Fault Diagnosis in Industrial IoT: AI-Driven Sensor Data Analytics for Predictive Maintenance

Abstract

Fault diagnosis in the Industrial Internet of Things (IoT), or Eliot, has drawn a lot of interest because of its potential to enhance predictive maintenance and operational efficiency. Traditional maintenance approaches often lead to high downtime and operational costs, necessitating AI-driven sensor data analytics for real-time fault detection. This study examines AI methods, including deep learning (DL), machine learning (ML), and signal processing for fault diagnosis in Eliot environments. Various sensors, including vibration, temperature, and acoustic sensors, are vital components of data acquisition. Advanced data analytics techniques, including feature extraction, anomaly detection, and predictive modeling, are examined for fault classification and prognosis. The integration of Real-time data processing is made possible by cloud and edge computing, which lowers bandwidth and increases the precision of defect detection. Furthermore, cybersecurity challenges in Eliot-based fault diagnosis systems are discussed, emphasizing the need for secure and resilient architectures. The study highlights various AI-driven fault diagnosis frameworks, their efficiency in minimizing failures, and their impact on industrial productivity. Comparative analysis of different AI models demonstrates their effectiveness in predictive maintenance applications. Future advancements in AI, sensor technology, and cloud-edge integration will further revolutionize fault diagnosis in Eliot, ensuring reliability, safety, and cost-effectiveness in industrial operations.