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Machines
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Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery
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Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 6861

Special Issue Editor

Prof. Dr. Dimitrios Giagopoulos

E-Mail Website
Guest Editor
Machine Dynamics Laboratory, School of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
Interests: structural dynamics; vibration and control of linear and nonlinear dynamical systems and mechanisms; optimal design and finite element analysis of structures; parametric modal identification, fault detection and finite element model updating techniques in structures and machines; integrated reverse engineering of structures; dynamic analysis, vibration monitoring and fault detection of geared rotor-bearing systems; structural health monitoring and fatigue analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore innovative methodologies and innovations in the field of fault detection, diagnosis, and prognostics for machinery. With the increasing complexity of industrial systems, ensuring reliable operation and minimizing downtime has become paramount. This issue highlights novel approaches, including advanced sensing technologies, data-driven analytics, machine learning algorithms, and predictive maintenance strategies. Contributions will cover various applications across various industries, addressing challenges such as early fault detection, accurate diagnosis, and reliable prognostics. By fostering interdisciplinary research and collaboration, this Special Issue endeavors to advance state-of-the-art machinery health monitoring and enhance industrial operations' overall reliability and efficiency.

By fostering interdisciplinary research and collaboration among researchers, engineers, and industry practitioners, this Special Issue aims to advance state-of-the-art machinery health monitoring. It seeks to contribute significantly to improving operational efficiency, reducing maintenance costs, and extending the lifespan of critical machinery components. Ultimately, the insights and innovations presented will pave the way for more resilient and sustainable industrial operations in the face of increasing complexity and technological advancement.

Dr. Dimitrios Giagopoulos
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 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly 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

  • fault detection
  • fault diagnosis
  • prognostics
  • predictive maintenance
  • condition monitoring
  • machinery health monitoring
  • machine learning
  • machinery
  • machines

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Published Papers (5 papers)

