Algorithm Application and Performance Improvement of Multi-Sensor Fusion in Pressure Pipeline Inspection

In this paper, the application of multi-sensor fusion technology in pressure pipeline inspection is deeply discussed, aiming at overcoming the limitations of traditional single sensor detection method and improving the detection accuracy and reliability. In the design of fusion algorithm, this paper puts forward a fusion algorithm based on weighted average, which extracts key features through preprocessing steps such as denoising, calibration and normalization, and assigns weights according to the accuracy and reliability of sensors, and finally evaluates the pipeline state and outputs early warning information. In order to optimize the performance of the algorithm, this paper improves the data preprocessing steps, adopts more efficient filtering methods and parallel computing technology, and simplifies the weight distribution mechanism. In the experimental part, a 500-meter-long carbon steel pressure pipeline is selected for testing. The pipeline includes elbows, joints and valves, and sensors are evenly distributed in these parts and along the pipeline. Experimental results show that the detection accuracy of the fusion algorithm reaches 95.0%, which is much higher than that of the single sensor method. Under different environmental conditions, the fusion algorithm can maintain high fault identification accuracy, while the false alarm rate and false alarm rate are low, showing good robustness. The processing time is stable between 0.8s and 0.95s, which meets the requirements of real-time monitoring. In addition, the algorithm can remain stable in normal operation and slight interference scenarios, but can effectively trigger early warning and accurately identify the abnormal state of the pipeline in abnormal situations.