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Convolutional neural network (CNN) based fault diagnosis method of DC/DC converter

A convolutional neural network and converter fault technology, applied in biological neural network models, neural architectures, instruments, etc., can solve problems such as complex and difficult models, and achieve high diagnostic performance and good diagnostic accuracy

Inactive Publication Date: 2019-03-08
WUHAN UNIV OF TECH
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Problems solved by technology

Researchers have implemented DNS in many applications, however, it is not easy to implement the model on common embedded devices due to the complexity of the model

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  • Convolutional neural network (CNN) based fault diagnosis method of DC/DC converter
  • Convolutional neural network (CNN) based fault diagnosis method of DC/DC converter
  • Convolutional neural network (CNN) based fault diagnosis method of DC/DC converter

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Embodiment Construction

[0045] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0046] like figure 1 As shown, the present invention provides a kind of DC / DC converter fault diagnosis method based on convolutional neural network, and this method comprises the following steps:

[0047] 1) Wrong data collection. In step 1), the DC / DC converter is composed of 12 IGBTs, storing 13 states in which each IGBT has an open circuit fault and all IGBTs have no faults, and collects the data corresponding to the above 13 states of the DC / DC converter. As the obtained data, the bus voltage is defined as X, and the 13 states are numbered and defined as Y.

[0048] 2) Data preprocessing. In step 2), data preprocessing includes the following steps:

[0049] 2.1) Reshape the sample data of step 1) into a picture, such as figure 2 shown. ...

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Abstract

The invention discloses a CNN based fault diagnosis method of a DC / DC converter. The method comprises the following steps including 1) error data collection; 2) data preprocessing; 3) CNN deep training; and 4) diagnosis precision test and use. The CNN is used to train the data, deep learning training skills are combined, data reinforcement and adaptive learning rate are used to solve the fitting problem, and a fault feature is extracted; and the representative feature can be extracted from original data based on signal processing knowledge and engineering experience of the spectif equipment and fault type needless of establishing an accurate mathematical model, and compared with traditional artificial feature extraction and deep neural network methods, the method of the invention has a higher diagnosis performance and can achieve high diagnosis precision.

Description

technical field [0001] The present invention relates to the technical field of DC / DC converter fault diagnosis, and more specifically, relates to a DC / DC converter fault diagnosis method based on a convolutional neural network. Background technique [0002] In order to meet the needs of the development of modern power systems, DC / DC converters play an increasingly important role, and their health conditions have a significant impact on the performance of power systems. Traditional diagnostic methods can be divided into model-based and data-driven methods. Model-based methods must analyze the electrical physical processes and interactions between components in power systems, however, in some cases, accurate mathematical models are difficult to build; traditional data-driven methods can be used for fault detection relying on manual feature extraction and classification, which requires extensive signal processing knowledge and equipment expertise. [0003] In recent years, th...

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Application Information

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IPC IPC(8): G01R31/00G06N3/04
CPCG01R31/00G06N3/045
Inventor 商蕾武美君高海波张泽辉张胜飞廖林豪盛晨兴
Owner WUHAN UNIV OF TECH
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