Engine surge fault prediction system and method based on fusion neural network model

Abstract

The invention discloses an engine surge fault prediction system and method based on a fusion neural network model, and belongs to the technical field of time series data prediction, and the system comprises a prediction module which is used for generating a prediction time series with a specified length through three-dimensional structure time series data of an engine; a feature extraction modulewhich is used for extracting local features of the prediction time sequence, semantic relationships among data and overall sequence trend features; and a classification module which is used for judging whether a surge fault exists or not according to the local features of the prediction time sequence, the semantic relationship among the data and the trend characteristics of the whole sequence. According to the method, the prediction time sequence with the specified length is generated firstly, that is, prediction of the working state data of the engine in a future period of time is achieved, and then whether the working state data of the engine in the future period of time include the surge fault data or not is judged so that the surge fault of the engine is predicted more accurately and quickly in advance.

Description

technical field [0001] The invention relates to the technical field of time series data prediction, in particular to an engine surge fault prediction system and method based on a fusion neural network model. Background technique [0002] The aero engine is the "heart" of the aircraft, and engine failure accounts for a considerable proportion of flight failures, and once a failure occurs, it will be very fatal. Therefore, how to predict aeroengine failure in advance is a difficult problem that needs to be solved for current flight safety. Aeroengine surge failure is a common abnormal working state, which will cause violent vibration of engine parts and overheating of hot end, and even endanger flight safety in severe cases. Therefore, when the engine is about to experience surge, it is one of the important prerequisites to avoid flight accidents by discovering and identifying the surge phenomenon in time, and then taking anti-surge measures. [0003] The research on fault p...

AI Technical Summary

Problems solved by technology

Although these methods can meet the real-time requirements, but because the engine itself is a complex nonlinear vibration system, it is very difficult to establish a predictive model

[0005] 2. Knowledge-based prediction; knowledge-based prediction does not require precise mathematical models, and can give full play to the knowledge and experience of experts in various disciplines of the engine. However, due to the limited failure modes covered by the expert knowledge base, there are still many in practical applications. problem to be solved

Among them, there are very few solutions that use machine learning algorithms or establish deep learning models based on data to make predictions. Most of them are based on models or knowledge to make predictions, which is not only time-consuming and labor-intensive, but also has low prediction accuracy.

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