Abstract: The rapid industrial advancements of Industry 4.0 make it necessary for companies to adopt maintenance systems which use intelligent and adaptive technology together with data analysis methods that exceed basic condition monitoring capabilities. Structured sensor data dominates predictive maintenance models, maintenance logs, operator reports, and technical documentation are underutilised. This paper presents a conceptual approach to integrate the NLP and hybrid intelligence frameworks into industrial condition monitoring and predictive maintenance systems. The study uses structured machine data with unstructured textual insights to improve defect identification, prognostics, and decision-making. It is based on interdisciplinary research in Machine Learning, AI, Computer Science, Statistics, and Automation Engineering. This study employs qualitative research approaches, including systematic literature analysis, thematic synthesis of AI-driven maintenance frameworks, and conceptual modelling to construct an integrated architecture for smart manufacturing. The proposed framework demonstrates that machine learning models which use sensors together with natural language processing-based information extraction systems, enhance the accuracy of detecting anomalies and their underlying causes while improving maintenance schedule establishment. The hybrid intelligence framework enables human-AI collaboration for decision-making processes, which improves situational understanding and process transparency. The research discovered that system resilience and operational downtime improvements result from using NLP together with predictive analytics, which help organizations develop sustainable automation strategies. The research presents an adaptable framework which enables smart factories to implement data-driven transformations within Industry 4.0 ecosystems. The study develops manufacturing analytics through conceptual advancements, which enable future empirical testing within industrial environments.
Keywords: Predictive Maintenance; Natural Language Processing (NLP); Hybrid Intelligence; Industry 4.0; Smart Manufacturing.
Title: A Conceptual Model for Integrating NLP and Hybrid Intelligence in Industrial Condition Monitoring and Predictive Maintenance
Author: Suraj Shrestha
International Journal of Engineering Research and Reviews
ISSN 2348-697X (Online)
Vol. 14, Issue 1, January 2026 - March 2026
Page No: 53-60
Research Publish Journals
Website: www.researchpublish.com
Published Date: 27-March-2026
