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摘要: 為了給數控機床故障的精準診斷提供保障，延長數控機床使用周期，以數控機床歷史維修記錄為研究對象，對數控機床設備故障領域的命名實體識別進行了研究。在分析歷史維修記錄中的故障描述特點后，提出了一種基于雙向長短期記憶網絡（Bidirectional long short-term memory, BLSTM）與具有回路的條件隨機場（Conditional random field with loop, L-CRF）相結合的命名實體識別方法。首先，對輸入語句進行分詞和標注，使用Word2vec中的Skip-gram模型對標注語料進行預訓練，將其生成的字向量通過詞嵌入層轉化為字向量序列；然后，將字向量序列輸入BLSTM學習長期依賴信息；最后將句子表達輸入L-CRF獲取全局最優序列。實驗結果表明，該方法明顯優于其他命名實體識別方法，為數控機床設備的智能檢修與實時診斷任務打下了堅實的基礎。
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關鍵詞:
- 數控機床 /
- 設備故障 /
- 雙向長短期記憶網絡 /
- 具有回路的條件隨機場 /
- 命名實體識別
Abstract: With 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 Word2vec, 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. -
表 1 句子序列標注方法
Table 1. Sentence sequence labeling method
Sentence Labeling Sentence Labeling Sentence Labeling 發 B-Dev 的 O 牙 I-Fau 動 I-Dev 螺 B-Dev 機 I-Dev 釘 I-Dev 中 O 滑 B-Fau 
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表 2 Word2vec的Skip-gram模型參數表
Table 2. Parameter list of Skip-gram model in Word2vec
Parameter Value Window size 10 Vector dimension 200 Minimum term frequency 5 Iterations 100
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表 3 BLSTM-L-CRF模型參數表
Table 3. BLSTM-L-CRF model parameter table
Network layer Parameter Value BLSTM Learning rate 0.002 BatchSize 20 Iterations 100 Dropout 0.68
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表 4 不同數據集在BLSTM-L-CRF模型中的識別結果
Table 4. Experiment result of BLSTM-L-CRF models in different data set
Date set Precision/% Recall/% F-measure/% People's daily corpus（1998） 83.07 83.40 83.23 MSRA corpus 82.23 80.35 81.28 Boson NLP corpus 79.45 80.18 79.81 CNC machine dataset 86.16 83.40 84.76
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中文字幕在线观看表 5 BLSTM-L-CRF與其他模型綜合性能對比
Table 5. Comparison of performance of BLSTM-L-CRF and other models
Model Precision/% Recall/% F-measure/% CRF 85.45 69.87 76.88 L-CRF 85.92 72.54 79.16 LSTM 78.90 77.84 78.37 BLSTM 80.71 79.00 79.85 CNN-LSTM 83.62 80.07 81.81 BLSTM-CRF 81.54 80.41 80.97 BLSTM-L-CRF 86.16 83.40 84.76
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