Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics

Abstract

Deep learning methods based on neural network models have been widely applied to bearing fault classification. Although they can achieve high accuracy, they also come with significant complexity. Bearing faults often generate impact vibrations, which produce regular fault characteristic peaks on the envelope spectrum. This paper utilizes the differences in frequency and intensity of the envelope spectrum characteristic peaks under different bearing fault conditions as fault features. By combining these features with the simple and efficient Naive Bayes classifier for fault diagnosis, the algorithm complexity is reduced from the perspective of feature extraction and fault identification. The proposed method was validated using bearing fault data from the Case Western Reserve University (CWRU) dataset and the Machinery Fault Prediction Technology (MFPT) dataset. The results show that the method can classify bearing faults and achieve accurate diagnostic results. The average diagnostic accuracy for the four groups from these two datasets was 99.90% and 99.65%, respectively. The Naive Bayes classification algorithm was compared with classic algorithms in terms of classification accuracy and classification time. Additionally, the algorithm was compared with recent bearing fault diagnosis methods using the CWRU dataset in terms of algorithm complexity. The complexity of the proposed algorithm is only O(N(2280)), which is lower than that of other bearing fault diagnosis methods, where N represents the number of fault samples. This demonstrates that the method significantly reduces algorithm complexity while ensuring accuracy, improving diagnostic efficiency, enhancing the timeliness of real-time industrial bearing fault diagnosis, and reducing hardware setup and operating costs.