TY  - CONF
N2  - One of the challenges in monitoring machinery vibration is handling the huge amount of data that must be transmitted, stored, and analyzed before transforming it into useful information. Many existing works aim at devising efficient and effective data reduction schemes to minimize this data load. Unfortunately, many of these techniques require the reconstruction of data, even in its reduced form, before proceeding with analysis. This is true even for compressive sampling (CS), the current state-of-the-art method for reducing sampling and data transfer volumes, as the vibration signal must be reconstructed before analysis can be applied. In this work, we capitalize on the nature of manufacturing machines, where vibrations often remain stationary over time and only change when the machine status changes. As such, we propose the use of a multilayer neural network and k-nearest neighbor (kNN) method to analyze the sparsely and randomly sampled data and subsequently identify the machine status without the need for data reconstruction. Experimental results demonstrate that the proposed solution can track a machine's status with an accuracy of 99.88, using only 15 of the vibration data. © 2024 IEEE.
KW  - Classification (of information); Compressed sensing; Data compression; Data reduction; Data transfer; Metadata; Multilayer neural networks; Nearest neighbor search; 'current; Compressive sampling; Data load; Machinery vibrations; Sparse sampling; State-of-the-art methods; Status tracking; Transfer volumes; Vibration monitoring; Vibration signal; Internet of things
ID  - scholars20424
AV  - none
SN  - 10915281
A1  - Ooi, Boonyaik Yaik
A1  - Kh'ng, Xin Yi
A1  - Beh, W. L.
A1  - Shirmohammadi, Shervin
N1  - Cited by: 0
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2024///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197754620&doi=10.1109%2FI2MTC60896.2024.10560888&partnerID=40&md5=71972686ef3aa0ebd065cdb5fde69bbb
TI  - Machine Status Tracking Using Vibration via Sparse Sampling and Without Reconstruction
ER  -