An early fault prediction method for hydraulic equipment based on the fusion of multi-source condition monitoring information and reliability features

A technology for monitoring information and early failures, applied in character and pattern recognition, instruments, computer components, etc.

Active Publication Date: 2018-12-25
SINOPEC OILFIELD EQUIP CORP +1
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  • Application Information

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Problems solved by technology

[0006] Aiming at the limitations of the application of the monitoring data-driven failure prediction method in hydraulic equipment failure prediction, the present invention proposes a proportional covariate model PCM

Method used

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  • An early fault prediction method for hydraulic equipment based on the fusion of multi-source condition monitoring information and reliability features
  • An early fault prediction method for hydraulic equipment based on the fusion of multi-source condition monitoring information and reliability features
  • An early fault prediction method for hydraulic equipment based on the fusion of multi-source condition monitoring information and reliability features

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[0059] Specific implementation method

[0060] 1. Identify the signal feature quantity, the method is as follows:

[0061] Take the state space of the feature quantity as the vertical direction and the time scale as the horizontal direction to quantitatively characterize the signal feature quantity;

[0062] 2. Based on the fusion of multi-source monitoring information for state comprehensive feature value recognition, the method is as follows:

[0063] The self-organizing mapping neural network is used to merge the feature layer of multi-source signals, and the minimum quantization error (MQE), that is, the distance between the input data and the normal state data, is used as the comprehensive feature quantity of the monitoring state of the device, which can be expressed by the following formula:

[0064] MQE(t)=||D(t)-m BMU ||

[0065] Among them, D(t) is the multi-source signal feature vector at time t, and it is used as the input of the neural network; m BMU Represents the weight vec...

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Abstract

The invention discloses an early fault prediction method of hydraulic equipment based on the fusion of multi-source condition monitoring information and reliability features, aiming at providing a method capable of identifying early fault signals, reducing false alarm and missed alarm of early warning, and improving the accuracy of fault prediction. The invention also discloses an early fault prediction method of hydraulic equipment based on the fusion of multi-source condition monitoring information and reliability features. The key points of the technical scheme are as follows: the association rules between the monitoring information of hydraulic equipment and the condition evaluation is mined; a multi-sensor based monitoring data fusion method is constructed to reduce the dimension of monitoring data; monitoring information and reliability life data are combined, a parameterized failure rate function (PCM) is constructed to predict the early fault evolution process of hydraulic equipment, and provide powerful early warning information for the operation and maintenance of hydraulic equipment. Compared with the traditional hydraulic fault prediction method, this method can effectively improve the prediction accuracy and broaden the prediction interval by fusing the reliability characteristics in the condition monitoring information of hydraulic equipment.

Description

technical field [0001] The invention relates to the field of hydraulic equipment failure early warning, in particular to an early failure prediction method for hydraulic equipment based on fusion of multi-source state monitoring information and reliability features. Background technique [0002] The hydraulic system is a subsystem with a high failure rate in mechanical equipment. At the same time, since the hydraulic system maintenance strategy mostly adopts preventive maintenance or maintenance after failure, it is easy to produce obvious "under-repair" or "over-repair" problems, resulting in high maintenance costs and long maintenance time, thus affecting the availability of equipment and production capacity. With the development of sensor technology, condition-based maintenance (CBM) has been more and more developed and applied in important fields. Among them, condition monitoring, health status assessment and fault prediction are the core contents of the condition main...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/22G06F18/2415G06F18/253
Inventor 钱新博黄剑黄家文赵慧徐军彭太峰
Owner SINOPEC OILFIELD EQUIP CORP
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