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

A neural network model and fault prediction technology, applied in biological neural network models, neural learning methods, neural architectures, etc., can solve the problems of limited fault modes, time-consuming and labor-intensive prediction accuracy, and low prediction accuracy, so as to achieve rapid prediction and improve accuracy , Improving the effect of precision and recall

Active Publication Date: 2020-12-25
SOUTHWEST PETROLEUM UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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.

Method used

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

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Embodiment 1

[0053] Such as figure 1 As shown, in Embodiment 1, the engine surge fault prediction system based on the fusion neural network model, the system based on the fusion neural network (PCFNN) of the present invention specifically includes a sequentially connected prediction module, feature extraction module and classification module. Specifically, the prediction module is used to generate a forecast time series of a specified length from the three-dimensional structure time series data of the engine; the feature extraction module is used to extract the local features of the forecast time series, the semantic relationship between data, and the overall sequence trend feature; the classification module, It is used to judge whether it is a surge fault based on the local characteristics of the predicted time series, the semantic relationship between data, and the overall sequence trend characteristics. The system prediction module of the present invention generates the three-dimensiona...

Embodiment 2

[0069] This embodiment has the same inventive concept as Embodiment 1. On the basis of the embodiment, a method for predicting engine surge faults based on a fusion neural network model is provided. The method includes the following steps:

[0070] S1: Generating the three-dimensional structural time series data of the engine into a predicted time series of a specified length;

[0071] S2: Extract the local features of the predicted time series, the semantic relationship between the data, and the overall sequence trend features;

[0072] S3: Determine whether it is a surge fault based on the local characteristics of the predicted time series, the semantic relationship between the data, and the overall sequence trend characteristics.

[0073]Further, in step S1, generating a forecast time series of a specified length from the three-dimensional structural time series data of the engine specifically includes:

[0074] S11: Encode the three-dimensional structural time series data...

Embodiment 3

[0095] This embodiment provides a storage medium, which has the same inventive concept as that of Embodiment 2, on which computer instructions are stored, and when the computer instructions are run, the engine surge fault prediction method based on the fusion neural network model in Embodiment 2 is executed. step.

[0096] Based on this understanding, the technical solution of this embodiment is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium. Several instructions are included to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Mem...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/15G06F30/27G06N3/04G06N3/08
CPCG06F30/15G06F30/27G06N3/084G06N3/044G06N3/045G05B23/024G06N3/048G06N3/0455G06N3/0464G06N3/0442G06N3/09G06N3/08G06N5/022
Inventor 郑德生唐晓澜张柯欣邓碧颖蒋东浦吴欣隆
Owner SOUTHWEST PETROLEUM UNIV
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