eprintid: 20418
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/04/18
datestamp: 2026-01-12 12:17:56
lastmod: 2026-01-12 12:17:56
status_changed: 2026-01-12 12:17:56
type: conference_item
metadata_visibility: show
creators_name: Ooi, Boonyaik Yaik
creators_name: Beh, W. L.
creators_name: Yi, Kh'Ng Xin
creators_name: Shirmohammadi, Shervin
title: Impulsive Vibrations Detection for Manufacturing Machines Using Machine Learning
ispublished: pub
keywords: Calibration; Correlation detectors; Learning systems; Time series analysis; Vibration measurement; Calibration process; Computational capability; Impulsive vibration detection; Machine-learning; Manufacturing machine; Neural base expansion analyze for time series; Production line; Times series; Vibration detection; Vibration monitoring; Vibration analysis
note: Cited by: 0
abstract: Manufacturing machines, especially those without computational capabilities, often require technicians to calibrate the machines to ensure the production line operates optimally. During the calibration process, there are times when the machine experiences transient faults, whose manual monitoring becomes unpractical and cost ineffective. In some cases, the machine's vibrations have regular beats, but when faults occur, there will be impulsive vibrations. This work addresses these cases and proposes a novel approach to detect packaging faults through monitoring the vibration of the machine by detecting impulsive vibrations from a series of beat vibrations. The novelty of the proposed solution is that it does not require calibration or fine-tuning. It uses Neural Basis Expansion Analysis for Time Series (N-BEATS) to learn and predict the beat vibration tempo. It detects impulsive vibrations by comparing incoming vibrations with the predicted beat using Spearman's rank correlation coefficient. Experimental results are promising, showing that the approach can achieve 97 accuracy in detecting impulsive vibrations. © 2025 IEEE.
date: 2025
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-105012211517&doi=10.1109%2FI2MTC62753.2025.11079010&partnerID=40&md5=eaa534623432c064c0296ca0503ddbea
id_number: 10.1109/I2MTC62753.2025.11079010
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 Beh, W. L. and Yi, Kh'Ng Xin and Shirmohammadi, Shervin  (2025) Impulsive Vibrations Detection for Manufacturing Machines Using Machine Learning.  In: UNSPECIFIED.