关键词: 故障诊断, 旋转机械, 深度森林, 全矢谱, 数据融合

Abstract: 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.

Key words:  , fault diagnosis, rotary machine, deep forest, full vector spectrum, data fusion