Aero-engine service life prediction method based on label-free, unbalanced and initial value uncertain data

An aero-engine and data-determining technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as unbalanced initial value, lack of prior knowledge, and imbalance

Pending Publication Date: 2019-05-17
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide an aero-engine life prediction method based on unlabeled, unbalanced, and uncertain initial values. In the case of deterministic problems, it solves the problem that the existing multivariate metho

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  • Aero-engine service life prediction method based on label-free, unbalanced and initial value uncertain data
  • Aero-engine service life prediction method based on label-free, unbalanced and initial value uncertain data
  • Aero-engine service life prediction method based on label-free, unbalanced and initial value uncertain data

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Embodiment Construction

[0048] In this embodiment, the civil turbine engine life cycle data set released by NASA's predictive failure prediction research is used, and the FD001 data set is selected. This data set simulates the end-of-life situation of aero-engines caused by the performance degradation of high-pressure compressors of aero-engines. The data set includes training data, test data and RUL of test data. The data set includes 21 sensor measurement variables, including multi-source data types such as temperature, pressure, and rotational speed. The specific sensor symbols, descriptions, and units are shown in Table 1. Furthermore, the dataset contains noise and the initial health state of each engine sample is uncertain.

[0049] Table 1 Description of aero-engine sensor variables

[0050]

[0051]

[0052] The overall process of this method is as follows figure 1 As shown, the specific implementation steps are as follows:

[0053] Step 1. Feature selection of aero-engine multi-sour...

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Abstract

The invention discloses an aero-engine service life prediction method based on label-free, unbalanced and initial value uncertain data, which comprises the following steps of firstly, carrying out feature screening on an engine training data set by utilizing a correlation index and a trend index; then, obtaining a health state label through quantum fuzzy clustering, training a multivariable deep forest classifier, and obtaining an aero-engine health assessment model; meanwhile, training a long-short cycle memory neural network (LSTM) time sequence prediction model by utilizing the engine training data set; and finally, obtaining the maintenance time and the final residual service life (RUL) of the engine at different health stages by using the engine test data set according to the trainedhealth assessment model and the time sequence prediction model. According to the method, the defects of no label, imbalance and initial value uncertainty of observation data are overcome, and a technical reference is provided for maintenance decisions of the aero-engine in different subsequent health stages.

Description

technical field [0001] The invention relates to an aero-engine life prediction method, in particular to an aero-engine life prediction method based on unlabeled, unbalanced and uncertain initial value data. Background technique [0002] The engine is the most sophisticated and complex subsystem in the aircraft. It provides the power for the aircraft to fly, and has extremely strict requirements on safety and reliability. During the long-term continuous operation of the engine in an extreme environment, its performance will inevitably degrade, which can easily cause various failures and threaten the safety of the system. The PHM technology with health assessment and failure prediction as the core is the key to realize the predictive maintenance of aeroengine and ensure the reliability of engine operation, and has become the focus of attention in the aviation field. In recent years, due to the promotion of big data analysis technology, cloud computing technology and artificia...

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Application Information

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IPC IPC(8): G06F17/50G06N3/04
CPCY02T90/00
Inventor 王村松陆宁云程月华姜斌
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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