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Research

18 pages, 935 KB  
Article
A Lightweight Audio Spectrogram Transformer for Robust Pump Anomaly Detection
by Hangyu Zhang and Yi-Horng Lai
Machines 2026, 14(1), 114; https://doi.org/10.3390/machines14010114 - 19 Jan 2026
Cited by 1 | Viewed by 747
Abstract
Industrial pumps are critical components in manufacturing and process plants, where early acoustic anomaly detection is essential for preventing unplanned downtime and reducing maintenance costs. In practice, however, strong background noise, severe class imbalance between rare faults and abundant normal data, and the [...] Read more.
Industrial pumps are critical components in manufacturing and process plants, where early acoustic anomaly detection is essential for preventing unplanned downtime and reducing maintenance costs. In practice, however, strong background noise, severe class imbalance between rare faults and abundant normal data, and the limited computing resources of edge devices make reliable deployment challenging. In this work, a lightweight Audio Spectrogram Transformer (Tiny-AST) is proposed for robust pump anomaly detection under imbalanced supervision. Building on the Audio Spectrogram Transformer, the internal Transformer encoder is redesigned by jointly reducing the embedding dimension, depth, and number of attention heads, and combined with a class frequency-based balanced sampling strategy and time–frequency masking augmentation. Experiments on the pump subset of the MIMII dataset across three SNR levels (−6 dB, 0 dB, 6 dB) demonstrate that Tiny-AST achieves an effective trade-off between computational efficiency and noise robustness. With 1.01 M parameters and 1.68 GFLOPs, it maintains superior performance under heavy noise (−6 dB) compared to ultra-lightweight CNNs (MobileNetV3) and offers significantly lower computational cost than standard compact baselines (ResNet18, EfficientNet-B0). Furthermore, comparisons highlight the performance gains of this lightweight supervised approach over traditional unsupervised benchmarks (e.g., autoencoders, GANs) by effectively leveraging scarce fault samples. These results indicate that a carefully designed lightweight Transformer, together with appropriate sampling and augmentation, can provide competitive acoustic anomaly detection performance while remaining suitable for deployment on resource-constrained industrial edge devices. Full article
(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)
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25 pages, 7587 KB  
Article
LiMS-MFormer: A Lightweight Multi-Scale and Multi-Dimensional Attention Transformer for Robust Rolling Bearing Fault Diagnosis Under Complex Conditions
by Haixiao Cao, Chuanlong Ding, Yonghong Zhang and Liang Jiang
Machines 2026, 14(1), 32; https://doi.org/10.3390/machines14010032 - 25 Dec 2025
Cited by 2 | Viewed by 647
Abstract
Bearings are critical components in industrial machinery, and their failures can lead to equipment downtime and significant safety hazards. Traditional fault diagnosis methods rely on manually crafted features and classical classifiers, often suffering from poor robustness, weak generalization under noisy or small-sample conditions, [...] Read more.
Bearings are critical components in industrial machinery, and their failures can lead to equipment downtime and significant safety hazards. Traditional fault diagnosis methods rely on manually crafted features and classical classifiers, often suffering from poor robustness, weak generalization under noisy or small-sample conditions, and limited suitability for lightweight deployment. This study proposes a Lightweight Multi-Scale Multi-Dimensional Self-Attention Transformer (LiMS-MFormer)—an end-to-end lightweight fault diagnosis framework integrating multi-scale feature extraction and multi-dimensional attention. The model integrates lightweight multi-scale convolutional feature extraction, hierarchical feature fusion, and a multi-dimensional self-attention mechanism to balance feature expressiveness with computational efficiency. Specifically, the front end employs Ghost convolution and enhanced residual structures for efficient multi-scale feature extraction. The middle layers perform cross-scale concatenation and fusion to enrich contextual representations. The back end introduces a lightweight temporal-channel-spatial attention module for global modeling and focuses on key patterns. Experiments on the Paderborn University (PU) dataset and the University of Ottawa bearing vibration dataset (Ottawa dataset) show that LiMS-MFormer achieves an accuracy of 96.68% on the small-sample PU dataset while maintaining minimal parameters (0.07 M) and low computational cost (13.55 M FLOPs). Moreover, under complex noisy conditions, the proposed model demonstrates strong fault diagnosis capability. On the University of Ottawa dataset, LiMS-MFormer consistently outperforms several state-of-the-art lightweight models, exhibiting superior accuracy, robustness, and generalization in challenging diagnostic tasks. Full article
(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)
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37 pages, 7149 KB  
Article
An AI Digital Platform for Fault Diagnosis and RUL Estimation in Drivetrain Systems Under Varying Operating Conditions
by Dimitrios M. Bourdalos, Xenofon D. Konstantinou, Josef Koutsoupakis, Ilias A. Iliopoulos, Kyriakos Kritikakos, George Karyofyllas, Panayotis E. Spiliotopoulos, Ioannis E. Saramantas, John S. Sakellariou, Dimitrios Giagopoulos, Spilios D. Fassois, Panagiotis Seventekidis and Sotirios Natsiavas
Machines 2026, 14(1), 26; https://doi.org/10.3390/machines14010026 - 24 Dec 2025
Viewed by 1074
Abstract
Drivetrain systems operate under varying operating conditions (OCs), which often obscure early-stage fault signatures and hinder robust condition monitoring (CM). This work introduces an AI digital platform developed during the EEDRIVEN project, featuring a holistic CM framework that integrates statistical time series methods—using [...] Read more.
