Decision-level information fusion-based motor fault diagnosis for electric propulsion systems
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摘要:
永磁同步电动机（PMSM）是电推进系统的核心部件，健康状态关系系统安全运行。其故障会引发多物理量变化，依靠单一的信号源难以实现准确故障诊断。针对这一问题，提出一种基于卷积神经网络与门控循环单元（CNN-GRU）和改进Dempster-Shafer（D-S）证据论证结合的决策级多通道信息融合故障诊断方法。采用解析法和有限元仿真对永磁同步电机局部退磁和转子偏心故障振动、电流频域特征进行定量分析，增加诊断结果的可解释性。基于Pignistic概率距离和加权邓熵改进D-S证据理论，建立了CNN-GRU与改进D-S证据理论结合的决策级融合诊断模型。最后，搭建了电机故障模拟实验器，利用实验数据对模型进行验证。结果表明：相比于单通道诊断结果，多通道故障诊断更具优势。融合4通道情况下，基于多源数据的决策级故障诊断，3种工况条件下的诊断准确率分别达到了100%、100%和99.3%。所提方法能够精确识别永磁同步电机故障类型，为电推进系统故障诊断提供了参考，具有一定的工程应用价值。
Abstract:The permanent magnet synchronous motor is a critical component of electric propulsion systems, and its operational status is integral to the system’s safe functioning. Failure of the permanent magnet synchronous motor (PMSM) can trigger multiple physical quantity changes, making it challenging to achieve accurate fault diagnosis relying on a single signal source. To address this issue, a decision-level multi-channel information fusion fault diagnosis method was proposed, by combining convolutional neural networks and gated recurrent units (CNN-GRU) and improved Dempster-Shafer (D-S) evidence argumentation. Initially, analytical and finite element methods were employed to quantitatively analyze the vibration and current frequency domain characteristics of local demagnetization and rotor eccentricity faults in permanent magnet synchronous motors, thereby enhancing the interpretability of diagnostic results. Subsequently, a decision-level fusion diagnostic model was established, by integrating CNN-GRU and improved D-S evidence theory based on Pignistic probability distance and weighted Deng entropy. Finally, a motor fault simulation tester was constructed, and the model was validated using experimental data. The results demonstrated that multi-channel fault diagnosis is superior to single-channel diagnosis results. The decision-level fault diagnosis based on multi-source data, with the fusion of 4 channels, achieved diagnostic accuracy of 100%, 100%, and 99.3%, respectively, under three operating conditions. The proposed method accurately identified the types of permanent magnet synchronous motor faults, providing a reference for fault diagnosis in electric propulsion systems and offering potential value for engineering applications.
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图 6 改进证据理论信息融合方法计算流程
Figure 6. Improve the calculation process of evidence theory information fusion method
图 12 基于电流信号的单通道初步诊断结果
Figure 12. Preliminary diagnosis results of single channel based on current signal
图 13 不同工况决策级融合诊断效果对比图
Figure 13. Comparison of fusion diagnosis effects at different decision levels under different operating conditions
表 1 偏心故障新增电磁力频率
Table 1. Eccentricity faults add electromagnetic force frequency
故障类型 转子静偏心 转子动偏心 永磁体磁场 $ （{\mu }_{1}\pm {\mu }_{2}） {f}_{\mathrm{e}} $
$ （{\mu }_{1}\pm {\mu }_{2}） $$ （{\mu }_{1}\pm {\mu }_{2}\pm 1/p） {f}_{\mathrm{e}} $
