Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.

Deep learning models (RNN & LSTM & WaveNet) for predicting the remaining useful life of rolling element bearings using time series health indicators. Compares performance between different architectures for predictive maintenance applications.

This project uses the NASA Turbofan Engine Dataset and an LSTM model to predict equipment failures, enabling proactive maintenance through time-series analysis.

Predictive maintenance using LSTM and anomaly detection

Remaining Useful Life (RUL) prediction for turbofan engines using Domain-Adversarial Neural Networks (DANN-LSTM). Addresses domain shift challenges in predictive maintenance with 30% RMSE improvement over baseline methods. PyTorch implementation on NASA CMAPSS dataset with SHAP interpretability.

This project, inspired by an earlier work on Predictive Maintenance using LSTM, employs a Decision Tree Classification Model to predict the remaining useful life of aircraft engines. Using simulated sensor data from gas turbine engines, the model seeks to optimize maintenance scheduling to minimize unexpected failures.

In this repository, I have used th ML model that LSTM for predictive modelling ans maintenance

Predictive Maintenance System Using Time-Series Sensor Data with Random Forest & LSTM.

In this notebook, we build an LSTM network for the data set and scenerio described at Predictive Maintenance Template to predict remaining useful life of aircraft engines. In summary, the template uses simulated aircraft sensor values to predict when an aircraft engine will fail in the future so that maintenance can be planned in advance.

A dynamic Predictive Maintenance system that auto-detects dataset type and uses ANN for failure classification and LSTM for RUL forecasting. Optimized with Adam and Early Stopping, the project includes a Streamlit web interface for real-time model training and machine health predictions.

LSTM network is used to predict the RUL of turbofan engines.

Predictive Maintenance Model for Turbofan Engines using LSTM

Advanced predictive maintenance for wind turbines using digital twin technology and a hybrid deep learning model. It integrates SCADA and historical fault data to create a digital twin. It combines transformer encoders, with LSTM, and GRU layers, forecasts faults while Microsoft Azure Digital Twins enable simulation, visualization, and monitoring.

Demonstration of predictive maintenance using LSTM via classification and regression method.

Predictive Maintenance: Use LSTM to predict failure (binary classification) and RUL (remaining useful life or time to failure with regression) of aircraft engines.

Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras

Predictive Maintenance using LSTM

An AI-powered computer vision project for textile defect detection, achieving over 85% accuracy. Includes predictive maintenance with IoT sensor data and supply chain forecasting using ARIMA/LSTM. Designed to improve quality control and efficiency in manufacturing.

This project predicts the Remaining Useful Life of aircraft engines using the NASA C-MAPSS dataset. Multiple machine learning and deep learning models (Linear Regression, Random Forest, SVR, and CNN+LSTM) are compared for time-series sensor data, with emphasis on feature engineering and real-world predictive maintenance applications.

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Predictive maintenance using LSTM

Predictive maintenance using LSTM/TCN

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git@github.com:umbertogriffo/Predictive-Maintenance-using-LSTM.git

NASA turbofan engine health monitoring using LSTM and predictive maintenance

Predictive Maintenance Using LSTM and GRU on NASA Turbofan Engine Dataset

Predictive maintenance for pump sensor data using a simple LSTM model

Remaining useful life prediction for predictive maintenance using an LSTM architecture. Based on the NASA CMAPSS dataset.

Use some open datasets to train deep learning models(CNN, RNN, LSTM, GRU) for industrial predictive maintenance