Production equipment health management system based on predictive maintenance

Abstract

The invention relates to the technical field of industrial production equipment operation state monitoring and fault diagnosis, and discloses a production equipment health management system based on predictive maintenance. The production equipment health management system based on predictive maintenance comprises an equipment state detection module, a fault diagnosis module, an equipment early warning module, a fault prediction module, a maintenance decision module, a maintenance activity module and a data server, and the data server is used for managing and storing data and transmitting the data to a cloud for backup storage. Related maintenance can be carried out at a proper time based on the health condition of the equipment, normal maintenance of the equipment can be guaranteed, the influence on production is reduced to the minimum, the maintenance cost is the minimum, meanwhile, it can be guaranteed that the equipment performance is always in the optimal working state, prediction can be carried out in advance, the diagnosis accuracy is high, and high value and good economic benefits are continuously created for enterprises. And the overall production capacity and profitability of the equipment can be improved.

Description

technical field [0001] The invention belongs to the technical field of operating state monitoring and fault diagnosis of industrial production equipment, and specifically relates to a predictive maintenance-based production equipment health management system. Background technique [0002] Intelligent maintenance expert and Professor Li Jie from the University of Cincinnati wrote in the book "Industrial Big Data": "Sudden occurrence of equipment failure in manufacturing enterprises will not only increase the maintenance cost of the enterprise, but also seriously affect the production efficiency of the enterprise. Huge losses. According to the survey, 60% of equipment maintenance costs are caused by sudden downtime. The indirect production losses are even greater." [0003] Common equipment maintenance methods mainly include after-event maintenance and preventive maintenance. After-event maintenance is the most common maintenance method in enterprises. It is to quickly comple...

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Problems solved by technology

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a production equipment health management system based on predictive maintenance, which effectively solves common equipment maintenance methods, mainly including post-event maintenance and preventive maintenance. Post-event maintenance is The most common maintenance method in enterprises is to quickly complete equipment maintenance in the shortest time after a failure occurs, minimizing downtime, but the failure maintenance time is longer due to damage to key components such as the spindle and screw of the machine tool , in addition to the direct loss of equipment, equipment failure will also have a more serious impact on the production schedule; preventive maintenance refers to avoiding sudden and gradual failures and prolonging equipment life, according to experience, relevant data or equipment user manuals, etc. The traditional means of inspecting, testing and replacing equipment on a regular basis or based on a certain amount of work (such as the number of products produced) can avoid potential failures to a certain extent, causing risks such as safety and downtime, but such regular or empirical maintenance There are shortcomings such as not being accurate enough and not economical enough. Some equipment may not be worn out or have not deteriorated to the point of maintenance. Early maintenance will cause a waste of labor and resources and affect normal production. De-maintenance may cause accelerated aging of equipment due to timing delays, affect product quality, and even bring serious safety hazards. Traditional equipment maintenance and maintenance management methods restrict the efficient, high-quality, and low-cost production of enterprises. It also lags far behind the needs of enterprises to realize intelligent manufacturing.

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