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Residual service life prediction method of complex equipment based on combined depth neural network

A deep neural network and life prediction technology, applied in neural learning methods, biological neural network models, neural architectures, etc., to achieve the effects of improving effective life, suppressing noise, and simple structure

Active Publication Date: 2019-03-26
ZHEJIANG UNIV
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

And this method can be widely used in various complex equipment

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  • Residual service life prediction method of complex equipment based on combined depth neural network
  • Residual service life prediction method of complex equipment based on combined depth neural network
  • Residual service life prediction method of complex equipment based on combined depth neural network

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Embodiment Construction

[0054] The present invention will be further described below in conjunction with accompanying drawing and turbine engine data set as specific example:

[0055] like figure 1 As shown, the embodiment of the present invention uses a turbine engine as an example for illustration, specifically including the following steps:

[0056] 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.

[0057] In the simulation program, the training set consists of sensor data records collected fr...

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Abstract

The invention discloses a method for predicting the remaining service life of complex equipment based on a combined depth neural network. The main steps are as follows: acquiring multi-sensor data ofcomplex equipment; Obtaining effective measurement data by feature selection; obtaining A plurality of slice samples by preprocessing; Establishing the neural network regression model which combines the attention mechanism and depth neural network; The slice samples and their corresponding labels are inputted into the neural network regression model to train the neural network regression model offline. inputting The slice samples of multi-sensor data to be predicted into the trained neural network regression model, and the remaining service life of complex equipment is obtained. Considering the data characteristics of the multi-sensor signal, the invention 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 pieces of complex equipment.

Description

technical field [0001] The invention relates to a method for predicting the performance of complex equipment, in particular to a method for predicting the remaining service life of complex equipment based on a combined deep neural network, and belongs to the field of system health management. Background technique [0002] In industry, it is very meaningful to predict the remaining service life of complex equipment, 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. Most importantly, critical failures can be prevented through lifetime prediction, which can provide necessary guarantees for industrial activities and human safety. [0003] Complex equipment plays a very important role in industrial and production activities. For example, the engine is the core component of the development of the aviation fi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06F2119/04G06F30/17G06F30/20G06N3/045G06F18/2414
Inventor 刘振宇刘惠郏维强张栋豪谭建荣
Owner ZHEJIANG UNIV
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