基于自适应多尺度注意力机制的CNN−GRU矿用电动机健康状态评估

谭东贵; 袁逸萍; 樊盼盼

doi:10.13272/j.issn.1671-251x.2023110024

基于自适应多尺度注意力机制的CNN−GRU矿用电动机健康状态评估

新疆大学智能制造现代产业学院，新疆乌鲁木齐　830017

基金项目: 国家自然科学基金资助项目（72361032）；新疆维吾尔自治区重点研发资助项目（2021B01003）。

详细信息

作者简介:
谭东贵（1997—），男，贵州毕节人，硕士研究生，主要研究方向为机械设备健康状态评估与预测性维护，E-mail：tan_0514@163.com

通讯作者:
袁逸萍（1973—），女，新疆石河子人，教授，博士研究生导师，主要研究方向为智能工厂/数字化车间、精益生产、预测性维护，E-mail:yipingyuan@163.com。

中图分类号: TD614
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出版历程
- 收稿日期: 2023-11-07
- 修回日期: 2024-02-24
- 网络出版日期: 2024-03-04
- 刊出日期: 2024-02-24

Health status evaluation of CNN-GRU mine motor based on adaptive multi-scale attention mechanism

Intelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi 830017, China

摘要

摘要: 利用多传感器信息融合技术进行电动机健康状态评估时，矿用电动机监测数据中存在异常值和缺失值，而卷积神经网络和循环神经网络等深度学习模型在数据质量下降严重的情况下难以有效提取数据特征和更新网络权重，导致梯度消失或爆炸等问题。针对上述问题，提出了一种基于自适应多尺度注意力机制的CNN−GRU（CNN−GRU−AMSA）模型，用于评估矿用电动机健康状态。首先，对传感器采集的电动机运行数据进行填补、剔除和标准化处理，并以环境温度变化作为依据对矿用电动机运行数据进行工况划分。然后，根据马氏距离计算出电动机电流、电动机三相绕组温度、电动机前端轴承温度和电动机后端轴承温度等健康评估指标的健康指数（HI），采用Savitzky–Golay滤波器对指标HI进行降噪、平滑、归一化处理，并结合主成分分析法计算的不同指标对矿用电动机的贡献度，对指标HI进行加权融合得到矿用电动机HI。最后，将矿用电动机HI输入CNN−GRU−AMSA模型中，该模型通过动态调整注意力权重，实现对不同尺度特征的信息融合，从而准确输出电动机健康状态评估结果。实验结果表明，与其他常见的深度学习模型CNN，CNN−GRU，CNN−LSTM，CNN−LSTM−Attention相比，CNN−GRU−AMSA模型在均方根误差、平均绝对误差、准确率、Macro F1及Micro F1等评价指标上更优，且预测残差的波动范围更小，稳定性更优。
- 电动机健康状态评估 /
- 自适应多尺度注意力机制 /
- CNN−GRU /
- 多传感器信息融合 /
- 主成分分析
Abstract: When using multi-sensor information fusion technology to evaluate the health status of motors, there are outliers and missing values in the monitoring data of mine motors. However, deep learning models such as convolutional neural networks and recurrent neural networks find it difficult to effectively extract data features and update network weights when the data quality is severely degraded, resulting in problems such as vanishing or exploding gradients. In order to solve the above problems, A CNN-GRU (CNN-GRU-AMSA) model based on adaptive multi-scale attention mechanism is proposed to evaluate the health status of mine motors. Firstly, the model fills in, removes, and standardizes the motor operation data collected by sensors, and classifies the operating conditions of mine motors based on environmental temperature changes. Secondly, based on the Mahalanobis distance, the health index (HI) of health evaluation indicators such as motor current, three-phase temperature of motor winding, front bearing temperature of motor, and rear bearing temperature of motor are calculated. The Savitzky Golay filter is used to denoise, smooth, and normalize the HI indicator. Combining the contribution of different indicators calculated by principal component analysis method to mine motors, the weighted fusion of indicator HI is used to obtain the mine motor HI. Finally, the mine motor HI is input into the CNN-GRU-AMSA model, which dynamically adjusts attention weights to achieve information fusion of features at different scales, thereby accurately outputting the health status evaluation results of the motor. The experimental results show that compared with other common deep learning models such as CNN, CNN-GRU, CNN-LSTM, and CNN-LSTM Attention, the CNN-GRU-AMSA model performs better in evaluation metrics such as root mean square error, mean absolute error, accuracy, Macro F1, and MicroF1. The model has a smaller fluctuation range and better stability in predicting residuals.
- health status evaluation of motors /
- adaptive multi-scale attention mechanism /
- CNN−GRU /
- multi sensor information fusion /
- principal component analysis

