Wind power gear box state monitoring method based on long-short-term neural network and automatic coding machine

A wind power gearbox and automatic encoder technology, applied in neural learning methods, biological neural network models, testing of mechanical components, etc., can solve problems such as large data volume, multi-source heterogeneous dynamics, and difficult big data processing.

Active Publication Date: 2021-03-09
ZHEJIANG UNIV OF TECH
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Problems solved by technology

However, the processing and analysis of multi-dimensional time series data is difficult: First, there are potential correlations and mutual influences between different dimensions of the data, which lead to the fault detection and identification ...

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  • Wind power gear box state monitoring method based on long-short-term neural network and automatic coding machine
  • Wind power gear box state monitoring method based on long-short-term neural network and automatic coding machine
  • Wind power gear box state monitoring method based on long-short-term neural network and automatic coding machine

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Embodiment Construction

[0021] The present invention will be further described below in conjunction with the accompanying drawings.

[0022] refer to Figure 1 ~ Figure 4 , a wind power gearbox condition monitoring method based on long-short-term neural network automatic encoding machine, including the following steps:

[0023] Step 1) For the original high-dimensional time series data of wind power gearbox D={N*F}, where N is the number of samples and F is the dimension of the samples; the samples are divided by sliding windows to obtain I new samples, and the window width is set to is set as L, the window sliding step is set as s, and the divided data set D'={I*N*F} is obtained;

[0024] Among them, for the time series data sample division method based on the sliding window, the window width setting value L is very important. When the window width is large, the inherent time series dependence of the data set samples can be better mined, but at the same time it will reduce the self-encoding The se...

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Abstract

The invention discloses a wind power gear box state monitoring method based on a long-short-term neural network and an automatic encoder, and the method comprises the steps: segmenting a sample through employing a sliding window in combination with a bidirectional long-short-term neural network, capturing a time dependence relation in multi-dimensional time series data, and carrying out the reconstruction mapping of multi-dimensional nonlinear time series data to a low-dimensional space through learning a large number of normal samples; on the basis, comparing and analyzing the difference between a reconstructed sample and an original sample to achieve state monitoring and fault diagnosis of the wind power gear box. The method provided by the invention can effectively process high-dimensional time series data, and can be better applied to a variable working condition operation environment of the wind power gear box.

Description

technical field [0001] The invention relates to a method for monitoring the state of a wind power gearbox based on a bidirectional long-short-time neural network automatic encoding machine. Background technique [0002] With the rapid development of information fusion technology, modern electromechanical systems can realize real-time monitoring and health assessment of operating status and processes by installing multiple types of sensors, smart meters and other equipment. With the rapid development of machine learning and deep learning technology, data-driven models have become an effective method for fault detection of electromechanical equipment, especially wind power gearboxes. Considering that the data collected during the operation of the wind power gearbox is multi-dimensional time-series data, it is very important to perform condition monitoring and fault diagnosis on the wind power gearbox based on the multi-dimensional time-series data. However, the processing and...

Claims

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Application Information

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IPC IPC(8): G01M13/021G06N3/04G06N3/08
CPCG01M13/021G06N3/084G06N3/044G06N3/045Y04S10/50
Inventor 傅雷朱添田
Owner ZHEJIANG UNIV OF TECH
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