Wind turbine blade image damage detection and positioning method

Abstract

The invention belongs to the technical field of fault detection, and particularly relates to a wind turbine blade image damage detection and positioning method. Based on a deep convolutional neural network, the method comprises two processes of model training and damage detection and positioning. According to the invention, the wind turbine blade images shot by the unmanned aerial vehicle and themonitoring camera can be automatically interpreted, and multiple types of wind turbine blade damage can be efficiently and accurately recognized and positioned. Blade damage evaluation and early warning are achieved, the frequency of accidental shutdown of the wind turbine caused by blade faults of the wind turbine is reduced, and the operation and maintenance cost of the wind turbine is reduced.The invention has the advantages of high recognition speed, high precision, full-automatic process, low operation threshold and the like, and makes up the defects of low efficiency, high misjudgment rate, time and labor waste and the like caused by manual operation in the traditional method.

Description

Technical field: [0001] The invention belongs to the technical field of fault detection, and in particular relates to a method for detecting and locating image damage of wind turbine blades. Background technique: [0002] my country is rich in wind energy resources. The country's developable wind energy resources are about 4350GW, and the reserves of wind energy resources rank among the top in the world. my country's popularization of wind turbines (referred to as "wind turbines") is rapid. With the continuous expansion of the deployment of wind turbines in China and the world, its operating status monitoring and safety maintenance work has attracted more and more attention. Wind turbines are installed in places with abundant wind resources, most of which are near the seaside or on the top of mountains near the seaside, and the environment is harsh. Therefore, wind turbines are usually exposed to changeable and harsh environments, such as high altitude, desert, Gobi and sea...

AI Technical Summary

Problems solved by technology

[0008] However, in actual operation, the above-mentioned detection method still has several shortcomings that are difficult to overcome: 1. The operating conditions of wind turbine blades are complex and changeable, the sensor signal is easily interfered by a large amount of noise, and the fault information is easily submerged, resulting in judgment errors. Difficult to extract robust fault features

2. It is difficult to detect early damage to blades using methods based on acoustic or vibration signals

3. During the operation of the wind turbine, different signal data are obtained through the sensors arranged on the blades of the wind turbine. The arrangement, service life and accuracy of the collected signals of the sensors will greatly affect the reliability of the fault detection method of the wind turbine blades

Based on the above factors, the current detection methods perform poorly in practical applications in terms of detection accuracy (high false alarm rate and missed detection rate) and recognition effect (either the damage location cannot be achieved, or the specific damage type cannot be classified)

Images