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Advances in Failure Detection and Diagnostic Strategies: Enhancing Reliability and...
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Advances in Failure Detection and Diagnostic Strategies: Enhancing Reliability and Safety

A Special Issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (31 May 2026) | Viewed by 6490

Editor

Dr. Rafael Palacios

E-Mail Website
Guest Editor
1. Instituto de Investigación Tecnológica, Universidad Pontificia Comillas (IIT-Comillas), 28015 Madrid, Spain
2. Cybersecurity at MIT Sloan (CAMS), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
Interests: failure detection; diagnosis; vibration analysis; digital image processing; advanced data analytics; artificial intelligence

Special Issue Information

Dear Colleagues,

With the proliferation of sensors, real-time data collection, and databases of maintenance actions, there has never been as much data available for monitoring, anomaly detection, diagnosis, prediction, failure of accident prevention, and for optimizing maintenance. All the data available, along with powerful tools based on machine learning and artificial intelligence for their analysis, significantly contribute to improvements in diagnosis in many different fields, as well as contributing to anticipating failures and reducing operating and maintenance costs. The benefits of these new analysis strategies help to avoid unexpected failures, improving the reliability and safety of systems while reducing costs related to unavailability and expensive unplanned repairs. In addition, monitoring, detection, diagnosis, and prediction can be applied to a variety of fields including medical diagnosis, telemedicine, signal processing, defect detection in materials, or education.

This Special Issue seeks articles that present and evaluate novel approaches of data acquisition and analysis, with the objective of improving diagnosis, preventing failures, optimizing maintenance, and minimizing costs. We also invite papers submissions that explore innovative technologies in this field.

Potential topics include the following:

  • Diagnostic strategies;
  • Incipient failure detection;
  • Reliability engineering;
  • Signal analysis for anomaly detection;
  • Medical diagnosis using soft computing techniques;
  • Telemedicine and remote monitoring;
  • Condition monitoring;
  • Prognostics and health management (PHM);
  • Reliability enhancement;
  • Accident prevention;
  • Fault-tolerant systems;
  • Defect detection on materials;
  • Data-driven maintenance;
  • AI and IoT;
  • Digital twins;
  • Prevention and detection of cyber attacks.

Dr. Rafael Palacios
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers 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 1800 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

  • incipient failure detection
  • diagnostic strategies
  • reliability engineering
  • anomaly detection
  • condition monitoring
  • prognostics and health management (PHM)
  • reliability enhancement
  • data-driven maintenance
  • AI and IoT
  • digital twins

