Task reliability state-based manufacturing system predictive maintenance method

Abstract

A task reliability state-based manufacturing system predictive maintenance method is disclosed and comprises the following steps: in a first step, a manufacturing system underlying database is built; in a second step, device performance variation trends and all relevant parameters are analyzed via use of underlying data; in a third step, corrective maintenance cost of a device is predicted; in a fourth step, planned maintenance cost of the device is predicted; in a fifth step, processing capability loss cost of the device is predicted; in a sixth step, indirect loss cost of the device is predicted; in a seventh step, product quality loss cost of the device is predicted; in an eighth step, comprehensive cost is predicted, a task reliability threshold value of the device is determined, and an optimal predictive maintenance strategy is further determined; in a ninth step, task demand of the last device is determined according to a result of the eighth step, and the second step to the eighth step are repeated till a predictive maintenance strategy of a whole manufacturing system is made; in a tenth step, via the above steps, an aim of combining device performance conditions with production tasks is attained, and problems of conventional neglect of production states and blind periodic maintenance can be solved.

Description

technical field [0001] The invention provides a predictive maintenance method for a manufacturing system based on task reliability status, which belongs to the field of production management. Background technique [0002] As a key measure to improve the reliability and usability of the manufacturing system to ensure production efficiency, product quality, and corporate profits, the maintenance strategy of the manufacturing system has always been the focus of manufacturing enterprises. According to statistics, usually equipment maintenance costs account for 15%-40% of manufacturing costs, and with the development of automation and intelligent technology, its annual increase rate is as high as 10%-15%. Therefore, formulating a reasonable predictive maintenance strategy for manufacturing systems has become an important means for enterprises to reduce operating costs and improve market competitiveness. [0003] With the rapid development of information technology and computing ...

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Problems solved by technology

The condition-based maintenance method pays more attention to the degradation state of the equipment itself, and establishes the maintenance strategy of the manufacturing system based on the basic reliability state of the equipment. This method undoubtedly ignores the requirements and restrictions from production tasks and product quality, and lacks practicability; periodic maintenance It is based on the system running time to establish the optimal preventive maintenance cycle of the manufacturing system. This method is widely used in actual production, but ignoring the method itself ignores the system running status, which often leads to unnecessary economic losses caused by excessive maintenance. Or maintenance does not affect production in time, and even cause safety accidents

Aiming at the limitations that the existing research ideas cannot comprehensively consider the polymorphism of the manufacturing system from the perspective of system engineering, and cannot formulate feasible maintenance strategies for enterprises, this patent proposes a predictive maintenance method for manufacturing systems based on task reliability status : By defining the task reliability of the manufacturing system (the ability of the manufacturing system to complete the specified production tasks under the specified conditions and within the specified time (ie R d )) Integrate equipment performance status and actual production task requirements to scientifically describe the production status of the manufacturing system, and then use the minimum comprehensive cost as the criterion, combined with the transfer of task requirements between the relevant stations of the manufacturing system, analyze the current manufacturing system sequentially. Optimal predictive maintenance strategy for each device in the system

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