| 引用本文: | 胡列豪,巩 宇,张勇军,等.考虑声-振模态结合的抽水蓄能机组轴承故障诊断[J].电力系统保护与控制,2024,52(11):1-10.[点击复制] |
| HU Liehao,GONG Yu,ZHANG Yongjun,et al.Pumped storage unit bearing fault diagnosis with a combination of sound and vibration dual-modal[J].Power System Protection and Control,2024,52(11):1-10[点击复制] |
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| 摘要: |
| 为解决抽水蓄能机组轴承磨损故障难以监测和识别的问题，提出一种结合声振数据的双模态神经网络机组轴承诊断模型。首先分析了抽水蓄能机组声振特性，融合相似软阈值对奇异值分解去噪方法进行改进，有效消除非接触式传感器固有噪声干扰。其次提出逆巴克频谱变换方法，并结合巴克频谱变换和格拉姆角和场变换等特征工程技术，提取机组轴承的声纹和振动特征图。通过融合相对位置编码的自注意力机制和深度可分离卷积，建立特征图传递网络。同时运用多头自注意力机制和双向长短期记忆网络搭建了时序数据传递网络，并以平行网格架构构建了机组轴承故障诊断模型。实验对比分析表明，所提方法具有较高的故障识别准确率，为抽水蓄能电站机组轴承监测问题提供了有效的解决途径。 |
| 关键词: 抽水蓄能机组 声-振模态 奇异值分解 特征工程 故障诊断 |
| DOI：10.19783/j.cnki.pspc.231422 |
| 投稿时间：2023-11-06修订日期：2024-04-22 |
| 基金项目:国家自然科学基金项目资助(52177085)；广东省重点领域研发计划项目资助(2021B0101230001) |
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| Pumped storage unit bearing fault diagnosis with a combination of sound and vibration dual-modal |
| HU Liehao1,GONG Yu1,2,ZHANG Yongjun1,AN Yuzheng1,JIANG Chongying1,LIAO Meiying3 |
| (1. School of Electric Power, South China University of Technology, Guangzhou 510641, China; 2. China Southern
Power Grid Energy Storage Co., Ltd., Guangzhou 510635, China; 3. Guangdong Polytechnic of
Science and Technology, Guangzhou 510600, China) |
| Abstract: |
| To address the problem of difficult monitoring and identification of a bearing wear damage fault in pumped storage unit, a dual-modal neural network model for unit bearing diagnosis that combines sound and vibration data is proposed. First, the sound and vibration characteristics of the unit are analyzed, and a soft threshold is combined to improve the singular value decomposition (SVD) denoising method. This effectively eliminates inherent noise interference from the non-contact sound sensor. In addition, an inverse Bark spectrum transform method is proposed, and combining with feature engineering technology such as the Bark spectrum transform and Gram’s angle summation field (GASF) transform, the sound and vibration feature map of the unit bearing is extracted. A feature map propagation network is established using a self-attention mechanism with relative position encoding and depth-wise separable convolution. Simultaneously, a time series data propagation network is constructed using a multi-head self-attention mechanism and a bidirectional long short-term memory network (BiLSTM). These components are integrated into a parallel architecture to build a fault diagnosis model for the unit bearing. Comparative analysis of results shows that the proposed method has high accuracy in fault recognition, providing an effective solution to the unit bearing monitoring problem in pumped storage power stations. |
| Key words: pumped storage unit sound-vibration modal singular value decomposition (SVD) feature engineering fault diagnosis |
