Application Research of Kalman Filter and SVM Applied to Condition Monitoring and Fault Diagnosis

Abstract:

In the condition monitoring and fault diagnosis, useful information about the incipient fault features in the measured signal is always corrupted by noise. Fortunately, the Kalman filtering technique can filter the noise effectively, and the impending system fault can be revealed to prevent the system from malfunction. This paper has discussed recent progress of the Kalman filters for the condition monitoring and fault diagnosis. A case study on the rolling bearing condition monitoring and fault diagnosis using Kalman filter and support vector machine (SVM) has been presented. The analysis result showed that the integration of the Kalman filter and SVM was feasible and reliable for the rolling bearing condition monitoring and fault diagnosis and the fault detection rate was over 96.5%.

• Periodical: Applied Mechanics and Materials (Volumes 121-126)
• Pages: 268-272
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.121-126.268
• Online since: October 2011
• Authors: Ke Li, Yue Lei Zhang, Zhi Xiong Li
• Keywords: Condition Monitoring, Fault Diagnosis, Kalman Filter (KF), Support Vector Machine (SVM)

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