Method for analyzing reliability of electrical control and drive system of heavy-duty numerical control machine tool

Abstract

The invention discloses a method for analyzing the reliability of an electrical control and drive system of a heavy-duty numerical control machine tool. The method comprises the steps: firstly dividing a system into subsystems; secondly finding out a subsystem which frequently has faults, drawing a reliability block diagram of the subsystem, and converting the reliability block diagram into a Bayesian network, thereby calculating the fuzzy probability of the subsystem in different states, and achieving the quantification and processing of the uncertainty of the system.

Description

technical field [0001] The invention belongs to the technical field of reliability analysis, in particular to a reliability analysis technology of the electrical control and drive system of a heavy-duty numerical control machine tool. Background technique [0002] CNC machine tools are complex mechatronic devices, and only when each part coordinates in an orderly manner can CNC machine tools operate efficiently and reliably. The electrical system is one of the subsystems with frequent failures of domestic CNC machine tools. It can be said that a machine tool is a device that converts electrical energy into mechanical energy required for processing. The electrical system is the core of the entire CNC machine tool, and its reliability directly affects the reliability of the entire CNC machine tool. sex. Therefore, it is very necessary to conduct reliability research on the electrical system of heavy-duty CNC machine tools. [0003] Due to the high manufacturing cost of heavy...

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Problems solved by technology

[0003] Due to the high manufacturing cost of heavy-duty CNC machine tools, the inability to conduct a large number of reliability tests, the lack of historical data for reference, and the lack of knowledge, the failure probability of parts cannot be accurately obtained, resulting in uncertainty; Uncertainty in differentiation and identification of failure modes

In addition, due to the complex structure and failure mechanism of heavy-duty CNC machine tools, there are uncertainties in the logical relationship between parts

In addition, the electrical system of heavy-duty CNC machine tools often exhibits multiple performance states during the working process of components and systems, not only the two states of normal operation and failure, but also the state of functional degradation

[0004] The traditional Bayesian network has been able to handle polymorphic problems well, but it is still insufficient in dealing with uncertain problems

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