This paper suggests an image gradient based method that determines the optimal image size for convolutional neural network (CNN)-based diagnosis of fluid-film rotorbearing systems. As distinct patterns improve the diagnosis performance, a criterion is defined to measure the intensity of patterns in an image. The proposed criterion is derived by segmenting an image by the size of the CNN filter and evaluating each segment through the use of image gradient analysis. Vibration signals from a testbed are used to demonstrate the proposed method. First, the signals are transformed into vibration images by using an omnidirectional regeneration technique. Then, vibration images of four different health states are analyzed using the suggested criterion. The analyzed results are compared to the performance of CNN based diagnosis. The results indicate that the proposed criterion can determine the optimal size range of the vibration image that gives the best performance for CNN-based diagnosis.

本文提出了一种基于图像梯度的方法，该方法可以确定基于卷积神经网络（CNN）的液膜转子轴承系统诊断的最佳图像尺寸。随着不同的图案改善诊断性能，定义了一个标准来测量图像中图案的强度。通过按CNN滤波器的大小对图像进行分割，并通过使用图像梯度分析评估每个分段，可以得出建议的标准。来自测试台的振动信号用于证明所提出的方法。首先，通过使用全向再生技术将信号转换为振动图像。然后，使用建议的标准分析四种不同健康状态的振动图像。将分析结果与基于CNN的诊断性能进行比较。