The vast majority of the existing diagnostic studies using deep learning techniques for rotating machinery focus on the vibration analysis under steady rotating speed. Nevertheless, the collected vibration signals are sensitive to time-varying speeds and the vibration sensors may cause structure damage of equipment after long-term close contact. Aiming at these aforementioned problems, a modified Gaussian convolutional deep belief network driven by infrared thermal imaging is proposed to automatically diagnose different faults of rotor-bearing system under time-varying speeds. First, infrared thermal images are measured to characterize the working states of rotor-bearing system to reduce the impact of changeable speeds. Second, Gaussian units are used to construct Gaussian convolutional deep belief network to well deal with infrared thermal images. Finally, trackable learning rate is designed to modify the training algorithm to enhance the performance. The comparison results verify the feasibility of the proposed method, which outperforms the other methods.

现有的使用深度学习技术对旋转机械进行诊断的绝大多数研究都集中在稳定转速下的振动分析。然而，收集到的振动信号对时变速度很敏感，并且在长期紧密接触后，振动传感器可能会损坏设备的结构。针对上述问题，提出了一种由红外热成像技术驱动的改进的高斯卷积深度置信网络，以在转速变化时自动诊断转子轴承系统的不同故障。首先，对红外热图像进行测量，以表征转子轴承系统的工作状态，以减少转速变化的影响。其次，使用高斯单元构造高斯卷积深度置信网络，以很好地处理红外热图像。最后，设计可跟踪的学习速率来修改训练算法以提高性能。比较结果验证了该方法的可行性，该方法优于其他方法。