整流电路是航空发电机的重要组成部分,存在故障频发且维修困难等问题.为对电励磁双凸极发电机(DSEG)的整流电路进行故障诊断,研究了一种基于长短时记忆(LSTM)网络的故障诊断方法.首先,采集多种故障模式下发电机的三相电枢电流信号.其次,利用不同的信号处理方法处理故障信号以获取故障特征信息.然后,将获得的故障特征数据分为训练和测试样本输入LSTM网络进行故障分类.最后,计算并分析诊断结果.仿真与试验结果表明所提方法具有良好的故障诊断效果.

针对碳化硅MOSFET运行工况复杂、易造成器件栅极老化、影响电力系统可靠性的问题,提出一种基于BP神经网络的碳化硅MOSFET栅极老化监测方法.以碳化硅MOSFET的阈值电压和体二极管通态压降作为栅极老化的敏感表征参数,设计、搭建测试实验平台,获取变测量条件下的电参数值,结合BP神经网络提取健康器件与老化器件样本数据间的特征差异,充分挖掘器件的可靠性信息.实验结果表明:该方法可对碳化硅MOSFET的栅极老化状态进行较为准确的检测和评估.

针对前期新冠疫情防控的要求和"数字信号处理器(DSP)"课程实验教学的特点,提出了基于线上和线下相结合的混合教学DSP实验教学模式.要求学生通过网络学习,充分利用线上的资源,以时间换空间的方式,获取课程所必须的预先知识;根据学校疫情防控需要,合理安排学生的线下实验,并能够实际操作实验平台,获得感性知识,保障了实验教学的质量.

The state of charge(SOC) of lithium-ion battery is an essential parameter of battery management system. Accurate estimation of SOC is conducive to give full play to the capacity and performance of the battery. For the problems of selection of forgetting factor and poor robustness and susceptibility to the noise of extended Kalman filtering algorithm, this paper proposes a SOC estimation method for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification. Firstly, the Thevenin equivalent circuit model is established and the recursive least squares with forgetting factor(FFRLS) method is used to achieve the parameter identification. Secondly, an evaluation factor is defined, and fuzzy control is used to realize the mapping between the evaluation factor and the correction value of forgetting factor, so as to realize the adaptive adjustment of forgetting factor. Finally, the noise adaptive algorithm is introduced into the extended Kalman filtering algorithm(AEKF) to estimate the SOC based on the identification results, which is applied to the parameter identification at the next time and executed circularly, so as to realize the accurate estimation of SOC. The experimental results show that the proposed method has good robustness and estimation accuracy compared with other filtering algorithms under different working conditions, state of health(SOH) and temperature.

The lithium-ion battery’s state of health (SOH) is one of the essential parameters of the battery management system. An accurate state of health estimation of the battery pack helps to improve the service life of the overall battery pack. Given the poor generalization ability of a single data-driven model during SOH online estimation and the lack of uncertainty expression ability in the estimation results, this paper proposes an online SOH estimation method of lithium-ion battery based on bat algorithm optimized relevance vector machine (BA-RVM) with dynamic integration. Firstly, we perform feature extraction and select equal voltage drop discharge time as an indirect health factor. Secondly, we establish the integration model, take the wavelet kernel relevance vector machine (RVM) as the sub-model, and use the bat algorithm (BA) to optimize its kernel parameters to improve the estimation accuracy of the sub-model. Then we use the online monitoring data to update the weights of the sub-models continuously and dynamically integrate the output of the sub-models to improve the accuracy of SOH online estimation further. Finally, the correctness and effectiveness of the method are verified based on battery data from NASA and compared with other data-driven methods. The experimental results show that compared with the method based on a single data-driven model, this method has higher accuracy and more vital generalization ability, and the estimation results have specific uncertainty expression ability.

