lohithgsk/intelligent-component-failure-forecasting
This application uses LSTM analysis to predict when heavy vehicle machines might fail, based on their history and usage patterns. Built with a user-friendly interface using HTML, CSS, JS, and a Flask backend, it also features real-time anomaly detection and collects customer feedback to improve accuracy.
No commits in the last 6 months.
Stars
—
Forks
2
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Jul 11, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/lohithgsk/intelligent-component-failure-forecasting"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
petrobras/BibMon
Python package that provides predictive models for fault detection, soft sensing, and process...
hustcxl/Deep-learning-in-PHM
Deep learning in PHM,Deep learning in fault diagnosis,Deep learning in remaining useful life prediction
kokikwbt/predictive-maintenance
Datasets for Predictive Maintenance
biswajitsahoo1111/rul_codes_open
This repository contains code that implement common machine learning algorithms for remaining...
liguge/Journals-of-Prognostics-and-Health-Management
智能故障诊断和寿命预测期刊(Journals of Intelligent Fault Diagnosis and Remaining Useful Life)