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| Fault Diagnosis of Rolling Bearings in the Supercharger of An Internal Combustion Engine Based on Cepstrum–Symmetrized Dot Pattern–Covolution Neural Network |
| DOI：10.13949/j.cnki.nrjgc.2023.06.009 |
| Key Words:rolling bearing fault diagnosis cepstrum symmetrized dot pattern （SDP） convolution neural network（CNN） |
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| Abstract:An intelligent fault diagnosis method based on cepstrum–symmetrized dot pattern（SDP）–convolution neural network（CNN） was proposed to solve the problems that the rolling bearing vibration signal of the supercharger of an internal combustion engine is easily affected by noise and the fault features are weak. The fault features of the original signal were extracted by using the cepstrum method to obtain enough feature vectors that can reflect the type of rolling bearing faults. Then， the SDP method was applied to map the one-dimensional cepstrum data to polar coordinate space and grayscale them to generate SDP feature grayscale maps， and the feature map was imported into CNN for feature mining and fault identification. After the failure test of the damaged outer raceway， inner raceway and rolling elements of the rolling bearings， nine sets of fault status raw signals were utilized to verify the intelligent fault diagnosis method based on cepstrum–SDP–CNN. The results show that the cepstrum–SDP–CNN method is simple， fast and less affected by noise， and can effectively diagnose supercharger rolling bearing faults. The diagnosis recognition accuracy rate for the test sets was 97.5%， and the proposed method can accurately determine the fault status and severity of rolling bearings. |
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