A data-driven deep learning based fault diagnosis application for radial, active distribution grids

ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification

Experimental bed to study Linux faults

A Proof-of-Concept Prototype for detecting and classifying defects on high quality images of semiconductor wafers

Drone malfunction finder is an end-to-end system for fault-detection-from-uav-sounds, leveraging ML-based audio analysis to identify and classify drone faults.

The objective is to determine the operational status of each device based on the 47 input features. The classification label is represented by the variable Class, which indicates whether the device is functioning normally or experiencing a fault condition.