基于全矢增强深度森林的旋转设备智能故障诊断方法

针对传统智能诊断方法需要专家知识和复杂特征提取，而深度神经网络模型复杂度高、构建难度大，以及单源信号信息不完备等问题，提出了一种新颖的全矢数据融合增强深度森林的旋转设备故障诊断方法。该方法根据旋转设备振动信号的特点，选择全矢谱技术与深度森林多粒度扫描相结合，用于接收同源双通道信号输入，增强了数据的完备性，并通过改善深度森林级联层来减少深层特征消失和特征冗余。为了验证所提出方法的有效性，分别进行了滚动轴承与轴向柱塞泵两例故障诊断实验研究，结果表明，该方法在不同旋转设备上都有很好的诊断效果，并可以实现端到端故障诊断。此外，该方法在小训练数据集上的故障识别准确率也非常高。

Intelligent Fault Diagnosis Method for Rotating Equipment Based on Full Vector Enhanced Deep Forest

A new fault diagnosis method for rotating equipment was proposed based on deep forest improved by full vector data fusion to solve the problems that traditional intelligent diagnostic methods required expert knowledge and complex feature extraction, deep neural network model had complex structure and was difficult to construct, and the single-channel signals were incompleteness. According to the characteristics of vibration signals of rotating equipment, the full vector data fusion technology was combined with multi-grained scanning of deep forest to receive the homologous double-channel signals, so as to enhance the completeness of data. At the same time, the cascade layer of deep forest was improved to reduce the deep feature disappearance and feature redundancy. In order to verify the effectiveness of the proposed method, two fault diagnosis experiments of rolling bearing and axial plunger pump were carried out respectively. The results show that this method achieves a good diagnostic effectiveness on different rotating equipment and end-to-end fault diagnosis may be realized. In addition, it is also very excellent in fault recognition accuracy on small training data sets.