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Shore bridge prediction method based on BP neural network

A BP neural network and prediction method technology, which is applied in the field of quay crane state prediction based on BP neural network, can solve the problems of unpredictable quay crane status, reducing the risk of quay cranes, and inability to maintain and maintain quay cranes.

Inactive Publication Date: 2018-05-29
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

These methods only classify the data, but do not predict the data. They cannot predict the status of the quay crane in the future, and cannot properly maintain and maintain the quay crane to reduce the working pressure of the quay crane. risk

Method used

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  • Shore bridge prediction method based on BP neural network
  • Shore bridge prediction method based on BP neural network
  • Shore bridge prediction method based on BP neural network

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example

[0104]The experimental data comes from the vibration signal on the shaft of the quay crane. The data on a measuring point of the quay crane (vibration in the traveling direction of the crossbeam on the land side of the quay crane (Z8H)) is selected, and the data from 0:00 on December 28 to The vibration data at 23:00 on January 03 was used as a training sample, and the vibration data from 14:00 on January 18 to 23:00 on February 7 was taken as a test sample. Since the sensor of the acquisition system collects one data every 10 to 20 seconds, about 8,000 data can be collected in one day. In order to analyze the load status of the quay crane for a period of 3 weeks, it is first necessary to perform K-means clustering on the data according to step 2 , take out more representative data.

[0105] Through the observation of these data, it can be seen that the daily vibration of the quay crane is very similar, and it can be known that the daily operation of the quay crane is the same...

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Abstract

The invention provides a shore bridge prediction method based on a BP neural network. The method is characterized by comprising the steps of collecting data by an acceleration sensor and a strain sensor on a shore bridge, determining a load clustering center of collected vibration data by using a K-means method, classifying the data of a cluster center point, and analyzing collected shore bridge state data by using a BP neural network algorithm and predicting data of a future state to achieve the purpose of shore bridge state prediction. The state of the shore bridge is analyzed through the data, and a conclusion that whether the shore bridge needs maintenance or repair in a period of time is obtained. According to the method provided by the invention, the actual engineering data of the shore bridge is analyzed, on the above basis, the data of the future operation state of the shore bridge is predicted by using the BP neural network algorithm, and a prediction result is accurate and reliable.

Description

technical field [0001] The invention relates to the field of port machinery, in particular to a method for predicting the state of a quay bridge based on a BP neural network. Background technique [0002] Due to the rapid growth of global trade and the rapid development of container transportation, the utilization rate of quayside cranes in port logistics transportation is getting higher and higher. Whether the normal operation of the quay crane can be guaranteed will directly affect the working efficiency and economic benefits of the port. Therefore, more and more ports are testing and evaluating the mechanical status of the quay crane. By installing sensors on the key parts of the quay crane, a large amount of data information is collected and stored in the database. These data information hide various information about the operating status of the equipment. However, due to the huge amount of data, the disorder and the lack of individual data, it is a great challenge to p...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/08G06N3/04G06N3/08
CPCG06N3/084G06Q10/04G06Q10/083G06N3/045
Inventor 唐刚杨辉黄婉娟顾邦平
Owner SHANGHAI MARITIME UNIVERSITY
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