STTR Phase II: Probabilistic and Explainable Deep Learning for the Intuitive Predictive Maintenance of Industrial and Agricultural Equipment

STTR 第二阶段：用于工业和农业设备直观预测维护的概率和可解释深度学习

• 批准号: 2222630
• 负责人: Andrew Zimmerman
• 金额: $ 100万
• 依托单位: PERCEV LLC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222630&HistoricalAwards=false
• 关键词: STTR; Phase; II; Probabilistic; Explainable

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II is to improve schedule-based maintenance programs to ensure that industrial and farming equipment can function 24 hours a day, 7 days a week. The downtime associated with such high productivity equipment can result in significant lost revenue, and research shows that the average manufacturer deals with 800 hours of downtime per year. The proposed technology seeks to effectively reduce or eliminate this downtime, creating value for manufacturers. This project proposes a novel deep learning approach to predicting bearing failure in rotating industrial equipment and enable maintenance teams to confidently plan optimal maintenance activities around equipment that is in the process of degrading. This solution also aims to cost-effectively use a patented methodology to monitor industrial material handling systems with a combination of stationary and mobile battery-powered wireless sensors.This Small Business Technology Transfer (STTR) Phase II project proposes a novel deep learning approach to machinery prognostics. Many existing deep learning approaches focus on the most likely failure scenarios given a set of training data. Monitored equipment may not exbibit behavior covered in that training set, leading to low-confidence predictions. The proposed approach may not only predict the remaining useful life of a machine component, but also seeks to quantify the uncertainty of a prediction through an ensemble of models and a temporal fusion of predictions. As a result, maintenance decisions may be made from a risk-based perspective, eliminating unnecessary maintenance stemming from low-confidence predictions. Additionally, many existing deep learning approaches also lack the ability to intuitively explain their predictions to human users. In critical applications where poor predictions have serious consequences, maintenance personnel must understand and trust an artificially intelligent predictive maintenance partner. The proposed solution produces an intuitive visual explanation for the model’s prediction by highlighting and animating the segments of a raw data signal that are contributing most significantly to the prediction. This technology may allow trained personnel to quickly make optimal maintenance decisions by fusing data-driven insights with their existing domain expertise.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.

这项小型企业技术转让（STTR）第二阶段的更广泛影响/商业潜力是改进基于时间表的维护计划，以确保工业和农业设备能够每周7天，每天24小时正常运行。与这种高生产率设备相关的停机时间可能会导致严重的收入损失，研究表明，制造商平均每年要处理800小时的停机时间。该技术旨在有效减少或消除停机时间，为制造商创造价值。该项目提出了一种新颖的深度学习方法来预测旋转工业设备的轴承故障，并使维护团队能够自信地围绕处于退化过程中的设备计划最佳维护活动。该解决方案还旨在经济有效地使用专利方法，通过固定和移动电池供电的无线传感器组合来监控工业物料处理系统。这个小型企业技术转移（STTR）第二阶段项目提出了一种新的机器预测深度学习方法。许多现有的深度学习方法专注于给定一组训练数据的最可能失败的场景。被监测的设备可能不会表现出该训练集所涵盖的行为，从而导致低置信度的预测。所提出的方法可能不仅预测机器部件的剩余使用寿命，而且还寻求通过模型集合和预测的时间融合来量化预测的不确定性。因此，维护决策可以从基于风险的角度进行，从而消除了由于低置信度预测而产生的不必要的维护。此外，许多现有的深度学习方法也缺乏直观地向人类用户解释其预测的能力。在糟糕的预测会导致严重后果的关键应用中，维护人员必须理解并信任人工智能的预测性维护合作伙伴。提出的解决方案通过突出显示对预测贡献最大的原始数据信号片段并将其动画化，为模型的预测提供直观的视觉解释。这项技术可以让训练有素的人员通过将数据驱动的见解与他们现有的领域专业知识相结合，快速做出最佳的维护决策。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。