An integrated framework for condition monitoring and fault diagnosis of electric machine drive systems

这个NSF项目旨在设计和展示一种创新的基于物理指导的基于特征的方法，用于电机网络的状态监测和故障诊断（CMFD）。随着二氧化碳排放量的减少，电机的数量迅速增长，大量的电机被连接起来形成电机网络。然而，传统的CMFD是基于单个机器传感器开发的，这需要大量的传感器，并且没有全面考虑故障和退化传播。这一限制将在一定程度上通过协调监测和分析电网中战略性放置的电波形传感器来解决。该项目的智力优势包括将电机网络的高保真物理模型集成到基于签名的CMFD中，以提高准确性和鲁棒性。该项目的更广泛的影响包括推进K-12和本科生的研究经验，包括代表性不足的学生。该研究将整合到电力工程本科和研究生课程中，以培养未来的工程师，使他们具备满足行业新需求的技能和知识。与基于单个机器传感器的传统方法相比，提出的基于物理指导的基于特征的电机网络状态监测和故障诊断（CMFD）方法将把文献推向一个新的方向。将追求四个具体目标：(1)评估由于故障和退化导致的电波形特征，这将为拟议的CMFD方法建立技术基础。(2)创建电机网络的高保真物理模型，该模型不仅反映了电机的故障、退化和非线性，而且反映了由于故障和退化引起的波形传播。(3)设计一种基于物理引导特征的方法，利用波形传播模型更有效地识别和定位电机网络中的故障或退化源。(4)建立电机网络的半虚拟试验台，以评估所提出的CMFD方法的性能。提出的CMFD方法通常可用于各种应用，包括制造和工业系统、智能建筑、风力发电场和电气化运输。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This NSF project aims to design and demonstrate an innovative physics-guided signature-based approach for condition monitoring and fault diagnosis (CMFD) of electric machine networks. As the number of electric machines grows rapidly in response to less carbon dioxide emissions, a large number of electric machines are connected to form electric machine networks. However, traditional CMFD were developed based on individual machine sensors, which requires a large number of sensors and do not comprehensively consider fault and degradation propagation. This limitation will be in part resolved by coordinated monitoring and analysis of strategically-placed electrical waveform sensors in power networks. The intellectual merits of the project include integrating high-fidelity physical model of electric machine networks to signature-based CMFD for improved accuracy and robustness. The broader impacts of the project include advancing the research experiences for K-12 and undergraduate students including underrepresented students. The research will be integrated into the undergraduate and graduate electric power engineering curriculum to educate future engineers who will have the skills and knowledge to meet the emerging needs of the industry. The proposed physics-guided signature-based approach for condition monitoring and fault diagnosis (CMFD) of electric machine networks will advance the literature in a new direction compared to the traditional approach based on individual machine sensors. Four specific objectives will be pursued: (1) Assess the electrical waveform signature due to faults and degradation that will build a technical foundation for the proposed CMFD approach. (2) Create a high-fidelity physical model of electric machine networks that reflects not only the fault, degradation and nonlinearity of the machine, but also waveform propagation due to faults and degradation. (3) Design a physics-guided signature-based method that leverages the waveform propagation model to more effectively and efficiently identify and locate faults or degradation source in electric machine networks. (4) Build a semi-virtual testbed of electric machine networks to evaluate the performance of the proposed CMFD approach. The proposed CMFD approach can be generally used in a variety of applications, including manufacturing and industrial systems, smart buildings, wind farms, and electrified transportation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.