Multi-sensor adaptive fusion and convolutional neural network-based acoustic emission diagnosis for initial damage of the engine

Aiming at the problems of traditional fault diagnosis means that are difficult to identify initial damage, as well as the poor reliability and fault tolerance with a single sensor, an acoustic emission (AE) diagnosis method for initial damage of the engine based on multi-sensor adaptive fusion and convolutional neural network (CNN) is proposed. Firstly, under the premise of utilizing parametric analysis to characterize the multi-sensor AE signals, the feature parameter entropy is used to determine the primary and secondary relationships between multi-sensor signals, and then the AE feature parameter matrix is formed by adaptive fusion. Secondly, CNN is employed to mine and learn the fault feature combinations from the AE feature parameter matrix by multi-layer fusion to realize the identification and diagnosis for initial damage of the engine. Finally, the proposed method is validated on the engine test bench designed for initial damage identification and is compared with conventional methods in terms of diagnostic performance. The results demonstrate that the proposed method can achieve an identification accuracy of 98.83% for initial damage, and has advantages in various aspects such as TAMSE, K, F1mic and F1mac, which explicitly provides a theoretical and methodological basis for identifying initial faults comprehensively and accurately. This research not only enriches the theory and methods in the field of structural health monitoring, but also provides strong technical support for engine health management, which is expected to play a key role in the maintenance and guarantee of aviation engines in the future.