Wind Turbine Blade Damage Detection via Acoustic Spectrogram Analysis
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Solution Overview
Problem
Manual inspection methods for wind turbine blades are costly and fail to provide real-time monitoring of their health state, limiting the efficiency of wind power generation.
Innovation Solution
A method and apparatus that utilize a sound acquisition device to capture wind impingement sounds, generate frequency spectrograms, and employ a neural network-based damage recognition model for image recognition to identify blade damage, reducing the need for manual inspection and enabling real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection method is used, then inspection can be performed, but operation and maintenance costs are high and real-time monitoring is not achieved
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated acoustic detection system. Sound acquisition devices capture blade sounds, which are then processed through signal processing and neural network-based damage recognition models to automatically identify blade damage, eliminating the need for manual inspection while enabling real-time monitoring.
Solution Approach 2:
The inspection system performs self-diagnosis by using the wind turbine's own operational sounds as the detection source. The neural network model automatically analyzes the acoustic signals to identify damage patterns, allowing the system to monitor its own health status without external intervention.
2Productivity
If manual inspection method is used, then inspection can be performed, but operation and maintenance costs are high
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated acoustic detection system. Sound acquisition devices capture blade sounds, which are then processed through signal processing and neural network-based damage recognition models to automatically identify blade damage, eliminating the need for manual inspection while enabling real-time monitoring.
Solution Approach 2:
The patent introduces sound acquisition devices and signal processing algorithms as intermediaries between the wind turbine blades and the inspection outcome. These intermediaries capture and analyze acoustic signals to provide automated damage detection, serving as a bridge that eliminates the need for direct manual inspection.
3Loss of information
If traditional inspection methods are used, then simple equipment is required, but real-time health state monitoring cannot be achieved
Solution Approach 1:
The patent focuses on detecting specific damage patterns in acoustic signals rather than attempting to analyze all possible blade conditions. The neural network model is trained to recognize particular damage signatures in the sound spectrum, allowing effective detection of relevant damage while filtering out irrelevant information.
Solution Approach 2:
The patent transforms the acoustic signals from the time domain to the frequency domain through spectrum analysis. This parameter transformation allows the neural network to detect damage patterns more effectively by analyzing frequency characteristics of the sounds rather than raw temporal waveforms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach accurately recognizes blade damage without manual intervention, reduces operational costs, and allows for real-time monitoring of wind turbine blade health without relying on wind turbine operating data, thereby enhancing the efficiency of wind power generation.
Implementation Method 1
acquiring a sound signal generated by an impingement of wind on the wind turbine blade using the sound acquisition device
Data Source
AI summary
A method and apparatus for inspecting a wind turbine blade. The method includes: acquiring a sound signal generated by an impingement of wind on the wind turbine blade using a sound acquisition device; generating a frequency spectrogram corresponding to the sound signal; and obtaining a damage recognition result of the wind turbine blade from the frequency spectrogram by performing image recognition on the frequency spectrogram based on a damage recognition model. With the method, a damage type of the wind turbine blade is accurately recognized based on the frequency spectrogram without manual inspection. Therefore, human resources are saved. In addition, the health state of the wind turbine blade can be monitored in real time.