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Multi-state equipment system multi-stage spare part demand prediction method facing repairable spare part

A multi-stage, spare parts technology that can be used in forecasting, instrumentation, data processing applications, etc., and can solve problems such as poor practicability

Inactive Publication Date: 2016-10-26
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0003] In order to overcome the shortcomings of poor practicability of existing spare parts demand forecasting methods, the present invention provides a multi-stage spare parts demand forecasting method for repairable spare parts-oriented multi-state equipment systems

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  • Multi-state equipment system multi-stage spare part demand prediction method facing repairable spare part
  • Multi-state equipment system multi-stage spare part demand prediction method facing repairable spare part
  • Multi-state equipment system multi-stage spare part demand prediction method facing repairable spare part

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Embodiment Construction

[0068] refer to Figure 1-3 . The specific steps of the multi-stage spare parts demand forecasting method of the repairable spare parts-oriented multi-state equipment system of the present invention are as follows:

[0069] 1. Determine the task requirements of each stage and the status of each component according to the component composition of the polymorphic repairable equipment system. The specific methods are as follows:

[0070] The equipment system for performing target positioning tasks includes three types of components c 1 ,c 2 ,c 3 , this task consists of three phases whose durations are T1, T2, and T3, respectively. Phase 1 requires component c 1 work, the task structure function at this time is F 1 = c 1 , the second stage requires component c 1 works and part c 2 ,c 3 There is at least one job, and the task structure function at this time is F 2 = c 1 c 2 +c 1 c 3 , the third stage requires component c 1 ,c 2 ,c 3 Working at the same time, the t...

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Abstract

The present invention discloses a multi-state equipment system multi-stage spare part demand prediction method facing a repairable spare part, used for solving the technical problem that the conventional spare part demand prediction method is poor in practicality. The technical scheme is that firstly the task demands of the stages and the states of the parts are determined respectively according to the equipment system part composition; secondly, the different parts and the corresponding spare parts in an equipment system are used as the repairable part sets respectively to consider overall, and an MMDD model of a task system is established; then a Markov chain is utilized to establish a failure model of each part set of a multi-state task system carrying the repairable spare parts in the multi-stage tasks, the paths from a root node to a final node 1 in the MMDD model are enumerated to form an non-intersect path set; finally, based on the established MMDD model and the Markov chain of the part sets, the reliability of the task system at a stage under a corresponding spare part number is calculated and is compared with the required reliability of the task system, and the repairable spare part demand amount of the multi-state equipment system is predicted.

Description

technical field [0001] The invention relates to a spare parts demand prediction method, in particular to a repairable spare parts-oriented multi-stage spare parts demand prediction method for a multi-state equipment system. Background technique [0002] The document "Chinese Invention Patent Application Publication No. CN101320455A" discloses a spare parts demand prediction method based on in-service life evaluation. The method first uses the historical records of parts replacement during equipment operation to establish a statistical model to evaluate the life of spare parts under current service conditions. Then, according to the estimated value of in-service life and the actual service time, the spare parts demand function is defined, and the total spare parts demand of multiple equipment in a certain time range is further predicted. However, this method is only based on statistical laws, and does not address the impact of multiple states of the system on the demand for ...

Claims

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Application Information

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IPC IPC(8): G06Q10/00G06Q10/04
CPCG06Q10/04G06Q10/20
Inventor 蔡志强蒋忠雨司书宾张帅李洋司伟涛
Owner NORTHWESTERN POLYTECHNICAL UNIV
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