Spare part demand predicting method based on state monitoring and reliability of equipment part

A technology of demand forecasting and reliability, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problem of high cost, achieve good universality, reduce costs, and save costs.

Active Publication Date: 2014-03-12
TSINGHUA UNIV
View PDF2 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 ...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Spare part demand predicting method based on state monitoring and reliability of equipment part
  • Spare part demand predicting method based on state monitoring and reliability of equipment part
  • Spare part demand predicting method based on state monitoring and reliability of equipment part

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] The method for predicting demand for equipment spare parts based on operating state monitoring and equipment reliability proposed by the present invention comprises the following steps:

[0027] (1) Obtain the failure life of equipment components from the equipment maintenance records, and use Weibull model, Weibull competition risk model or truncated normal distribution model to fit the probability cumulative distribution function F( t); Taking the Weibull distribution as an example, the cumulative distribution function F(t)=1-exp[-(t / α) β ], where α and β are the parameters to be fitted. For the specific process of fitting the cumulative distribution function according to the reliability theory, please refer to "Reliability Model and Application" written by Jiang Renyan and Zuo Mingjian of Mechanical Engineering Press.

[0028] (2) Monitor the status of N devices, and obtain the status of each device j in the N devices at the current time T i The total working hours...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F19/00
Inventor 张力刘英博王建民曹原
Owner TSINGHUA UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap