Config files for my GitHub profile.

ML 4 PE

This project focuses on detecting and analyzing wear in drill bits during the drilling process. It involves studying three types of drill wear (flank, chisel, and outer corner wear) along with a healthy drill condition, using four corresponding datasets. The goal is to determine the most effective strategy for identifying drill bit wear.

Fault Detection and Classification techniques using Deep Learning and Machine Learning based architectures to detect the UAV maloperation in Accelerometer and Gyroscope sensors.

Multirobot supervision system using Nagios and ROS

ahuDoctor detects hard and soft faults in multiple zone VAV AHU systems

This problem statement involved predicting fault impacts on Radio Access Networks KPIs and prioritizing issues that affect data rates—a critical step toward enhancing network performance and preventing customer churn in the telecommunication space.

FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids

Student

Fault Bearing Classification Analysis dashboard to explore, diagnose and highlight potential factors to predict the fault class based on bearing statistical manufacturing data.

mutation testing techniques comparison w.r.t. fault detection.

TLA+ specifications for BFT algorithms

PCA for multivariate statistical process monitoring.

In this repo, a few topics about useful Machine Learning and Deep Learning algorithms for Fault Detection and Identification are taught.

A wrapper for connecting to Ecorithm's API Platform through Python

Fault Detection System Development at eParampara Technologies (Test Repo)

A predictive TinyML model to run classification task on edge MCUs.

An investigation into the advantages/disadvantages of some image noise removal techniques