针对碳化硅(SiC)MOSFET存在的栅氧可靠性问题,对其展开高温栅偏(HTGB)试验研究.以阈值电压(VTH)和体二极管通态压降(VSD)作为特征参数,设计搭建应力及测试试验平台,研究SiC MOSFET在高温栅偏应力下的特征参数退化特性,并对短期恢复下特征参数的不稳定现象以及长期恢复对特征参数的影响进行了分析.试验结果表明,SiC MOSFET的VTH和VSD均受负向和正向高温栅偏的影响,并能够产生相反方向的参数漂移.撤去应力后存在恢复现象,使电参数受可恢复部分偏移量的影响具有不稳定性,且经过长期室温储存后仍存在进一步的恢复.

利用快速傅里叶变换(FFT)研究了一种角度轨迹监测技术,并将其用于航空发电机旋转整流器故障特征提取及在线诊断应用中.首先,连续采集发电机交流励磁机励磁电流信号,每相邻两次数据采集间隔同样的时间.其次,对每次采集的数据均进行FFT处理以获取故障特征信息,该过程在每两次数据采集的间隔内完成.每次处理得到的故障特征信息会形成连续的变化轨迹,通过对该轨迹的研究可以在线监测并诊断旋转整流器的故障模式.最后,通过试验对所提方法进行了验证.

SiC功率管器件广泛应用在航空领域的电能变换、配电等场合,其健康状况十分重要.在SiC器件的健康监测应用中,导通电阻的检测是一项十分重要的技术.为了能够简单准确地得到碳化硅(SiC)MOSFET功率器件的导通电阻,本文提出了一种基于神经网络的SiC MOSFET器件导通电阻估测方法.本文搭建SiC MOSFET导通电阻测试电路仿真和物理试验平台,并使用BP神经网络(BP neural networks,BPNN)对不同温度、不同栅极电压以及不同漏极电流下SiC MOSFET器件的导通电阻数据进行详细描述.最后,对基于BPNN的SiC MOSFET导通电阻估测方法进行效果验证.结果表明,该方法具有精度高和泛化能力强的优点,能够实现SiC MOSFET器件导通电阻的有效估测.

由于SiC MOSFET开关速度较快,不能用普通Si MOSFET的栅极驱动电路来驱动.设计一种基于F28335的SiC MOSFET栅极驱动电路,利用键盘调节F28335输出PWM信号的频率、占空比、死区和移相值,并在LCM12864液晶屏上实时显示调节值.将PWM信号作为SiC MOSFET栅极驱动电路输入信号应用于驱动电路,从而实现对SiC MOSFET通断控制.实验结果表明:相比于Si MOSFET栅极驱动电路而言,所提出的基于F28335的SiC MOSFET栅极驱动电路操作方便,体积小,稳定性好.

A fault diagnosis method for the rotating rectifier of a brushless three-phase synchronous aerospace generator is proposed in this article. The proposed diagnostic system includes three steps: data acquisition, feature extraction and fault diagnosis. Based on a dynamic Fast Fourier Transform (FFT), this method processes the output voltages of aerospace generator continuously and monitors the continuous change trend of the main frequency in the spectrum before and after the fault. The trend can be used to perform fault diagnosis task. The fault features of the rotating rectifier proposed in this paper can quickly and effectively distinguish single and double faulty diodes. In order to verify the proposed diagnosis system, simulation and practical experiments are carried out in this paper, and good results can be achieved.

Accurately predicting the remaining useful life of lithium-ion batteries is critical to battery health management systems. Aiming at the problems of low long-term prediction accuracy, unstable model output, and difficult key parameter selection, this paper proposes a self-adaptive differential evolution optimized monotonic echo state network prediction method. First, we analyze the life decay characteristics of Li-ion batteries and select appropriate indirect health indicators to replace the capacity based on the partial correlation coefficient analysis. Then use the self-adaptive differential evolution algorithm to optimize the free parameters of the monotonic echo state network to maintain the monotonic relationship between input and output. Finally, the remaining useful life indirect prediction model is established. This paper uses NASA Li-ion battery experimental data and independent experimental data to verify the feasibility, followed by the different starting points experiments and cut-off voltage experiments. The accuracy of the proposed method is compared with other commonly used artificial intelligence prediction algorithms. Experimental results prove that this method has high prediction accuracy and stable output.

