Handling irregular and noisy field data is challenging in condition monitoring. In contrast to refined lab data, where external influences are kept to a minimum, acquired signal from accelerometer attached to mechanical devices involves a great deal of uncontrollable variables. Especially, irregular operation cycles of the process make difficult to specify significant vibration signals for monitoring without mechanical expertise and information of the manipulators' motion. In this study, we distinguish motion signals from noisy raw signal using Shannon Energy Envelope (SEE). The extracted individual motion signals are algorithmically clustered through the signal graph characteristics for each robot motion. Clusters are evaluated for the effectiveness of monitoring, and it enables users to obtain a reference whose signal can perform the same accuracy for condition monitoring with expert knowledge.
segmentation, data preprocessing, field application, condition monitoring
Deng, D., (2020). DBSCAN clustering algorithm based on density. 2020 7th International Forum on Electrical Engineering and Automation (IFEEA) (pp. 949-953), September 25-27, Hefei, China. doi: 10.1109/IFEEA51475.2020.00199
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