A Spare Parts Requirement Prediction Method Based on Condition Monitoring and Equipment Component Reliability

Abstract

The invention relates to a spare part demand predicting method based on state monitoring and reliability of an equipment part and belongs to the technical field of mechanical manufacturing. The method includes the following steps that firstly, according to a maintenance record list of equipment, the reliability theory is used for processing burn-out life data of parts in the maintenance record with the reliability theory, and the probability cumulative distribution function of the burn-out life of the parts is obtained; the probability cumulative distribution function of the burn-out life of the parts, the total working time of the equipment, the history record of the real spare part demand quantity, the year-to-year value record and the month-to-month value record of the spare part demand quantity, the experience predicting values of planners and the like are compared with errors, used for comparison predicting, of the real spare part demand quantity so as to obtain the predicting value of the demand quantity of equipment spare parts. According to the method, the prediction result of the method has the practical bases, the inactive stock of an enterprise can be effectively reduced, resource waste is reduced, cost is reduced and the method has good adaptability to different spare parts which are prone to damage.

Description

technical field [0001] The invention relates to a method for predicting demand for spare parts based on state monitoring and equipment component reliability, and belongs to the technical field of mechanical manufacturing. Background technique [0002] The maintenance process of large and complex equipment is complicated. In order to shorten the maintenance downtime and ensure maintenance efficiency, a certain amount of spare parts inventory is necessary. The shortage of spare parts inventory may cause the equipment to not be repaired and put into use in time, causing huge economic losses; excessive spare parts reserves will generate a large amount of sluggish inventory, resulting in additional consumption such as warehouse site fees, storage fees, and losses caused by damage to spare parts, occupying a large amount of business flow funds, affecting business operations. Therefore, it is of great significance to reasonably predict the demand for spare parts and reduce the inv...

AI Technical Summary

Problems solved by technology

[0006] Existing technologies have proposed methods for spare parts prediction using condition monitoring information, but they are usually based on a basic condition: the parts to be predicted must have sensing devices, and their status data can be obtained

However, in reality, not all parts of the device can be monitored due to the cost and volume of the sensor

The focus of condition monitoring is often on the more critical or expensive parts of the equipment. For some vulnerable parts that are in high demand but relatively cheap, it is too expensive to install sensors alone to predict the demand for spare parts in the future.

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