No abstract

Diagnosing the failure or predicting the performance state of low-speed and heavy-load slewing bearings is a practical and effective method to reduce unexpected stoppage or optimize the maintenances. Many literatures focus on the performance prediction of small rolling bearings, while studies on slewing bearings' health evaluation are very rare. Among these rare studies, supervised or unsupervised data-driven models are often used alone, few researchers devote to remaining useful life (RUL) prediction using the joint application of two learning modes which could fully take diversity and complexity of slewing bearings' degradation and damage into consideration. Therefore, this paper proposes a clustering-based framework with aids of supervised models and multiple physical signals. Correlation analysis and principle component analysis (PCA)-based multiple sensitive features in time-domain are used to establish the performance recession indicators (PRIs) of torque, temperature, and vibration. Subsequently, these three indicators are divided into several parts representing different degradation periods via optimized self-organizing map (OSOM). Finally, corresponding data-driven life models of these degradation periods are generated. Experimental results indicate that multiple physical signals can effectively describe the degradation process. The proposed clustering-based framework is provided with a more accurate prediction of slewing bearings' RUL and well reflects the performance recession periods.

In this work, an integral-type terminal sliding mode (TSM) controller with an asymptotic disturbance observer is developed. The proposed controller can be applied to a class of uncertain integrator systems with unknown external disturbances. In addition, the chattering phenomenon and the singularity problem in traditional TSM control and the influence of unknown external disturbances on the system are taken into consideration. For unknown external disturbances in practical applications, a disturbance observer with asymptotic estimation performance is constructed. Thus, the control strategy is developed to ensure chattering-free and eliminate the singularity problem. By comparing with the traditional TSM control, we expect to synthesize a more efficient chattering-free sliding mode controller. The integral-type TSM transforms the discontinuous sign function into the continuous smooth function. Furthermore, the proposed control algorithm can accurately compensate for the external unknown disturbance and exhibits better robustness. Finally, the simulation results demonstrate the effectiveness of the controller and the stability of the whole closed-loop system is strictly proved.

Disk-type gear gasher is promised by a cutter body and indexable inserts, affording gear gashing which belongs to no free, heavy cutting. By applying nonlinear empirical exponential formula and using Time Finite Element Analysis method, the model of gashing forces was obtained. The corresponding gashing torque was unanimity as the spindle current measured. Based on the intermittent force model, the machining condition was focused along the gear lead for the forces' amplitude and phase on the profile time. The value changed greatly until 0. There were two manifestations called phase congruency and phase imbalance which led to negative deviations or positive deviations. So, the force of rough gear gashing had been completed preliminarily, providing guidance for processing.