Fault Diagnosis in the Internet of Things Applications

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Network Services and Applications".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 10217

Special Issue Editors


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Guest Editor
Department of Engineering, University of Messina, 98166 Messina, Italy
Interests: anomaly detection; fault diagnosis; IIoT; machine learning; deep Learning; edge computing; cloud computing; cyber physical systems; embedded systems

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Guest Editor
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy
Interests: machine learning; deep learning; cyber physical systems; Internet of Things; anomaly detection; fault diagnosis; edge computing; cloud computing

Special Issue Information

Dear Colleagues,

Recent years have observed phenomenal market growth of Internet of Things (IoT) applications with strong economic potential. The advancements of technology, both in terms of hardware and software, and the spread of IoT, have acted as catalysts for the development of a wide array of systems, where hundreds of devices cooperatively interact with the cyber and physical worlds.

At present, aspects related to fault diagnosis are becoming an important requirement for modern IoT applications to prevent or avoid potentially harmful conditions. In this sense, efficient monitoring and regular maintenance are crucial for the timely detection of faults (or anomalies) that can have, in the worst cases, catastrophic consequences. This is even more evident in the industrial sector, where the pervasive use of sensors and actuators engendered the Industry 4.0 paradigm that leverages the IoT to provide an estimate on the “health” condition of an industrial plant. In such a context, technologies such as artificial intelligence (AI), cloud computing, and edge computing emerge as the key enabling technologies of novel infrastructures for efficient monitoring, data collection, and analysis, which play a proactive role during the diagnosis process.

This Special Issue aims to collate original, unpublished and high-quality research articles focused on fault diagnosis solutions applied to the IoT and Industry 4.0.

The topics of interest include, but are not limited to, the following:

  • AI methods for industrial applications;
  • Machine learning applications at the edge;
  • Deep learning fault diagnosis models;
  • Intelligent fault detection;
  • Industrial IoT applications;
  • IoT energy efficient algorithms;
  • Data fusion;
  • IoT privacy and security;
  • Predictive maintenance;
  • Anomaly detection;
  • Edge/cloud monitoring frameworks;
  • Digital twins for fault diagnosis;
  • Fault tolerance models.

Dr. Fabrizio De Vita
Dr. Giovanni Cicceri
Guest Editors

Manuscript Submission Information

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Keywords

  • fault diagnosis
  • anomaly detection
  • Industry 4.0
  • IoT
  • edge computing
  • cloud computing
  • deep learning
  • data fusion

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Published Papers (6 papers)

