Equipment degradation analysis method based on parameter residual error

Abstract

The invention discloses an equipment degradation analysis method based on a parameter residual error, and the method comprises the steps of building a neural network model through an improved convolutional neural network algorithm and a large amount of historical data, training an equipment model, and simulating the operation of the equipment; model prediction; and expert rule matching. Through the online comparative analysis of the model calculation value and the real-time monitoring value of the parameters, the residual prediction of the parameters is realized, and the degradation analysis and the fault prediction of the equipment are realized in combination with correlation analysis. According to the method, based on the big data, the improved convolutional neural network algorithm andthe expert rules, in combination with an equipment degradation analysis method, the model prediction accuracy can be improved to 99% or above after learning and perfecting the all-working-condition full-sample data, the prediction alarm time can be brought forward 10-15 days earlier than the fault occurrence time, the gateway moves forwards, the precious time is gained for equipment maintenance, the equipment availability is improved, the safety risk caused by equipment faults is reduced, the maintenance cost is reduced, the non-stop is reduced, and the overall economic benefit is improved.

Description

technical field [0001] The invention belongs to the technical field of equipment degradation management, and in particular relates to an equipment degradation analysis method based on parameter residuals. Background technique [0002] Equipment management is a huge industry and an area that cannot be ignored. There are more than 7.8 million manufacturing enterprises in China, 200 million pieces of equipment, 25 million equipment management personnel, and 72 million equipment maintenance personnel. [0003] Equipment is a very important asset of a manufacturing company and the core of a stable and reliable manufacturing system. With the continuous improvement of modern production management mode, manufacturing enterprises have higher and higher requirements for equipment safety, economy and environmental protection. [0004] During the use of the equipment, it will gradually become unusable due to many reasons such as wear and tear. The service life of the equipment can be e...

AI Technical Summary

Problems solved by technology

The equipment parameter residual analysis of the present invention is based on the neural network algorithm and big data correlation analysis, and performs correlation degradation analysis on multiple parameters representing the equipment state, determines the correlation between each parameter and a certain fault of the equipment, and constructs a surface diagra

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