Equipment abnormality diagnosis method and device based on energy consumption model and data interaction

Abstract

The invention relates to the technical field of computer data processing, in particular to an equipment exception diagnosis method and device based on an energy consumption model and data interaction.The invention provides an equipment exception diagnosis method based on an energy consumption model and data interaction. The method comprises the following steps: acquiring time history data of a selected variable for one or more components of to-be-diagnosed equipment according to a specified operating parameter variable, then obtaining a time period set in a steady-state working condition by using a probability statistics method in a sliding window, further calculating to obtain a steady-state working condition statistics set, screening reachable values of performance coefficients based onthe steady-state working condition statistics set, and constructing a performance coefficient dynamic alarm curve of to-be-diagnosed equipment through a neural network model; and inputting operationparameter variables collected in real time into a neural network model, and if the output value of the operation parameter variables exceeds the performance coefficient dynamic alarm curve, judging that the to-be-diagnosed equipment is suspected to have a fault and sending out an alarm signal.

Description

technical field [0001] The present application relates to the technical field of computer data processing, in particular to a device abnormality diagnosis method and device based on energy consumption model and data interaction. Background technique [0002] Equipment abnormality refers to the material damage caused by the automatic operation of the equipment, or the unexpected shutdown during the manufacturing process, the decline in efficiency, or the failure of the manipulator and other electromechanical equipment, and the irregular stoppage of operation is a common problem. These problems are usually caused by equipment operation. Or the terminal manufactured by the equipment, which adds a considerable cost to the user. [0003] In the implementation of some equipment abnormality diagnosis methods, for example, it involves the field of building energy consumption diagnosis, including refrigeration unit equipment, water pump equipment, cooling tower equipment, water syste...

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Problems solved by technology

[0004] However, during the actual operation of the above method, when the equipment is aging or the equipment is on the verge of failure, although the sensor values ​​at various positions remain normal, the overall performance coefficient of the equipment often fluctuates greatly, that is, when the equipment failure occurs Previously, the above methods could not make predictions in advance, and could not make real-time detection and prediction of the overall performance of the equipment

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