Affiliations: [a] School of Automobile and Transportation, Xihua University, Chengdu, China
| [b] Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China
Abstract: Rolling element bearings are widely used in machinery, such as cooling fan, railway axle, centrifugal pump, transaction motor, gas turbine engine, wind turbine gearbox, etc., to support rotating shafts. Bearing failures will accelerate failures of other adjacent components and finally result in the breakdown of systems. To prevent any unexpected accidents and reduce economic loss, condition monitoring and fault diagnosis of rolling element bearings should be immediately conducted. Ensemble empirical mode decomposition (EEMD) as an improvement on empirical mode decomposition is a data-driven algorithm to adaptively decompose vibration signals collected from the casing of machinery for bearing fault feature extraction without the requirement of expertise and thus its easy usage attracts much attention in recent years from readers and engineers. The direct applications of EEMD to preprocessing bearing fault signals for intelligent bearing fault diagnosis can be found in lots of publications and conferences every year. However, such applications are not always effective in extracting bearing fault features because the Fourier spectrum of the first intrinsic mode function is too wide and contains many unwanted strong low-frequency periodic components. In this paper, according to results from the analyses of industrial railway axle bearing fault signals, we experimentally show that the direct use of EEMD is not always effective in extracting bearing fault features. Further, to make EEMD more effective, we introduce the concept of blind fault component separation to separate low-frequency periodic vibration components from high-frequency random repetitive transients, such as bearing fault signals. Results show that the idea of blind fault component separation is much helpful in enhancing the effectiveness of EEMD in extracting bearing fault features in the case of industrial railway axle bearing fault diagnosis.
