A Signal Pre-processing Method for Condition Monitoring based on Vibration Signals from On-Site Manipulators



Published Sep 4, 2023
Hea-Ryeon Seo Geonhwi Lee Gun Sik Jae Min Deog Hyeon Kim Jin Woo Park Hae-Jin Choi


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.

Abstract 187 | PDF Downloads 217



segmentation, data preprocessing, field application, condition monitoring

Pech, M., Vrchota, J., & Bednář, J. (2021). Predictive Maintenance and Intelligent Sensors in Smart Factory: Review. Sensors, vol. 21(4), pp. 1470. doi:10.3390/s21041470

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
Regular Session Papers