Depending on the amount of data to process, file generation may take longer.

If it takes too long to generate, you can limit the data by, for example, reducing the range of years.

Article

Download file Download BibTeX

Title

Machine Fault Diagnosis through Vibration Analysis: Continuous Wavelet Transform with Complex Morlet Wavelet and Time–Frequency RGB Image Recognition via Convolutional Neural Network

Authors

[ 1 ] Instytut Robotyki i Inteligencji Maszynowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technology

Year of publication

2024

Published in

Electronics

Journal year: 2024 | Journal volume: vol. 13 | Journal number: iss. 2

Article type

scientific article

Publication language

english

Keywords
EN
  • fault diagnosis
  • vibration analysis
  • continuous wavelet transform
  • complex Morlet wavelet
  • convolutional neural network
  • RGB image recognition
  • feature extraction
  • time–frequency domain
  • short-time Fourier transform
  • MQTT
Abstract

EN In pursuit of advancing fault diagnosis in electromechanical systems, this research focusses on vibration analysis through innovative techniques. The study unfolds in a structured manner, beginning with an introduction that situates the research question in a broader context, emphasising the critical role of fault diagnosis. Subsequently, the methods section offers a concise summary of the primary techniques employed, highlighting the utilisation of short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for extracting time–frequency components from the signal. The results section succinctly summarises the main findings of the article, showcasing the results of features extraction by CWT and subsequently utilising a convolutional neural network (CNN) for fault diagnosis. The proposed method, named CWTx6-CNN, was compared with the STFTx6-CNN method of the previous stage of the investigation. Visual insights into the time–frequency characteristics of the inertial measurement unit (IMU) data are presented for various operational classes, offering a clear representation of fault-related features. Finally, the conclusion section underscores the advantages of the suggested method, particularly the concentration of single-frequency components for enhanced fault representation. The research demonstrates commendable classification performance, highlighting the efficiency of the suggested approach in real-time scenarios of fault analysis in less than 50 ms. Calculation by CWT with a complex Morlet wavelet of six time–frequency images and combining them into a single colour image took less than 35 ms. In this study, interpretability techniques have been employed to address the imperative need for transparency in intricate neural network models, particularly in the context of the case presented. Notably, techniques such as Grad-CAM (gradient-weighted class activation mapping), occlusion, and LIME (locally interpretable model-agnostic explanation) have proven instrumental in elucidating the inner workings of the model. Through a comparative analysis of the proposed CWTx6-CNN method and the reference STFTx6-CNN method, the application of interpretability techniques, including Grad-CAM, occlusion, and LIME, has played a pivotal role in revealing the distinctive spectral representations of these methodologies.

Date of online publication

22.01.2024

Pages (from - to)

452-1 - 452-16

DOI

10.3390/electronics13020452

URL

https://www.mdpi.com/2079-9292/13/2/452

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Release date

22.01.2024

Date of Open Access to the publication

at the time of publication

Full text of article

Download file

Access level to full text

public

Ministry points / journal

140

Impact Factor

2,9 [List 2022]

This website uses cookies to remember the authenticated session of the user. For more information, read about Cookies and Privacy Policy.