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Equipment degradation analysis method based on parameter residual error

An analysis method and parameter technology, applied in the field of equipment deterioration analysis based on parameter residuals, can solve problems such as failure to identify equipment failure early on deterioration, and achieve the effects of reducing maintenance costs, improving economic benefits, and improving availability

Inactive Publication Date: 2019-08-06
朗坤智慧科技股份有限公司
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  • Application Information

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 diagram of equipment degradation trend , so as to carry out equipment abnormal state prediction, health identification and fault diagnosis, solve the problem that existing technical means cannot identify signs of deterioration in the early stage of equipment failure and give early warning, and perform real-time diagnosis, avoiding equipment damage and non-stop losses.

Method used

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  • Equipment degradation analysis method based on parameter residual error
  • Equipment degradation analysis method based on parameter residual error
  • Equipment degradation analysis method based on parameter residual error

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Embodiment Construction

[0055] Embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0056] This embodiment takes a power plant induced draft fan as an example.

[0057] Such as Figure 1-4 As shown, a method for analyzing equipment degradation based on parameter residuals of the present invention includes the following steps:

[0058] 1. Measuring point selection

[0059] The list of original measurement points is as follows:

[0060]

[0061]

[0062] 2. Data cleaning strategy

[0063] (1) Remove duplicate data

[0064] A measure of similarity calculated from the contents of two numeric fields. The range is 0-1, the closer to 1, the greater the similarity.

[0065] S(s1,s2)=|s1-s2| / (max(s1,s2))

[0066] Set the threshold. When the similarity is greater than the threshold, it will be identified as a duplicate value, and then the duplicate data will be eliminated according to the actual situation.

[0067] (2) Rem...

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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...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06Q10/04G06N3/08G06N3/045
Inventor 武爱斌陈松赵永江康建辉陈道文卞志刚胡杰英张翔
Owner 朗坤智慧科技股份有限公司
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