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With the advancement of electronic technology, neural networks, and artificial intelligence, new research methods for processing magnetic flux leakage signals are continuously being explored both domestically and internationally. Traditional methods, however, are heavily influenced by human factors, leading to a high risk of false detection and reduced accuracy. This has created a strong demand for intelligent signal processing techniques that can enhance detection reliability. One such technique is multi-sensor data fusion, which integrates information from multiple sources to improve the quality and accuracy of the results. By leveraging the complementary strengths of different sensors, this approach not only enhances detection capabilities but also reduces errors caused by individual sensor failures.
In this study, we employ a wavelet threshold denoising algorithm to highlight defect characteristics in the signal before feeding it into an RBF (Radial Basis Function) neural network-based data fusion center. The redundancy between two types of sensors—electromagnetic induction sensors and Hall sensors—is utilized to improve measurement accuracy and reduce uncertainty. A sensor array consisting of 32 elements, divided into two groups of 16 each, is used to collect data. These sensors are alternately arranged on a probe that matches the surface of the steel pipe, ensuring comprehensive coverage and sensitivity.
Electromagnetic induction sensors work by detecting changes in the magnetic flux through a coil as it scans the surface of the steel pipe. When a defect causes a leakage magnetic field, the induced electromotive force (EMF) is generated, forming a defect signal. These sensors are robust, with long lifespans and resistance to environmental factors like dust, water, and oil.
Hall sensors, on the other hand, detect the magnetic flux leakage by measuring the Hall potential when current flows perpendicular to the magnetic field. Their output depends solely on the magnetic field strength, making them less affected by movement or non-uniformity in the pipeline.
Signal preprocessing plays a crucial role in ensuring accurate measurements. Techniques such as median smoothing and noise filtering are applied to eliminate external disturbances. The use of wavelet denoising further refines the signal, highlighting key features for better defect identification. After normalization, the processed signals are sent to the RBF neural network for fusion.
The RBF neural network is particularly effective due to its fast learning speed and strong approximation capabilities compared to traditional BP networks. Its structure includes an input layer, a hidden layer with radial basis functions, and an output layer. The parameters of the hidden layer, such as the center values and standard deviations, are determined using clustering algorithms like K-means. The connection weights are then optimized using gradient descent to minimize the mean square error.
Experiments conducted in MATLAB using 40 artificial crack samples showed that the RBF network outperformed conventional BP networks in terms of accuracy and learning efficiency. The average absolute error was significantly lower—2.69% for RBF versus 5.47% for BP. This demonstrates the effectiveness of the proposed method in quantitatively analyzing crack depth and improving the reliability of the system.
In conclusion, the integration of wavelet denoising and RBF-based data fusion offers a powerful solution for magnetic flux leakage signal analysis. It not only enhances detection accuracy but also reduces uncertainties, making it a promising approach for real-time and reliable defect detection in industrial applications.