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

ERI：利用概率深度学习方法与定制特征相结合，增强实时机械故障诊断和预测

• 批准号: 2138522
• 负责人: Shangyan Zou
• 金额: $ 19.91万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138522&HistoricalAwards=false
• 关键词: ERI; Harnessing; Probabilistic; Deep; Learning

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.

该奖项是根据2021年《美国救援计划法》的全部或部分资助（公共法117-2）。本工程研究启动（ERI）赠款将资助研究研究，以实现实时质量控制和在复杂机械运营中的准确决策，包括现代制造业的关键组成部分，从而促进科学的进步和促进国家繁荣的进步。机械系统的故障诊断和预后在确保完整性，安全性和性能中起着必不可少的作用。由于不确定性的许多来源，可靠的实时故障监视和预测超出了当前功能。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，但是最先进的工具缺乏识别在先前收集的数据培训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局限性，该框架使用概率思想来解释测量不确定性，对时变的故障签名敏感，并不断对实时数据进行培训。新框架将在航空航天，运输和基础设施行业的机械故障诊断和预后的可靠性，效率，实用性和鲁棒性方面进行大量绩效提高。相关的软件工具和策划数据集将公开共享，以促进技术转移并广泛访问研究方法。 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为了检测和预测机械故障，在给定的培训数据集中的机械故障方面，对于测量噪声和随时间变化的操作条件，甚至可以可靠地给出有限的数据。它将通过依靠贝叶斯卷积神经网络体系结构来实现这一结果，该贝叶斯卷积神经网络体系结构构建了特征检测的概率模型，长期的短期内存体系结构对故障渐进性的内在时间相关性敏感，并且基于变异的参与优化算法的渐进性特征，以实时更新，以前更新了一个对实时模型，以前进行了构图。 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.

项目成果

A hierarchical deep learning framework for combined rolling bearing fault localization and identification with data fusion

• DOI: 10.1177/10775463221091601
• 发表时间: 2022-04
• 期刊: Journal of Vibration and Control
• 影响因子: 2.8
• 作者: Mingxuan Liang;Kai Zhou

A Deep Long Short-Term Memory Network for Bearing Fault Diagnosis Under Time-Varying Conditions

用于时变条件下轴承故障诊断的深度长短期记忆网络

• DOI: 10.1115/detc2022-88808
• 发表时间: 2022
• 期刊: ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
• 作者: Zhou, Kai

Probabilistic Gear Fault Diagnosis Using Bayesian Convolutional Neural Network

• DOI: 10.1016/j.ifacol.2022.11.279
• 发表时间: 2022
• 期刊: IFAC-PapersOnLine
• 作者: K. Zhou;Jiong Tang

Damage identification using piezoelectric electromechanical Impedance: A brief review from a numerical framework perspective

• DOI: 10.1016/j.istruc.2023.03.017
• 发表时间: 2023-03-11
• 期刊: STRUCTURES
• 影响因子: 4.1
• 作者: Cao，Pei;Zhang，Shengli;Zhou，Kai
• 通讯作者: Zhou，Kai

Probabilistic Multi-Objective Inverse Analysis for Damage Identification Using Piezoelectric Impedance Measurement Under Uncertainties

• DOI: 10.3389/fbuil.2022.904690
• 发表时间: 2022-06
• 作者: K. Zhou;Yang Zhang;Q. Shuai;Jiong Tang