Feature Selection Strategies in Failure Prediction

Failure prediction is a pivotal element in ensuring the reliability of systems, minimizing downtime, and maximizing operational efficiency across various industries, to prevent costly breakdowns and ensure seamless operations. However, selecting pertinent features for accurate failure prediction has become a critical challenge. Addressing these challenges is made difficult by the extensive and varied data produced by modern systems, including sensor reading, environmental factors, and historical performance metrics. This paper provides a comprehensive review of feature selection strategies in failure prediction, encompassing both traditional and advanced methodologies. By examining real-world case studies and applying feature selection techniques to standard datasets, we underscore the importance of selecting the most informative attributes while balancing interpretability and predictive performance. Through this review, we aim to offer practitioners and researchers valuable insights into optimizing failure prediction models and enhancing system reliability. Additionally, we underscore the importance of integrating domain expertise into the feature selection process to enhance model interpretability and effectiveness.