A precision monitoring and diagnosis method for the electric spindle of ultra-high-speed CNC grinding machine based on differential chaos

Abstract

The invention discloses a precision monitoring diagnosis method based on differential chaos and used for an electric spindle of an ultra-high-speed numerical-control grinding machine. The precision monitoring diagnosis method comprises the following steps: firstly, counting frequency, which corresponds to different error factors, of the spindle; then, constructing a duffing oscillator signal differential dual-chaos detecting system based on a duffing equation, respectively carrying out positive-phase input detection and reversed-phase input detection on a detected signal, carrying out vectorization and OR operation on a maximal Lyapunov index of the positive-phase input detection and the reversed-phase input detection under the same error factor, thereby obtaining discriminant parameters L, discriminating that the precision of the spindle is normal and ending this monitoring diagnosis if all discriminant parameters L are 0; otherwise, amplifying the detected signal and inputting the duffing equation in an inverted-phase mode to obtain a phase diagram discriminating track, and comparing the phase diagram discriminating track with a chaos track phase diagram under each error factor to determine the error factors. The precision monitoring diagnosis method has the advantages of being high in detecting sensitivity, remarkably reducing an omission ratio of the chaos detecting system, reducing fault factor diagnosis discriminating difficulty, greatly improving a diagnosis abduction accurate rate and the like.

Description

technical field [0001] The present invention designs the technical field of precision detection in the CNC machine tool industry, and in particular relates to a method for monitoring and diagnosing the precision of an electric spindle of an ultra-high-speed CNC grinding machine based on differential chaos. Background technique [0002] Ultra-high-speed grinding and its equipment with a grinding wheel line speed exceeding 150m / s are known as "the highest peak of modern grinding technology" in developed countries such as Europe, Japan, and the United States. Compared with traditional grinding machines, it has great advantages : Double the grinding efficiency and reduce the number of equipment used; greatly improve the machining accuracy of the parts and reduce the surface roughness of the workpiece. For example, when other conditions are the same, the surface roughness values ​​are respectively It is Ra2.0μm and Ra1.2μm; the life of the grinding wheel is extended by more than ...

AI Technical Summary

Problems solved by technology

[0004] Common factors that affect the accuracy of the electric spindle include bearing wear, poor coaxiality between the stator and the rotor, unbalance, misalignment, loose support, etc. These faults will be contained in the vibration information of the electric spindle. The existing traditional method is through vibration Spectrum analysis method diagnoses the occurrence of characteristic frequency components to determine the generation of corresponding faults, which has the following disadvantages: 1) Spectrum analysis method cannot realize the early detection of electric spindle

The early degradation signal of the motorized spindle accuracy is not obvious, and the fault spectrum characteristics of the early faults that are different from the normal state of the motorized spindle are usually very weak, and no obvious frequency components of the fault spectrum are formed. Therefore, the spectrum analysis method cannot detect Weak signal of early precision degradation of electric spindle

2) The spectrum analysis method is greatly affected by the interference signal

However, because there is a transition region from the chaotic region to the large-scale periodic region, and the critical threshold of the driving force of the traditional chaotic system detection system cannot be accurately selected, the traditional chaotic method is prone to missing detection, resulting in poor detection accuracy. less accurate

In addition, because the signal to be detected is weak, the phase trajectory discrimination of the traditional chaotic system detection system is low, and it is difficult to distinguish

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