Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
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Updated
Feb 12, 2024 - Python
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.
time-series prediction for predictive maintenance
False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics
ARAKAT - Big Data Analysis and Business Intelligence Application Development Platform
CeRULEo: Comprehensive utilitiEs for Remaining Useful Life Estimation methOds
Evolutionary Neural Architecture Search on Transformers for RUL Prediction
This is a Machine Learning Practice Case of Predictive Maintenance by Python with NASA's Turbofan Engine Degradation Simulation Data Set.
ProFeld: survival analysis, predictive maintenance, churn analysis, and remaining useful life prediction in Python
The Unbreakable Project is a working prototype of predictive maintenance, a branch of the Industry 4.0.
IoT Plateform for Predective Maintenance
Industrial Predictive Maintenance using Sony Spresense and edgeML
Sample code as a template for developing custom machine learning algorithms to be used with SAP Predictive Maintenance and Service.
Atlantic: Automated Data Preprocessing Framework for Supervised Machine Learning
Evolutionary Neural Architecture Search for Remaining Useful Life Prediction
A framework to implement Machine Learning methods for time series data.
Multi-Objective Optimization of ELM for RUL Prediction
Scalable framework for Predictive Maintenance applications
SMWiredPy is python library for Sensemore vibration/accelerometer sensors
Partially Hidden Markov Chain Multivariate Linear AutoRegressive model
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