Artificial Intelligence–Based Fault Detection in Industrial Equipment

Jan Dimon Bendtsen

Artificial Intelligence–Based Fault Detection in Industrial Equipment

Author(s)	Prof. Jan Dimon Bendtsen
Country	United States
Abstract	The increasing complexity of industrial equipment and the demand for reliable production systems have accelerated the adoption of artificial intelligence-based fault detection technologies. Traditional maintenance approaches often depend on scheduled inspections or reactive responses after equipment failure, which may result in production downtime, increased maintenance costs, and operational inefficiencies. Artificial intelligence provides advanced capabilities for detecting equipment abnormalities through machine learning, predictive analytics, and real-time data processing. This study examines the role of artificial intelligence-based fault detection systems in improving industrial equipment reliability and maintenance performance. The research explores how machine learning algorithms, sensor data analysis, predictive models, anomaly detection, and intelligent monitoring systems contribute to early fault identification and operational optimization. A conceptual analytical framework supported by simulated observations is developed to evaluate the influence of AI-based fault detection capabilities on industrial performance. The findings indicate that predictive analytics accuracy, real-time monitoring, anomaly detection capability, machine learning performance, and automated maintenance decision support significantly contribute to improving fault detection efficiency. However, challenges including data quality limitations, cybersecurity risks, implementation costs, algorithm complexity, and technical skill requirements continue to influence adoption. The study contributes to industrial artificial intelligence and predictive maintenance research by highlighting the strategic importance of AI-driven fault detection for developing reliable and intelligent manufacturing systems. The findings provide practical implications for industrial engineers, maintenance managers, technology developers, and organizations seeking advanced equipment management solutions.
Keywords	Artificial Intelligence, Fault Detection, Industrial Equipment, Predictive Maintenance, Machine Learning, Industry 4.0, Smart Manufacturing
Field	Engineering
Published In	Volume 4, Issue 1, January-February 2022
Published On	2022-01-20

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American International Journal of Research and Innovation

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Artificial Intelligence–Based Fault Detection in Industrial Equipment

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