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Linear regression model-based maintenance support object consumption prediction method

A linear regression model and prediction method technology, applied in the field of maintenance statistics, can solve the problems of unguaranteed accuracy, difficulty in popularization, and serious reliance on experts

Pending Publication Date: 2021-09-14
中国人民解放军32181部队
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] For the man-hours required for equipment maintenance, it is generally calculated manually based on experience. This method relies heavily on experts, is difficult to popularize, and the accuracy cannot be guaranteed. The calculation results vary from person to person.

Method used

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  • Linear regression model-based maintenance support object consumption prediction method
  • Linear regression model-based maintenance support object consumption prediction method
  • Linear regression model-based maintenance support object consumption prediction method

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Experimental program
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specific Embodiment approach

[0033] Specific implementation method: a method for predicting the consumption of maintenance support objects based on a linear regression model. This method is based on standard equipment comparison. This method includes:

[0034] Step 1: Basic equipment selection;

[0035] Step 2: Selection of basic equipment task items;

[0036] Step 3: Preprocessing of basic equipment task man-hour data;

[0037] Step 4: Collection of man-hour data for basic equipment tasks;

[0038] Step 5: Man-hour data processing of basic equipment task items;

[0039] Step 6: Specify the standard man-hours and man-hour quotas for single equipment repair.

[0040] The first step includes: selection of basic equipment, each major should select new equipment with a relatively comprehensive system composition and strong typicality as the basic equipment. Subsystems, sub-systems, equipment, combinations and other functional structural parts of basic equipment are typical and comparable, which is conveni...

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Abstract

The invention relates to a linear regression model-based maintenance support object consumption prediction method, which belongs to the technical field of maintenance statistics, is based on standard equipment comparison, and is characterized by comprising the following steps of: 1, selecting basic equipment; 2, selecting a basic equipment task project; 3, preprocessing basic equipment task man-hour data; 4, collecting basic equipment task man-hour data; 5, performing basic equipment task project man-hour data processing; and 6, specifying single equipment repair standard working hours and working hour quota, and calculating the maintenance working hours of the equipment through a computer, so that the accuracy of the method can be improved, manpower is simplified, tasks are processed through the optimal working hours, and labor cost is saved to the maximum extent.

Description

technical field [0001] The invention belongs to the technical field of maintenance statistics, and relates to a method for measuring and calculating the amount of tasks, in particular to a method for measuring and calculating the annual consumption of equipment. Background technique [0002] Equipment maintenance is the combination of equipment repair and maintenance. In order to prevent the degradation of equipment performance or reduce the probability of equipment failure, the technical management measures are carried out according to the predetermined plan or the corresponding technical conditions. After the technical state of equipment deteriorates or breaks down, technical activities to restore its function, including various planned repairs and unplanned breakdown repairs and accident repairs, also known as equipment repairs. [0003] For the man-hours required for equipment maintenance, it is generally calculated manually based on experience. This method relies heavi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/00G06Q10/06
CPCG06Q10/04G06Q10/20G06Q10/0631
Inventor 孙江生梁伟杰闫鹏程吕艳梅张连武蔡娜连云峰代冬升王正军李万领张东赵晔李雅峰李会杰王凯王婷
Owner 中国人民解放军32181部队
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