Abstract:
In the present study, a new bearing fault detection and recognition methodology is proposed based on complementary ensemble empirical mode decomposition method
(CEEMD) and a newly developed weighted multiscale entropy method. The need for this
methodology is felt due to the inability of the existing multiscale entropy methods in correctly identifying the nature of the signal, particularly in the initial scales. The implication
of this drawback is strongly perceived in the experimental analysis in the present work.
Vibration signals acquired from test machines/working machines have a substantial presence of noise which severely affects the consistency and reliability of the extracted features. Therefore, for effective implementation and comparing the efficiency of the
proposed methods, the original signal is firstly processed with CEEMD. The processing of
the signal includes its decomposition into several modes thereafter reconstructing a new
signal from the modes chosen through Hurst exponent threshold analysis. From the reconstructed signals, the faulty feature vectors are extracted by the weighted multiscale
entropy methods. The capabilities of the proposed method are intensively tested through
simulation and experimental analysis. From the analysis of simulated signals, it is demonstrated that the drawback prevailing in the established entropy methods have strongly
been mitigated by the newly developed weighted entropy methods. On the experimental
front, an impressive improvement is observed by the proposed methods both qualitatively
(in indicating the faulty system from the normal system) and quantitatively (in recognizing
the fault type and severity by the support vector machine classifier). Apart from the analysis of vibration signals, the versatility of the proposed method is also verified on the
acoustic signals acquired under similar experimental conditions.
