Transaction on Machine Design Hsing-Cheng Liu 14 2 2026 https://doi.org/10.6025/tmd/2026/14/2/47-66 https://www.dline.info/tmd/fulltext/v14n2/tmdv14n2_1.pdf Accurately assessing machine health in Industry 4.0 smart manufacturing is challenging due to complex, heterogeneous data streams. Existing diagnostic models often focus on isolated faults or individual sensor signals, lacking a comprehensive, multidimensional evaluation of overall equipment condition. To address this gap, this study proposes a hybrid Principal Component Analysis (PCA) and Entropy weighting framework to generate a robust Composite Machine Health Score (CMHS). Empirically validated using a smart manufacturing dataset comprising 10,000 records and 15 operational, environmental, and production variables, the framework integrates PCA for variance based dimensionality reduction with entropy weighting for objective, information theoretic indicator evaluation. Results demonstrate that the CMHS effectively differentiates machine conditions, successfully separating highly efficient operations from severely degraded states. Entropy analysis revealed that downtime duration and environmental humidity possess the highest discriminatory power in distinguishing machine health states. Furthermore, the hybrid fusion approach proved significantly more robust than single method evaluations, notably identifying latent anomalies where machines exhibited extreme performance degradation without triggering explicit fault alarms. Ultimately, this framework offers a highly interpretable, data driven decisionsupport tool for predictive maintenance. By synthesizing multidimensional manufacturing data into a single actionable metric, it enables organizations to accurately prioritize maintenance interventions, mitigate hidden inefficiencies, and enhance overall operational resilience. This advancement directly supports the critical transition from reactive maintenance to proactive, condition-based strategies in modern production environments.