학술논문
Acoustic-Based Detection Technique for Identifying Worn-Out Components in Large-Scale Industrial Machinery
Document Type
Periodical
Source
IEEE Sensors Letters IEEE Sens. Lett. Sensors Letters, IEEE. 7(9):1-4 Sep, 2023
Subject
Language
ISSN
2475-1472
Abstract
This letter addresses the challenge of monitoring large-scale machine halls, particularly in the context of iron making processes. We propose an acoustic sound-based condition monitoring (ASCM) system to detect potential faults and damages in machinery. The letter focuses on selecting suitable audio features, integrating physical insights regarding the fault, and determining optimal window lengths for feature extraction. Our fault detection method utilizes outlier detection with a Gaussian mixture model trained on features extracted only from normal operating conditions. We compare conventional audio features with physically motivated features and conduct a window length analysis. The results demonstrate the effectiveness of our approach and the impact of incorporating physically motivated features for fault detection performance.