Knowledge mining method for intelligent IETM fault maintenance record text

Abstract

The invention discloses a knowledge mining method for an intelligent IETM fault maintenance record text. The knowledge mining method comprises the steps of 1, collecting equipment fault maintenance record text data; 2, establishing an equipment fault word bank; 3, obtaining a lexical item document matrix; 4, generating a theme document matrix; 5, training a label feature matrix; 6, constructing aneural network; and 7, classifying the equipment fault maintenance record texts. According to the method, the LDA topic model is used for extracting lexical item document matrix features; obtaining label feature matrix, providing a classification label of the fault maintenance record text; equipment fault maintenance record texts can be quickly classified; in addition, a pooling layer in the convolutional neural network is replaced with a circulating layer, the convolutional layer in the convolutional neural network has the advantage of weight sharing, the circulating layer has the advantage of solving the problem of long-term dependency of texts, and sufficient knowledge data is provided for a fault maintenance auxiliary system in an intelligent IETM platform.

Description

technical field [0001] The invention belongs to the technical field of knowledge mining, and in particular relates to a knowledge mining method for an intelligent IETM fault maintenance record text. Background technique [0002] With the continuous promotion of human-computer interaction technology, big data technology, virtual reality technology and other technologies, the traditional IETM (Interaction Electronic Technical Manuel) can no longer meet the needs of equipment fault maintenance and support work. Intelligent IETM should have the thinking logic of automatic learning, correct analysis and judgment ability, and knowledge is an indispensable component element. Text knowledge mining is to extract effective knowledge data from equipment operating status information data that contains a lot of noise, and complete the acquisition of fault diagnosis knowledge. Knowledge acquisition is the core of the entire intelligent IETM fault maintenance assistance system, and it is ...

AI Technical Summary

Problems solved by technology

At present, in the field of intelligent IETM fault maintenance, there are still insufficient text processing problems: First, in the process of text feature extraction of equipment fault maintenance, equipment fault maintenance records are recorded by different maintenance personnel, and there will be inconsistent text lengths. The text may have several characters, and the longest text reaches dozens of characters. There are stop words such as quantifiers and numbers in a considerable part of the text record data; Insufficient processing of the relationship between semantics and semantics, as well as the dependency between the context of the text; the classic text classification algorithm cannot meet the above problems, so there are problems of low accuracy and fault diagnosis errors

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