Fault diagnosis and prediction method for numerical control machine tool spindle

Abstract

The invention discloses a fault diagnosis and prediction method for a numerical control machine tool spindle, and belongs to the field of numerical control machine tools. The method includes the following steps of database establishment, signal acquisition and analysis, fault diagnosis and fault prediction. Advanced artificial intelligence technologies such as a nerve network, an expert system and a support vector machine are used for conducting fault diagnosis and prediction, measured data transmitted from a high-precision laser displacement sensor, an infrared temperature sensor and an acceleration sensor are rapidly analyzed through a mixed reasoning model, fault sources are effectively inferred and fault diagnosis and prediction of the numerical control machine tool spindle are conducted in the running process of the numerical control machine tool spindle according to numerical control machine tool spindle models established by various machine tools, and therefore the fault diagnosis accuracy and efficiency of the numerical control machine tool spindle are greatly improved, relative prediction is conducted on the tendency of the state of the numerical control machine tool spindle, and the maintenance cost of the numerical control machine tool spindle is effectively reduced.

Description

technical field [0001] The invention belongs to the field of numerically controlled machine tools, and in particular relates to a method for fault diagnosis and prediction of the spindle of the numerically controlled machine tool, which can be widely used in the fault diagnosis and prediction of the spindle of the numerically controlled machine tool. Background technique [0002] The failure of the mechanical aspects of CNC machine tools is the most common failure of the spindle part, and it is the most difficult to judge. Although the numerical control system has its own diagnostic system, it is mainly limited to electrical faults, and it is slightly insufficient in terms of mechanical faults. In daily maintenance, when there are problems with the spindle of the CNC machine tool, the traditional method can only be eliminated one by one based on experience. It is often necessary to check every transmission link, and a large number of internal structures of the spindle of the...

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Problems solved by technology

[0005] The state detection method of the CNC machine tool spindle disclosed in this patent document needs to process the obtained fault characteristic parameters through the expert system, artificial neural network system and support vector machine respectively to obtain three fault diagnosis results, and then the three fault diagnosis results Through genetic algorithm fusion, the detection is quite complicated, the diagnosis result is prone to errors, the accuracy of fault diagnosis is low, and the prediction of machine tool spindle failure cannot be realized, which increases the maintenance cost of machine tool spindle

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