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Health monitoring method of complex equipment based on attention mechanism and neural network

A neural network and health state technology, applied in biological neural network models, neural architectures, special data processing applications, etc., to achieve the effect of easy operation of the prediction process, ensuring reliability, and enhancing the effect of the model

Active Publication Date: 2019-03-08
ZHEJIANG UNIV
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And this method can be widely used in various complex equipment

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  • Health monitoring method of complex equipment based on attention mechanism and neural network
  • Health monitoring method of complex equipment based on attention mechanism and neural network
  • Health monitoring method of complex equipment based on attention mechanism and neural network

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Embodiment Construction

[0052] The present invention will be further described below in conjunction with accompanying drawing and turbine engine as specific examples:

[0053] Such as figure 1 As shown, the embodiment of the present invention uses a turbine engine as an example for illustration, specifically including the following steps:

[0054] This example uses the C-MAPSS dataset of the National Aeronautics and Space Administration (NASA) Forecast Data Warehouse to verify the effectiveness of the proposed method. This data set is simulated data obtained through simulation using the Commercial Modular Aerospace Propulsion System (C-MAPSS) developed by NASA. According to different operating states and failure modes, it can be further divided into 4 independent subsets, each subset contains a training set and a test set, and each subset contains engine operating data obtained through 21 sensors.

[0055] In the simulation program, the training set consists of sensor data records collected from mu...

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Abstract

The invention discloses a health condition monitoring method of complex equipment based on an attention mechanism and a neural network. The main steps comprise acquiring tge multi-sensor data of complex equipment; obtaining the effective measurement data by feature selection; obtaining a plurality of slice samples by preprocessing; establishing the neural network classification model which combines the attention mechanism and the depth neural network; inputting the slice samples and their corresponding labels into the neural network classification model to train the neural network classification model offline; inputting the slice samples of multi-sensor data to be predicted into the trained neural network classification model to obtain the health status of complex equipment. The method considers the data characteristics of the multi-sensor signal, fully excavates the local characteristics and the time sequence information in the data, has high prediction accuracy and wide applicability, and can be widely applied to various complex equipment.

Description

technical field [0001] The invention relates to a complex equipment state monitoring method, in particular to a complex equipment health state monitoring method based on an attention mechanism and a neural network, belonging to the field of system health management. Background technique [0002] In industrial production, real-time monitoring of the health status of complex equipment is very meaningful, which can provide condition-based maintenance capabilities and provide guidance for maintenance activities. It can also reduce the cost of inspection, reduce the cost of the whole life cycle, and avoid unnecessary expenses. It can also monitor the use of equipment in time, so that technicians can make relevant adjustments in time. [0003] The monitoring model based on neural network has become one of the commonly used methods in condition-based maintenance of complex equipment. The monitoring model based on neural network relies on a large amount of effective historical dat...

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Application Information

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IPC IPC(8): G06F16/215G06N3/04
CPCG06N3/049G06N3/045
Inventor 刘振宇刘惠郏维强张栋豪谭建荣
Owner ZHEJIANG UNIV
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