针对轮毂电机驱动汽车(hub motor driven vehicle,HMDV)因开关磁阻电机自重和电机气隙偏心导致产生的垂向振动负效应严重恶化车辆的平顺性和操稳性的问题,提出一种基于分数阶滑模控制的HMDV可控动惯性悬架优化设计方法。首先,在轮毂驱...针对轮毂电机驱动汽车(hub motor driven vehicle,HMDV)因开关磁阻电机自重和电机气隙偏心导致产生的垂向振动负效应严重恶化车辆的平顺性和操稳性的问题,提出一种基于分数阶滑模控制的HMDV可控动惯性悬架优化设计方法。首先,在轮毂驱动电机气隙偏心产生的不平衡径向力基础上,建立HMDV 1/4混棚动惯性悬架,理论证明二阶混棚正实网络的优异性能;其次,采用HMDV二阶混棚正实网络作为参考模型,构建基于分数阶滑模控制理论的HMDV协调控制系统,在随机路面下进行平顺性仿真和分析;最后,进行HMDV 1/4悬架台架试验。试验结果表明,HMDV可控动惯性悬架与HMDV传统被动悬架相比,车身加速度均方根值、悬架动行程均方根值以及轮胎动载荷均方根值最大降幅分别为7.72%、30.64%以及11.54%。验证了所设计的HMDV可控动惯性悬架对于由开关磁阻电机造成的垂向振动负效应有优异的抑制性能。展开更多
直流母线电容作为电机驱动变换器中最薄弱的元件之一,其老化会导致系统故障的概率增大,因此对电容老化进行在线监测至关重要。针对现有监测方法存在经济性差、采样频率高、影响系统正常运行等问题,提出一种基于长周期暂态信号分析的电...直流母线电容作为电机驱动变换器中最薄弱的元件之一,其老化会导致系统故障的概率增大,因此对电容老化进行在线监测至关重要。针对现有监测方法存在经济性差、采样频率高、影响系统正常运行等问题,提出一种基于长周期暂态信号分析的电容在线监测方法,用于估计电机驱动变换器直流母线等值串联电容(equivalent series capacitance,ESC)。首先,根据系统负载切换过程建立共节点感-容等值暂态模型,分析长周期暂态信号特点。其次,推导基于长周期暂态信号的在线监测模型,确定监测程序启动判定条件。然后,提出一种基于多项式重构的电容电流基线校准方法,消除传感器零漂影响,提高监测精度。最后,仿真和实验表明所提出方法的监测精度满足电容监测的要求。展开更多
Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing faults.Manual fault diagnosis poses safety risks and economic instability,necessitating an automated approach.Thi...Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing faults.Manual fault diagnosis poses safety risks and economic instability,necessitating an automated approach.This study proposes FTCNNLSTM(Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory),an algorithm combining Convolutional Neural Networks,Long Short-Term Memory Networks,and Attentive Interpretable Tabular Learning.The model preprocesses the CWRU(Case Western Reserve University)bearing dataset using segmentation,normalization,feature scaling,and label encoding.Its architecture comprises multiple 1D Convolutional layers,batch normalization,max-pooling,and LSTM blocks with dropout,followed by batch normalization,dense layers,and appropriate activation and loss functions.Fine-tuning techniques prevent over-fitting.Evaluations were conducted on 10 fault classes from the CWRU dataset.FTCNNLSTM was benchmarked against four approaches:CNN,LSTM,CNN-LSTM with random forest,and CNN-LSTM with gradient boosting,all using 460 instances.The FTCNNLSTM model,augmented with TabNet,achieved 96%accuracy,outperforming other methods.This establishes it as a reliable and effective approach for automating bearing fault detection in electric motor-driven systems.展开更多
文摘直流母线电容作为电机驱动变换器中最薄弱的元件之一,其老化会导致系统故障的概率增大,因此对电容老化进行在线监测至关重要。针对现有监测方法存在经济性差、采样频率高、影响系统正常运行等问题,提出一种基于长周期暂态信号分析的电容在线监测方法,用于估计电机驱动变换器直流母线等值串联电容(equivalent series capacitance,ESC)。首先,根据系统负载切换过程建立共节点感-容等值暂态模型,分析长周期暂态信号特点。其次,推导基于长周期暂态信号的在线监测模型,确定监测程序启动判定条件。然后,提出一种基于多项式重构的电容电流基线校准方法,消除传感器零漂影响,提高监测精度。最后,仿真和实验表明所提出方法的监测精度满足电容监测的要求。
基金supported by King Abdulaziz University,Deanship of Scientific Research,Jeddah,Saudi Arabia under grant no. (GWV-8053-2022).
文摘Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing faults.Manual fault diagnosis poses safety risks and economic instability,necessitating an automated approach.This study proposes FTCNNLSTM(Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory),an algorithm combining Convolutional Neural Networks,Long Short-Term Memory Networks,and Attentive Interpretable Tabular Learning.The model preprocesses the CWRU(Case Western Reserve University)bearing dataset using segmentation,normalization,feature scaling,and label encoding.Its architecture comprises multiple 1D Convolutional layers,batch normalization,max-pooling,and LSTM blocks with dropout,followed by batch normalization,dense layers,and appropriate activation and loss functions.Fine-tuning techniques prevent over-fitting.Evaluations were conducted on 10 fault classes from the CWRU dataset.FTCNNLSTM was benchmarked against four approaches:CNN,LSTM,CNN-LSTM with random forest,and CNN-LSTM with gradient boosting,all using 460 instances.The FTCNNLSTM model,augmented with TabNet,achieved 96%accuracy,outperforming other methods.This establishes it as a reliable and effective approach for automating bearing fault detection in electric motor-driven systems.
