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.
Narendra Karmarkar· International Journal of Mod...· 0 citations
Scientific publishing, digital repositories, patents, and multidisciplinary research datasets have expanded rapidly, making traditional literature review methods increasingly inefficient. Large Language Models (LLMs) address this challenge by enabling intelligent knowledge discovery, semantic search, literature summarization, research gap identification, hypothesis generation, citation assistance, and academic writing support. By integrating Retrieval-Augmented Generation (RAG), vector databases, knowledge graphs, citation networks, and domain-specific ontologies, LLMs improve contextual relevance, reduce hallucinations, and enhance research accuracy. These capabilities accelerate interdisciplinary collaboration, automate research workflows, and support evidence-based decision-making. However, challenges such as hallucination, bias, outdated knowledge, explainability, privacy, intellectual property, reproducibility, and computational requirements remain significant. Modern AI-assisted research systems increasingly incorporate human-in-the-loop validation, explainable AI, and responsible governance to ensure trustworthy outcomes. This study presents a conceptual framework that combines semantic retrieval, intelligent reasoning, automated literature analysis, and workflow orchestration, demonstrating how LLM-powered systems can transform scientific research into scalable, accurate, ethical, and collaborative knowledge discovery processes.
Narendra Karmarkar, Iyengar P.K· International Journal of Eme...· 0 citations