Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/7675
Title: Fault Detection in Rotating Machinery Based on Sound Signal Using Edge Machine Learning
Authors: Shubita, Rashad R. 
Alsadeh, Ahmad 
Khater, Ismail M. 
Keywords: Fault diagnosis;Fault location (Engineering);Fault tolerance (Engineering);Electric machinery - Monitoring;Acoustic emission;Frequency curves;Frequency domain analysis;Electric circuits, Nonlinear
Issue Date: 16-Jan-2023
Publisher: Institute of Electrical and Electronics Engineers ({IEEE})
Source: R. R. Shubita, A. S. Alsadeh and I. M. Khater, "Fault Detection in Rotating Machinery Based on Sound Signal Using Edge Machine Learning," in IEEE Access, vol. 11, pp. 6665-6672, 2023, doi: 10.1109/ACCESS.2023.3237074.
Abstract: Fault detection at the early stage is very important in modern industrial processes to avoid failure with life-threatening results and to reduce the cost of maintenance and machine downtime. In this paper, we present a workflow for building a fault diagnosis system based on acoustic emission (AE) using machine learning (ML) techniques. Our fault diagnosis approach is implemented on an embedded device with the internet of things (IoT) connectivity for real-time faults detection and classification in rotating machines. The achieved accuracy for our approach with a fine decision tree ML model is 96.1%.
URI: http://hdl.handle.net/20.500.11889/7675
DOI: http://dx.doi.org/10.1109/access.2023.3237074
127054012
http://dx.doi.org/10.1109/access.2023.3237074
127054012
http://dx.doi.org/10.1109/access.2023.3237074
127054012
http://dx.doi.org/10.1109/access.2023.3237074
127054012
http://dx.doi.org/10.1109/access.2023.3237074
127054012
http://dx.doi.org/10.1109/access.2023.3237074
127054012
http://dx.doi.org/10.1109/access.2023.3237074
10.1109/access.2023.3237074
http://dx.doi.org/10.1109/access.2023.3237074
127054012
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