Detection Algorithm and Application Based on Work Status Evaluator

A novel approach for pipeline leak fault detection and work status identification based on fuzzy clustering neural network has been studied. This approach do not need construct exact mathematical model. First of all, we preprocess dataset by extended sigmoid function to normalize each input status vector. Together with prior knowledge, a competitive learning neural network is then used to identify work status, and then the structure and detection scheme of the adaptive algorithm were developed to diagnose the leak fault. An experiment was performed at oil pipeline in Shengli oil field. We can learn by experiment results that the proposed method has shown the feasibility and effectiveness