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.
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Stefaniak, P., Śliwiński, P., Poczynek, P., Wyłomańska, A., Zimroz, R. (2019). The Automatic Method of Technical Condition Change Detection for LHD Machines - Engine Coolant Temperature Analysis. In: Fernandez Del Rincon, A., Viadero Rueda, F., Chaari, F., Zimroz, R., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2018. Applied Condition Monitoring, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-11220-2_7
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DOI: https://doi.org/10.1007/978-3-030-11220-2_7
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