基于数控机床设备故障领域的命名实体识别

Named entity recognition based on equipment and fault field of CNC machine tools

为了给数控机床故障的精准诊断提供保障,延长数控机床使用周期,以数控机床历史维修记录为研究对象,对数控机床设备故障领域的命名实体识别进行了研究.在分析历史维修记录中的故障描述特点后,提出了一种基于双向长短期记忆网络(Bidirectional long short-term memory, BLSTM)与具有回路的条件随机场(Conditional random field with loop, L-CRF)相结合的命名实体识别方法.首先,对输入语句进行分词和标注,使用Word2vec中的Skip-gram模型对标注语料进行预训练,将其生成的字向量通过词嵌入层转化为字向量序列;然后,将字向量序列输入BLSTM学习长期依赖信息;最后将句子表达输入L-CRF获取全局最优序列.实验结果表明,该方法明显优于其他命名实体识别方法,为数控机床设备的智能检修与实时诊断任务打下了坚实的基础.

ith the advent of intelligent manufacturing and big data, the Made in China 2025 Initiative and Industry 4.0 have been paying increasing attention to automation and intelligent industrial equipment. In the background of the present times, the complexity and intelligence of computer numerical control(CNC) machine tools have been continuously improved, and the types and descriptions of CNC machine tools’ faults have increased, presenting serious challenges to equipment maintenance and diagnosis of CNC machine tools. In order to provide guarantee for accurate fault diagnosis of CNC machine tools, and to prolong the service life of CNC machine tools, it is necessary to improve the performance of named entity recognition system. Accordingly, the named entity recognition in the equipment and faults field of CNC machine tools were studied, taking the historical examinations and repair records of CNC machine tools as the research object. After analyzing the characteristics of fault description in the historical examinations and repair records, a named entity recognition method was proposed based on the combination of bidirectional long short-term memory(BLSTM) and conditional random field with loop(L-CRF). The first step is to input a sentence and segment and label the input sentence. The annotation corpus is combined with the pre-trained generated word vector by using Skip-gram model in Word2 vec, and the word vector is converted into a word vector sequence through the word embedding layer. In the second step, the word vector sequence is integrated into the BLSTM layer to learn long term dependency information. The final step is to input the sentence expression into the L-CRF layer to obtain the global optimal sequence.The experimental results show that the method is superior to other named entity recognition methods,which lays a solid foundation for the intelligent maintenance and the real-time diagnostic tasks of CNC machine tools.