As a new type of non-destructive testing equipment, the pen type electromagnetic ultrasonic high-temperature corrosion detector has demonstrated advantages in the field of high-temperature corrosion detection due to its non-contact and high-temperature resistance characteristics. Traditional corrosion detection methods have many limitations in high-temperature environments and are difficult to meet the detection needs of modern industry.
1、 Working principle
The pen type electromagnetic ultrasonic high-temperature corrosion detector is based on electromagnetic ultrasonic technology, which excites and receives ultrasonic waves in conductive materials through the principle of electromagnetic induction. The detector consists of an electromagnetic ultrasonic transducer, a signal processing unit, and a display control module. During operation, alternating current generates an alternating magnetic field through the transducer coil, which in turn induces eddy currents in the measured material. The interaction between eddy currents and magnetic fields generates Lorentz force, which excites ultrasonic waves to propagate in materials.
When ultrasonic waves encounter corrosion defects, physical phenomena such as reflection and scattering occur. By analyzing the characteristics of the received ultrasonic signals, the corrosion status of the material can be determined. This technology does not require coupling agents and can achieve non-contact detection, making it particularly suitable for corrosion detection in high-temperature environments. The system consists of high-temperature electromagnetic ultrasonic probes, high-temperature cables, signal excitation/reception devices, data acquisition systems, and analysis software. Each component works together to complete the detection task.
2、 Key technologies of pen type electromagnetic ultrasonic high-temperature corrosion detector
The design of electromagnetic ultrasonic transducer is one of the core technologies of this detector. The high temperature environment imposes special requirements on the material and structure of the transducer, requiring the use of high-temperature resistant magnetic materials and special coil design. The frequency characteristics, sensitivity, and directionality of the transducer directly affect the detection effect, and precise electromagnetic field simulation and optimization design are required to achieve optimal performance.
The high-temperature signal processing algorithm is a key technology to ensure the reliability of detection. Signal attenuation is severe and noise interference is significant in high-temperature environments, requiring the development of specialized high-temperature signal enhancement algorithms. Advanced signal processing methods including time-frequency analysis, wavelet transform, and temperature compensation algorithms are used to eliminate the influence of temperature on signals. These algorithms need to be optimized and adjusted for different materials and temperature ranges.
The extraction and recognition technology of corrosion characteristics determines the accuracy of detection. Extract feature parameters related to corrosion degree through multi-dimensional feature analysis in time domain, frequency domain, and time-frequency domain. Using pattern recognition and machine learning algorithms to establish the correspondence between corrosion characteristics and defect types, achieving quantitative evaluation of corrosion degree. This process needs to consider the impact of high temperature environment on characteristic parameters and make corresponding adjustments.
3、 Optimization strategy
For optimizing the performance of transducers, new high-temperature magnetic materials such as samarium cobalt magnets can be used to improve the stability of magnetic properties at high temperatures. Optimize coil design by adopting a multi-layer segmented structure to enhance magnetic field uniformity. Introduce an active cooling system to control the working temperature of the transducer and ensure its stable operation in high-temperature environments.
In terms of signal processing algorithms, develop adaptive denoising algorithms based on deep learning to effectively separate signals from noise. Optimize the temperature compensation model, consider nonlinear temperature effects, and improve compensation accuracy. Adopting multi-sensor data fusion technology to integrate information from multiple detection points and improve signal reliability.
To improve detection accuracy, a more accurate corrosion feature database can be established, which includes the characteristic changes of different materials at different temperatures. Develop intelligent diagnostic algorithms that combine expert knowledge and machine learning to improve defect recognition rates. Optimize the distance control between the probe and the tested surface to ensure optimal detection conditions. These optimization measures will significantly improve the performance of the detector in high-temperature environments.