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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
