This research highlights the potential benefits of AI-based predictive maintenance, including proactive equipment failure detection, maintenance schedule optimization, and reduced downtime, and identifies emerging trends and future directions in AI-powered predictive maintenance.
Experimental results demonstrate that the proposed framework achieves high fault prediction accuracy, enhances system reliability, reduces maintenance costs, and supports data-driven decision-making in industrial environments.
Govind D. More, Shreyas Hon, Piyush Kotkar et al.· International Journal of Cre...· 0 citations
The findings reveal that ML algorithms significantly improve fault detection, Remaining Useful Life (RUL) estimation, and maintenance decision-making, but challenges related to data quality, model interpretability, cybersecurity, and integration with legacy systems continue to affect implementation effectiveness.
P. Siva, Sankar Shunmuga, Sundaram et al.· Stanzaleaf International Jou...· 0 citations
The proposed Explainable AI-Based Predictive Maintenance Framework (XAI-PMF) addresses this challenge by integrating IIoT sensing, intelligent feature engineering, hybrid machine learning, and explainability techniques such as SHAP, LIME, and rule extraction.
Narendra Karmarkar· International Journal of Mod...· 0 citations
An integrated AI-IoT framework for smart manufacturing that continuously acquires machine data, performs real-time analytics, predicts equipment failures, optimizes production scheduling, and supports data-driven decision-making is proposed.
Anand Singh, B. Mishra, Amjid Nadeem et al.· Journal of Intelligent Decis...· 0 citations
Findings indicate that AI-enabled digital twins significantly improve equipment reliability, reduce unexpected failures, enhance resource utilization, and enable proactive manufacturing strategies, however, challenges related to interoperability, cybersecurity, computational complexity, data quality, and governance remain critical barriers to widespread industrial adoption.
H. Mahmood· European International Journ...· 0 citations
Analytical preservation has become an vital strategy in Industrial Internet of Things (IIoT) locations for enlightening equipment reliability, dropping unforeseen machine failures, and enlightening trade productivity. This paper suggests an AI-driven predictive maintenance framework using the Google Cloud AI Platform for smart monitoring and fault estimate of engineering refining machines. The planned framework assembles and procedures real-time device data such as vibration, temperature, turning speed, and instrument wear from IIoT-enabled plans. Three machine learning algorithms, namely Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are practical and assessed to classify potential equipment letdowns before disappointment occurrence. Among the manufacturing models, the XGBoost classifier achieved the highest prediction accuracy of 99.18% with strong accuracy, recall, and AUC performance, on behalf of superior ability in early fault finding and analytical analytics. The grouping of Google Cloud AI services allows walkable model training, cloud-based supply, real-time specialist care, and efficient data organization for smart manufacturing applications. New results show significant improvements in upkeep efficiency, decrease in working downtime, and lower upkeep costs associated with traditional sensitive conservation approaches. The study highlights the productivity of joining IIoT sensor analytics, cloud computation, and progressive artificial intelligence methods for evolving smart and proactive industrial conservation arrangements in Industry 4.0 surroundings.
More Praveen, A. Lakshman, V.Jyothi2 et al.· 2026 4th International Confe...· 0 citations
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