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Research

20 pages, 3912 KiB  
Article
A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance
by Joon-Hyuk Lee, Chibuzo Nwabufo Okwuosa, Baek Cheon Shin and Jang-Wook Hur
J. Sens. Actuator Netw. 2024, 13(5), 64; https://doi.org/10.3390/jsan13050064 - 9 Oct 2024
Viewed by 596
Abstract
The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery, and have a direct impact on its longevity and profitability. Therefore, the failure of a mechanical system or any of its components would [...] Read more.
The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery, and have a direct impact on its longevity and profitability. Therefore, the failure of a mechanical system or any of its components would be detrimental to production continuity and availability. Consequently, this study proposes a robust diagnostic framework for analyzing the blade conditions of shot blast industrial machinery. The framework explores the spectral characteristics of the vibration signals generated by the industrial shot blast for discriminative feature excitement. Furthermore, a peak detection algorithm is introduced to identify and extract the unique features present in the peak magnitudes of each signal spectrum. A feature importance algorithm is then deployed as the feature selection tool, and these selected features are fed into ten machine learning classifiers (MLCs), with extreme gradient boosting (XGBoost (version 2.1.1)) as the core classifier. The results show that the XGBoost classifier achieved the best accuracy of 98.05%, with a cost-efficient computational cost of 0.83 s. Other global assessment metrics were also implemented in the study to further validate the model. Full article
(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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30 pages, 4047 KiB  
Article
Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps
by Dong-Yun Kim, Akeem Bayo Kareem, Daryl Domingo, Baek-Cheon Shin and Jang-Wook Hur
J. Sens. Actuator Netw. 2024, 13(5), 60; https://doi.org/10.3390/jsan13050060 - 25 Sep 2024
Viewed by 1619
Abstract
This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and [...] Read more.
This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and signal stretching (SS), with advanced models, including Long Short-Term Memory (LSTM) networks, Autoencoders (AE), and Generative Adversarial Networks (GANs). Our approach significantly improves the robustness and accuracy of machine learning (ML) models for fault detection and classification. Key findings demonstrate a marked reduction in false positives and a substantial increase in fault detection rates, particularly in complex operational scenarios where traditional statistical methods may fall short. The experimental results underscore the effectiveness of combining these augmentation techniques, achieving up to a 30% improvement in fault detection accuracy and a 25% reduction in false positives compared to baseline models. These improvements highlight the practical value of the proposed framework in ensuring reliable operation and the predictive maintenance of centrifugal pumps in diverse industrial environments. Full article
(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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20 pages, 566 KiB  
Article
Predictive Maintenance in IoT-Monitored Systems for Fault Prevention
by Enrico Zero, Mohamed Sallak and Roberto Sacile
J. Sens. Actuator Netw. 2024, 13(5), 57; https://doi.org/10.3390/jsan13050057 - 19 Sep 2024
Viewed by 3278
Abstract
This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to [...] Read more.
This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed. Full article
(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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23 pages, 16203 KiB  
Article
Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications
by Luca Patanè, Francesca Sapuppo, Giuseppe Napoli and Maria Gabriella Xibilia
J. Sens. Actuator Netw. 2024, 13(5), 49; https://doi.org/10.3390/jsan13050049 - 27 Aug 2024
Viewed by 735
Abstract
The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary [...] Read more.
The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data. Full article
(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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19 pages, 6382 KiB  
Article
Tool Condition Monitoring in the Milling Process Using Deep Learning and Reinforcement Learning
by Devarajan Kaliyannan, Mohanraj Thangamuthu, Pavan Pradeep, Sakthivel Gnansekaran, Jegadeeshwaran Rakkiyannan and Alokesh Pramanik
J. Sens. Actuator Netw. 2024, 13(4), 42; https://doi.org/10.3390/jsan13040042 - 30 Jul 2024
Cited by 2 | Viewed by 1154
Abstract
Tool condition monitoring (TCM) is crucial in the machining process to confirm product quality as well as process efficiency and minimize downtime. Traditional methods for TCM, while effective to a degree, often fall short in real-time adaptability and predictive accuracy. This research work [...] Read more.
Tool condition monitoring (TCM) is crucial in the machining process to confirm product quality as well as process efficiency and minimize downtime. Traditional methods for TCM, while effective to a degree, often fall short in real-time adaptability and predictive accuracy. This research work aims to advance the state-of-the-art methods in predictive maintenance for TCM and improve tool performance and reliability during the milling process. The present work investigates the application of Deep Learning (DL) and Reinforcement Learning (RL) techniques to monitor tool conditions in milling operations. DL models, including Long Short-Term Memory (LSTM) networks, Feed Forward Neural Networks (FFNN), and RL models, including Q-learning and SARSA, are employed to classify tool conditions from the vibration sensor. The performance of the selected DL and RL algorithms is evaluated through performance metrics like confusion matrix, recall, precision, F1 score, and Receiver Operating Characteristics (ROC) curves. The results revealed that RL based on SARSA outperformed other algorithms. The overall classification accuracies for LSTM, FFNN, Q-learning, and SARSA were 94.85%, 98.16%, 98.50%, and 98.66%, respectively. In regard to predicting tool conditions accurately and thereby enhancing overall process efficiency, SARSA showed the best performance, followed by Q-learning, FFNN, and LSTM. This work contributes to the advancement of TCM systems, highlighting the potential of DL and RL techniques to revolutionize manufacturing processes in the era of Industry 5.0. Full article
(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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19 pages, 7146 KiB  
Article
Structured Data Ontology for AI in Industrial Asset Condition Monitoring
by Jacob Hendriks, Mana Azarm and Patrick Dumond
J. Sens. Actuator Netw. 2024, 13(2), 23; https://doi.org/10.3390/jsan13020023 - 26 Mar 2024
Cited by 1 | Viewed by 1309
Abstract
This paper proposes an ontology for prognostics and health management (PHM) applications involving sensor networks monitoring industrial machinery. Deep learning methods show promise for the development of autonomous PHM systems but require vast quantities of structured and representative data to realize their potential. [...] Read more.
This paper proposes an ontology for prognostics and health management (PHM) applications involving sensor networks monitoring industrial machinery. Deep learning methods show promise for the development of autonomous PHM systems but require vast quantities of structured and representative data to realize their potential. PHM systems involve unique and specialized data characterized by time and context, and thus benefit from tailored data management systems. Furthermore, the use of dissimilar standards and practices with respect to database structure and data organization is a hinderance to interoperability. To address this, this paper presents a robust, structured data ontology and schema that is designed to accommodate a wide breadth of PHM applications. The inclusion of contextual and temporal data increases its value for developing and deploying enhanced ML-driven PHM systems. Challenges around balancing the competing priorities of structure and flexibility are discussed. The proposed schema provides the benefits of a relational schema with some provisions for noSQL-like flexibility in areas where PMH applications demand it. The selection of a database engine for implementation is also discussed, and the proposed ontology is demonstrated using a Postgres database. An instance of the database was loaded with large auto-generated fictitious data via multiple Python scripts. CRUD (create, read, update, delete) operations are demonstrated with several queries that answer common PHM questions. Full article
(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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