ERI: Harnessing Probabilistic Deep Learning Method Integrated with Tailored Features for Enhanced Real-Time Machinery Fault Diagnosis and Prognosis

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公共法律117-2)。这项工程研究启动(ERI)拨款将资助在复杂机械(包括现代制造业中的关键部件)的操作中实现实时质量控制和准确决策的研究，从而促进科学进步和国家繁荣。机械系统的故障诊断和预测在保证系统的完整性、安全性和性能方面起着不可或缺的作用。由于不确定性的来源很多，可靠的实时故障监测和预测超出了当前的能力范围。基于机器学习的技术在克服传统测量方法的局限性方面表现出了希望，但最先进的工具缺乏识别在以前收集的数据的训练期间没有遇到的故障的能力。该项目将通过创建一个新的机器学习框架来克服这些限制，该框架使用概率思想来解释测量的不确定性，对时变的故障特征敏感，并根据实时数据连续训练。新框架将在航空航天、交通运输和基础设施行业的机械故障诊断和预测的可靠性、效率、实用性和稳健性方面实现实质性的性能增强。将公开分享相关的软件工具和经过整理的数据集，以促进技术转让和更广泛地利用研究方法。本科生研究机会将提供实践学习经验，并利用密歇根理工大学和三所密歇根社区学院之间的合作伙伴关系，帮助来自目前代表性不足的群体和为他们提供服务的机构的学生更广泛地参与STEM。这项研究旨在为几种深度学习技术与优化传感器布置的算法的集成做出基本贡献，从而能够使用实时振动测量来检测和预测给定训练数据集中的机械故障，对测量噪声和时变的操作条件具有很强的鲁棒性，即使给定的数据也是有限的。它将依靠贝叶斯卷积神经网络体系结构实现这一结果，该体系结构构建用于特征检测的概率模型，对故障渐进性质的内在时间相关性特征敏感的长短期记忆体系结构，以及用于实时模型更新的基于变分推理的反向传播优化算法，进一步促进对以前未见的故障的概括。该项目将使用Shapley加法解释方法来量化信号特征对于故障检测的重要性，并将应用该度量来优化用于测试和验证算法框架的试验性变速箱的传感器布置。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This Engineering Research Initiation (ERI) grant will fund research that enables real-time quality control and accurate decision making in the operation of complex machinery, including critical components in modern manufacturing, thereby promoting the progress of science and advancing the national prosperity. Fault diagnosis and prognosis for machinery systems play an indispensable role in ensuring integrity, safety, and performance. Due to many sources of uncertainty, reliable real-time fault monitoring and prediction are beyond current capabilities. Machine learning-based techniques show promise in overcoming the limitations of traditional measurement approaches, but state-of-the-art tools lack the ability to identify faults that were not encountered during training on previously collected data. This project will overcome these limitations by creating a new machine-learning framework that uses probabilistic ideas to account for measurement uncertainty, is sensitive to time-varying fault signatures, and trains continuously on real-time data. The new framework will enable substantial performance enhancements in reliability, efficiency, practicality, and robustness of machinery fault diagnosis and prognosis in aerospace, transportation, and infrastructure industries. Related software tools and curated datasets will be shared publicly in order to promote technology transfer and broad access to the research methodology. Undergraduate research opportunities will provide hands-on learning experiences and, leveraging a partnership between Michigan Tech University and three Michigan community colleges, help broaden participation in STEM of students from currently underrepresented groups and the institutions that serve them.This research aims to make fundamental contributions to the integration of several deep-learning technologies with an algorithm for optimized sensor placement to enable the use of real-time vibration measurements for detection and prediction of machinery faults also outside of those in a given training data set, robustly to measurement noise and time-varying operating conditions, and reliably even given limited data. It will achieve this outcome by relying on a Bayesian convolutional neural network architecture that builds a probabilistic model for feature detection, a long short-term memory architecture that is sensitive to intrinsic temporal correlations characteristic of the progressive nature of faults, and a variational inference-based backpropagation optimization algorithm for real-time model updating, further facilitating generalizations to previously unseen faults. This project will use the Shapley Additive Explanations approach to quantify the importance of signal features for fault detection, and will apply this metric to optimize sensor placement for an experimental gearbox that will be used to test and validate the algorithmic framework.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.