Optimal Statistical Feature Subset Selection for Bearing Fault Detection and Severity Estimation by Chhaya Grover, Neelam Turk

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Early Fault Detection in FOC Driven Induction Motors: A Case Study by Isabela O. Zaparoli, Arismar M. G. Junior, Marco Tulio A. Evo, Diogo S. C. Souza, Helder de Paula

“…The work presented by those authors significantly contributed to the area of fault detection by proposing the fault index called Gama. …”
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An Integrated Remote Sensing and Near-Surface Geophysical Approach to Detect and Characterize Active and Capable Faults in the Urban Area of Florence (Italy) by Luigi Piccardi, Antonello D’Alessandro, Eutizio Vittori, Vittorio D’Intinosante, Massimo Baglione

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Predictive Maintenance of HVAC Systems using Deep Learning for Optimized Building Energy Efficiency by Seyed Abolfazl Aghili, Amin Haji Mohammad Rezaei, Mostafa Khanzadi

“…Past research has underestimated qualitative faults analysis in HVAC systems or suffered from inaccurate identification using AI. …”
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Identification of short circuits in electronic boards using electrical impedance spectroscopy by Yves Santos Borges, Pedro Bertemes Filho

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Detecting Software Anomalies in Robots by Means of One-class Classifiers by Héctor Quintián, Esteban Jove, Francisco Zayas-Gato, Nuño Basurto, Carlos Cambra, Álvaro Herrero

“…The growing dependence on collaborative robots in essential industrial and service sectors raises urgent concerns regarding their reliability and ability to handle faults. …”
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A Comprehensive Review of Blockchain and Machine Learning Integration for Peer-to-Peer Energy Trading in Smart Grids by Sameen Fatima, Muhammad Junaid Arshad

“…Blockchain enhances energy transactions by making them safer, more transparent, and tamper-proof, while ML optimizes grid performance by improving predictions, fault detection, and anomaly identification. This systematic review examines the application of blockchain and ML in peer-to-peer (P2P) energy trading within smart grids and analyzes how these technologies complement each other in mitigating risks and enhancing the efficiency of smart grids. …”
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A novel dynamic machine learning-based explainable fusion monitoring: application to industrial and chemical processes by Husnain Ali, Rizwan Safdar, Yuanqiang Zhou, Yuan Yao, Le Yao, Zheng Zhang, Weilong Ding, Furong Gao

“…Traditional monitoring techniques for automatic anomaly detection, identifying the potential variables, and root cause analysis for fault information are not intelligent enough to tackle the intricate problems of real-time practices in the industrial and chemical sectors. …”
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