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

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Research

Jump to: Review

15 pages, 4357 KB  
Article
A Bearing Fault Diagnosis Method for Variable Operating Conditions Based on MMDSC-CBAM-BiLSTM
by Yufang Wang, Cairong Li and Jianan Wang
Computers 2026, 15(9), 614; https://doi.org/10.3390/computers15090614 - 14 Sep 2026
Abstract
Bearing fault diagnosis under variable operating conditions is challenging because changes in rotational speed and load alter the vibration response and cause substantial distribution shifts between operating domains. To improve cross-condition fault recognition, this study proposes an MMDSC-CBAM-BiLSTM model that combines multi-scale depthwise [...] Read more.
Bearing fault diagnosis under variable operating conditions is challenging because changes in rotational speed and load alter the vibration response and cause substantial distribution shifts between operating domains. To improve cross-condition fault recognition, this study proposes an MMDSC-CBAM-BiLSTM model that combines multi-scale depthwise separable convolution (MMDSC), convolutional block attention (CBAM), and bidirectional long short-term memory (BiLSTM). The vibration signals are first transformed into time–frequency representations using a wavelet transform. MMDSC then extracts complementary fault features at multiple spatial scales with reduced convolutional cost, CBAM adaptively reweights informative channel and spatial responses, and BiLSTM models bidirectional temporal dependencies before feature fusion and Softmax classification. The physical interpretation of the diagnosis is linked to the characteristic vibration responses generated by localized defects on the inner race, outer race, and rolling element, while the network itself learns discriminative representations rather than explicitly reconstructing defect morphology. Cross-condition experiments on the CWRU and Jiangnan University bearing datasets yield average accuracies of 98.22% and 93.26%, respectively, demonstrating improved robustness and generalization under varying operating conditions. The results indicate that the proposed architecture provides an effective data-driven solution for variable-condition bearing fault diagnosis. Full article
(This article belongs to the Special Issue Advances in Failure Detection and Diagnostic Strategies: Enhancing Reliability and Safety)
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26 pages, 9517 KB  
Article
Deep Learning-Based Automated Industrial Surface Defect Classification
by Rana Alrayes and Atta Rahman
Computers 2026, 15(7), 417; https://doi.org/10.3390/computers15070417 - 29 Jun 2026
Viewed by 607
Abstract
Materials such as steel, concrete, and various alloys are used to build infrastructure and machinery across all industries. Due to their long service life, some of these materials will eventually develop surface damage (such as crazing, corrosion, and pitting) that will negatively affect [...] Read more.
Materials such as steel, concrete, and various alloys are used to build infrastructure and machinery across all industries. Due to their long service life, some of these materials will eventually develop surface damage (such as crazing, corrosion, and pitting) that will negatively affect both the structural integrity and the reliability of the machinery/infrastructure. Thus, the rapid and accurate classification of defects on material surfaces is crucial for ensuring high-quality materials and a continuous process without machinery breakdowns. In this work, we compare the effectiveness of two types of deep learning models (a VGG16 convolutional neural network with transfer learning and the state-of-the-art YOLOv8) for automatic defect classification on surfaces. The dataset used in our experiment included data from the Phase 5 Capstone Corrosion and the NEU Surface Defects Databases, resulting in eight distinct classes of surface defects. The effectiveness of both models was determined using stratified 10-fold cross-validation. The results of the experiment revealed that YOLOv8 achieved 98.5% accuracy, whereas VGG16 achieved only 92.5%. Moreover, YOLOv8 exhibited greater consistency under noise perturbations, demonstrating superior robustness compared with VGG16. Beyond model comparison, this study introduces a unified benchmark constructed from heterogeneous industrial defect datasets. It systematically evaluates classification performance, generalization capability, and robustness using stratified cross-validation and noise-based testing. The results indicate that YOLOv8 is a practical solution for automated industrial surface defect classification. Full article
(This article belongs to the Special Issue Advances in Failure Detection and Diagnostic Strategies: Enhancing Reliability and Safety)
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21 pages, 793 KB  
Article
Beyond the Norm: Unsupervised Anomaly Detection in Telecommunications with Mahalanobis Distance
by Aline Mefleh, Michal Patryk Debicki, Ali Mubarak, Maroun Saade and Nathanael Weill
Computers 2025, 14(12), 561; https://doi.org/10.3390/computers14120561 - 17 Dec 2025
Viewed by 1506
Abstract
Anomaly Detection (AD) in telecommunication networks is critical for maintaining service reliability and performance. However, operational networks present significant challenges: high-dimensional Key Performance Indicator (KPI) data collected from thousands of network elements must be processed in near real time to enable timely responses. [...] Read more.
Anomaly Detection (AD) in telecommunication networks is critical for maintaining service reliability and performance. However, operational networks present significant challenges: high-dimensional Key Performance Indicator (KPI) data collected from thousands of network elements must be processed in near real time to enable timely responses. This paper presents an unsupervised approach leveraging Mahalanobis Distance (MD) to identify network anomalies. The MD model offers a scalable solution that capitalizes on multivariate relationships among KPIs without requiring labeled data. Our methodology incorporates preprocessing steps to adjust KPI ratios, normalize feature distributions, and account for contextual factors like sample size. Aggregated anomaly scores are calculated across hierarchical network levels—cells, sectors, and sites—to localize issues effectively. Through experimental evaluations, the MD approach demonstrates consistent performance across datasets of varying sizes, achieving competitive Area Under the Receiver Operating Characteristic Curve (AUC) values while significantly reducing computational overhead compared to baseline AD methods: Isolation Forest (IF), Local Outlier Factor (LOF) and One-Class Support Vector Machines (SVM). Case studies illustrate the model’s practical application, pinpointing the Random Access Channel (RACH) success rate as a key anomaly contributor. The analysis highlights the importance of dimensionality reduction and tailored KPI adjustments in enhancing detection accuracy. This unsupervised framework empowers telecom operators to proactively identify and address network issues, optimizing their troubleshooting workflows. By focusing on interpretable metrics and efficient computation, the proposed approach bridges the gap between AD and actionable insights, offering a practical tool for improving network reliability and user experience. Full article
(This article belongs to the Special Issue Advances in Failure Detection and Diagnostic Strategies: Enhancing Reliability and Safety)
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15 pages, 2871 KB  
Article
TD3 Reinforcement Learning Algorithm Used for Health Condition Monitoring of a Cooling Water Pump
by Miguel A. Sanz-Bobi, Inés Rodriguez, F. Javier Bellido-López, Antonio Muñoz, Javier Anguera, Daniel Gonzalez-Calvo and Tomas Alvarez-Tejedor
Computers 2025, 14(12), 540; https://doi.org/10.3390/computers14120540 - 9 Dec 2025
Viewed by 796
Abstract
In this paper, we describe the procedure of implementing a reinforcement learning algorithm, TD3, to learn the performance of a cooling water pump and how this type of learning can be used to detect degradations and evaluate its health condition. These types of [...] Read more.
In this paper, we describe the procedure of implementing a reinforcement learning algorithm, TD3, to learn the performance of a cooling water pump and how this type of learning can be used to detect degradations and evaluate its health condition. These types of machine learning algorithms have not been used extensively in the scientific literature to monitor the degradation of industrial components, so this study attempts to fill this gap, presenting the main characteristics of these algorithms’ application in a real case. The method presented consists of several models for predicting the expected evolution of significant behavior variables when no anomalies exist, showing the performance of different aspects of the pump. Examples of these variables are bearing temperatures or vibrations in different pump locations. All of the data used in this paper come from the SCADA system of the power plant where the cooling water pump is located. Full article
(This article belongs to the Special Issue Advances in Failure Detection and Diagnostic Strategies: Enhancing Reliability and Safety)
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Review

