Machine Learning–Based Predictive Maintenance in Industrial Robotics
DOI:
https://doi.org/10.59675/E123Keywords:
Predictive maintenance, Machine learning, Industrial robotics, Industry 4.0, Deep learning, Remaining useful life, Condition monitoringAbstract
The fast development of the industry 4.0 technologies is radically changing the operations and maintenance approaches of manufacturing activities in the global arena. Machine learning predictive maintenance is a paradigm shift of the old reactive and preventive methods which allow real time fault detection and predictions of the remaining useful life in industrial robots. In this paper, the technical framework and operation models of predictive maintenance in robotic systems by integrating machine learning algorithms with sensor networks and Industrial Internet of Things infrastructure are analyzed along with their implications. It is a critical analysis of different methodologies such as supervised learning, unsupervised learning, and deep learning approaches, and it discusses implementation issues involving the data quality, model interpretability, and system integration. The paper also assesses the regulatory factors and cybersecurity systems needed to deploy in industries. Using automotive, electronic, and aerospace manufacturing case studies, this study shows that predictive maintenance with the help of ML can reduce unexpected downtimes by up to 40 percent and increase the life of components. It is concluded in the paper that machine learning-driven predictive maintenance systems are revolutionary systems to provide efficient, reliable, and cost-effective industrial operations in the smart manufacturing era.
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