关键词: 管道泄漏; , 熵方法; , 特征提取; , 极限学习机

Abstract:

Due to the influence of instrument and equipment work, outdoor environment and other factors, there will be some random noise in the collected pipeline signal, which will make the original signal lose its characteristics, leading to the failure to accurately identify the pipeline signal. Therefore, a feature extraction method based on VMD (Variational Mode Decomposition) algorithm-entropy method is proposed. First VMD algorithm based on working condition of gathering pipeline deals with the noise signal, then from energy, impact properties, three angles, complexity of time series extracts signal characteristics under different working conditions of three kinds of signal reconstruction after the signal are calculated separately, and the energy entropy, kurtosis entropy and fuzzy entropy, and finally establishs characteristic vector input to the extreme learning machine to identify the condition. The experimental results show that the method proposed can classify and recognize pipeline working condition signals more accurately than other feature parameters, and the recognition rate is up to 98. 33% , which proves the feasibility of this method to classify and recognize pipeline leakage signals.

Key words: pipeline leakage; , entropy method; , feature extraction; , extreme learning machine