Vibration Signal ESSA-CVMD and Entropy Method for the Leakage Assessment of Pipe

Pipe is a critical component of urban infrastructure. Cracks would lead to accidents such as fluid leakage and explosion. It is of great significance to assess the pipe leakage in real time. In this article, an enhanced sparrow search algorithm-correlation variational modal decomposition (ESSA-CVMD) and entropy assessment method for pipe leakage were proposed. First, the penalty factor and the order of modal decomposition were optimized by enhanced SSA step factor. In this way, the pipe vibration signal could be filtered according to the correlation between the intrinsic mode function (IMF) and the original signal. Then, multiparameters of vibration signals were extracted. The weight vector was calculated by the entropy method. Furthermore, the membership matrix was constructed by means of the self-information of conditional entropy. The leakage level of the pipe could be assessed through the fuzzy calculation of the weight vector and membership matrix. The numerical simulation and experiment showed that this data-driven method could monitor and assess the leakage state of the pipe. It is practical for urban underground pipe engineering with complex working conditions.