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All Journal Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health
Rafasya Nayira
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Computer Science & IT Education Engineering Public Health Social Sciences Other

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Artificial Intelligence in Predictive Maintenance for Industrial IoT Nayira Azeema; Rafasya Nayira
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 3 No. 2 (2023): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v3i2.125

Abstract

The adoption of the Industrial Internet of Things (IIoT) has enabled real-time monitoring of machinery, leading to increased efficiency and reduced downtime. This paper presents an AI-driven predictive maintenance system that utilizes machine learning algorithms to detect potential failures before they occur. Using historical sensor data, the proposed model achieves high accuracy in predicting equipment malfunctions, allowing timely intervention. The study demonstrates the potential of AI in optimizing industrial processes and reducing operational costs.
Artificial Intelligence in Predictive Maintenance for Industrial IoT Nayira Azeema; Rafasya Nayira
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 3 No. 2 (2023): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v3i2.125

Abstract

The adoption of the Industrial Internet of Things (IIoT) has enabled real-time monitoring of machinery, leading to increased efficiency and reduced downtime. This paper presents an AI-driven predictive maintenance system that utilizes machine learning algorithms to detect potential failures before they occur. Using historical sensor data, the proposed model achieves high accuracy in predicting equipment malfunctions, allowing timely intervention. The study demonstrates the potential of AI in optimizing industrial processes and reducing operational costs.
Co-Authors Nayira Azeema