Abstract
This paper presents the application of new time frequency method, ensemble empirical mode decomposition (EEMD), in purpose to detect localized faults of damage at an early stage. EEMD is a self adaptive analysis method for non-linear and non-stationary signals and it was recently proposed by Huang and Wu to overcome the drawbacks of the traditional empirical mode decomposition (EMD). The vibration signal is usually noisy. To detect the fault at an early stage of its development, generally the residual signal is used. There exist different methods in literature to calculate the residual signal, in this paper we mention some of them and we propose a new method which is based on EEMD. The results given by the different methods are compared by using simulated and experimental signals.
Highlights
Fault diagnosis of gearboxes has shown a great development in techniques based on the analysis of vibration signals [1,2,3,4,5,6], because vibration signals carry a great deal of information, which can be used to detect early faults in rotating machines
There exist different methods to calculate the residual signal, in this work, first we give a preview on some methods that already exist and we propose a new method based on the ensemble empirical mode decomposition
Intrinsic Mode Functions (IMFs) Signal IMF1 IMF2 IMF3 Kurtosis 2.501 2.13 2.03 1.53 the IMFs obtained by Empirical Mode Decomposition (EMD) are distorted seriously and the mode mixing is occurring between IMFs
Summary
Fault diagnosis of gearboxes has shown a great development in techniques based on the analysis of vibration signals [1,2,3,4,5,6], because vibration signals carry a great deal of information, which can be used to detect early faults in rotating machines. The resulting signals are non stationary and nonlinear To analyze such signals, time-frequency analysis has been applied to fault diagnosis of gearboxes in order to combine the advantages of both time and frequency domains. In the field of fault diagnosis of rotating machines, the EMD method has been widely applied for identification of faults [5, 14,15,16,17,18,19,20]. To alleviate these difficulties, in this work we use the EEMD method to calculate the residual signal (RS).
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