Tools
Tools
Safaei, Mahmood, Soleymani, Seyed Ahmad, Safaei, Mitra, Chizari, Hassan 
ORCID: 0000-0002-6253-1822 and Nilashi, Mehrbaksh
(2023)
Deep learning algorithm for supervision process in production using acoustic signal.
Applied Soft Computing, 146.
Art 110682.
doi:10.1016/j.asoc.2023.110682
|
Text (Peer-reviewed version)
13007 CHIZARI Hassan (2023) Deep learning algorithm for supervision article.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1MB) | Preview |
Abstract
In an industrial environment, accurate fault diagnosis of machines is crucial to prevent shutdowns, failures, maintenance costs, and production downtime. Existing methods for system failure prevention are often unsatisfactory and expensive, prompting the need for alternative approaches. Acoustic signals have emerged as a new method for predicting machine component lifespan, but recognizing relevant features and distinguishing them from noise remains challenging. To address the aforementioned challenges, we present a comprehensive model that integrates various components to enhance the accuracy and effectiveness of machine process identification. The proposed model incorporates a deep learning algorithm, which enables the forecasting of machine operation based on acoustic signals. In addition, we employ a customized Continuous Wavelet Transformation (CWT) technique to convert the acoustic signals into CWT images, preserving vital information such as signal amplitude. This transformation allows for a more comprehensive analysis and representation of the acoustic data. Furthermore, a Convolutional Neural Network (CNN) is utilized as a powerful classifier to accurately classify and differentiate between different machine processes based on the extracted features from the CWT images. By combining these elements, our model provides a robust and efficient framework for machine process identification using acoustic signals. Testing our model on a dataset generated from the Institute for Manufacturing Technology and Machine Tools (IFW) for the Gildemeister machine (CTX420 linear), we achieve over 97% accuracy in discovering and early detecting emerging faults and machine processes based on acoustic signals.
| Item Type: | Article |
|---|---|
| Article Type: | Article |
| Uncontrolled Keywords: | Deep learning; Acoustic; Production; Fault diagnosis |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD2321 Industry Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Research Priority Areas: | Applied Business & Technology |
| Depositing User: | Rhiannon Goodland |
| Date Deposited: | 07 Aug 2023 15:32 |
| Last Modified: | 27 Jul 2024 04:15 |
| URI: | https://eprints.glos.ac.uk/id/eprint/13007 |
University Staff: Request a correction | Repository Editors: Update this record