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45 pages, 1364 KB  
Review
Deep Learning for Short-Circuit Fault Diagnostics in Power Distribution Grids: A Comprehensive Review
by Fathima Razeeya Mohamed Razick and Petr Musilek
Computers 2026, 15(2), 76; https://doi.org/10.3390/computers15020076 - 1 Feb 2026
Cited by 4 | Viewed by 2602
Abstract
In modern power distribution networks, robust and intelligent fault management techniques are increasingly important as system complexity grows with the integration of distributed energy resources. This article reviews the use of deep learning methods for short-circuit fault detection, classification, and localization in power [...] Read more.
In modern power distribution networks, robust and intelligent fault management techniques are increasingly important as system complexity grows with the integration of distributed energy resources. This article reviews the use of deep learning methods for short-circuit fault detection, classification, and localization in power distribution systems, including symmetrical, asymmetrical, and high-impedance faults. The approaches examined include convolutional neural networks, recurrent neural networks, deep reinforcement learning, graph neural networks, and hybrid architectures. A comprehensive taxonomy of these models is presented, followed by an analysis of their application across the stages of fault diagnostics. Major contributions to the field are highlighted, and research gaps are identified in relation to data scarcity, model interpretability, real-time responsiveness, and deployment scalability. The paper provides an in-depth technical and performance comparison of deep learning approaches based on current research trends, and it also outlines the limitations of previous review studies. The objective of this work is to support researchers in selecting and implementing appropriate deep learning techniques for fault analytics in complex smart electricity grids with significant penetration of distributed energy resources. The review is intended to serve as an initial foundation for continued research and development in intelligent fault analytics for reliable and sustainable power distribution systems. Full article
(This article belongs to the Special Issue Advances in Failure Detection and Diagnostic Strategies: Enhancing Reliability and Safety)
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