Qual Maintenance Eng 19(1):74–88CrossRefGoogle Scholar
  8. 8.
    Laukka A, Saari J, Ruuska J, Juuso E, Lahdelma S (2016) Condition-based monitoring for underground mobile machines. Int J Industr Syst Eng 23(1):74–89CrossRefGoogle Scholar
  9. 9.
    Wyłomańska A, Zimroz R (2014) Signal segmentation for operational regimes detection of heavy duty mining mobile machines-a statistical approach. Diagnostyka 15Google Scholar
  10. 10.
    Sawicki M, Zimroz R, Wyłomańska A, Obuchowski J, Stefaniak P, Żak G (2015) An automatic procedure for multidimensional temperature signal analysis of a SCADA system with application to belt conveyor components. Procedia Earth Planet Sci 15:781–790CrossRefGoogle Scholar
  11. 11.
    Wodecki J, Stefaniak P, Michalak A, Wyłomańska A, Zimroz R (2017) Technical condition change detection using Anderson-Darling statistic approach for LHD machines-engine overheating problem. Int J Mining Reclam Environ 32:392–400CrossRefGoogle Scholar
  12. 12.
    Kucharczyk D, Wyłomańska A, Zimroz R (2017) Structural break detection method based on the adaptive regression splines technique. Phys A: Stat Mech Appl 471:499–511MathSciNetCrossRefGoogle Scholar
  13. 13.
    Gajda J, Sikora G, Wyłomańska A (2013) Regime variance testing - a quantile approach. Acta Phys Polon B 44(5):1015–1035MathSciNetCrossRefGoogle Scholar
  14. 14.
    Tsay RS (1988) Outliers, level shifts, and variance changes in time series. J Forecast 7(1):1–20CrossRefGoogle Scholar
  15. 15.
    Wyłomańska A, Zimroz R, Janczura J, Obuchowski J (2016) Impulsive noise cancellation method for copper ore crusher vibration signals enhancement. IEEE Trans Industr Electron 63(9):5612–5621CrossRefGoogle Scholar
  16. 16.
    Lopatka M, Laplanche C, Adam O, Motsch JF, Zarzycki J (2005) Non-stationary time-series segmentation based on the Schur prediction error analysis. In: 2005 IEEE/SP 13th workshop on statistical signal processing. IEEE, pp 251–256Google Scholar
  17. 17.
    Makowski R, Zimroz R (2013) A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings. Mech Syst Sig Process 38(1):65–77CrossRefGoogle Scholar
  18. 18.
    Makowski R, Zimroz R (2014) New techniques of local damage detection in machinery based on stochastic modelling using adaptive Schur filter. Appl Acoust 77:130–137CrossRefGoogle Scholar
  19. 19.
    Li C, Liang M, Wang T (2015) Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals. Mech Syst Sig Process 64:132–148CrossRefGoogle Scholar
  20. 20.
    Popescu TD, Aiordachioaie D (2013) Signal segmentation in time-frequency plane using Renyi entropy-application in seismic signal processing. In: 2013 conference on control and fault-tolerant systems (SysTol). IEEE, pp 312–317Google Scholar
  21. 21.
    Obuchowski J, Wyłomańska A, Zimroz R (2014) Selection of informative frequency band in local damage detection in rotating machinery. Mech Syst Sig Process 48(1):138–152CrossRefGoogle Scholar
  22. 22.
    Crossman JA, Guo H, Murphey YL, Cardillo J (2003) Automotive signal fault diagnostics-part i: signal fault analysis, signal segmentation, feature extraction and quasi-optimal feature selection. IEEE Trans Veh Technol 52(4):1063–1075CrossRefGoogle Scholar
  23. 23.
    Chen C (1984) On a segmentation algorithm for seismic signal analysis. Geoexploration 23(1):35–40CrossRefGoogle Scholar
  24. 24.
    Gaby JE, Anderson KR (1984) Hierarchical segmentation of seismic waveforms using affinity. Geoexploration 23(1):1–16CrossRefGoogle Scholar
  25. 25.
    Popescu TD (2014) Signal segmentation using changing regression models with application in seismic engineering. Digit Sig Process 24:14–26CrossRefGoogle Scholar
  26. 26.
    Pikoulis EV, Psarakis EZ (2012) A new automatic method for seismic signals segmentation. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 3973–3976Google Scholar
  27. 27.
    Azami H, Mohammadi K, Bozorgtabar B (2012) An improved signal segmentation using moving average and Savitzky-Golay filter. J Sig Inf Process 3(01):39Google Scholar
  28. 28.
    Bowman AW, Azzalini A (1997) Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations, vol 18. OUP, OxfordGoogle Scholar
  29. 29.
    Rathi Y, Michailovich O, Malcolm J, Tannenbaum A (2006) Seeing the unseen: segmenting with distributions. In: International conference on signal and image processingGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Paweł Stefaniak
    • 1
    Email author
  • Paweł Śliwiński
    • 2
  • Paula Poczynek
    • 1
  • Agnieszka Wyłomańska
    • 1
  • Radosław Zimroz
    • 1
  1. 1.Research and Development Centre, KGHM Cuprum Ltd.WroclawPoland
  2. 2.KGHM Polska MiedźLubinPoland

Personalised recommendations