基于深度學(xué)習(xí)的數(shù)控機(jī)床故障知識圖譜構(gòu)建探索

中圖分類號：G424

文獻(xiàn)標(biāo)識碼：A DOI：10.16400/j.cnki.kjdk.2025.20.023

Exploration on the Construction of Fault Knowledge Graph for CNC Machine Tools Based on Deep Learning

SHENGJunfei

（Yixing Higher Vocational School， Yixing， Jiangsu ）

AbstractRapid diagnosis and repair ofCNC machine tool faultsare crucial for ensuring production effciency and reducing maintenance costs.However，the scatered and heterogeneous nature of machine tool fault knowledge poses challenges to fault diagnosis.This paper explores a method ofconstructing a fault knowledge graph for CNC machine tools by integratingdeep learningtechnology，expounds on the important roleofknowledge graphs in integrating CNC machinetol fault knowledge and asisting intelligent diagnostic decision-making，and proposes a systematic technical route for knowledge graph construction. Taking the faults ofthe feed axis of CNC machine tools as an example， the application process of the proposed method is demonstrated.

KeywordsCNC machine tools; fault diagnosis; knowledge graph; deep learning; intelligent manufacturing

隨著智能制造的快速發(fā)展，數(shù)控機(jī)床作為離散制造業(yè)的關(guān)鍵裝備，其可靠性和智能化水平備受關(guān)注，然而，由于數(shù)控機(jī)床結(jié)構(gòu)日益復(fù)雜，工況多變，故障問題頻發(fā)，嚴(yán)重影響生產(chǎn)效率和產(chǎn)品質(zhì)量。（剩余4803字）