K. Sravan Kumar, Naguru Navya, Surayapalem V Naga Dharmika, Perumalla Vyshnavi 03 04 Recent studies report that unplanned machine failures account for nearly 2030 of total production downtime in smart manufacturing environments With Industry 40 enabling continuous sensorbased monitoring industries now generate massive volumes of operational data yet effective fault analysis and decisionmaking remain a major challenge Conventional manual fault analysis systems suffer from critical limitations including heavy dependence on human expertise and periodic inspections which often result in delayed fault detection and inconsistent decisionmaking In this study a comprehensive data set is utilized containing parameters such as timestamp machine id temperature vibration level power consumption pressure material flow rate cycle time error rate downtime maintenance flag efficiency score and production status The data undergoes systematic preprocessing including noise handling normalization missingvalue treatment and feature alignment followed by Exploratory Data Analysis EDA to understand operational patterns fault correlations and feature importance Existing machine learning models such as AdaBoostCART XGBoostCART and PassiveAggressive PA CART are implemented as baseline methods To overcome the limitations of fixedfeature learning and manual feature engineering a proposed Neural Architecture Search NASbased feature extraction framework integrated with a Greedy Rule Forest GRFCART model is introduced The NAS component automatically learns optimal feature representations from complex sensor interactions while the GRFCART enhances interpretability and decision robustness The proposed framework performs classification tasks to predict downtime occurrence maintenance requirement maintenance flag and production status along with a regression task to accurately estimate the efficiency score Experimental results demonstrate that the proposed NASGRFCART approach significantly improves fault prediction accuracy reduces false maintenance alerts and provides reliable efficiency assessment making it wellsuited for intelligent datadriven maintenance strategies in Industry 40 environments Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026