$ （{\mu }_{1}\pm {\mu }_{2}\pm 2/p） {f}_{\mathrm{e}} $电枢反应磁场 $ （{m}_{1}\pm {m}_{2}） {f}_{\mathrm{e}} $
$ （{\mu }_{1}\pm {\mu }_{2}） $$ （{m}_{1}\pm {m}_{2}\pm 1/p） {f}_{\mathrm{e}} $
$ （{m}_{1}\pm {m}_{2}\pm 2/p） {f}_{\mathrm{e}} $永磁体和电枢反应相互作用 $ （m\pm \mu ） {f}_{\mathrm{e}} $ $ （m\pm \mu \pm 1/p） {f}_{\mathrm{e}} $
$ （m\pm \mu \pm 2/p） {f}_{\mathrm{e}} $
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表 2 故障特征仿真分析结果
Table 2. Simulation analysis results of fault characteristics
故障类型 电流特征 定子振动特征 转子振动特征 随故障程度变化趋势 退磁故障 $ （2k\pm 1/p） {f}_{\mathrm{e}} $ $ （2k\pm n/p） {f}_{\mathrm{e}} $ $ （2k+1） {f}_{0} $ 成正比 转子静偏心 $ 3{f}_{\mathrm{e}} $ $ 2k{f}_{\mathrm{e}} $ $ {f}_{0} $ 成正比 转子动偏心 $ 3{f}_{\mathrm{e}} $ $\left(2k\pm \dfrac{1}{p}\right){f}_{\mathrm{e}} $
$ （2k\pm 2/p） {f}_{\mathrm{e}} $$ 2{f}_{0} $ 成正比
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表 3 故障模拟实验工况
Table 3. Fault simulation experimental conditions
通道 转速/（r/min） 负载/（N·m） 工况1 1500 25 工况2 1500 50 工况3 3000 25
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表 4 电机具体参数
Table 4. Specific parameters of the motor
参数 数值及详情 槽数 36 定子外径/mm 155 转子外径/mm 96.4 电枢长度/mm 60 永磁体厚度/mm 4 额定功率/kW 5.5 转子铁心材料 50W465 永磁体材料 N38SH 极对数 4 定子内径/mm 98 转子内径/mm 38 气隙宽度/mm 0.8 线圈匝数 20 额定转速/（r/min） 6000 定子铁心材料 50W465 导线材料 铜
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表 5 数据说明
Table 5. Operating condition parameters
序号 故障类型 故障程度/% 原始样本长度 样本量 训练量 测试量 标签值 1 正常 0 1024 512 990 330 1 2 动偏心 10 1024 512 990 330 2 3 动偏心 20 1024 512 990 330 3 4 动偏心 30 1024 512 990 330 4 5 静偏心 10 1024 512 990 330 5 6 静偏心 20 1024 512 990 330 6 7 静偏心 30 1024 512 990 330 7 8 局部退磁 25 1024 512 990 330 8 9 局部退磁 50 1024 512 990 330 9 10 局部退磁 75 1024 512 990 330 10 11 局部退磁 100 1024 512 990 330 11
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表 6 故障样本数据集
Table 6. Operating condition parameters
样本序号 训练集 测试集 电流数据样本 990×512 330×512 转子振动数据样本 990×512×2 330×512×2 定子振动数据样本 990×512 330×512 输入样本 990×512×4 330×512×4
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表 7 CNN-GRU模型参数选取
Table 7. Parameter selection for CNN-GRU model
模型 参数 数值 卷积层 卷积核 16×4 步长 1 GRU层1 输入层维度 512 隐藏层维度 512 dropout 0.3 GRU层2 输入层维度 512 隐藏层维度 512 dropout 0.3 线性层1 输入维度 512 输出维度 120 偏置项 True 线性层2 输入维度 120 输出维度 11 偏置项 True Softmax层 尺度 1
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表 8 双通道融合诊断精度对比
Table 8. Comparison of diagnostic accuracy of dual channel fusion
通道 诊断精度/% D-S证据 本文方法 提升比例 1&2 86.4 90.7 4.3 1&3 95.9 97.8 2.3 1&4 97.3 98.9 1.6 2&3 99.3 99.5 0.2 2&4 99.2 99.8 0.6 3&4 97.3 98.1 0.8
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表 9 三通道融合诊断精度对比
Table 9. Comparison of diagnostic accuracy of three channel fusion
通道 诊断精度/% D-S证据 本文方法 提升比例 1&2&3 98.9 99.2 0.3 1&3&4 99.6 99.8 0.2 1&2&4 99.4 99.8 0.4 2&3&4 100 100 4通道融合 100 100
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表 10 不同融合诊断方法诊断精度对比
Table 10. Comparison of diagnostic accuracy between different fusion diagnostic methods
融合
诊断方法诊断精度/% 工况1 工况2 工况3 平均 特征层融合 98.48 88.79 96.67 94.65 数据层融合 99.09 99.09 98.48 98.89 决策层融合 100 100 99.3 99.77
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