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

该奖项全部或部分由2021年美国救援计划法案（公法117 - 2）资助。该工程研究启动（ERI）拨款将资助在复杂机械操作中实现实时质量控制和准确决策的研究，包括现代制造中的关键部件，从而促进科学进步和促进国家繁荣。机械系统的故障诊断和预测在确保系统的完整性、安全性和性能方面起着不可或缺的作用。由于许多不确定性来源，可靠的实时故障监测和预测超出了当前的能力。基于机器学习的技术在克服传统测量方法的局限性方面显示出了希望，但最先进的工具缺乏识别在对先前收集的数据进行训练期间未遇到的故障的能力。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不确定性，对时变故障特征敏感，并在实时数据上连续训练。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、实用性和鲁棒性方面实现大幅性能提升。相关软件工具和精选数据集将公开共享，以促进技术转让和广泛获取研究方法。本科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关系，帮助扩大学生从目前代表性不足的群体和为他们服务的机构的STEM参与。这项研究旨在为几个深层次的整合做出根本性的贡献-- 利用用于优化传感器布置的算法来学习技术，以使得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之外的机械故障，对测量噪声和时变操作条件具有鲁棒性，并且即使在有限的数据下也是可靠的。它将通过依赖于贝叶斯卷积神经网络架构来实现这一结果，该架构构建了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敏感的长短期记忆架构，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，进一步促进了对以前未见过的故障的概括。该项目将使用Shapley加法简化方法来量化信号特征对故障检测的重要性，并将此度量用于优化实验齿轮箱的传感器位置，该齿轮箱将用于测试和验证算法框架。该奖项反映了NSF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 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.