Fault Diagnosis Method of Aircraft Engine Based on ISO-BiLSTM

Aero-engine is an important component of aviation equipment. Due to the special working environment and the poor temporality of fault diagnosis, it is difficult for aircraft maintenance personnel to make accurate fault diagnosis of aero-engine. To address this problem, this paper proposes an aero-engine fault diagnosis method based on an improved snake optimization algorithm (ISO) and a bidirectional long and short-term memory network (BiLSTM). The idea of balanced pool of EO algorithm is introduced in the snake optimization algorithm to build an elite pool to compensate for the low accuracy of calculation caused by randomly selected individuals, while the Lévy flight perturbation mechanism is introduced to enhance the SO’s performance of finding the best; the optimized parameters are substituted into the BiLSTM to reconstruct the model, train the fault data for prediction and output the results. In the simulation experiments, five failure modes are diagnosed with real monitoring data of a certain type of aero-engine and compared with the BiLSTM and SO-BiLSTM fault diagnosis models. The experimental results show that the ISO-BiLSTM fault diagnosis method proposed in this paper has better fault diagnosis effect compared with BiLSTM and SO-BiLSTM, and provides a new idea for aero-engine fault diagnosis.