Intelligent Evaluation Method for Ball Screw Degradation State
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摘要:
现有滚珠丝杠副退化状态评估方法通常假设已有充足且带标签的数据集，但实际工程应用中故障成本过高、获取标签难度过大，难以在特定工况下获得大量带标签数据集. 针对上述问题，提出一种基于多尺度对抗域对抗学习的智能化状态评估方法，结合注意力卷积神经网络模块和域对抗学习模块，利用不同工况下采集的传感器信号建立深度学习模型，从而自适应地学习域不变特征并实现高效的知识复用和特征迁移；利用多工况下采集的滚珠丝杠副退化信号构建试验数据集来验证方法的有效性. 研究结果表明：本文方法在6个标签缺失跨工况条件下的滚珠丝杠副退化状态识别子任务中均取得了高于89.02%的识别准确率；能够充分迁移带标签数据的关键特征，实现了标签样本缺失条件下目标工况退化状态识别.
Abstract:The existing ball screw degradation assessment method usually assumes that sufficient labeled data sets are available. However, it is difficult to obtain massive labeled data sets under practical projects due to excess failure cost and difficulty of obtaining labels. To solve the above problems, an intelligent state evaluation method based on multi-scale adversarial domain adversarial learning is proposed. Combining an attention convolution neural network module and a domain adversarial learning module, a deep learning model is established by using sensor signals collected under different working conditions, so as to learn domain invariant features adaptively and realize efficient knowledge reuse and feature migration. The experimental data sets are constructed by using the ball screw degradation signals collected under multiple working conditions to verify the effectiveness of the method. The results show that the proposed method achieves a recognition accuracy higher than 89.02% in six sub-tasks of degradation state identification of ball screw under cross-working conditions with missing labels. The proposed method can fully migrate key features with labeled data and achieve the degradation state identification of target operating conditions under missing label samples.
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Key words:
- ball screw /
- degradation state evaluation /
- deep learning /
- domain adversarial learning
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图 5 多尺度域对抗学习框架
Figure 5. Structure of multi-scale adversarial domain adversarial learning
图 8 滚珠丝杠副退化状态识别子任务结果
Figure 8. Results of ball screw degradation state recognition tasks
图 9 不同样本数量下的退化状态识别结果
Figure 9. Results of ball screw degradation state recognition tasks with various samples quantities
表 1 MACNN模块基础参数
Table 1. Basic parameters of the MACNN modules
模块名 卷积核大小 卷积核个数 MACNN 1 3 × 1/5 × 1/16 × 1 1 × 3 MACNN 2 3 × 1/5 × 1/16 × 1 10 × 3 MACNN 3 3 × 1/5 × 1/16 × 1 10 × 3 MACNN 4 3 × 1/5 × 1/16 × 1 10 × 3 MACNN 5 3 × 1/5 × 1/16 × 1 10 × 3 
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表 2 所选FFZD4010-3型滚珠丝杠副基础参数
Table 2. Basic parameters of selected FFZD4010-3 ball screw
参数 取值 参数 取值 公称直径/mm 40 丝杠底径/mm 34.3 导程/mm 10 循环总圈数/圈 3 丝杠外径/mm 39.5 额定动载荷/kN 30 滚珠直径/mm 7.14 额定静载荷/kN 66.3
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表 3 试验工况详细参数
Table 3. Detailed parameters of test working conditions
工况 轴向载荷/kN 丝杠转速/（r•min−1） 1 0 100 2 1 300 3 2 800
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表 4 滚珠丝杠副退化状态识别试验
Table 4. Ball screw degradation state identification test
名称 详情 名称 详情 任务 A 工况 1→工况 2 任务 D 工况 2→工况 3 任务 B 工况 1→工况 3 任务 E 工况 3→工况 1 任务 C 工况 2→工况 1 任务 F 工况 3→工况 2
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表 5 退化状态识别试验结果
Table 5. Results of degradation state identification tests
% 名称 识别精度 名称 识别精度 任务 A 90.76 任务 D 90.51 任务 B 89.75 任务 E 91.17 任务 C 92.13 任务 F 89.02
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表 6 所提方法与对比方法的结果比较
Table 6. Comparison between the proposed method and the state-of-the-art methods
名称 识别精度/% 精度标准差 CNN 73.57 5.33 JDAN 86.24 0.70 DANN 87.26 1.47 本文所提方法 91.84 1.28
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