Method for optimal maintenance decision-making of hydraulic equipment with risk control

A hydraulic equipment and optimal technology, applied in special data processing applications, instruments, biological neural network models, etc., can solve problems such as different status, improve accuracy, improve fault diagnosis accuracy and algorithm efficiency, and speed up diagnosis. Effect

Active Publication Date: 2011-01-19
天津开发区精诺瀚海数据科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0051] Although the weighted association rules are applied to equipment fault mining and diagnosis, it solves the problems caused by the different status of each component of the equipment in the equipment.

Method used

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  • Method for optimal maintenance decision-making of hydraulic equipment with risk control
  • Method for optimal maintenance decision-making of hydraulic equipment with risk control
  • Method for optimal maintenance decision-making of hydraulic equipment with risk control

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0163] Embodiment 1: Using variable weight association rule algorithm DVWAR to calculate the probability value of the corresponding potential fault

[0164] In this embodiment, two methods are used for analysis, one is the weighted association rule algorithm, and the other is the variable weight association rule algorithm. The same set of data is used in the example, and the final results are obtained through calculation and compared. .

example

[0165] Example: The hydraulic equipment M is composed of 5 components, and there are 7 possible failures. Table 1 is the initial weight of each component, and Table 2 is the fault database, indicating the components that can be traced when a certain fault occurs. Set the minimum support threshold wminsup to 1.

[0166] Table 1 Initial weights of each component of equipment M Table 2 Each fault database of equipment M

[0167]

[0168] 1. Mining using weighted association rule algorithm

[0169] Since the weighted association rule algorithm is given to each component only once in the life cycle of the device, the initial weight of each component of the device M is constant, that is, the weight of component A is 0.2, and the weight of component B is 0.2. The weight is 0.1, the weight of component C is 0.3, the weight of component D is 0.4, and the weight of component E is 0.8. According to the mining method of weighted association rules, mining is carried out according to ...

Embodiment 2

[0180] Embodiment 2: Using neural network modeling to obtain the consequence value of the corresponding fault

[0181] In order to illustrate the application of BP neural network in the prediction of failure consequence value, five items of hydraulic equipment risk value, personal risk value, environmental risk value, social risk value and system risk value are selected as input items, and the output item is the comprehensive potential failure consequence value. Evaluation value. Due to the large data, Table 6 lists the learning samples for predicting the comprehensive evaluation value of some fault consequences.

[0182] Table 6 Learning samples for prediction of comprehensive evaluation value of partial failure consequences

[0183]

[0184] In the design of BP neural network, if the number of nodes in the hidden layer of the network is too small, the nonlinear mapping function and fault tolerance of the network will be poor. If the number of nodes is too large, the lear...

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Abstract

The invention belongs to the field of maintenance decision-making of hydraulic equipment, and relates to a method for the optimal maintenance decision-making of hydraulic equipment with risk control. The method mainly comprises three steps: 1) judging whether a system is in a status of defect by using a variable-weight association rule algorithm, if so, calculating the probability values of occurrences of latent faults of the system; 2) calculating the comprehensive evaluation value for the consequence of each latent fault by using a BP neural network; and 3) multiplying the probability values obtained in step 1 by the comprehensive evaluation values obtained in step 2 so as to obtain the VaRs (values-at-risk) of the latent faults, judging whether the VaRs are more than a specified threshold, if so, ranking the VaRs in descending order so as to determine the maintenance sequence; otherwise, returning to the step of monitoring. The method can judge whether a device is in a status of defect, judge the type of the latent fault and calculate the probability values of occurrences of latent faults only through a calculation; and compared with traditional risk maintenance methods, the method of the invention improves the accuracy of fault diagnosis, speeds up the diagnosis speed, and provides a better reference for online decision-making.

Description

technical field [0001] The invention belongs to the field of hydraulic equipment maintenance decision-making, and relates to a hydraulic equipment optimal maintenance decision-making method with risk control. Background technique [0002] Hydraulic equipment has the characteristics of complexity, precision, high price, and high power. At the same time, the working status of hydraulic equipment determines the production efficiency and the quality of steel smelting, and its safety and reliability requirements are relatively high. Since the hydraulic components in the hydraulic system work in a closed oil circuit, the flow state of the oil in the pipeline and the condition of the internal parts cannot be directly observed. Therefore, the fault diagnosis of the hydraulic system is more difficult than the fault diagnosis of general mechanical and electrical equipment. difficulty. It is very important to establish an effective and accurate fault diagnosis and early warning system...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/00G06F19/00G06N3/02
Inventor 刘晶蔡大勇季海鹏朱清香
Owner 天津开发区精诺瀚海数据科技有限公司
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