Equipment fault diagnosis method

Abstract

The invention belongs to the technical field of equipment operation guarantee, and particularly relates to an equipment fault diagnosis method. The method comprises the following steps: determining the category, rated parameters and initial life cycle of research equipment; establishing an expert system model rule base according to object category characteristics; establishing an object historical database; introducing an artificial neural network to model a high-dimensional nonlinear problem; fault diagnosis is realized by using a BP neural network; according to the equipment fault diagnosis method, technologies such as big data and an artificial intelligence algorithm are introduced to digitalize, model and standardize equipment rated parameters, equipment operation conditions, equipment operation and maintenance history and experience and the like, meanwhile, a diagnosis system is endowed with intelligent learning and changing capabilities, and debugging parameters are fed back and corrected according to result output; and finally, the full life cycle state of the equipment is displayed and reflected through a visual platform, and data support is provided for fault early warning, diagnosis analysis and fault maintenance decision, so that the fault rate of the equipment is reduced.

Description

technical field [0001] The invention belongs to the technical field of equipment operation guarantee, and in particular relates to a method for diagnosing equipment faults. Background technique [0002] At present, the early warning and regular inspection diagnosis of operating equipment failures are completely based on the experience of operation and maintenance personnel. However, due to the work experience, working status and individual differences of operation and maintenance personnel, there will be deviations in the diagnosis of faults, and at the same time, personnel flow , Changes in the division of labor lead to the inability to effectively continue the inheritance of the experience value of the operation and maintenance personnel, the cost of trial and error and opportunity cost are in a state of fluctuation, the number of equipment is large, and the energy of the operation and maintenance personnel cannot be fully concentrated, which will also lead to delays in jud...

AI Technical Summary

Problems solved by technology

[0002] At present, the early warning and regular inspection diagnosis of operating equipment failures are completely based on the experience of operation and maintenance personnel. However, due to the work experience, working status and individual differences of operation and maintenance personnel, there will be deviations in the diagnosis of faults, and at the same time, personnel flow , Changes in the division of labor lead to the inability to effectively continue the inheritance of the experience value of the operation and maintenance personnel, the cost of trial and error and opportunity cost are in a state of fluctuation, the number of equipment is large, and the energy of the operation and maintenance personnel cannot be fully concentrated, which will also lead to delays in judgment and omissions. Unable to complete advance warning and effective regular inspection decision-making

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