Method for predicting facility and equipment failure

A technology for equipment failures and facilities, applied in neural learning methods, systems based on fuzzy logic, biological neural network models, etc., can solve problems such as different service life of parts

Inactive Publication Date: 2009-09-23
DA-YEH UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] If there are consumable parts in facilities and equipment, they must be repaired or replaced at regular int

Method used

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  • Method for predicting facility and equipment failure
  • Method for predicting facility and equipment failure
  • Method for predicting facility and equipment failure

Examples

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Example Embodiment

[0015] In this embodiment, the so-called facilities and equipment include three parts: facilities, equipment and furniture. Facilities may refer to a building or place to provide specific services or specific industrial purposes; equipment may be required for a job or service tools; furniture can be household appliances, such as tables, chairs, cabinets, etc.

[0016] Please refer to figure 1 , which shows a flow chart of a method for predicting facility and equipment failures according to a preferred embodiment of the present invention. The method 200 of predicting facility and equipment failure includes selecting a specified part, step 210 . Factors affecting the service life of the specified part are selected, as in step 220 . A fuzzy rule for predicting the useful life of a given part is defined (step 230). Build a fuzzy neural network, as in step 240. The fuzzy neural network is trained as in step 250. The likely useful life of the specified parts in the facility equ...

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Abstract

The invention provides a method for predicting facility and equipment failure by using a fuzzy neural network. The method predicts the service life of facility and equipment parts by the importance that historical data automatically adjusts factors influencing the service life through a learning mode of the fuzzy neural network.

Description

technical field [0001] The present invention relates to a method for predicting failures of facilities and equipment, and in particular to a method for predicting failures of facilities and equipment parts using a fuzzy neural network. Background technique [0002] If there are consumable parts in facilities and equipment, they must be repaired or replaced at regular intervals. However, the service life of parts varies depending on the environment and the characteristics of materials. Generally speaking, the service life of parts is affected by some factors of use, and in different situations, the importance of each influencing factor will change. In order to prevent the parts of facilities and equipment from being damaged or exhausted before maintenance, the impact Its proper function, so if it can further proactively predict the time when the parts of the facility equipment may be damaged or exhausted, it can be prevented before it happens. Contents of the invention [...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06N7/02
Inventor 柯千禾
Owner DA-YEH UNIVERSITY
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