Abstract
In the paper we will show specific case study related to long-term temperature data from electric motor bearings (with progressing fault) used in belt conveyor operating in open-cast mine. Existing SCADA system for data acquisition has built-in simple decision making rules based on static thresholds. Due to time-varying environmental and operational conditions, i.e. machine is heavily influenced by ambient temperature (−20 up to +30 \(^\circ \)C) and external load (no operation, idle mode, startup with heavily overloaded belt). Hence, basic analytical methods based on simple statistics are sometimes not sufficient to determine the change of technical condition of the bearing. In order to address this issue authors propose an analytical method based on multidimensional distribution analysis. Finally, a clustering method can be applied to multidimensional representation of the initial data. This approach allows to differentiate the technical condition across the investigated time period.
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Migdał, W., Wodecki, J., Wuczyński, M., Stefaniak, P., Wyłomańska, A., Zimroz, R. (2019). Long Term Temperature Data Analysis for Damage Detection in Electric Motor Bearings with Density Modeling and Bhattacharyya Distance. 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_16
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DOI: https://doi.org/10.1007/978-3-030-11220-2_16
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