2024· International Journal of Intelligent Automation & Robotics Engineering· Vol 7, pp. 01-16· 0 citations
TL;DR
The proposed approach improves fault detection accuracy, reduces downtime and maintenance costs, enhances robot reliability and productivity, and supports the development of self-aware robotic systems for Industry 4.0 and Industry 5.0 smart manufacturing environments.
Abstract
Intelligent robotic systems increasingly require predictive health monitoring to ensure reliability, safety, and operational efficiency. Traditional maintenance methods cannot accurately predict failures or estimate component lifespan. This paper proposes an AI-driven predictive health monitoring framework that integrates multi-sensor data, intelligent feature engineering, deep learning, anomaly detection, fault diagnosis, and Remaining Useful Life (RUL) estimation. Using data from vibration, temperature, motor current, torque, acoustic signals, batteries, and controller logs, the framework continuously assesses robot health and predicts failures in real time. It supports intelligent maintenance scheduling, resource optimization, and autonomous maintenance decisions through adaptive learning. The proposed approach improves fault detection accuracy, reduces downtime and maintenance costs, enhances robot reliability and productivity, and supports the development of self-aware robotic systems for Industry 4.0 and Industry 5.0 smart manufacturing environments.
Autonomous robotic platforms have become an integral part of industrial automation, intelligent manufacturing, autonomous transportation, precision agriculture, healthcare robotics, and hazardous environment exploration. The operational reliability of these robotic systems strongly depends on the accuracy and health of their sensing infrastructure. Sensors including inertial measurement units (IMUs), LiDAR, cameras, ultrasonic sensors, GPS modules, encoders, force-torque sensors, and proximity sensors continuously provide environmental and operational information for autonomous decision-making. However, sensor degradation, calibration drift, communication failures, environmental interference, and hardware aging can significantly deteriorate robotic performance and may even lead to catastrophic failures. Consequently, intelligent sensor fault diagnosis has emerged as a critical research area that combines artificial intelligence, machine learning, data analytics, and model-based reasoning to identify, classify, and predict sensor faults in real time. In this paper, a complete intelligent sensor fault diagnosis framework for autonomous robotic platforms is proposed. The proposed framework utilizes the integration of multi-sensor data fusion, feature extraction, deep learning-based fault classification, anomaly detection, and predictive maintenance components into a unified architecture. Experimental performance evaluation shows significant enhancements versus traditional threshold-based diagnostic approaches in terms of high fault detection accuracy, lower false alarms and reduction in the latency for diagnosis among different faults with an increased reliability level on system diagnosis. These results demonstrate that AI-assisted diagnostic models significantly increased robotic autonomy through the ability to proactively manage faults, reduce downtime and enhance operational safety. The proposed framework is suitable for deployment in Industry 5.0 manufacturing systems, autonomous vehicles, collaborative robots, and intelligent service robotics.
James Carter, Patricia Hall· International Journal of Int...· 0 citations
This study presents a comprehensive research framework for real-time embedded AI-based industrial robot monitoring that combines edge computing, embedded deep learning, multi-sensor fusion, anomaly detection, predictive maintenance, and intelligent decision-making and provides a scalable, energy-efficient, and intelligent monitoring solution suitable for next-generation smart factories and Industry 5.0 environments.
Corrado Böhm, Corrado Gini· International Journal of Int...· 0 citations
This research proposes an AI-driven, edge-based system for electric motor health monitoring and predictive maintenance using multi-sensor data, and achieves low latency, reduced bandwidth usage, and fast on-site decision-making using TinyML, an edge AI framework.
F. Haruna, M. Abdulraheem, I. O. Durotoye et al.· SPE Nigeria Annual Internati...· 0 citations
This study proposes a comprehensive research framework that investigates the integration of hybrid artificial intelligence models into predictive maintenance systems for Industry 4.0 and aims to improve prediction accuracy, operational reliability, resource utilization, and sustainability while enabling autonomous maintenance decisions in smart factories.
Dr. T. Thirumalaikumari¹, V. Naga, Dr Kishore Thota² et al.· Journal of Intelligent Decis...· 0 citations
Artificial Intelligence (AI), machine learning, and adaptive control technologies have greatly improved industrial automation by enabling intelligent and flexible manufacturing systems. Traditional control methods, such as PID controllers, are less effective in handling dynamic industrial environments and complex production conditions. AI-based adaptive control systems use technologies like neural networks, fuzzy logic, reinforcement learning, and deep learning to optimize industrial processes in real time. These systems dynamically adjust control parameters using sensor feedback, improving efficiency, process stability, predictive maintenance, fault detection, and product quality.This research proposes an AI-driven adaptive control architecture that includes intelligent sensing, machine learning optimization, predictive analytics, reinforcement learning, and fault diagnosis modules for smart industrial automation. The system is evaluated using performance metrics such as response time, energy efficiency, production throughput, and operational reliability. The results show that AI-based adaptive control systems outperform traditional industrial controllers in adaptability, robustness, and intelligent decision-making. The study concludes that AI-powered adaptive control is essential for future Industry 4.0 and Industry 5.0 applications, supporting autonomous operations, smart manufacturing, and resilient industrial ecosystems.
Rajesh K. Sharma, Priya Natarajan· International Journal of Int...· 0 citations
This study finds no evidence that attention-enhanced temporal autoencoders offer a universal advantage over simpler deep learning baselines for industrial robotic predictive maintenance; any benefit appears concentrated in detecting subtle, temporally extended fault signatures, is small in magnitude, and was not confirmed as statistically significant in the present sample.
S. Musa, Sanjay Choudhary, Bashir M. Ahmad· Cureus Journal of Computer S...· 0 citations
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