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Applied Sciences
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Mechanical Fault Diagnosis and Signal Processing
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Mechanical Fault Diagnosis and Signal Processing

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

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 6814

Editors

Dr. Juan Jose Saucedo-Dorantes

E-Mail Website
Guest Editor
Engineering Faculty, Autonomous University of Queretaro (UAQ), San Juan del Rio 76806, Mexico
Interests: condition monitoring; fault detection; artificial intelligence; deep learning; signal processing; electromechanical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Delgado-Prieto

E-Mail Website
Guest Editor
Automatic Control Department, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain
Interests: cyber-physical systems; fault detection and diagnosis methods; machine and deep learning; electrical machines and drives
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect and publish the results of innovative research in the field of mechanical fault diagnosis and signal processing. Electrical and mechanical machines such as motors, pumps, gearboxes, and compressors are widely used in industrial processes; although most of these elements have been previously assessed, new challenges arise as industry applications and machinery configurations become increasingly complex. While any machine is strictly composed of mechanical elements that can fail due to extreme operating conditions, the most common faults are related to problems in shafts, bearings, couplings, gears, and rotors.

This Special Issue will share novel signal processing methods applied to the detection and identification of mechanical faults, involving applications based on classic or advanced processing techniques like Fourier Transform, Wavelet Transform, Hilbert Transform, and MUSIC.

Thus, this Special Issue is focused on, but not limited to, the following topics:

  • Intelligent fault detection;
  • High-resolution signal processing;
  • Noise elimination and feature extraction;
  • Non-linear operating conditions;
  • Electromechanical systems.

Dr. Juan Jose Saucedo-Dorantes
Dr. Miguel Delgado-Prieto
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • intelligent fault detection
  • high-resolution signal processing
  • noise elimination and feature extraction
  • non-linear operating conditions
  • electromechanical systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

