In view of the shortcomings of traditional fault diagnosis methods based on time domain vibration analysis, which require complicated calculations of feature vectors, and are over-dependent on a prior diagnosis knowledge, effort for the design of the feature extraction algorithms, and have limited ability to extract the complex relationships in fault signals, in this paper we propose a convolutional neural network (CNN) framework for machine health monitoring based on the encoding of one-dimension (1-D) time series to two-dimension (2-D) images. This paper defines a new Gram matrix and through the Python programming environment, we emulate the new Gram matrix in 2-D images, thus feature extraction and recognition can be performed by CNNs. The proposed method which is tested on two datasets, including multi-stage centrifugal fan dataset for our lab, motor bearing dataset for Case Western Reserve University, has achieved prediction average accuracy of 94.07% and 96.28%, respectively. The results have been compared with other deep learning and traditional methods, including Recurrent neural network (RNN), Support Vector Machines (SVM), Multi-Genetic algorithm, shallow CNN and BP neural network. The results show that the method can improve fault diagnosis accuracy in an effective way and stability than other advanced techniques.

中文翻译：

---

基于编码时间序列和卷积神经网络的故障诊断方法

针对传统基于时域振动分析的故障诊断方法存在特征向量计算复杂、过分依赖先验诊断知识、特征提取算法设计费力、能力有限的缺点为了提取故障信号中的复杂关系，在本文中，我们提出了一种用于机器健康监测的卷积神经网络 (CNN) 框架，该框架基于将一维 (1-D) 时间序列编码为二维 (2-D)图片。本文定义了一个新的 Gram 矩阵，并通过 Python 编程环境，在二维图像中模拟新的 Gram 矩阵，从而可以通过 CNN 进行特征提取和识别。所提出的方法在两个数据集上进行了测试，包括我们实验室的多级离心风机数据集，凯斯西储大学电机轴承数据集，预测平均准确率分别达到 94.07% 和 96.28%。结果已与其他深度学习和传统方法进行了比较，包括循环神经网络 (RNN)、支持向量机 (SVM)、多遗传算法、浅层 CNN 和 BP 神经网络。结果表明，与其他先进技术相比，该方法能够有效地提高故障诊断的准确性和稳定性。