Fault detection and identification method for civil aircraft system based on LSTM-AE depth learning framework

Abstract

The invention discloses a fault detection and identification method for civil aircraft system based on LSTM-AE depth learning framework and relates to the technical field of condition monitoring and fault diagnosis of complex civil aircraft system, and can be used to realize the monitoring and identification of flight faults. The invention comprises the following steps: selecting time series dataof multi-state parameters of an aircraft in flight under a certain stable condition, and according to the characteristics of the monitored object, the time series data of state parameters under suitable conditions are selected for the training of the system reconstruction model, then the fault-free state of civil aircraft system is modeled and reconstructed by making full use of the long-time series-dependent memory ability of LSTM model. The fault monitoring and identification are realized by further analyzing the reconstruction error of its state parameters. The invention solves the problemof insufficient fault monitoring means of civil aircraft system, utilizes the advantage of deep learning in big data analysis to mine massive operation and maintenance data of civil aircraft, and provides important support for fault monitoring of civil aircraft system and route fault isolation.

Description

technical field [0001] The invention relates to the technical field of status monitoring and fault diagnosis of complex civil aircraft systems, and in particular to a civil aircraft system fault monitoring and identification method based on the LSTM-AE deep learning framework. Background technique [0002] The complexity and integration of modern engineering systems are increasing day by day, and at the same time, they are facing the challenges of dynamic diversity of tasks and operating environments, and the resulting reliability and safety issues are also becoming more prominent. In addition, higher requirements are put forward for its "economic affordability". At the same time, with the development of low-cost sensing and communication technologies, modern engineering systems are usually equipped with various monitoring systems to monitor and record parameters such as the status, performance, operating environment and load of the system online. The dynamic, continuous sa...

AI Technical Summary

Problems solved by technology

In the field of civil aviation maintenance, more and more data collection and intelligent analysis tools are being developed and used, but the application of artificial intelligence technology in the aviation maintenance industry is still in the initial stage, and the operation and maintenance data generated by the new generation of aircraft presents Order of magnitude growth, but data utilization is far from enough to fully release the value of massive aircraft operation data, information and knowledge in the field of aircraft maintenance

[0004] In summary, there is a lack of a system health monitoring method in the existing technology, which can mine the massive operation and maintenance data of civil aircraft, and provide support for civil aircraft system fault monitoring and route fault isolation

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