| 孟庆旭,沈功田,俞跃,胡斌,王宝轩,李志农.深度残差收缩网络的含噪微泄漏超声识别方法*[J].,2022,41(6):964-972 |
| 深度残差收缩网络的含噪微泄漏超声识别方法* |
| An ultrasonic identification method of noised micro-leakage based on deep residual shrinkage networks |
| 投稿时间：2021-08-31 修订日期：2022-10-08 |
| 中文摘要: |
| 在利用声学信号进行泄漏检测时，复杂的背景噪声往往会淹没微弱的泄漏信号，导致误判率高。针对微小泄漏在含噪环境中识别困难的问题，提出了基于深度残差收缩网络（DRSN）的含噪微泄漏识别方法。在提出的方法中，添加不同强度高斯噪声，建立数据集，使用DRSN网络进行训练，验证DRSN对不同泄漏强度、不同噪声含量样本识别的有效性。实验结果表明：DRSN对于微弱泄漏可以达到较理想的识别率，即使在高度杂糅数据识别时仍能达到较理想的识别效果，而且噪声含量并不会对DRSN迭代次数产生明显的影响。将提出的方法与CNN识别方法对比，DRSN具有明显的优势。 |
| 英文摘要: |
| Using acoustic signals for leak detection, complex background noise tends to drown out weak leak signals, resulting in a high rate of false positives. Due to the small leakage is difficult to be identified in noisy environments, a noised micro-leakage identification method based on deep residual shrinkage network ( DRSN ) is proposed. In the proposed method, the data set is built by superimposing Gaussian noise of different intensities. The DRSN network is used for training to verify the effectiveness of DRSN for sample identification with different leakage intensities and different noise contents. The experimental results show that DRSN can achieve a better recognition rate for weak leakage, and maintain a better recognition effect even when the data is highly hybrid, and the noise content does not have a significant impact on the number of DRSN iterations. Compared with CNN recognition method, DRSN has obvious advantages. |
| DOI：10.11684/j.issn.1000-310X.2022.06.014 |
| 中文关键词: 气体管道 泄漏检测 深度残差收缩网络 声波 |
| 英文关键词: gas pipeline leak detection deep residual shrinkage network acoustic wave |
| 基金项目:国家重点研发计划、国家自然科学基金项目、航空科学基金重点项目 |
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