###### Abstract  
The diagnosis and prognosis of aeronautical-bearing health conditions are essential to proactively ensuring efficient power transmission, safety, and reduced downtime. The rarity of failures in such safety-critical systems drives this process towards data-driven analytics of fault injection and aging experiments, rather than complex physics-based modeling. Nonetheless, data-based condition monitoring is very challenging due to data complexity, unavailability, and drift resulting from distortions generated by harsh operating conditions, scarcity of failure patterns, and rapid data change, respectively. Accordingly, the objective of this work is three-fold. First, to reduce data complexity and improve feature space representation, a robust data engineering scheme, including feature extraction, denoising, outlier removal, filtering, smoothing, scaling, and balancing, is introduced in this work. Second, collaborative selection-based incremental deep transfer learning (CSIDTL) is introduced to overcome the problem of the lack of patterns, incrementing the number of source domains in different training rounds. Third, long short-term memory (LSTM) adaptive learning rules are fully taken into account to combat further data complexity and data change problems. The well-structured methodology is applied on a huge dataset of aeronautical bearings dedicated to both diagnostic and prognosis studies, which perfectly addresses the above challenges in a form of a classification problem with 13 different conditions, 7 operating modes, and 3 stages of damage severity. Conducting CSIDTL following a three-fold cross-validation process allows us to improve classification performance by about 12.15% and 10.87% compared with state-of-the-art methods, reaching classification accuracy rates of 93.63% and 95.65% in diagnosis and prognosis, respectively. aircraft bearing; aircraft engine; deep learning; fault diagnosis; health stage; long short-term memory; prognosis; transfer learning Article Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach  
[PERSON], [PERSON], [PERSON] and Prognosis of Faults in High-Speed Aeronautical Bear  
reduction, heat dissipation, vibration absorption, reliability, durability, and overall safety. For numerous reasons, monitoring system vibration and bearing quality is critical in high-speed aerospace applications [2]. First, vibrations might suggest possible problems within the aircraft, such as misalignment or imbalanced loads, which can result in mechanical breakdowns and jeopardize safety. Second, bearing condition monitoring assists in identifying wear, excessive heat, and lubrication concerns that, if not treated promptly, can lead to catastrophic failures. Finally, monitoring these indicators allows for preventative maintenance, which reduces downtime and increases aircraft availability [2]. In another way, their proper operation and maintenance are essential to the efficient and safe operation of aircraft engines. In this context, bearing failure and degradation modeling can usually be performed through physics-based modeling, data-driven modeling, or hybrid physics- and data-driven modeling. However, various difficulties exist in this particular case. Extreme temperatures and strong G-forces are common operating conditions in high-speed aerospace applications that can impair the accuracy and reliability of monitoring systems. Furthermore, the complexity of aerospace systems, as well as the requirement for real-time data processing, presents hurdles in terms of data interpretation and rapid decision making [3]. Because of the intrinsic complexity of aircraft engine modeling, physics-based modeling is a difficult undertaking that induces less generalization, forcing it to be constrained to particular defined components and conditions [4; 5].