Abstract
Predictive Engineering Analytics (PEA) and Performance Forecasting (PF) have emerged as transformative tools in modern engineering, combining data-driven techniques with classical engineering principles to enhance system reliability, operational efficiency, and lifecycle management. This paper reviews the state-of-the-art methodologies in PEA and PF, highlighting machine learning, digital twins, and physics-informed modeling approaches. Key applications in aerospace, manufacturing, energy, and automotive sectors are discussed, emphasizing the role of predictive analytics in minimizing downtime, optimizing maintenance schedules, and reducing operational costs. Challenges such as data quality, model interpretability, and integration with legacy systems are addressed. The paper also presents comparative analyses, illustrating how predictive analytics frameworks can guide decision-making and improve overall system performance.
Keywords: Predictive Engineering Analytics, Performance Forecasting, Machine Learning, Digital Twin, Reliability, Maintenance Optimization, Physics-Informed Modeling
Full Issue
| View or download the full issue | PDF 99-111 |