Drivetrain systems operate under varying operating conditions (OCs), which often obscure early-stage fault signatures and hinder robust condition monitoring (CM). This work introduces an AI digital platform developed during the EEDRIVEN project, featuring a holistic CM framework that integrates statistical time series methods—using Generalized AutoRegressive (GAR) models in a multiple model fault diagnosis scheme—with deep learning approaches, including autoencoders and convolutional neural networks, enhanced through a dedicated decision fusion methodology. The platform addresses all key CM tasks, including fault detection, fault type identification, fault severity characterization, and remaining useful life (RUL) estimation, which is performed using a dynamics-informed health indicator derived from GAR parameters and a simple linear Wiener process model. Training for the platform relies on a limited set of experimental vibration signals from the physical drivetrain, augmented with high-fidelity multibody dynamics simulations and surrogate-model realizations to ensure coverage of the full space of OCs and fault scenarios. Its performance is validated on hundreds of inspection experiments using confusion matrices, ROC curves, and metric-based plots, while the decision fusion scheme significantly strengthens diagnostic reliability across the CM stages. The results demonstrate near-perfect fault detection (99.8%), 97.8% accuracy in fault type identification, and over 96% in severity characterization. Moreover, the method yields reliable early-stage RUL estimates for the outer gear of the drivetrain, with normalized errors < 20% and consistently narrow confidence bounds, which confirms the platform’s robustness and practicality for real-world drivetrain systems monitoring. Full article
(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)
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42 pages, 8498 KB  
Article
Encoding Multivariate Time Series of Gas Turbine Data as Images to Improve Fault Detection Reliability
by Enzo Losi, Mauro Venturini, Lucrezia Manservigi and Giovanni Bechini
Machines 2025, 13(10), 943; https://doi.org/10.3390/machines13100943 - 13 Oct 2025
Viewed by 1050
Abstract
The monitoring and diagnostics of energy equipment aim to detect anomalies in time series data in order to support predictive maintenance and avoid unplanned shutdowns. Thus, the paper proposes a novel methodology that utilizes sequence-to-image transformation methods to feed Convolutional Neural Networks (CNNs) [...] Read more.
The monitoring and diagnostics of energy equipment aim to detect anomalies in time series data in order to support predictive maintenance and avoid unplanned shutdowns. Thus, the paper proposes a novel methodology that utilizes sequence-to-image transformation methods to feed Convolutional Neural Networks (CNNs) for diagnostic purposes. Multivariate time series taken from real gas turbines are transformed by using two methods. We study two CNN architectures, i.e., VGG-19 and SqueezeNet. The investigated anomaly is the spike fault. Spikes are implanted in field multivariate time series taken during normal operation of ten gas turbines and composed of twenty gas path measurements. Six fault scenarios are simulated. For each scenario, different combinations of fault parameters are considered. The main novel contribution of this study is the development of a comprehensive framework, which starts from time series transformation and ends up with a diagnostic response. The potential of CNNs for image recognition is applied to the gas path field measurements of a gas turbine. A hard-to-detect type of fault (i.e., random spikes of different magnitudes and frequencies of occurrence) was implanted in a seemingly real-world fashion. Since spike detection is highly challenging, the proposed framework has both scientific and industrial relevance. The extended and thorough analyses unequivocally prove that CNNs fed with images are remarkably more accurate than TCN models fed with raw time series data, with values higher than 93% if the number of implanted spikes is 10% of the total data and a gain in accuracy of up to 40% in the most realistic scenario. Full article
(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)
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20 pages, 816 KB  
Article
Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model
by Shuxia Ye, Bin Da, Liang Qi, Han Xiao and Shankai Li
Machines 2025, 13(1), 7; https://doi.org/10.3390/machines13010007 - 25 Dec 2024
Cited by 6 | Viewed by 2371
Abstract
As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, [...] Read more.
As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, the proposed approach eliminates the need for explicit modeling and leverages a novel optimization algorithm for data denoising. Additionally, a new noise-resistant monitoring index is introduced to enhance monitoring reliability. The paper is structured into two main sections for validation. The first section addresses advanced data preprocessing, where the Improved Sparrow Search Algorithm (ISSA) is employed to optimize the parameters of Random Singular Value Decomposition (RSVD). This step effectively minimizes noise, reduces manual intervention, and handles high-dimensional data. The second section focuses on analyzing the data characteristics using the Random Matrix Theory (RMT) and establishing novel condition monitoring indicators to achieve more reliable monitoring outcomes. The proposed methodology captures the intricate relationships among key variables within the system, providing a more robust framework for condition monitoring. Applied to a marine diesel engine lubrication system, the method demonstrates significant improvements in noise immunity and monitoring reliability. Comparative analyses of condition monitoring models before and after denoising reveal that the relative error of the proposed monitoring index under varying noise amplitudes is within 1%, substantially lower than that of other indices. Furthermore, the monitoring accuracy is improved by 4.95% when the proposed index is employed for system condition monitoring. Full article
(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)
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