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Applied Sciences
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Artificial Intelligence in Fault Diagnosis and Signal Processing
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Artificial Intelligence in Fault Diagnosis and Signal Processing

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 3815

Special Issue Editors

Prof. Dr. Roque A. Osornio-Rios

E-Mail Website
Guest Editor
HSPdigital CA-Mecatronica Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76806, Mexico
Interests: condition monitoring; power quality; fault diagnosis; signal processing; vibration analysis; electrical power engineering; control theory; instrumentation
Special Issues, Collections and Topics in MDPI journals
Dr. Athanasios Karlis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering Democritus University of Thrace, 67100 Komotini, Greece
Interests: electrical machines and drives; diagnostics of electrical machines; renewable energies and smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Andres Bustillo Iglesias

E-Mail Website
Guest Editor
Department of Informatic and Machine Learning, Universidad de Burgos, 09006 Burgos, Spain
Interests: machine learning; virtual reality; 3D modelling; manufacturing industry; cultural heritage

Special Issue Information

Dear Colleagues,

The detection and diagnosis of faults is essential in industrial processes, as the early detection of faults avoids damage that may be irreparable to machinery, which would reduce the performance of the control system and reduce the process efficiency, which would result in a decrease in production. Additionally, in terms of industrial safety, this would facilitate safer operations, reducing the risk to plant workers. Therefore, the early detection and correct diagnosis of faults will facilitate decision making that allows corrective actions to be taken to repair damaged components. In recent years, various machine fault detection techniques have emerged; additionally, artificial intelligence and signal processing are essential to achieving this goal. However, the topic continues to generate new trends in methodologies related to multiple fault detection, novelty detection, data mining, development in hardware, etc.

The goal of this issue is to bring researchers and industrial practitioners together to share their research findings and present ideas that are relevant in the field of fault diagnosis using artificial intelligence and signal processing. 

Prof. Dr. Roque A. Osornio-Rios
Dr. Athanasios Karlis
Dr. Andres Bustillo Iglesias
Guest Editors

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • neural networks
  • machine learning
  • sensors
  • novelty detection
  • data mining
  • signal processing methods
  • signal processing implementation
  • FPGA
  • HIL
  • industrial applications

Published Papers (4 papers)

