Application issues of data-driven intelligent fault diagnosis technologies for liquid rocket engines

Fault diagnosis is one of the key technologies to ensure the safety of liquid rocket engines. The model-based diagnostic methods are limited by the irreconcilable contradiction between diagnostic accuracy and model accuracy, while data-driven diagnostic methods, typified by signal processing techniques, rely heavily on expert domain knowledge. With the rapid development of artificial intelligence and big data, the data-driven intelligent fault diagnosis methods have received extensive attention and achieved great success in a great variety of engineering applications. Therefore, the application modes of the data-driven intelligent fault diagnosis methods in liquid rocket engines was reviewed from the perspectives of model structures and feature engineering of machine learning. The three major challenges faced by the the data-driven intelligent fault diagnosis methods in the practical health monitoring application of liquid rocket engines were further analyzed, and the corresponding solutions based on research achievements of our team were presented, respectively. Finally, review conclusions and future works of the data-driven intelligent fault diagnosis technology were proposed to inspire further exploration in this field.