Objectives A small sample intelligent fault diagnosis method for rolling bearings based on PSR-PCA-CNN is proposed to address the problems of traditional fault diagnosis methods being unable to fully explore the fault information contained in one-dimensional nonlinear temporal vibration signals, the large number of samples required for model training, and insufficient generalization.
Methods First, phase space reconstruction (PSR) based on chaos theory is used to recover potential dynamic features of data, achieving high-dimensional mapping of signals in phase space. Then, principal component analysis (PCA) is used to reduce data dimensionality redundancy and generate concise and informative fault feature reconstruction phase diagrams. Finally, an improved convolutional neural network (CNN) is used to automatically learn and extract features from complex data, achieving intelligent fault diagnosis of rolling bearings in small sample scenarios.
Results The PSR-PCA-CNN method was validated using two bearing datasets, and the experimental diagnostic accuracy was over 97%. In a small sample scenario with a 10% training set, the testing accuracy was higher than 90%. Compared with other feature extraction methods and intelligent algorithms, the PSR-PCA-CNN method has higher experimental accuracy.
Conclusions Compared with other image encoding methods and intelligent algorithms, this intelligent diagnostic method has superior ability to extract sample features in small sample scenarios, and has good diagnostic performance. It is an effective model for solving the small sample fault diagnosis task of rolling bearings.
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