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Applied Sciences
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Fault Classification and Detection Using Artificial Intelligence
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Fault Classification and Detection Using Artificial Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 3474

Special Issue Editors

Dr. Tahir Abdul Hussain Ratlamwala

E-Mail Website
Guest Editor
Department of Engineering Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan
Interests: AI; neural network; renewable energy; hydrogen
Prof. Dr. Khurram Kamal

E-Mail Website
Guest Editor
Department of Engineering Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan
Interests: AI; mechatronics; computational intelligence; machine learning

Special Issue Information

Dear Colleagues,

In order to run machine operations smoothly, machine faults need to be detected, located, and classified quickly. For this, artificial neural network approaches are considered significant tools in related applications of machine operations. Faults can occur in any machine or operation. They need to be identified in a timely manner, or else they can cause severe damage to operations. This special issue focuses on fault detection and classification using an intelligent approach of artificial neural networks applied to different sectors. The Special Issue will provide a single platform for researchers and industrialists to find research related to applications of AI for fault classification and detection in different sectors.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the area of fault classification and detection using artificial intelligence. We welcome both theoretical and experimental studies in this area that will be beneficial to the readers.

Dr. Tahir Abdul Hussain Ratlamwala
Prof. Dr. Khurram Kamal
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

  • artificial intelligence
  • artificial neural network
  • fault classification
  • pattern recognition
  • supervised learning

Published Papers (4 papers)

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Research

29 pages, 2422 KiB  
Article
AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors
by Syed Safdar Hussain and Syed Sajjad Haider Zaidi
Appl. Sci. 2024, 14(7), 3105; https://doi.org/10.3390/app14073105 - 07 Apr 2024
Viewed by 498
Abstract
This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers [...] Read more.
This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated. Full article
(This article belongs to the Special Issue Fault Classification and Detection Using Artificial Intelligence)
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17 pages, 7588 KiB  
Article
LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites
by Syed Haider Mehdi Rizvi, Muntazir Abbas, Syed Sajjad Haider Zaidi, Muhammad Tayyab and Adil Malik
Appl. Sci. 2024, 14(7), 2925; https://doi.org/10.3390/app14072925 - 30 Mar 2024
Viewed by 546
Abstract
Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about [...] Read more.
Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure’s health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT). layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency. Full article
(This article belongs to the Special Issue Fault Classification and Detection Using Artificial Intelligence)
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21 pages, 5358 KiB  
Article
Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
by Kai Wang, Bo Gao, Shijie Shan, Rong Wang and Xueyang Wang
Appl. Sci. 2024, 14(2), 551; https://doi.org/10.3390/app14020551 - 08 Jan 2024
Cited by 1 | Viewed by 653
Abstract
Most fault diagnosis models use a single input and have weak generalization performance. In order to obtain more fault information, a fault diagnosis method based on a Multi-channel Residual Attention Network with Efficient Channel Attention (ECA-MRANet) is proposed in this paper. In this [...] Read more.
Most fault diagnosis models use a single input and have weak generalization performance. In order to obtain more fault information, a fault diagnosis method based on a Multi-channel Residual Attention Network with Efficient Channel Attention (ECA-MRANet) is proposed in this paper. In this method, the original time domain signal is first processed by a multi-domain transform, the result of which is input to the MRANet for feature extraction. Finally, the extracted features are fused by ECA to realize fault identification. The experimental results show that the proposed method can enhance the ability of the network to discriminate key features, and shows good generalization performance under different working conditions and with small-sample transfer between data sets. Full article
(This article belongs to the Special Issue Fault Classification and Detection Using Artificial Intelligence)
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15 pages, 2828 KiB  
Article
Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost
by Tonglei Wang, Qun Li, Jinggang Yang, Tianxi Xie, Peng Wu and Jiabi Liang
Appl. Sci. 2023, 13(13), 7539; https://doi.org/10.3390/app13137539 - 26 Jun 2023
Cited by 2 | Viewed by 910
Abstract
Dissolved gas analysis is an important method for diagnosing the operating condition of power transformers. Traditional methods such as IEC Ratios and Duval Triangles and Pentagon methods are not applicable in the case of abnormal or missing values of DGA data. A novel [...] Read more.
Dissolved gas analysis is an important method for diagnosing the operating condition of power transformers. Traditional methods such as IEC Ratios and Duval Triangles and Pentagon methods are not applicable in the case of abnormal or missing values of DGA data. A novel transformer fault diagnosis method based on an extreme gradient boosting algorithm is proposed in this paper. First, the traditional statistical method is replaced by the random forest regression algorithm for filling in missing values of dissolved gas data. Normalization and feature derivation of the outlier data is adopted based on the gas content. Then, hyperparameter optimization of the transformer fault diagnosis model based on an extreme gradient boosting algorithm is carried out using the tree-structured probability density estimator algorithm. Finally, the influence of missing data and optimization algorithms on transformer fault diagnosis models is analyzed. The effects of different algorithms based on incomplete datasets are also discussed. The results show that the performance of the random forest regression algorithm on missing data filling is better than classification and regression trees and traditional statistical methods. The average accuracy of the fault diagnosis method proposed in the paper is 89.5%, even when the missing data rate reaches 20%. The accuracy and robustness of the TPE-XGBoost model are superior to other machine learning algorithms described in this paper, such as k-nearest neighbor, deep neural networks, random forest, etc. Full article
(This article belongs to the Special Issue Fault Classification and Detection Using Artificial Intelligence)
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