PFI-TT: Physics-based Deep Transfer Learning for Predictive Maintenance of Industrial and Agricultural Machinery

PFI-TT：基于物理的深度迁移学习，用于工业和农业机械的预测性维护

• 批准号: 1919265
• 负责人: Simon Laflamme
• 金额: $ 23.95万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1919265&HistoricalAwards=false
• 关键词: PFI; TT; Physics; based; Deep

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to provide the industrial and agricultural sectors with a practical and scalable solution for proactively predicting and preventing the failures of rotating machinery. An unexpected failure of a rotating machine may incur high maintenance and downtime costs, reduce customer satisfaction in a produced good, and cause human injuries and fatalities. These consequences not only impact the end user of the rotating machine, but also the machinery manufacturer by tarnishing its reputation and potentially impacting its competitive advantage. This PFI-TT project will accelerate the commercialization of deep learning in predictive maintenance to provide more accurate failure predictions than current solutions and is easily deployed across different types of machines and equipment. By making predictive maintenance practical and scalable, this project is expected to produce major advancements in developing machine systems that are more reliable and safer, as well as incurring lower maintenance and downtime costs than existing systems. Ultimately, the economic competitiveness of the U.S. industrial and agricultural sectors will be enhanced based on more reliable, safer and lower-cost rotating machinery.The proposed project will create a cost-effective, easy-to-implement, easy-to-scale Industrial Internet of Things (IIoT) platform for remotely monitoring machine health and predicting when and where maintenance actions need to be taken. The core of the proposed IIoT platform is a new deep learning solution that exploits the concepts of physics-based learning, transfer learning and online learning. To date, deep learning approaches to diagnostics/prognostics have been mostly relying on large volumes of training data and largely in isolation from the underlying physics of component faults. This project will overcome these limitations by integrating physics-based modeling and data-driven transfer learning. The resulting solution does not simply use run-to-failure data to train a deep learning model. Instead, training data is used, in conjunction with known physics and previously learned knowledge, to achieve more accurate predictions than possible from using training data alone. Additionally, the solution offers the capability of online learning that may lead to a paradigm shift in machinery prognostics toward unit-specific learning and prediction. The proposed deep learning solution has the potential to make predictive maintenance practical and scalable, thereby significantly promoting the wide-scale adoption of this maintenance strategy.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.

这个创新技术转化伙伴关系（PFI-TT）项目的更广泛的影响/商业潜力是为工业和农业部门提供一个实用的和可扩展的解决方案，以主动预测和预防旋转机械的故障。旋转机器的意外故障可能导致高昂的维护和停机成本，降低生产产品的客户满意度，并导致人员伤亡。这些后果不仅影响旋转机器的最终用户，而且通过玷污其声誉和潜在影响其竞争优势的机械制造商。这个PFI-TT项目将加速深度学习在预测性维护中的商业化，提供比当前解决方案更准确的故障预测，并且很容易部署在不同类型的机器和设备上。通过实现预测性维护的实用性和可扩展性，该项目有望在开发更可靠、更安全的机器系统方面取得重大进展，并且比现有系统产生更低的维护和停机成本。最终，基于更可靠、更安全、成本更低的旋转机械，美国工农业部门的经济竞争力将得到增强。拟议的项目将创建一个成本效益高、易于实施、易于扩展的工业物联网（IIoT）平台，用于远程监控机器健康状况，并预测何时何地需要采取维护行动。提出的IIoT平台的核心是一个新的深度学习解决方案，它利用了基于物理的学习、迁移学习和在线学习的概念。迄今为止，用于诊断/预测的深度学习方法主要依赖于大量的训练数据，并且在很大程度上与组件故障的底层物理隔离。该项目将通过整合基于物理的建模和数据驱动的迁移学习来克服这些限制。由此产生的解决方案并不是简单地使用运行到故障的数据来训练深度学习模型。相反，将训练数据与已知的物理和以前学过的知识结合使用，以获得比单独使用训练数据更准确的预测。此外，该解决方案提供了在线学习的能力，这可能导致机器预测向特定单元学习和预测的范式转变。提出的深度学习解决方案有可能使预测性维护变得实用和可扩展，从而显著促进这种维护策略的广泛采用。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。

项目成果

A physics-informed deep learning approach for bearing fault detection

• DOI: 10.1016/j.engappai.2021.104295
• 发表时间: 2021-05-18
• 期刊: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
• 影响因子: 8
• 作者: Shen, Sheng;Lu, Hao;Kenny, Shawn
• 通讯作者: Kenny, Shawn

IIoT Deployment of a Physics-Informed Deep Learning Model for Online Bearing Fault Diagnostics

IIoT 部署基于物理的深度学习模型，用于在线轴承故障诊断

• 发表时间: 2023
• 期刊: Proceedings of the 2022 International Symposium on Flexible Automation
• 作者: Lu, Hao;Allen, Cade;Nemani, Venkat;Hu, Chao;Zimmerman, Andrew
• 通讯作者: Zimmerman, Andrew

Ensembles of probabilistic LSTM predictors and correctors for bearing prognostics using industrial standards

• DOI: 10.1016/j.neucom.2021.12.035
• 发表时间: 2022-04-28
• 期刊: NEUROCOMPUTING
• 影响因子: 6
• 作者: Nemani, Venkat P.;Lu, Hao;Zimmerman, Andrew T.
• 通讯作者: Zimmerman, Andrew T.

A physics-informed feature weighting method for bearing fault diagnostics

• DOI: 10.1016/j.ymssp.2023.110171
• 发表时间: 2023-02-07
• 期刊: MECHANICAL SYSTEMS AND SIGNAL PROCESSING
• 影响因子: 8.4
• 作者: Lu, Hao;Nemani, Venkat Pavan;Zimmerman, Andrew T.
• 通讯作者: Zimmerman, Andrew T.

Joint training of a predictor network and a generative adversarial network for time series forecasting: A case study of bearing prognostics

• DOI: 10.1016/j.eswa.2022.117415
• 发表时间: 2022-05-17
• 期刊: EXPERT SYSTEMS WITH APPLICATIONS
• 影响因子: 8.5
• 作者: Lu, Hao;Barzegar, Vahid;Zimmerman, Andrew Todd
• 通讯作者: Zimmerman, Andrew Todd