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Applied Sciences
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Artificial Intelligence in Machinery Fault Diagnosis
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Artificial Intelligence in Machinery Fault Diagnosis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 September 2025 | Viewed by 981

Special Issue Editor

Dr. Marco Cristoforetti

E-Mail Website
Guest Editor
Bruno Kessler Foundation, 38123 Trento, Italy
Interests: digital industry; AI; deep learning; applied physics; data science

Special Issue Information

Dear Colleagues,

In recent years, data-driven artificial intelligence (AI) has become very relevant to diagnostics. Traditional methodologies are being complemented and, in some cases, supplanted by AI-powered solutions, suggesting a possible paradigm shift. AI algorithms have demonstrated remarkable capabilities in analyzing vast numbers of data with speed and precision, enabling the early detection of anomalies and predictive insights into potential faults. This Special Issue will discuss recent efforts and advances in AI for use in diagnosis.

Dr. Marco Cristoforetti
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI
  • deep learning
  • diagnosis
  • manufacturing

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Published Papers (1 paper)

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Research

22 pages, 7015 KB  
Article
Induction Motor Fault Diagnosis Using Low-Cost MEMS Acoustic Sensors and Multilayer Neural Networks
by Seon Min Yoo, Hwi Gyo Lee, Wang Ke Hao and In Soo Lee
Appl. Sci. 2025, 15(17), 9379; https://doi.org/10.3390/app15179379 - 26 Aug 2025
Viewed by 487
Abstract
Induction motors are the dominant choice in industrial applications due to their robustness, structural simplicity, and high reliability. However, extended operation under extreme conditions, such as high temperatures, overload, and contamination, accelerates the degradation of internal components and increases the likelihood of faults. [...] Read more.
Induction motors are the dominant choice in industrial applications due to their robustness, structural simplicity, and high reliability. However, extended operation under extreme conditions, such as high temperatures, overload, and contamination, accelerates the degradation of internal components and increases the likelihood of faults. These faults are challenging to detect, as they typically develop gradually without clear external indicators. To address this issue, the present study proposes a cost-effective fault diagnosis system utilizing low-cost MEMS acoustic sensors in conjunction with a lightweight multilayer neural network (MNN). The same MNN architecture is employed to systematically compare three types of input feature representations: raw time-domain waveforms, FFT-based statistical features, and PCA-compressed FFT features. A total of 5040 samples were used to train, validate, and test the model for classifying three conditions: normal, rotor fault, and bearing fault. The time-domain approach achieved 90.6% accuracy, misclassifying 102 samples. In comparison, FFT-based statistical features yielded 99.8% accuracy with only two misclassifications. The FFT + PCA method produced similar performance while reducing dimensionality, making it more suitable for resource-constrained environments. These results demonstrate that acoustic-based fault diagnosis provides a practical and economical solution for industrial applications. Full article
(This article belongs to the Special Issue Artificial Intelligence in Machinery Fault Diagnosis)
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