Deep learning algorithm for supervision process in production using acoustic signal

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

[img] Text (Peer-reviewed version)
13007 CHIZARI Hassan (2023) Deep learning algorithm for supervision article.pdf - Accepted Version
Restricted to Repository staff only until 27 July 2024. (Publisher Embargo).
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (1MB)


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: 31 Oct 2023 11:10

University Staff: Request a correction | Repository Editors: Update this record

University Of Gloucestershire

Bookmark and Share

Find Us On Social Media:

Social Media Icons Facebook Twitter Google+ YouTube Pinterest Linkedin

Other University Web Sites

University of Gloucestershire, The Park, Cheltenham, Gloucestershire, GL50 2RH. Telephone +44 (0)844 8010001.