25 pages, 6168 KB  
Article
PerDCGAN: A Perceptual Generative Framework for High-Fidelity Bearing Fault Diagnosis
by Yuantao Li, Ao Li, Xiaoli Wang and Jiancheng Yin
Appl. Sci. 2026, 16(8), 4054; https://doi.org/10.3390/app16084054 - 21 Apr 2026
Cited by 1 | Viewed by 722
Abstract
Data imbalance significantly hinders the performance of deep learning models in rolling bearing fault diagnosis. While Generative Adversarial Networks (GANs) are widely used for data augmentation, traditional architectures employing pixel-level loss functions often fail to capture complex time-frequency textures, resulting in blurred spectrograms [...] Read more.
Data imbalance significantly hinders the performance of deep learning models in rolling bearing fault diagnosis. While Generative Adversarial Networks (GANs) are widely used for data augmentation, traditional architectures employing pixel-level loss functions often fail to capture complex time-frequency textures, resulting in blurred spectrograms and the loss of transient fault characteristics. To address this, we propose a data augmentation framework based on a Perceptually Optimized Deep Convolutional GAN (PerDCGAN). By integrating a perceptual loss function derived from a pre-trained VGG-16 network, the generator is constrained at the feature level rather than the pixel level, explicitly enforcing the preservation of structural details and high-frequency impact patterns. Extensive experiments on the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets demonstrate that the proposed method effectively mitigates spectral blurring. Ablation studies confirm the synergistic effect of the joint loss function. Furthermore, under extreme 0 dB noise conditions, the classifier augmented by PerDCGAN maintains a robust diagnostic accuracy of 89.65% on the PU dataset, significantly outperforming standard DCGAN and demonstrating strong potential for complex industrial applications. Full article
(This article belongs to the Special Issue Mechanical Fault Diagnosis and Signal Processing)
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26 pages, 1294 KB  
Article
Anomaly Detection and Fault Diagnosis Based on Action States for Excavators
by Jaehyun Soh, Changmin Lee, Wonkyung Kim, Byungmun Kang and DaeEun Kim
Appl. Sci. 2026, 16(5), 2414; https://doi.org/10.3390/app16052414 - 2 Mar 2026
Viewed by 704
Abstract
Anomaly detection has been a challenging subject in many industrial fields. In industrial machinery such as hydraulic excavators, sensor data distributions are inherently multimodal because different operating conditions produce distinct sensor signatures, and conventional algorithms struggle to establish clear normal–abnormal boundaries when these [...] Read more.
Anomaly detection has been a challenging subject in many industrial fields. In industrial machinery such as hydraulic excavators, sensor data distributions are inherently multimodal because different operating conditions produce distinct sensor signatures, and conventional algorithms struggle to establish clear normal–abnormal boundaries when these conditions are mixed. We propose an action-state decomposition framework that partitions multimodal sensor data into homogeneous subsets based on discretized control inputs, thereby reducing the ambiguity of normal–abnormal boundaries by learning state-conditional distributions. The approach comprises a reactive method that evaluates each sample within its action state, and a history-based method that incorporates temporal context from previous action states. This decomposition is algorithm-agnostic and can improve detection performance across diverse anomaly detection algorithms. The framework is further extended to Bayesian fault diagnosis that identifies the root cause of failures using action-state-conditional detection probabilities. Experiments on simulated excavator data and two real-world benchmark datasets (UCI Hydraulic Systems and SKAB) demonstrate the generalizability of the proposed mode decomposition and provide insights into factors that may influence its effectiveness. The history-based method achieves a mean AUC of 0.89 across sensor fault types, outperforming all baseline algorithms, and the Bayesian fault diagnosis achieves 86.7% accuracy in identifying the root cause among six action fault types. For the proposed GMM-based methods, the decomposition also substantially reduces per-sample inference time by approximately 10× (from 8.68 μs to 0.75 μs), enabling real-time deployment in industrial settings. Full article
(This article belongs to the Special Issue Mechanical Fault Diagnosis and Signal Processing)
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25 pages, 5217 KB  
Article
Adaptive Extraction of Acoustic Emission Features for Gear Faults Based on RFE-SVM
by Lehan Cui, Yang Yu and Nan Lu
Appl. Sci. 2026, 16(1), 191; https://doi.org/10.3390/app16010191 - 24 Dec 2025
Viewed by 874
Abstract
Gears, as critical components of rotating machinery, are prone to wear and fracture due to their complex structural dynamics and harsh operating conditions, leading to catastrophic failures, economic losses, and safety risks. AE technology enables real-time fault diagnosis by capturing stress wave emissions [...] Read more.
Gears, as critical components of rotating machinery, are prone to wear and fracture due to their complex structural dynamics and harsh operating conditions, leading to catastrophic failures, economic losses, and safety risks. AE technology enables real-time fault diagnosis by capturing stress wave emissions from material defects with high sensitivity. However, mechanical background noise significantly corrupts AE signals, while optimal selection of gear health indicators remains challenging, critically impacting fault feature extraction accuracy. This study develops an adaptive feature extraction method for fault diagnosis using AE. Through gear fault simulation experiments, VMD analyzes mode number and penalty factor effects on signal decomposition. Correlation coefficient-based reconstruction optimization is implemented. For feature selection challenges, SVM-RFE enables adaptive parameter ranking. Finally, SVM with optimized kernel parameters achieves effective fault classification. Optimized VMD enhances signal decomposition, while SVM-RFE reduces feature dimensionality, addressing manual selection uncertainty and computational redundancy. Experimental results demonstrate superior accuracy in gear fault classification. This study proposes an AE-based adaptive feature extraction method with three innovations: (1) establishing VMD parameter–decomposition quality relationships; (2) developing an SVM-RFE feature selection framework; (3) achieving high-accuracy gear fault classification. The method provides a novel technical approach for rotating machinery diagnostics with significant engineering value. Full article
(This article belongs to the Special Issue Mechanical Fault Diagnosis and Signal Processing)
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19 pages, 7034 KB  
Article
Dynamic Characteristics Analysis of Cylindrical Roller Bearing with Dimensional Deviations in Cage Pocket
by Jiajia Xin, Zhi Wang, Xu Hao, Xiaoye Qi, Yongjie Wang and Baogang Wen
Appl. Sci. 2024, 14(20), 9433; https://doi.org/10.3390/app14209433 - 16 Oct 2024
Cited by 5 | Viewed by 2457
Abstract
Dimensional deviations in the cage pocket of a roller bearing can significantly affect the bearing’s dynamic performance, directly determining the positional stability of the roller. These deviations can result in roller misalignment, increasing friction and wear. Deviations arise from machining errors and deformation [...] Read more.
Dimensional deviations in the cage pocket of a roller bearing can significantly affect the bearing’s dynamic performance, directly determining the positional stability of the roller. These deviations can result in roller misalignment, increasing friction and wear. Deviations arise from machining errors and deformation during motion, etc. A dynamic model of a cylindrical roller bearing that accounts for cage flexibility was developed to explore the impact of deviations. The flexible cage provides a more realistic representation compared to the rigid cage. The effects of deviations in the length and width of the cage pocket on the bearing’s dynamic behavior were analyzed, and the results show that deviations in cage pocket dimensions lead to notable changes in bearing dynamics. Specifically, when the length deviation is negative and increasing, the amplitude of cage motion decreases, while both transitional and rotational speeds rise. It also causes greater fluctuations in the rotational speeds of the inner ring and rollers. Conversely, the cage’s equivalent stress and the contact load decrease and the amplitude of cage motion increases with increases in width deviation. Full article
(This article belongs to the Special Issue Mechanical Fault Diagnosis and Signal Processing)
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