Vibration-based techniques are among the most common condition monitoring approaches. With the advancement of computers, these approaches have also been improved such that recently, these approaches in conjunction with deep learning methods attract attention among researchers. This is mostly due to the nature of the deep learning method that could facilitate the monitoring procedure by integrating the feature extraction, feature selection, and classification steps into one automated step. However, this can be achieved at the expense of challenges in designing the architecture of a deep learner, tuning its hyper-parameters. Moreover, it sometimes gives low generalization capability. As a remedy to these problems, this study proposes a framework based on ensemble deep learning methodology. The framework was initiated by creating a pool of Convolutional neural networks (CNN). To create diversity to the CNNs, they are fed by frequency responses which are passed through different functions. As the next step, proper CNNs are selected based on an information criterion to be used for fusion. The fusion is then carried out by improved Dempster-Shafer theory. The proposed framework is applied to real test data collected from Equiax Polycrystalline Nickel alloy first-stage turbine blades with complex geometry.
翻译:以振动为基础的技术是最常见的条件监测方法之一。随着计算机的进步,这些方法也得到了改进,最近,这些方法与深层次学习方法相结合,吸引了研究人员的注意。这主要是由于深层次学习方法的性质,这种深层次的学习方法可以通过将特征提取、特征选择和分类步骤整合为一个自动化步骤来便利监测程序。然而,这可以牺牲设计深层次学习者架构方面的挑战,调整其超参数。此外,它有时提供较低的概括能力。作为解决这些问题的一种补救办法,本研究建议了一个基于共同深层次学习方法的框架。这个框架是通过建立一个动态神经网络集合(CNN)而启动的。为了创建CNN的多样性,它们受到频率反应的支撑,而频度反应是通过不同的功能传递的。下一步,适当的CNN是根据用于聚变的信息标准选择的。然后,通过改进Dempster-Shafer理论来进行聚变。拟议框架将适用于从Equiax 质质质质系线上采集的真正测试数据,并带有复杂层核糖层全面涡轮。