多尺度特征自适应融合的气动控制阀故障诊断

气动控制阀作为过程工业典型的终端执行机构,由于故障发生率高、故障类型繁多,导致故障识别难度大,且故障后果 严重,因此对其进行智能的故障检测和诊断具有重要的实际意义。 本文提出了一种多尺度特征自适应融合网络用于气动控制 阀故障诊断。 首先,搭建了融合自注意力机制的多尺度特征提取网络自动提取信号的空间特征和细节特征。 然后,设计了权重 自适应特征融合网络对多尺度特征加权融合,提高模型对故障特征的表征能力。 最后,由长短时记忆神经网络和 SoftMax 函数 实现特征识别和故障分类。 实验结果表明,该模型在 DAMADICS 阀门基准实验平台上的平均检测准确率达到 96. 82% ,均高于 其他对比模型。 与最近发表文献中的检测结果对比发现,本文开发的模型在可检测的故障数量和检测准确率方面也具有一定 的优势,并且通过实验验证了模型的检测性能。

Fault diagnosis of pneumatic control valves with multi-scale features adaptive fusion

Pneumatic control valves act as typical terminal actuators in the process industry, which suffer from difficulties in fault identification and severe consequences of faults due to the high incidence of faults and diverse fault types. Therefore, intelligent fault detection and diagnosis of pneumatic control valves have crucial practical significance. In this paper, an adaptive multi-scale features fusion network is proposed for the pneumatic control valve fault diagnosis. Firstly, a multi-scale feature extraction network with fusion self-attention mechanism is constructed to automatically extract spatial and detail features of signals. Then a weighted adaptive feature fusion network is designed to perform the weighted fusion of multi-scale features to improve the fault feature characterization capability of model. Finally, the feature identification and fault classification are performed by the Long short-term memory neural network with SoftMax function. The experimental results show that the model achieves an average accuracy of 96. 82% on the DAMADICS valve benchmark experimental platform, which are higher than other comparative models. Comparison with the detection results in the latest literature reveals that the model developed in this paper also has certain advantages such as the number of detectable faults and detection accuracy, and the detection performance of model is experimentally verified.