基于门控循环神经网络的阀门泄漏检测方法

Valve leak detection method based on gated recurrent neural networks

目的:针对阀门常见的泄漏故障,研究一种阀门泄漏故障检测方法。方法:采用声发射技术与深度学习相结合的方法,通过声发射技术对阀门内漏信号进行了采集,搭建了以门控循环神经网络(GRU)为核心的阀门内漏诊断模型。该方法针对阀门内漏的特点,发挥了声发射技术在无损检测领域上的优越性,并利用GRU捕捉所采集声发射数据的长期依赖性,最终改善了传统循环神经网络的梯度弥散的问题。结果:经过实验和数据分析,提出了基于声发射技术和门控循环神经网络的故障检测方法,从而提升了阀门内漏故障识别的准确率。结论:本方法利用重构的去噪数据进行处理来提高模型效果,并充分发挥GRU在时间序列预测上的优势,最后提升了对阀门微小泄漏检测的精度。

Aims:This paper aims to study a valve leakage detection method aiming at common failures of valve leakage.Methods:The valve internal leakage signal was collected with the acoustic emission technology.The valve internal leakage diagnosis model was built with the deep learning method of gated recurrent neural networks(GRU).The method collected acoustic emission data to improve the gradient dispersion of the traditional neural networks.Results:The experiments and analysis showed that the proposed method improved the accuracy of valve internal leakage detection.Conclusions:This method uses reconstructed denoising data to improve the effect of the model and takes the advantages of GRU to improve the accuracy of valve micro-leak detection.