Health status identification of rolling bearing based on SVM and improved evidence theory

The health condition assessment of Electric Multiple Unit (EMU) traction motor ball bearing is one of the key issues of high-speed train running safety. In order to assess health condition of EMU traction motor ball bearing, an online-sequential extreme learning machine algorithm based on TensorFlow (TOSELM) is proposed. Samples data set is divided into normal condition and fault condition using vibration data of ball bearings. This paper uses health condition accuracy rate index to evaluate TOSELM algorithm performance. The proposed approach is verified by public data set and private data set. The experiment results show the proposed method is an effective method for ball bearing health status assessment.

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Health status identification of rolling bearing based on SVM and improved evidence theory

Cited by 2 publications

References 9 publications

Fault Diagnosis of a Double-Row Spherical Roller Bearing for Induction Motor Using Vibration Monitoring Technique

Fault Diagnosis of a Double-Row Spherical Roller Bearing for Induction Motor Using Vibration Monitoring Technique

Health condition assessment of ball bearings using TOSELM

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