Marine auxiliary equipment fault intelligent early warning design method based on artificial intelligence

Abstract

The invention discloses a marine auxiliary engine equipment fault intelligent early warning design method based on artificial intelligence in the technical field of fault intelligent early warning. The method comprises the following steps: 1, naming an entity extraction rule and a semantic sentiment analysis algorithm; 2, calling data of samples in the historical knowledge base for study and judgment, and performing secondary processing on operation data of auxiliary equipment; 3, obtaining key operation information of the auxiliary machine equipment; and 4, extracting actual operation data and key operation indexes of the marine auxiliary equipment, carrying out data classification, and selecting classification analysis in a manual supervision mode to realize intelligent monitoring and state self-analysis of the ship auxiliary equipment. Deep utilization of historical operation data of the ship auxiliary equipment is realized, heterogeneous data intercommunication and information fusion are fully realized, the intelligent level of operation and maintenance can be improved in actual engineering application, and the maintenance cost of ship auxiliary equipment can be reduced.

Description

technical field [0001] The invention relates to the technical field of fault intelligent early warning, in particular to an artificial intelligence-based design method for intelligent fault early warning of ship auxiliary equipment. Background technique [0002] There are many types of sensors for marine auxiliary equipment and high frequency of use. Equipment troubleshooting and maintenance are important daily tasks; however, traditional manual operation and maintenance methods have problems such as low accuracy and poor timeliness. At the same time, in order to maintain related equipment, many enterprises Choose to set up a special management department and recruit professional operation and maintenance personnel for maintenance. This method brings about an increase in personnel costs and maintenance costs. When the traditional marine auxiliary equipment fails, the equipment will stop working, and spare parts need to be dispatched, and then the corresponding operation and ...

AI Technical Summary

Problems solved by technology

[0002] There are many types of sensors for marine auxiliary equipment and high frequency of use. Equipment troubleshooting and maintenance are important daily tasks; however, traditional manual operation and maintenance methods have problems such as low accuracy and poor timeliness. At the same time, in order to maintain related equipment, many enterprises Choose to set up a special management department and recruit professional operation and maintenance personnel to carry out maintenance. This method brings about an increase in personnel costs and maintenance costs

When the traditional marine auxiliary equipment fails, the equipment will stop working, and spare parts need to be dispatched, and then the corresponding operation and maintenance personnel will be coordinated to troubleshoot the equipment. This will bring additional time costs, and the equipment stopping will also cause daily run impact

At the same time, the traditional method has a low degree of informatization and slow information interaction; many operation and maintenance departments focus too much on the hardware itself, ignoring software monitoring, and the timeliness of response is poor; there is no long-term planning for the operation and maintenance of ship auxiliary equipment, and the operation and maintenance Efficiency is low, based on this, the present invention has designed a kind of artificial intelligence-based intelligent early warning design method of marine auxiliary equipment failure to solve the above problems

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