基于MFS-DCGNNN的水力发电机组主导轴承的摆动趋势预测
Xu Li1, Zhuofei Xu1, Pengcheng Guo1
1School of Water Resources and Hydroelectric Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种新的扩展卷积图神经网络 (DCGNN),用于预测水电单元的摆动趋势. 通过提高运营安全和降低成本,DCGNN增强了故障预测和健康管理.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 预测性维护是指预测性维护.
背景情况:
- 水力发电机组对于发电至关重要,需要强大的故障预测和健康管理.
- 准确的摆动趋势预测对于确保水电运营的安全性,稳定性和可靠性至关重要.
- 现有的方法经常在复杂系统中与空间依赖性和图形结构定义作斗争.
研究的目的:
- 开发一种先进的算法,用于预测水力发电机组中主导轴承的摆动趋势.
- 提高水电站故障预测和健康管理系统的准确性和有效性.
- 解决现有的图形神经网络 (GNN) 方法在预定义图形结构和过度平滑方面的局限性.
主要方法:
- 使用多指数算法和皮尔森/距离相关系数来识别关键状态变量的特征选择.
- 实现扩展卷积图神经网络 (DCGNN) 模型用于摆动趋势预测.
- DCGNN利用扩展卷积和图形学习来捕捉没有明确的图形结构的空间依赖性,减轻过度平滑.
主要成果:
- 与RNN-GRU,LSTNet和TAP-LSTM相比,DCGNN算法表现出更高的性能.
- DCGNN实现了高达6.32%的较低平均绝对误差 (MAE) 和高达9.21%的根平均平方误差 (RMSE).
- 相关性 (CORR) 值提高了高达1.05%,表明预测准确度显著提高.
结论:
- DCGNN算法有效地预测水电单元的摆动趋势,比现有方法有了显著的改进.
- 这种方法提高了故障预测和健康管理,有助于更安全,更可靠的水电运营.
- DCGNN在没有预定义图形的情况下学习空间依赖的能力使其成为复杂时间序列分析的强大工具.
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