TY - JOUR A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Bhanja, Tapanta T1 - Exploring distance based approaches for reducing sensor data in defect related prognosis JF - Procedia Computer Science N2 - Vibration data consists of batches of time series which if accumulated over a period of time is a huge collection of numeric data. Reducing such data for use in deep learning models for computational effciency is a challenge. Combinatorial and discrete approaches, on the other hand, is not an extensively explored area when it comes to datasets. This paper aims to identify feature reduction techniques based on discrete approaches such as euclidean distance using dot products on vibration data samples from accelerometers fitted on bearings. In this limited experimentation, the procured dataset by this approach is considerably smaller in size as compared to the actual complete data, and with comparable results in prediction models, it can be used as a smaller representation of a sensor timeline. The results based on different models show that such reductions can be considered in building IoT applications in industries based on sensors. UR - https://doi.org/10.1016/j.procs.2021.03.076 KW - sensors KW - data KW - reduced distance KW - machine learning KW - accuracy scores Y1 - 2021 UR - https://doi.org/10.1016/j.procs.2021.03.076 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13111 SN - 1877-0509 VL - 2021 IS - 184 SP - 614 EP - 621 PB - Elsevier CY - Amsterdam ER -