Abstract
Rolling element bearings are of great importance in planetary gearboxes. Monitoring their operation state is the key to keep the whole machine running normally. It is not enough to just apply traditional fault diagnosis methods to detect the running condition of rotating machinery when weak faults occur. It is because of the complexity of the planetary gearbox structure. In addition, its running state is unstable due to the effects of the wind speed and external disturbances. In this paper, a signal model is established to simulate the vibration data collected by sensors in the event of a failure occurred in the planetary bearings, which is very useful for fault mechanism research. Furthermore, an improved wavelet scalogram method is proposed to identify weak impact features of planetary bearings. The proposed method is based on time-frequency distribution reassignment and synchronous averaging. The synchronous averaging is performed for reassignment of the wavelet scale spectrum to improve its time-frequency resolution. After that, wavelet ridge extraction is carried out to reveal the relationship between this time-frequency distribution and characteristic information, which is helpful to extract characteristic frequencies after the improved wavelet scalogram highlights the impact features of rolling element bearing weak fault detection. The effectiveness of the proposed method for weak fault recognition is validated by using simulation signals and test signals.
Highlights
Condition monitoring, fault diagnosis and periodic maintenance are indispensable steps to maintain the normal operation of rotating machinery [1,2]
Signal processing methods are the most popular fault diagnosis techniques due to the fact vibration signals are collected by sensors, but feature extraction is an essential and complex question for fault diagnosis of rotating machinery based on vibration signal analysis due to the fact vibration signals are usually disturbed by the external circumstances in practical engineering applications
Inthe order to prove signal the validity the waveletbyridge line method based on improved wavelet scalogram, simulation that isofestablished researching the characteristics of rolling element scalogram, simulation established bywhite researching the characteristics of rolling
Summary
Fault diagnosis and periodic maintenance are indispensable steps to maintain the normal operation of rotating machinery [1,2]. The main time-frequency distribution methods, including Short Time Fourier Transform and Wigner-Ville distribution, are very useful for dynamic vibration signal analysis They have a poor performance in extracting weak impact features in the case where the signal is disturbed by strong background noise. To solve this problem, another method named Hilbert spectrum is studied in signal processing [26,27], it has a limitation in time-frequency resolution. The background noise can be ignored as an insignificant ingredient because it has nothing to do with fault diagnosis Based on this property, several new time-frequency analysis methods are extensively used in weak fault feature extraction.
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