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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: 31 October 2025 | Viewed by 1841

Special Issue Editor

Dr. Dimitrios Giagopoulos

E-Mail Website
Guest Editor
Laboratory of Machine Dynamics, Department of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 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 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. 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 (2 papers)

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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 (registering DOI) - 13 Oct 2025
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 1 | Viewed by 1224
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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