Research on fault warning technology for cutting part of cantilever roadheader based on virtual and real fusion data
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摘要: 目前悬臂式掘进机截割部故障预警技术依赖于传统的数据采集方法，在掘进机截割部实际运行过程中存在信号获取困难、噪声较多等问题，导致掘进机截割部故障预测预警能力受到限制。针对上述问题，提出一种基于虚实融合数据的悬臂式掘进机截割部故障预警方法。对悬臂式掘进机截割部进行三维实体建模，利用机械系统动力学自动分析软件(ADAMS)获取截割部机械系统虚拟数据，构建其动力学仿真模型以获取虚拟数据，并采用余弦相似度函数表征其与真实数据的相似度，验证虚拟数据的可信度。将虚拟、真实数据分别采用贝叶斯估计与自适应互补加权融合方法进行相似关联与互补关联融合，获得虚实融合数据。针对传统自组织映射（SOM）神经网络学习效率易受学习速率的影响问题，建立了基于改进SOM神经网络的故障预警模型，引入关于时间的单调递减函数对SOM神经网络进行训练，在保证学习速率的同时，兼顾模型的稳定性。将融合数据输入基于SOM神经网络的故障预警模型以确定获胜神经元并进行权值调整，计算真实数据与获胜神经元间的距离并进行权值调整，进而实现故障预警。实验结果表明，改进SOM神经网络的平均运行效率可提高35.84%；基于虚实融合数据的悬臂式掘进机截割部故障预警方法可成功实现单一故障和复合故障的类型预测，其预测准确率达83.33%。Abstract: Currently, the fault warning technology for the cutting part of cantilever roadheader relies on traditional data collection methods. In the operation process of the cutting part of the roadheader, problems such as difficulty in obtaining signals and high noise limit the capability to predict and warn faults in the cutting part of the roadheader. In order to solve the above problems, a fault warning method for the cutting part of cantilever roadheader based on virtual and real fusion data is proposed. The method performs three-dimensional solid modeling of the cutting section of a cantilever roadheader. It uses the automatic dynamic analysis of mechanical systems (ADAMS) to obtain virtual data of the cutting section's mechanical system, constructs its dynamic simulation model to obtain virtual data. The method uses the cosine similarity function to characterize its similarity with real data to verify the credibility of the virtual data. The method uses Bayesian estimation and adaptive complementary weighted fusion methods to perform similarity association and complementary association fusion on virtual and real data, respectively, to obtain virtual and real fusion data. In response to the problem that the learning efficiency of traditional self-organizing mapping (SOM) neural networks is easily affected by the learning rate, a fault warning model based on an improved SOM neural network is established. A monotonic decreasing function about time is introduced to train the SOM neural network, ensuring both the learning rate and the stability of the model. The method inputs the fused data into the fault warning model based on SOM neural network to determine the winning neuron and adjust its weight. The method calculates the distance between the real data and the winning neuron and adjusts its weight to achieve fault warning. The experimental results show that the average operating efficiency of the improved SOM neural network can be improved by 35.84%. The fault warning method for the cutting part of a cantilever roadheader based on virtual and real fusion data can successfully predict the types of single and composite faults, with a prediction accuracy of 83.33%.
