This paper introduces a novel method for fast calculating the electromagnetic forces in interior permanent magnet synchronous machines(IPMSMs)under pulse width modulation(PWM)voltage source inverter(VSI)supply based o...This paper introduces a novel method for fast calculating the electromagnetic forces in interior permanent magnet synchronous machines(IPMSMs)under pulse width modulation(PWM)voltage source inverter(VSI)supply based on the small-signal time-harmonic finite element analysis(THFEA),which has been successfully utilized for fast calculating the PWMinduced losses in silicon steel sheets and permanent magnets.Based on the small-signal THFEA,the functional relationships between high-frequency harmonic voltages(HFHVs)and corresponding airgap flux densities are established,which are used for calculating the flux density spectra caused by each HFHV in the PWM voltage spectra.Then,the superposition principle is applied for calculating the flux density spectra caused by fundamental currents and all HFHVs,which are converted to the electromagnetic force spectra at last.The relative errors between the force density spectra calculated with the proposed method and those obtained from traditional time-stepping finite element analysis(TSFEA)using PWM voltages as input are within 3.1%,while the proposed method is 24 times faster than the traditional TSFEA.展开更多
振动声调制(vibro-acoustic modulation,VAM)利用低频振动和高频信号在损伤处相互作用所产生的非线性旁瓣信号进行结构损伤识别。在实际工程中,边界条件往往会影响被监测结构,干扰旁瓣的识别,导致损伤状况的误判。针对边界条件影响下的...振动声调制(vibro-acoustic modulation,VAM)利用低频振动和高频信号在损伤处相互作用所产生的非线性旁瓣信号进行结构损伤识别。在实际工程中,边界条件往往会影响被监测结构,干扰旁瓣的识别,导致损伤状况的误判。针对边界条件影响下的螺栓连接状态监测问题,提出了基于VAM信号协整分析的螺栓预紧状态识别方法。首先提取不同边界固定力下旁瓣信号的幅值作为协整变量,然后根据残差序列判断螺栓的预紧状态,建立具有鲁棒性的螺栓预紧力状态量化指标。实验结果表明:协整分析可以消除边界条件对VAM的影响,能够很好地表征螺栓的状态;协整残差的均方根(root mean square,RMS)值作为量化指标,能够有效地识别螺栓预紧力状态。展开更多
基金supported in part by the National Natural Science Foundation of China under projects 51907053by Natural Science Foundation of Jiangsu Province of China under Project BK20190489+1 种基金by the Fundamental Research Funds for the Central Universities under grant B200202167by the China Postdoctoral Science Foundation under grant no.2019M661708。
文摘This paper introduces a novel method for fast calculating the electromagnetic forces in interior permanent magnet synchronous machines(IPMSMs)under pulse width modulation(PWM)voltage source inverter(VSI)supply based on the small-signal time-harmonic finite element analysis(THFEA),which has been successfully utilized for fast calculating the PWMinduced losses in silicon steel sheets and permanent magnets.Based on the small-signal THFEA,the functional relationships between high-frequency harmonic voltages(HFHVs)and corresponding airgap flux densities are established,which are used for calculating the flux density spectra caused by each HFHV in the PWM voltage spectra.Then,the superposition principle is applied for calculating the flux density spectra caused by fundamental currents and all HFHVs,which are converted to the electromagnetic force spectra at last.The relative errors between the force density spectra calculated with the proposed method and those obtained from traditional time-stepping finite element analysis(TSFEA)using PWM voltages as input are within 3.1%,while the proposed method is 24 times faster than the traditional TSFEA.
文摘振动声调制(vibro-acoustic modulation,VAM)利用低频振动和高频信号在损伤处相互作用所产生的非线性旁瓣信号进行结构损伤识别。在实际工程中,边界条件往往会影响被监测结构,干扰旁瓣的识别,导致损伤状况的误判。针对边界条件影响下的螺栓连接状态监测问题,提出了基于VAM信号协整分析的螺栓预紧状态识别方法。首先提取不同边界固定力下旁瓣信号的幅值作为协整变量,然后根据残差序列判断螺栓的预紧状态,建立具有鲁棒性的螺栓预紧力状态量化指标。实验结果表明:协整分析可以消除边界条件对VAM的影响,能够很好地表征螺栓的状态;协整残差的均方根(root mean square,RMS)值作为量化指标,能够有效地识别螺栓预紧力状态。
基金supported by the Basic Science (Natural science)Research Project of Higher Education of Jiangsu Province (Grant No.23KJB460019)the National Natural Science Foundation of China (Grant Nos.12302355 and 52075548)+2 种基金the Taishan Scholar Program of Shandong Province (Grant No.tsqn201909068)the Excellent Young Scientists Fund of Shandong Province (Grant No.2022HWYQ-071)the Fundamental Research Funds for the Central Universities (Grant No.20CX06074A)。