Harnessing Probabilistic Deep Learning Method Integrated with Tailored Features for Enhanced Real-Time Machinery Fault Diagnosis and Prognosis (National Science Foundation)
The overaching goal of this research is to make fundamental contributions to the integration of several deep-learning technologies with the novel algorithm for optimized sensor placement to enable the use of real-time vibration measurements for detection and prediction of machinery faults beyond those in a given training data set, robustly to measurement noise and time-varying operating conditions, and reliably even given limited data.