2025· International Journal of Modern Research in Science & Engineering· 0 citations
TL;DR
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.
Abstract
Industry 4.0 has transformed manufacturing through the integration of Industrial IoT (IIoT), cyber-physical systems, cloud computing, and artificial intelligence, making predictive maintenance (PdM) a key strategy for improving equipment reliability. Unlike traditional maintenance, AI-driven PdM analyzes real-time sensor data to predict equipment failures before they occur. However, many AI models operate as black boxes, limiting trust and interpretability. The proposed Explainable AI-Based Predictive Maintenance Framework (XAI-PMF) addresses this challenge by integrating IIoT sensing, intelligent feature engineering, hybrid machine learning (Random Forest, Gradient Boosting, LSTM, and Transformers), and explainability techniques such as SHAP, LIME, and rule extraction. These methods provide transparent fault predictions and maintenance recommendations by highlighting the factors influencing equipment degradation. Continuous learning further enables adaptive model updates as new operational data become available. Overall, the framework improves prediction accuracy, reduces downtime and false alarms, enhances maintenance scheduling, and supports trustworthy, intelligent asset management for next-generation smart factories.
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
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
A number of physically meaningful indicators, including thermal instability, vibration anomalies, smoke-related features, and aggregated risk scores, have a significant impact on maintenance predictions and hence provide better operational interpretability and maintenance transparency.
Chitranjanjit Kaur, S. Chopra, Chitta Ranjan Tripathy· Automation· 0 citations
Artificial intelligence-driven predictive maintenance represents a critical enabler of operational excellence, resilient manufacturing systems, and sustainable industrial transformation in the era of Industry 4.0.
Banoth Samya, V. Ramesh, A. Vathsala et al.· Journal of Intelligent Decis...· 0 citations
A structured methodology for ML-based PdM frameworks, covering data-driven, physics-based, and hybrid approaches, including supervised, unsupervised, and deep learning models is proposed, offering valuable insights for developing efficient and scalable PdM solutions.
Sithik Shah· International Journal of App...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