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图 1 矿用电动机健康状态评估总体框架

Figure 1. General framework of health state assessment of mine motor

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图 2 CNN−GRU模型

Figure 2. CNN-GRU model

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图 3 AMSA结构

Figure 3. Structure of adaptive multi-scale attention mechanism

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图 4 降噪处理后的HI曲线

Figure 4. Health index curve after noise reduction

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图 5 矿用电动机HI曲线

Figure 5. Health index curve of mine motor

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图 6 CNN−GRU−AMSA模型损失值

Figure 6. Loss value of CNN−GRU−AMSA model

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图 7 不同工况下CNN−GRU−AMSA模型预测结果的混淆矩阵

Figure 7. Confusion matrix of CNN−GRU−AMSA model prediction results under different working conditions

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图 8 不同模型的HI对比

Figure 8. Health index comparison of different models

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图 9 不同模型的预测残差对比

Figure 9. Comparison of predicted residuals of different models

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表 1 健康评估指标的个体贡献度与累计贡献度

Table 1 Individual and cumulative contribution degree of health assessment index

%
主成分	个体贡献度	累计贡献度
主成分1	85.68	85.68
主成分2	12.86	98.54
主成分3	0.94	99.48
主成分4	0.26	99.74
主成分5	0.24	99.98
主成分6	0.02	100.00

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表 2 矿用电动机健康状态评估类别

Table 2 Mine motor health state assessment categories

状态	健康（S₁）	良好（S₂）	恶化（S₃）	故障（S₄）
HI	（0.8，1.0]	（0.6，0.8]	（0.3，0.6]	[0，0.3]

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表 3 工况1下不同模型性能比较

Table 3 Performance comparison of different models under working condition 1

模型	RMSE	MAE	MAX	ACC/%	Macro F1	Micro F1
CNN	0.0332	0.0235	0.2639	88.58	0.5648	0.5840
CNN−GRU	0.0386	0.0275	0.2656	89.29	0.6003	0.6374
CNN−LSTM	0.0310	0.0218	0.2467	88.20	0.5507	0.5602
CNN−LSTM−Attention	0.0312	0.0219	0.239 1	87.95	0.5415	0.5442
CNN−GRU−AMSA	0.009 6	0.006 4	0.2634	97.83	0.933 8	0.950 5

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表 4 工况2下不同模型性能比较

Table 4 Performance comparison of different models under working condition 2

模型	RMSE	MAE	MAX	ACC/%	Macro F1	Micro F1
CNN	0.0300	0.0229	0.3287	89.96	0.7134	0.7867
CNN−GRU	0.0527	0.0349	0.3671	85.49	0.6128	0.6443
CNN−LSTM	0.0346	0.0261	0.3638	86.93	0.6486	0.6958
CNN−LSTM−Attention	0.0371	0.0276	0.3874	86.57	0.6405	0.6822
CNN−GRU−AMSA	0.0141	0.0080	0.3733	96.79	0.9359	0.9426

