AI-Powered Telemetry for Predictive Maintenance in Enterprise Devices

Authors

  • Ravi Kiran Gadiraju

Keywords:

Artificial Intelligence (AI), Predictive Maintenance, Telemetry Data, Enterprise Devices, Machine Learning, Anomaly Detection, Real-Time Monitoring, Condition-Based Maintenance, IoT, Data Analytics, Fault Prediction, Preventive Maintenance, Edge Computing, Device Health Monitoring, Smart Maintenance Systems

Abstract

Enterprise IT infrastructures, increasing in complexity and scale, have given rise to bigger and bigger needs for efficient maintenance strategies to minimize downtime and operational costs. Predictive maintenance, based on and enabled by telemetry data and AI, has become the approach to prevent failures from actually happening. The paper continues with integrating AI-based telemetry in enterprise environments to proactively monitor and maintain devices. By utilizing streaming sensor data, machine learning tools, and anomaly detection, organizations can forecast failures better and initiate corrective measures beforehand. The research provides a deeper analysis of the system architecture, data pipelines, key technologies required to craft such solutions, and a detailed presentation of model evaluation metrics. Using actual telemetry datasets for experimentation, the paper verifies the efficacy of AI models in device health forecasting, minimizes unscheduled downtimes, and optimizes preventive maintenance scheduling. Moreover, the discussion considers challenges to realize the solution, such as data security, compliance, and interpretability of AI decisions. The findings emphasize AI-powered telemetry as a key enabling technology for smart, cost-efficient, and resilient enterprise device management.

DOI: https://doi.org/10.17762/ijisae.v12i23s.7612

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Published

31.10.2024

How to Cite

Ravi Kiran Gadiraju. (2024). AI-Powered Telemetry for Predictive Maintenance in Enterprise Devices. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 3063 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7612

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

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