The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project proposes a deep learning approach to predictive maintenance of industrial and agricultural equipment. High productivity demands and just-in-time approaches to manufacturing mean that equipment downtime can be extremely expensive; Research shows that the average manufacturer deals with 800 hours of downtime per year. Further, an increasing quantity of aging equipment and a maintenance workforce that is largely reaching retirement age have led to a situation where maintenance staff often lack the knowledge, training, and/or manpower to address a growing pool of aging assets. This project intends to bring forward a novel suite of intuitive and explainable technologies that can help reduce or eliminate unexpected downtime and help digitally capture and transfer expert knowledge from the retiring workforce.
This Small Business Technology Transfer (STTR) Phase I 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. However, monitored equipment may not exbibit behavior covered in that training set, leading to low-confidence predictions. This approach will not only predict the remaining useful life of a machine component, but it will also quantify the uncertainty of a prediction through an ensemble of models and a temporal fusion of predictions. As a result, maintenance decisions can be made from a risk-based perspective, eliminating unnecessary maintenance stemming from low-confidence predictions. Furthermore, many existing deep learning approaches lack the ability to intuitively explain their predictions to human users. In critical applications where bad 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 will 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.