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表 5 工况3下不同模型性能比较

Table 5 Performance comparison of different models under working condition 3

模型	RMSE	MAE	MAX	ACC/%	Macro F1_c	Micro F1
CNN	0.0384	0.0313	0.1284	78.31	0.4246	0.4373
CNN−GRU	0.0347	0.0259	0.1521	90.16	0.5995	0.6079
CNN−LSTM	0.0387	0.0293	0.1336	76.82	0.3979	0.3956
CNN−LSTM−Attention	0.0368	0.0286	0.1318	78.64	0.4252	0.4346
CNN−GRU−AMSA	0.0080	0.0057	0.0622	97.38	0.9567	0.9698

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表 6 工况4下不同模型性能比较

Table 6 Performance comparison of different models under working condition 4

模型	RMSE	MAE	MAX	ACC/%	Macro F1_c	Micro F1
CNN	0.0445	0.0306	0.1986	96.93	0.6331	0.6419
CNN−GRU	0.0739	0.0443	0.2817	83.04	0.4527	0.4748
CNN−LSTM	0.0508	0.0337	0.2164	95.66	0.6177	0.6299
CNN−LSTM−Attention	0.0500	0.0334	0.2119	95.75	0.6186	0.6307
CNN−GRU−AMSA	0.0250	0.0129	0.1428	97.69	0.9650	0.8232

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表 7 工况5下不同模型性能比较

Table 7 Performance comparison of different models under working condition 5

模型	RMSE	MAE	MAX	ACC/%	Macro F1	Micro F1
CNN	0.0288	0.0245	0.1152	91.01	0.5166	0.5521
CNN−GRU	0.0305	0.0238	0.1472	92.51	0.6401	0.6656
CNN−LSTM	0.0261	0.0218	0.1107	91.80	0.5336	0.5667
CNN−LSTM−Attention	0.0263	0.0222	0.1153	91.77	0.5322	0.5659
CNN−GRU−AMSA	0.0075	0.0054	0.0905	97.52	0.7687	0.8153

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表 8 工况6下不同模型性能比较

Table 8 Performance comparison of different models under working condition 6

模型	RMSE	MAE	MAX	ACC/%	Macro F1	Micro F1
CNN	0.0253	0.0210	0.2475	95.22	0.4725	0.5213
CNN−GRU	0.0301	0.0244	0.2421	95.28	0.4750	0.5238
CNN−LSTM	0.0220	0.0187	0.2426	95.13	0.4689	0.5174
CNN−LSTM−Attention	0.0228	0.0195	0.2428	95.28	0.4750	0.5238
CNN−GRU−AMSA	0.0085	0.0053	0.2722	98.81	0.6242	0.6395

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表 9 工况7下不同模型性能比较

Table 9 Performance comparison of different models under working condition 7

模型	RMSE	MAE	MAX	ACC/%	Macro F1	Micro F1
CNN	0.0290	0.0200	0.3783	87.87	0.4115	0.4394
CNN−GRU	0.0332	0.0226	0.3605	86.27	0.3880	0.4004
CNN−LSTM	0.0255	0.0177	0.3470	88.80	0.4520	0.5029
CNN−LSTM−Attention	0.0291	0.0206	0.3568	86.62	0.3846	0.4023
CNN−GRU−AMSA	0.0091	0.0064	0.2634	95.03	0.6081	0.6446

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表 10 工况8下不同模型性能比较

Table 10 Performance comparison of different models under working condition 8

模型	RMSE	MAE	MAX	ACC/%	Macro F1	Micro F1
CNN	0.0378	0.0284	0.2710	85.00	0.5879	0.5724
CNN−GRU	0.0464	0.0334	0.2745	83.75	0.5756	0.5634
CNN−LSTM	0.0358	0.0241	0.2553	93.94	0.6394	0.6313
CNN−LSTM−Attention	0.0348	0.0236	0.2645	93.22	0.6352	0.6265
CNN−GRU−AMSA	0.0140	0.0090	0.2733	96.18	0.9447	0.9421

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