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In order to ensure the structural health of civil aircraft in service, health monitoring systems that can collect large amounts of data to reflect the structural condition of civil aircraft have been put into use, which has led to the "big data" era of structural health monitoring of civil aircraft. The data collected by health monitoring systems is characterized by large capacity, diversity and real-time. The research of advanced theoretical methods to extract effective information from health monitoring data and efficiently and accurately identify the structural health status has become a new problem in the field of structural health monitoring of civil aircraft. Theories such as data-driven deep learning and machine learning, as the latest research results in pattern recognition, have achieved fruitful results in various fields of big data processing with powerful modelling and characterization capabilities. Combining the characteristics of health monitoring data with the advantages of data-driven theoretical approaches, this paper proposes a new method for processing health monitoring data of civil aircraft structures. The method uses machine learning and deep learning algorithms to train structural state prediction models. The advantage of this method is that it is able to extract and make intelligent judgements on the state of the airframe without relying on the processing technology and engineering diagnosis experience of a large amount of collected data. This paper also compares the accuracy of various machine learning and deep learning algorithms and explores the differences in the weight of the acquisition signals and the dimensionality reduction of the acquisition data. Experimental results show that the method achieves accurate identification and prediction of the number of structural cycle take-offs and landings and crack expansion states in segment-level fatigue testing of aircraft.
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