[1]杨正理,吴馥云,陈海霞.深度残差收缩网络的多特征锅炉炉管声波信号故障识别[J].智能系统学报,2023,18(5):1108-1116.[doi:10.11992/tis.202207012]
YANG Zhengli,WU Fuyun,CHEN Haixia.Fault identification of multi-feature boiler tube acoustic signal based on deep residual shrinkage network[J].CAAI Transactions on Intelligent Systems,2023,18(5):1108-1116.[doi:10.11992/tis.202207012]
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YANG Zhengli,WU Fuyun,CHEN Haixia.Fault identification of multi-feature boiler tube acoustic signal based on deep residual shrinkage network[J].CAAI Transactions on Intelligent Systems,2023,18(5):1108-1116.[doi:10.11992/tis.202207012]
深度残差收缩网络的多特征锅炉炉管声波信号故障识别
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第5期
页码:
1108-1116
栏目:
学术论文—机器学习
出版日期:
2023-09-05
- Title:
- Fault identification of multi-feature boiler tube acoustic signal based on deep residual shrinkage network
- Keywords:
- deep learning; fault identification; deep residual shrinkage network; bidirectional short and long memory network; attention mechanism; convolutional neural network; boiler tube; acoustic signal
- 分类号:
- TP391；TN912.3；TM621.2
- 摘要:
- 为了提高锅炉炉管声波信号故障识别的学习效果和识别精度，采用特征向量并行和拼接两种融合方式构成特征层，以及平均得分和最大值得分两种融合方式构建决策层等不同信息融合机制，提出基于深度残差收缩网络的多特征锅炉炉管声波信号故障识别方法。首先，考虑锅炉炉管上各声波传感器的差异性，分别计算声波信号谱特征一阶和二阶差分构建三通道特征集作为二维网络的输入特征向量；然后，在卷积神经网络和双向长短时记忆网络基础上引入注意力机制构建基线模型，并采用深度残差收缩网络对二维网络的通道权重进行优化分配，提高模型的故障识别精度。大量实验结果表明：采用特征向量并行融合方式构成特征层的信息融合机制是一种更有效的策略；本文模型的识别精度得到较大程度提高，与基线模型相比较，未加权平均召回率提高了4.32%。
- Abstract:
- To improve the learning effect and recognition accuracy in fault identification of boiler tube acoustic signals, different information fusion mechanisms were used in this study. Specifically, feature vector parallelism and splicing techniques are used to form the feature layer, while the decision layer is formed using average and maximum score approaches. A fault identification method of multifeature boiler tube acoustic signal based on a deep residual shrinkage network is proposed. First, the first-order and second-order differential characteristics of the acoustic signal spectrum were calculated separately, considering the variations of acoustic wave sensors on the boiler tube. This process constructs a three-channel feature set, which serves as the input feature vector of the two-dimensional network. Subsequently, an attentional mechanism is introduced to construct a baseline model using a combination of a convolutional neural network and a bidirectional short and long memory network. Additionally, a deep residual shrinkage network is used to optimize the allocation of channel weights within the two-dimensional network to improve the fault identification accuracy of the model. Extensive experimental results show that: it is a more effective strategy to construct the information fusion mechanism of the feature layer by using feature vector parallel fusion; compared with the baseline model, the unweighted average recall rate has increased by 4.32%, highlighting significant improvements in the recognition accuracy of the model presented in this paper.
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备注/Memo
收稿日期:2022-7-11。
基金项目:江苏省高校自然科学基金面上项目(20KJB470029).
作者简介:杨正理，副教授，主要研究方向为复杂系统与计算智能、软件工程。发表学术论文63篇;吴馥云，讲师，主要研究方向为新能源接入电网控制技术。发表学术论文16篇;陈海霞，副教授，主要研究方向为海量信息处理的计算模型、自动推理。发表学术论文33篇
通讯作者:杨正理.E-mail：Zhengli-yang@163.com
更新日期/Last Update:
1900-01-01