TY  - CONF
PB  - Institute of Electrical and Electronics Engineers Inc.
N1  - Cited by: 0
TI  - Impulsive Vibrations Detection for Manufacturing Machines Using Machine Learning
Y1  - 2025///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-105012211517&doi=10.1109%2FI2MTC62753.2025.11079010&partnerID=40&md5=eaa534623432c064c0296ca0503ddbea
KW  - 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
N2  - 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.
A1  - Ooi, Boonyaik Yaik
A1  - Beh, W. L.
A1  - Yi, Kh'Ng Xin
A1  - Shirmohammadi, Shervin
AV  - none
SN  - 10915281
ID  - scholars20418
ER  -