eprintid: 20424
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/04/24
datestamp: 2026-01-12 12:18:08
lastmod: 2026-01-12 12:18:08
status_changed: 2026-01-12 12:18:08
type: conference_item
metadata_visibility: show
creators_name: Ooi, Boonyaik Yaik
creators_name: Kh'ng, Xin Yi
creators_name: Beh, W. L.
creators_name: Shirmohammadi, Shervin
title: Machine Status Tracking Using Vibration via Sparse Sampling and Without Reconstruction
ispublished: pub
keywords: 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
note: Cited by: 0
abstract: 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.
date: 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197754620&doi=10.1109%2FI2MTC60896.2024.10560888&partnerID=40&md5=71972686ef3aa0ebd065cdb5fde69bbb
id_number: 10.1109/I2MTC60896.2024.10560888
full_text_status: none
publication: Conference Record - IEEE Instrumentation and Measurement Technology Conference
refereed: TRUE
isbn: 9780879425791; 0879425792; 9781665453837; 9781665483605; 9781467346221; 9781467392204; 0780372182; 1424415411; 0780388798; 078038248X
issn: 10915281
citation:   Ooi, Boonyaik Yaik and Kh'ng, Xin Yi and Beh, W. L. and Shirmohammadi, Shervin  (2024) Machine Status Tracking Using Vibration via Sparse Sampling and Without Reconstruction.  In: UNSPECIFIED.