To eliminate the influence of thermally induced error from a machine tool on machining accuracy, a comprehensive error compensation method for thermal displacement of the screw shaft and spindle is put forward. Based on a heat transfer mechanism and experimental analysis, a model of screw thermal expansion error is built. Modeling of spindle thermal growth that depends on speed variations is also proposed. Thermal tests for studying thermal behavior of the spindle and screw axis are carried out on the vertical drilling center TC500R. Finally, the compensation effect of the robust model is validated via experimental tests and machining. Experimental results show that thermal displacement variations are controlled within 2 μm when the compensation system is activated. The suggested model can achieve high accuracy and good applicability in different moving states. Machining results indicate that dimensional accuracy of the workpiece is significantly improved after implementation of compensation. Feasibility of the thermal error compensation system is satisfactory in applications for drilling operations.

A reliable condition monitoring system is very useful in a wide range of industries to detect the occurrence of incipient defects so as to prevent machinery performance degradation, malfunction and sudden failure. Among the rotating machinery, many mechanical problems are attributed due to bearing failures. So implementing condition monitoring for bearing is critically needed. Considering that most research for condition monitoring only focus on detecting the existing fault, this paper add degradation tendency prognostics into the condition monitoring process. The kernel of bearing condition monitoring method presented in this paper is related to condition features extraction and remaining useful life prediction. The former is realized by the comprehensive vibration analysis for specific fault frequencies. The latter is achieved by adaptive neuron-fuzzy inference system based on extracted degradation signal. For illustration purpose, a bearing case from NASA data repository is used to validate the feasibility of the proposed method. The result indicates that the performance degradation of bearing can be effectively monitored and the predicted remaining useful life with 5.6% relative error can be the important reference for maintenance decision making.

Thermal errors caused by spindle rotation is a major factor that influences the precision stability of CNC machine tools. To determine an effective method for reducing thermal errors, a thermal experiment was carried out on the spindle of a vertical drilling center. The thermal deformation mechanism and thermal error variations of the spindle are presented. Based on the generation, convection, and conduction theory of heat, the thermal field model of a spindle system is derived. The relationship between the thermal field and the radial thermal error is established using a physically based method. Finally, the effect of the thermal error model proposed is verified by both a simulation and experiment. The results recorded on the two CNC machining centers indicate that the average fitting accuracy of the theoretical model is up to 94.1%, which validates the high accuracy and strong robustness of the presented model.

Structural damage often happens in rotating machinery such as steam engines, aircraft engines, and compressors due to the high-speed rotating of the shaft. The most common structural damages in rotating machinery are rotor shaft crack, rotor to stator rub, and bolts looseness and so on. In the present paper, the model based identification method is used to detect single structural damage such as crack, rotor to stator rub, pedestal looseness, and also, coupling fault such as rotor to stator rub and crack, crack and pedestal looseness. Utilizing the characteristic that equivalent loads of rub forces are internal forces, and the equivalent loads of the crack are external moments, the coupling faults of crack and rub-impact and crack and pedestal looseness are analyzed and exampled. The merit of the method is that it is an online diagnosis method, which provides early warning of machine failure. Theoretical simulation and laboratory testing are conducted to validate the method.

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