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

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2222630
Agency Tracking Number: 2222630
Amount: $1,000,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: IT
Solicitation Number: NSF 22-552
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2022-12-01
Award End Date (Contract End Date): 2024-11-30
Small Business Information
1515 East Kimberly Road
Davenport, IA 52807
United States
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Andrew Zimmerman
 (563) 823-5511
Business Contact
 Andrew Zimmerman
Phone: (563) 823-5511
Research Institution
 University of Connecticut
STORRS, CT 06269
United States

 Nonprofit College or University

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._x000D_
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._x000D_
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.

* Information listed above is at the time of submission. *

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