eprintid: 20433
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/04/33
datestamp: 2026-01-12 12:18:05
lastmod: 2026-01-12 12:18:05
status_changed: 2026-01-12 12:18:05
type: article
metadata_visibility: show
creators_name: Lim, Jason Jing Wei
creators_name: Ooi, Boonyaik Yaik
creators_name: Lee, Wai Kong
creators_name: Tan, Teik Boon
creators_name: Liew, Soung Yue
title: Noncontact machinery operation status monitoring system with gated recurrent unit model
ispublished: pub
keywords: Acoustic noise; Machinery; Neural network models; Background noise; Legacy machine monitoring; Machine monitoring; Monitoring system; Non-contact; Operation status; Operation status tracking; Sound recognition; Status monitoring; Status tracking; Internet of things
note: Cited by: 0; All Open Access; Bronze Open Access
abstract: In manufacturing industry, assembly line monitoring provides statistical information about overall performance and reliability of the legacy machines, ensuring that the machines give maximum yield output. However, most legacy machines lack internet connectivity and advanced functionality, increasing the difficulty for tracking task. Therefore, this work seeks to introduce a noncontact acoustic method to track machines rather than the mainstream vibrational approach. In order to provide accurate tracking of the daily machine operation for our machine tracking system, we consider scenario of background noises such as environmental sounds from multiple sources as well as neighbouring machine's sound. Thus, several neural networks are employed to recognize the machine status accurately. The objective of our work is to investigate the effect of machine types and states on recognition performance of neural network models under extremely noisy environments as well as to demonstrate the possibility of recognizing the sound on edge device. The main contribution of this article is the proposal of lightweight recurrent and convolutional-based models for machine sound recognition. The experimental results of our extensive testing included with multiple types of machines and background noises show that the proposed system with gated recurrent unit model has the best recognition accuracy of F1 score 0.913 with standard uncertainty of 0.026 with decent inference speed on edge device. © 2022 Turkiye Klinikleri. All rights reserved.
date: 2022
publisher: Turkiye Klinikleri
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141920613&doi=10.55730%2F1300-0632.3944&partnerID=40&md5=33a2bbe17a39ada93cabe78144b89bc9
id_number: 10.55730/1300-0632.3944
full_text_status: none
publication: Turkish Journal of Electrical Engineering and Computer Sciences
volume: 30
number: 6
pagerange: 2373 - 2384
refereed: TRUE
issn: 13000632
citation:   Lim, Jason Jing Wei and Ooi, Boonyaik Yaik and Lee, Wai Kong and Tan, Teik Boon and Liew, Soung Yue  (2022) Noncontact machinery operation status monitoring system with gated recurrent unit model.  Turkish Journal of Electrical Engineering and Computer Sciences, 30 (6).  2373 - 2384.  ISSN 13000632