An optimized variational mode decomposition and symmetrized dot pattern image characteristic information fusion-Based enhanced CNN ball screw vibration intelligent fault diagnosis approach

The failure of the ball screw in the machine tool presents various types and complex coupling characteristics, which pose challenges in extracting fault features from vibration signals and lead to decreased accuracy in fault diagnosis classification. To tackle this issue, an improved intelligent fault diagnosis method based on enhanced CNN is proposed by optimizing the variational mode decomposition and symmetrized dot pattern image features. Initially, VMD is employed to decompose the preprocessed signals, incorporating information entropy for selecting decomposition layers to enhance accuracy. Different image features are generated using SDPI, and differential information is introduced to enhance feature expressive capability through fusion. Subsequently, considering the limited classification ability of traditional SoftMax classifiers in CNNs, SVM is integrated for classification purposes to optimize convolutional neural networks' structure and improve their classification performance. Finally, the proposed method was ultimately validated by means of comparative analysis of screw vibration signals, thereby affirming its exceptional accuracy in fault diagnosis and classification.