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Critical failure of a slewing bearing used in large machines would entail high costs to an enterprise. Designing the condition monitoring system to diagnose the failure or predict the residual life of the slewing bearing is a practical and effective method to reduce unexpected stoppage or optimize the maintenances. Many literatures mentioned the life prediction of small typical rolling bearings based on the vibration signal. However slewing bearing is a large low-speed heavy-load bearing completely different from small bearing. Some researchers focused on the fault diagnosis of slewing bearing using non-traditional methods with vibration signals. And no published literatures mention the life prediction researches of slewing bearings based on the condition monitoring. Therefore, this paper presents a residual life model for slewing bearing based on multiple physical signals (torque, temperature and vibration). The correlation analysis and principal component analysis (PCA) based multiple sensitive features in time-domain were used to establish the performance recession indicators of temperature, torque and vibration, and these three indicators are input to the support vector regression (SVR) to construct the residual life model. The test results show that the PCA fusion combined correlation based features selection is an effective method of choosing the performance regression indicators, which is able to make full use of various features. The residual life prediction model based on temperature, torque and vibration signals can well reflect the performance recession trend and is suitable to predict the residual life of slewing bearing effectively.

Multiple physical signals based residual life prediction model of slewing bearing