Accurately predicting failures before they occur can transform industrial operations by reducing downtime, improving safety, and optimizing maintenance strategies. However, achieving this requires overcoming key challenges, including handling irregularly sampled data, integrating sparse yet critical failure-time information, and addressing privacy concerns in data-sensitive applications. This Engineering Research Initiation (ERI) award supports research that aims to develop a scalable predictive modeling framework that leverages both high-dimensional sensor signals and failure event data for online system failure prognosis while preserving industrial data privacy. By combining longitudinal analysis, survival modeling, and decentralized learning methodologies, this approach looks to enhance predictive accuracy and enable robust decision-making in industrial settings. If successful, the project will significantly advance data-driven monitoring, prognostics, and contribute to the emerging personalized predictive maintenance activities. Beyond research, this project intends to enrich undergraduate education by integrating real-world applications into classroom learning, providing students with hands-on opportunities to engage in model development, simulation, and testing in predictive maintenance and reliability. Additionally, the research will offer K-12 students opportunities to engage with cutting-edge technologies, fostering a new generation of innovators ready to tackle futu