Abstract
Condition monitoring and fault diagnosis via vibration signal processing play an important role to avoid serious accidents. Aiming at the complexity of multiple faults in a rotor-bearing system and drawback, the characteristic frequency of relevant fault could not be determined effectively with traditional method. The Spectral Kurtosis (SK) is useful for the bearing fault detection. Nevertheless, the simulation of experiment in this paper shows that the SK is unable to identify multi-fault of rotor-bearing system fully when different faults excite different resonance frequencies. A new multi-fault detection method based on EEMD and spectral kurtosis (SK) is proposed in order to overcoming the shortcoming. The proposed method is applied to multi-faults of rotor imbalance and faulty bearings. The superiority of the proposed method based on spectral kurtosis (SK) and EEMD is demonstrated in extracting fault characteristic information of rotating machinery.
Highlights
Rotating machinery is widely used for energy transformation and power transmission in industry
We propose a new method with combining the advantages of ensemble EMD (EEMD) and Spectral Kurtosis (SK) for rotor bearing system multi-fault diagnosis
The ensemble empirical mode decomposition (EEMD) method provides a powerful tool for nonlinear signal analysis
Summary
Rotating machinery is widely used for energy transformation and power transmission in industry. For condition monitoring of rotor bearing system, the vibration signals associated to damaged bearing typically produce an impulsive signature In such case, the Spectral Kurtosis (SK) is useful by calculating Kurtosis value across different frequency bands [6]. EMD has been introduced for the analysis of nonlinear and non-stationary signals of rotating machinery fault detection [12,13,14]. We propose a new method with combining the advantages of EEMD and SK for rotor bearing system multi-fault diagnosis. It can solve the challenging task of rotor bearing system multi-fault information and extract the different fault value.
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