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图 1 悬臂式掘进机截割部故障预警总体方案
Figure 1. Overall scheme of fault early warning for cutting part of cantilever roadheader
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图 2 截割部行星减速器动力学仿真模型
Figure 2. Dynamics simulation model of planetary reducer in cutting part
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图 3 正常状态下行星减速器传动轴（输入端）幅值
Figure 3. Amplitude diagram of planetary reducer drive shaft (input end) under normal condition
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图 4 断齿状态下行星减速器传动轴（输入端）幅值
Figure 4. Amplitude diagram of transmission shaft (input end) of planetary reducer under broken tooth condition
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图 5 正常状态下行星减速器传动轴（输出端）幅值
Figure 5. Amplitude diagram of planetary reducer drive shaft (output) under normal condition
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图 6 断齿状态下行星减速器传动轴（输出端）幅值
Figure 6. Amplitude diagram of transmission shaft (output end) of planetary reducer under broken tooth condition
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图 7 截割部某时刻真实运行数据与虚拟运行数据对比
Figure 7. Comparison of real operation data and virtual operation data of cutting part at a certain time
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图 8 截割部行星减速器传动轴（输入端）加速度虚实数据相似关联融合
Figure 8. Similar correlation fusion of virtual and real acceleration data of transmission shaft (input end ) of planetary reducer of cutting part
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图 9 截割部行星减速器传动轴（输入端）加速度虚实数据互补关联融合
Figure 9. Complementary correlation fusion of virtual and real acceleration data of transmission shaft (input end ) of planetary reducer of cutting part
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图 10 基于改进SOM神经网络的故障预警模型
Figure 10. Fault early warning model based on improved self-organizing map neural network
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图 14 SOM神经网络相邻权重之间的距离曲线
Figure 14. Distance curve between adjacent weights of self-organizing map neural network
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图 15 改进前后不同迭代次数的平均运行时间
Figure 15. Average running time of different iterations before and after improvement
表 1 余弦相似度对应变量相关性
Table 1 Cosine similarity corresponding variable correlation
余弦相似度 变量相关性 $0 \leqslant \cos ( {\boldsymbol{X}}, {\boldsymbol{Y}}) ＜ 0.4$ 无关 $0.4 \leqslant \cos ( {\boldsymbol{X}}, {\boldsymbol{Y}}) ＜ 0.8$ 相关 $0.8 \leqslant \cos ( {\boldsymbol{X}}, {\boldsymbol{Y}}) ＜ 1$ 强相关 
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表 2 悬臂式掘进机截割部故障类型
Table 2 Fault types of cutting part of cantilever roadheader
截割部部位 故障类型 截割头 截割头转速 行星减速器 传动轴（输入端）加速度 传动轴（输出端）加速度 轴承加速度 太阳轮加速度 截割电动机 截割电动机转速
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表 3 部分正常样本数据
Table 3 Partial sample data
截割头
转速/
(m·s−1)输入轴
加速度/
(m·s−2)输出轴
加速度/
(m·s−2)轴承
加速度/
(m·s−2)太阳轮
加速度/
(m·s−2)截割电动
机转速/
(m·s−2)0.423 0.003 0.003 −0.073 0.285 1.000 0.442 0 −0.001 −0.151 0.732 0.960 0.426 −0.032 0.001 −0.115 0.978 0.040 0.430 −0.011 0.003 −0.140 0.820 0.560 0.434 0.037 0 −0.167 −0.080 0.600 0.434 0.111 0.004 −0.097 −0.104 0.320 0.438 −0.006 0.003 −0.218 −0.200 0.640 0.446 0.121 0.002 −0.149 −0.292 0.720 0.450 0.092 −0.002 −0.155 −0.728 0.240 0.453 0.057 0.004 −0.176 −0.268 0.040
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表 4 单一故障测试
Table 4 Single fault test
类别 截割头 输入轴 输出轴 轴承 太阳轮 截割电动机 样本获胜
神经元2 10 51 91 100 56 测试获胜
神经元— 10 — — — —
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表 5 复合故障测试
Table 5 Composite fault testing
类别 截割头 输入轴 输出轴 轴承 太阳轮 截割电动机 样本获胜神经元 2 10 51 91 100 56 测试获胜神经元 — 10 — — 99 —
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表 6 改进前后SOM神经网络不同迭代次数下的运行时间
Table 6 Running time of self-organizing map neural network under different iteration times before and after improvement
迭代次数 单次运行时间/s 平均时间/s 第1次 第2次 第3次 第4次 第5次 10 改进前 0.0536 0.0618 0.0532 0.0534 0.0540 0.0552 改进后 0.0532 0.0434 0.0384 0.0412 0.0454 0.0443 100 改进前 0.2098 0.1963 0.2073 0.1994 0.2149 0.2055 改进后 0.1297 0.1236 0.1365 0.1357 0.1257 0.1302 200 改进前 0.3976 0.3642 0.3660 0.3682 0.3700 0.3732 改进后 0.2154 0.1904 0.2105 0.2038 0.2066 0.2053 500 改进前 0.8637 0.8571 0.8339 0.8337 0.8385 0.8454 改进后 0.4839 0.4990 0.4902 0.4939 0.4837 0.4901
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