Accurate diagnosis of aero-engine faults and precise signal characterization are crucial to ensuring operational reliability and service life prediction. The structural complexity of engines and the variability of operating conditions pose significant challenges for fault diagnosis and identification. Based on an analysis and emphasis on the critical importance of aero-engine fault signal recognition and diagnosis, this paper comprehensively reviews and discusses the classification and evolution of aero-engine fault signal recognition techniques. The review traces this evolution along its developmental trajectory, from classical methods to emerging approaches such as quantum signal processing for weak feature extraction. It also examines characteristics of different types of aviation engine failures and the progression of diagnostic research over time. This review provides multiple tables to compare the applicability, advantages, and limitations of various signal recognition methods and deep learning diagnostic architectures. Detailed discussions synthesize the relative merits of different approaches and their selection trade-offs. Based on this overview, the paper outlines the complexity of real aero-engine faults and key research directions. Building on these developments in fault signal recognition and diagnosis, the paper addresses the complexity and the research areas receiving particular attention within real aero-engine faults. It highlights key research areas, including handling data imbalance, adapting to variable and cross-domain conditions, and advancing diagnostic and data enhancement methods for weak composite faults. Finally, the paper analyzes the multifaceted challenges in the field and identifies future trends in aero-engine fault signal recognition and intelligent diagnosis.
Li et al. (Mon,) studied this question.