Reverse engineering EGT Margin for P&W engines (ECM)
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Updated
May 23, 2024 - Jupyter Notebook
Reverse engineering EGT Margin for P&W engines (ECM)
The Predictive Maintenance Game (🛠️ WIP)
Nvidia DLI workshop on AI-based predictive maintenance techniques to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and map anomalies to failure conditions.
[IEEEHTC2023] Repo to analyse and predict cutting tool wear using acoustic signals.
A fast solver for Markov Decision Processes
Python codes “Jupyter notebooks” for the paper entitled "A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay, IEEE Trans. on Instrumentation and Measurement, Aug. 2022. Techniques used: Wavelet Packet Transform (WPT) & Fast Fourier Transform (FFT). Application: vibration-based fault diagnosis.
CeRULEo: Comprehensive utilitiEs for Remaining Useful Life Estimation methOds
This repository contains code and documentation for a machine learning project focused on predictive maintenance in industrial machinery. The project explores the development of a comprehensive predictive maintenance system using various machine learning techniques.
Code repository for the book 'Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance'
Implementation of Predicting Survival Time of Ball Bearings in the Presence of Censoring (AAAI Fall Symposium 2023)
Industrial Predictive Maintenance using Sony Spresense and edgeML
Papers and datasets for Vibration Analysis
Developed at the IEEE CS SPIT Aeravat 1.0 AI Hackathon
Predictive maintenance can help companies minimize downtime, reduce repair costs, and improve operational efficiency. Developing a web application for predictive maintenance can provide users with real-time insights into equipment performance, enabling proactive maintenance, and reducing unplanned downtime.
Repository for my FYP (Final Year Project) thesis.
Expandable Isotropic Multimodal Patch Learning Neural Architecture for the Nano-modal (9) time-series and images data.
Application of machine/deep learning models & algorithms in the energy sector
Remaining Useful Life (RUL) prediction of Multi-Stiffener composite Panels from Single-Stiffener Panels histories using SVR and LSTM
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