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Research

14 pages, 3052 KiB  
Article
Time Series Feature Selection Method Based on Mutual Information
by Lin Huang, Xingqiang Zhou, Lianhui Shi and Li Gong
Appl. Sci. 2024, 14(5), 1960; https://doi.org/10.3390/app14051960 - 28 Feb 2024
Viewed by 927
Abstract
Time series data have characteristics such as high dimensionality, excessive noise, data imbalance, etc. In the data preprocessing process, feature selection plays an important role in the quantitative analysis of multidimensional time series data. Aiming at the problem of feature selection of multidimensional [...] Read more.
Time series data have characteristics such as high dimensionality, excessive noise, data imbalance, etc. In the data preprocessing process, feature selection plays an important role in the quantitative analysis of multidimensional time series data. Aiming at the problem of feature selection of multidimensional time series data, a feature selection method for time series based on mutual information (MI) is proposed. One of the difficulties of traditional MI methods is in searching for a suitable target variable. To address this issue, the main innovation of this paper is the hybridization of principal component analysis (PCA) and kernel regression (KR) methods based on MI. Firstly, based on historical operational data, quantifiable system operability is constructed using PCA and KR. The next step is to use the constructed system operability as the target variable for MI analysis to extract the most useful features for the system data analysis. In order to verify the effectiveness of the method, an experiment is conducted on the CMAPSS engine dataset, and the effectiveness of condition recognition is tested based on the extracted features. The results indicate that the proposed method can effectively achieve feature extraction of high-dimensional monitoring data. Full article
(This article belongs to the Special Issue Artificial Intelligence in Fault Diagnosis and Signal Processing)
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25 pages, 7014 KiB  
Article
Machinery Fault Signal Detection with Deep One-Class Classification
by Dosik Yoon and Jaehong Yu
Appl. Sci. 2024, 14(1), 221; https://doi.org/10.3390/app14010221 - 26 Dec 2023
Viewed by 610
Abstract
Fault detection of machinery systems is a fundamental prerequisite to implementing condition-based maintenance, which is the most eminent manufacturing equipment system management strategy. To build the fault detection model, one-class classification algorithms have been used, which construct the decision boundary only using normal [...] Read more.
Fault detection of machinery systems is a fundamental prerequisite to implementing condition-based maintenance, which is the most eminent manufacturing equipment system management strategy. To build the fault detection model, one-class classification algorithms have been used, which construct the decision boundary only using normal class. For more accurate one-class classification, signal data have been used recently because the signal data directly reflect the condition of the machinery system. To analyze the machinery condition effectively with the signal data, features of signals should be extracted, and then, the one-class classifier is constructed with the features. However, features separately extracted from one-class classification might not be optimized for the fault detection tasks, and thus, it leads to unsatisfactory performance. To address this problem, deep one-class classification methods can be used because the neural network structures can generate the features specialized to fault detection tasks through the end-to-end learning manner. In this study, we conducted a comprehensive experimental study with various fault signal datasets. The experimental results demonstrated that the deep support vector data description model, which is one of the most prominent deep one-class classification methods, outperforms its competitors and traditional methods. Full article
(This article belongs to the Special Issue Artificial Intelligence in Fault Diagnosis and Signal Processing)
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17 pages, 4854 KiB  
Article
Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data Augmentation
by Sungjun Kim, Muhammad Muzammil Azad, Jinwoo Song and Heungsoo Kim
Appl. Sci. 2023, 13(21), 11837; https://doi.org/10.3390/app132111837 - 29 Oct 2023
Cited by 2 | Viewed by 774
Abstract
As laminated composites are applied more commonly, Prognostics and Health Management (PHM) techniques for the maintenance of composite systems are also attracting attention. However, applying PHM techniques to a composite system is challenging due to the data imbalance problem from the lack of [...] Read more.
As laminated composites are applied more commonly, Prognostics and Health Management (PHM) techniques for the maintenance of composite systems are also attracting attention. However, applying PHM techniques to a composite system is challenging due to the data imbalance problem from the lack of failure data and unpredictable failure cases. Despite numerous studies conducted to address this limitation, including techniques like data augmentation and transfer learning, significant challenges remain. In this study, the Wasserstein Generative Adversarial Network (WGAN) model using a time-series data augmentation technique is proposed as a solution to the data imbalance problem. To ensure the performance of the WGAN model, time-series data augmentation of experimental data is executed with a frequency analysis. After that, a One-Dimensional Convolutional Neural Network (1D CNN) is used for fault diagnosis in laminated composites, validating the performance improvement after data augmentation. The proposed data augmentation significantly elevated the performance of the 1D CNN classification model compared to its non-augmented counterpart. Specifically, the accuracy increased from 89.20% to 91.96%. The precision improved remarkably from 29.76% to 74.10%, and its sensitivity rose from 33.33% to 94.39%. Collectively, these enhancements highlight the vital role of data augmentation in improving fault diagnosis performance. Full article
(This article belongs to the Special Issue Artificial Intelligence in Fault Diagnosis and Signal Processing)
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25 pages, 2816 KiB  
Article
FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots
by Ervin Galan-Uribe, Luis Morales-Velazquez and Roque Alfredo Osornio-Rios
Appl. Sci. 2023, 13(14), 8493; https://doi.org/10.3390/app13148493 - 23 Jul 2023
Cited by 2 | Viewed by 1021
Abstract
Industrial processes involving manipulator robots require accurate positioning and orienting for high-quality results. Any decrease in positional accuracy can result in resource wastage. Machine learning methodologies have been proposed to analyze failures and wear in electronic and mechanical components, affecting positional accuracy. These [...] Read more.
Industrial processes involving manipulator robots require accurate positioning and orienting for high-quality results. Any decrease in positional accuracy can result in resource wastage. Machine learning methodologies have been proposed to analyze failures and wear in electronic and mechanical components, affecting positional accuracy. These methods are typically implemented in software for offline analysis. In this regard, this work proposes a methodology for detecting a positional deviation in the robot’s joints and its implementation in a digital system of proprietary design based on a field-programmable gate array (FPGA) equipped with several developed intellectual property cores (IPcores). The method implemented in FPGA consists of the analysis of current signals from a UR5 robot using discrete wavelet transform (DWT), statistical indicators, and a neural network classifier. IPcores are developed and tested with synthetic current signals, and their effectiveness is validated using a real robot dataset. The results show that the system can classify the synthetic robot signals for joints two and three with 97% accuracy and the real robot signals for joints five and six with 100% accuracy. This system aims to be a high-speed reconfigurable tool to help detect robot precision degradation and implement timely maintenance strategies. Full article
(This article belongs to the Special Issue Artificial Intelligence in Fault Diagnosis and Signal Processing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Insulator Defect Detection Algorithm Based on Multi-Scale Detection Transformer
Author: Zou
Highlights: 1. To alleviate the confusion between the foreground and background, we introduce a context-based attention module to fully learn the relationship between defects and their backgrounds. 2. We introduce the insulators defect IDIoU loss to optimize the instability issues caused by small defects in the matching process, thereby accelerating training speed.

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