Abstract
Self-propelled Mining Machines constitute large group of basic machines in underground copper ore mining in Poland. Depends on their purpose and design there are several key parameters that (according to mining companies suggestions) should be monitored and processed in order to assess machine efficiency, its condition, proper operation (according to manufacturer recommendation), human factors influence and so on. Several studies have been done regarding selection of parameters, developing algorithms of data processing, data storage and management and finally reporting and visualization of knowledge extracted from measured data. Although serious efforts have been done in this field, there is still some work to do. In this paper, a new look on the problem will be presented including data acquisition process validation, importance of data quality for automatic processing and analysis. Finally new approach for signal analysis will be proposed and compared with already existing parameters. Also kind of target re-definition attempt will be discussed. All discussed issues will be illustrated using real data acquired during machine operation.
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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, Cham. https://doi.org/10.1007/978-3-319-02678-7_124
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DOI: https://doi.org/10.1007/978-3-319-02678-7_124
Publisher Name: Springer, Cham
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