Work

Acoustic Feature Extraction Based on Wavelet Transform for Industrial Induction Motor-Driven Belt Conveyor Condition Monitoring

Year: 2024

Type: article

Abstract: The industrial belt conveyor is a critical component within manufacturing facilities, and any unexpected shutdown can lead to significant disruptions, causing financial losses amounting to thousands of dollars per hour. Unfortunately, efficient mechanisms for monitoring the conveyor's condition are often lacking. Therefore, it is crucial to ensure early, precise, and effective detection of malfunctions in belt conveyors. This necessitates the identification of distinctive anomalies stemming from initial damage to the rotating machinery and motor components. This paper presents a non-invasive acoustic monitoring technique designed specifically for industrial belt conveyors. The method employed relies on wavelet transform-based feature extraction, offering notable advantages in terms of classification accuracy, time efficiency, and the quantity of feature vectors required for classifier training.

Source: Oriental Journal of Physical Sciences

DOI: 10.13005/ojps09.01.08

Authors: Long Xiao, Zhiping Wang

Institutions: Agency for Science, Technology and Research, Singapore Institute of Manufacturing Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Singapore Institute of Manufacturing Technology