排灌机械工程学报2026，Vol.44Issue(4)：405-414,10.DOI:10.3969/j.issn.1674－8530.25.0112
基于双模态特征变换PMSCNN－BiGRU－SA的机组故障诊断模型
Fault diagnosis model for hydropower units based on dual-modal feature transformation with PMSCNN－BiGRU－SA
摘要
Abstract
To accurately identify potential faults in the operation of hydropower units,a novel fault di-agnosis model based on dual-modal feature transformation,parallel multi-scale convolutional neural network(PMSCNN),bidirectional gated recurrent units(BiGRU),and a self-attention mechanism(SA)was proposed.One-dimensional time-series signals were first converted into dual-modal images through an improved Markov transition field(IMTF)and synchrosqueezed wavelet transform(SWT),to capture temporal correlations inherent in the fault data.These representations were then fed into the PMSCNN－BiGRU－SA network to fuse,extract,and refine fault features,and a Softmax layer was fi-nally applied for fault classification.The effectiveness,advancement,and universality of the proposed model were verified using measured data from the SK power plant and the XJTU－SY bearing dataset,and a comparative analysis was conducted with other methods.The results indicate that the proposed model achieves accuracies of 100.00%and 98.75%in the two engineering applications,which is sig-nificantly better than the compared methods,providing a novel technical solution for fault diagnosis in hydropower units.关键词
水电机组/故障诊断/多尺度卷积神经网络/双向门控循环单元/特征变换Key words
hydropower units/fault diagnosis/multi-scale convolutional neural networks/bidirectional gated recurrent units/feature transformation分类
建筑与水利引用本文复制引用
田祖哲,郑寓,周廷鑫,俞晓东..基于双模态特征变换PMSCNN－BiGRU－SA的机组故障诊断模型[J].排灌机械工程学报,2026,44(4):405-414,10.基金项目
江苏省自然科学基金资助项目(BK20240084) （BK20240084）