旋转整流器为同步发电机提供励磁,其可靠性十分重要.主要采用FPGA平台设计了一套故障监测系统,可以对发电机的整流器进行运行状态监测.实验采用三级式同步发电机进行了验证,结果表明,该系统具有较好的监测性能.

为了方便实现离心机转速的实时监测和计量,基于LabVIEW平台,采用振动频率测量的方法,设计了离心机转速计量系统.该系统可以实现离心机的转速测量和离心机振动状态的分析.实验在一台医用离心机上进行,结果表明,该套监测系统的转速误差测量误差小于0.1％,能够满足系统设计的要求.

该文研究一种基于深度置信网络(deep belief network,DBN)的改进方法,并将其用于航空发电机旋转整流器故障特征提取及诊断操作中.首先,采集发电机交流励磁机励磁电流信号.其次,利用粒子群算法(particle swarm optimization,PSO)对深度置信网络进行训练,用于优化和确定深度置信网络的结构.最后,对所研究的方法进行仿真模型和实际平台的算法验证,并设计一个基于数字信号控制器(digital signal controller,DSC)的紧凑型实时诊断系统,成功实现了算法的移植工作,并取得了理想的诊断效果.

研究一种基于宽度学习系统(broad learning system,BLS)的特征自适应提取方法,并将其应用于航空发电机旋转整流器二极管故障分类问题.针对目前宽度学习系统中参数选择等问题,尝试将网格搜索法与宽度学习系统进行结合,提出一种改进的宽度学习系统,该方法可以自适应的计算网络结构,并进行故障特征提取.通过建立的航空发电机仿真模型和实际民用发电机平台的诊断实验表明,BLS与现有的一些典型故障诊断方法相比,在诊断性能相近的情况下,还具有较高的诊断速度.

基于深度置信网络技术,使用C++编程语言设计了发电机旋转整流器故障诊断平台,实现了对故障信号特征的提取与分类.选择三级式发电机进行了实验验证,结果表明,该设计具有良好的故障诊断效果.

为提高嵌入式技术实验教学效果,基于桌面云构建了嵌入式技术创新实践硬件实验平台,开发了虚拟实验系统,研究了桌面云下虚实结合的实验教学模式,探索了基于桌面云的开放共享的互联网+实验教学模式,以及理论、实验和网络教学交叉融合的教学架构.研究成果可用于嵌入式技术课程教学,提高学生嵌入式应用系统设计、工程实践和科技创新能力,并可为互联网+教育背景下实验教学提供参考.

针对光伏逆变器软故障特征提取难、诊断效果差等问题,对光伏逆变器三相桥臂中点间的线电压信号,采用排列熵优化变分模态分解的模态分量数后,利用小波分解提取出变分模态分解的各模态分量的小波能量,作为光伏逆变器的软故障特征;然后,利用支持向量机识别多种不同软故障;最后,对NPC三电平光伏逆变器分压电容软故障进行诊断实验.实验结果表明,相对于传统的小波能量、EMD小波能量等方法,该方法诊断精度高、速度快,适用于光伏逆变器的软故障诊断.

科研成果向教学内容的转化是很有意义的,转化的形式也比较多.本文主要讨论了在DSP课堂教学和实验教学过程中,合理引入科研成果的一些思路和做法,以此激发学生的课堂兴趣,锻炼学生的动手能力,夯实其解决领域内复杂工程问题的基本能力.

研究了一种基于深度置信网络的故障诊断方法,主要应用于针对航空发电机旋转整流器所进行的故障诊断中,对方法进行了仿真及实际实验验证.采集主励磁机励磁电流作为故障诊断所使用的有效信号,对所采集到的励磁电流信号进行快速傅里叶变换以获取其频域信息,将所得到的频域数据分为训练样本和测试样本输入至深度置信网络中进行故障分类,计算诊断正确率并做出分析.实验证明,所提出的方法具有良好的故障分类效果.