Nonstationary signal de-noising method of slow-speed large-size slewing bearing using robust local mean decomposition

As a key rotary connection component of construction machinery, the operation performance of slewing bearing has an impact on the stability of engineering construction. Condition monitoring for slewing bearing is essential to their high availability and profitable operation. However, the characteristics of slow-speed large-size slewing bearing make the weak vibration signal corrupted with noise. Therefore, effective signal de-noising for preprocessing technique is difficult but crucial. To solve this problem, a novel signal de-nosing method using robust local mean decomposition is proposed with a product function selection strategy based on kernel principal component analysis. The effectiveness is validated by using simulated as well as experimental vibration signals obtained through a slewing bearing highly accelerated life test. The results illustrate that proposed method can perform effective signal de-noising of slewing bearing compared with other conventional method.