Defect recognition method based on a power equipment defect recognition learning model

A technology for defect identification and power equipment, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve the problems of low precision, large number of power equipment defect detection samples, and long time consumption, etc., to ensure accurate Sex and efficiency, save human resources and time management costs, and improve the effect of automation

Inactive Publication Date: 2019-06-11
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

[0005] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides a defect recognition method based on a learning model for ele

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  • Defect recognition method based on a power equipment defect recognition learning model
  • Defect recognition method based on a power equipment defect recognition learning model
  • Defect recognition method based on a power equipment defect recognition learning model

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Embodiment Construction

[0033] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0034] Such as figure 1 As shown, a defect recognition method based on the learning model of electric equipment defect recognition includes the following steps:

[0035] S1. Collect image data of electrical equipment in batches through X-ray imaging equipment;

[0036] The internal structural components or state of the power equipment obtained through the principle of X-ray imaging are relatively accurate. A high-definitio...

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Abstract

The invention discloses a defect recognition method based on a power equipment defect recognition learning model. The method is based on multi-layer label labeling and identification; According to thenovel learning model integrating feature extraction, boundary frame regression and classifier technology set, a priori probability is replaced by a clustering algorithm in the model, a training set is generated by training power equipment samples, the type and the defect of the power equipment can be detected in real time, the accuracy and the high efficiency of defect recognition are ensured, and the safe operation of the power equipment is ensured. The automation degree of power equipment state monitoring and defect recognition is improved, a power grid big data analysis and processing system is researched and developed, the power equipment defect recognition technical concept is innovated, and technical development in the field of power systems is promoted. Advanced technologies are applied to the field of power systems, manpower resources and time management cost are saved, and field development is promoted.

Description

technical field [0001] The invention relates to the technical field of electrical equipment detection, in particular to a defect identification method based on a learning model for identification of electrical equipment defects. Background technique [0002] With the interconnection of large power grids and the continuous expansion of the grid scale, the safety and stability of power equipment has attracted widespread attention. The traditional types of electrical equipment defects are judged by inspectors based on their own experience. On the one hand, with the renewal of power equipment and the continuous change of the operating environment, the types of equipment defects increase, and the corresponding detection experience is also constantly changing. On the other hand, with the extension of transmission lines and the increase of electrical equipment, the contradiction between the increase in the workload of equipment defect detection and the shortage of corresponding de...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08
Inventor 苟先太刘琪芬郭竞邓方薛宏强张鹏举
Owner SOUTHWEST JIAOTONG UNIV
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