管道漏磁信号分类的多特征融合网络研究

Multi-feature Fusion Network for Classification of Pipeline Magnetic Leakage Signals

【目的】地下管道如预应力钢筒混凝土管(PCCP)等检漏是城市基础设施管理和维护中至关重要的一项工作。提出一种识别地下管道弱磁分布类型的磁异常多特征融合网络(MMF)。【方法】充分利用标准正交基函数(OBF)和最小熵(MED)两种检测特征,以全面而准确地捕捉漏磁信号的复杂特性。首先,在不同物径距离上利用OBF和MED进行磁异常检测,获取实测目标磁场特征;其次,融合磁场特征设计多特征融合网络MMF,并引入多头注意力机制捕捉序列磁场中的复杂关系和特征;最后,采用多特征熵权法MFEW,根据输入特征熵分配网络权重。【结果】实验结果显示,MMF网络异常分类达到了98.86%的精度,AUC评估结果为99.25%,同时模型更加精简,具有更高的计算效率,能够在相对较短的训练时间内取得令人满意的性能。

【Purposes】 Leakage detection in underground pipelines such as prestressed concrete cylinder pipes is a crucial aspect of urban infrastructure management and maintenance.In this study,an innovative magnetic anomaly multi-feature fusion network(MMF) is designed and introduced to identify weak magnetic distribution types in underground pipelines.【Methods】 The network leverages standard orthogonal basis functions(OBF) and minimum entropy detection(MED) features were used to comprehensively and accurately capture the complex characteristics of magnetic leakage signals.First,magnetic anomaly detection was conducted by using OBF and MED at different radial distances to acquire measured target magnetic field features.Second,an MMF was devised to integrate magnetic field features,and a multi-head attention mechanism was incorporated to capture intricate relationships and features within the sequence of magnetic fields.Finally,a multi-feature entropy weighting method was employed to allocate network weights on the basis of input feature entropy.【Findings】 Experimental results demonstrate that the MMF network achieves a precision of 98.86% in anomaly classification,with an AUC evaluation result of 99.25%.Additionally,the model is more streamlined,exhibiting higher computational efficiency,and is capable of delivering satisfactory performance within a relatively short training period.