Rotary mechanical equipment fault diagnosis method based on MP-CNN and SVM

The invention belongs to the field of rotary mechanical equipment fault diagnosis, and particularly relates to a rotary mechanical equipment fault diagnosis method based on MP-CNN and SVM, and the method comprises the steps: obtaining a to-be-diagnosed rotary mechanical equipment image, carrying out the preprocessing of the to-be-diagnosed rotary mechanical equipment image, and obtaining a to-be-diagnosed rotary mechanical equipment image; inputting the preprocessed image into a rotating mechanical equipment fault diagnosis model based on MP-CNN and SVM to obtain a fault diagnosis result of the mechanical equipment; according to the method, the strong feature extraction capability of the layering thought applied to the field of mechanical fault diagnosis and the efficient classification function of the SVM are combined, and the problem that the diagnosis accuracy is low due to the fact that the number and types of samples are large when CNN-SVM is used is solved. 本发明属于旋转机械设备故障诊断领域，具体涉及一种基于MP-CNN与SVM的旋转机械设备故障诊