From data analysis to intelligent maintenance: a survey on visual defect detection in aero-engines

Abstract In this paper, a systematic review of aero-engine defect detection methods is presented, encompassing the general procedure, traditional and intelligent detection algorithms, performance optimization, and future trends. The complete process and innovative theories of aero-engine visual defect detection are analyzed in this overview. Specifically, a five-level taxonomy is designed, with each level further subdivided to provide deeper insights, from data acquisition and task-oriented detection with nondestructive testing (NDT), to practical applications. By leveraging multiscale feature fusion-based detection, these methods achieve enhanced precision in identifying defects across varying scales and complexities. Moreover, in-depth discussions and outlooks on performance optimization and efficient deployment strategies are provided to promote advanced intelligent maintenance solutions for high-end equipment, which may encourage more multidisciplinary collaborations. Compared to other existing surveys, this work comprehensively outlines how computer vision (CV)-based methods can assist in aero-engine defect detection for intelligent decision-making, and a connection between NDT technology and CV-based inspection has been established, thereby drawing greater attention to the application of artificial intelligence to further enhance the development of industrial predictive maintenance.