lilinglu / Device-failure-prediction
Company has a fleet of devices transmitting daily aggregated telemetry attributes.Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks a…
☆15Updated 3 years ago
Alternatives and similar repositories for Device-failure-prediction:
Users that are interested in Device-failure-prediction are comparing it to the libraries listed below
- A low capacity and high capacity plant across 5 countries are considered and a linear programming model is built to determine the total l…☆19Updated 3 years ago
- Complementary Jupyter notebooks for load forecasting tutorial.☆12Updated 4 years ago
- The aim of the project is leverage linear programming in supply chain optimization problem☆16Updated 4 years ago
- End-to-end automated pipeline in Python that forecasts weekly demand for products & recommends corresponding optimal prices for a retail …☆35Updated 5 years ago
- Sales forecasting for the supply chain industry.☆11Updated 3 years ago
- This work explains how OR and ML in tandem can help us making a cost efficient decisions. I have used a Supply Chain Network Design use c…☆22Updated 4 years ago
- Algorithms Library for Supply Chain Inventory Optimization☆16Updated 5 years ago
- Deterministic and Stochastic Dynamic Programs for optimization of Supply Chain☆19Updated 2 years ago
- optimizing locations of electric vehicle charging stations in the city of Toronto☆29Updated 2 years ago
- Food Demand Forecasting Challenge☆16Updated 5 years ago
- Predict seasonal item sales using classical time-series forecasting methods like Seasonal ARIMA and Triple Exponential Smoothing and curr…☆29Updated 4 years ago
- Multivariate time series prediction using LSTM in keras☆33Updated 7 years ago
- ☆15Updated 3 years ago
- Geoffrey-Z / Multivariate-Time-Series-Forecasting-with-LSTMs-in-Keras-for-CORN-SWEET-Terminal-Market-Price☆16Updated 3 years ago
- Multivariate Time series Analysis Using LSTM & ARIMA☆37Updated 5 years ago
- Using machine learning models like linear regression to make predictions for time series data☆11Updated 3 years ago
- Illustrating a typical Predictive Maintenance use case in an Industrial IoT Scenario. By using Statistical Modelling and Data Visualizati…☆22Updated 2 years ago
- Work done for paper (Load Forecasting using Deep Neural Networks) at IEEE SmartGridComm 2016 — Edit☆19Updated 8 years ago
- Predicting Global Supply Chain Outcomes for Essential HIV Medicines using Machine Learning Techniques☆35Updated 5 years ago
- Classifing the iris dataset with fuzzy logic, genetic algorithm and particle swarm optimization.☆11Updated 5 years ago
- Application of Deep Reinforcement Learning to Supply Chain management. Reference: https://blog.griddynamics.com/deep-reinforcement-learni…☆10Updated 3 years ago
- 🕸 CNN + 🛍 BoVW + 💼 BoCF + 🐺 Grey Wolf Optimization & Comparision ⚖☆11Updated 4 years ago
- Use of time series modelling tools including ARIMA, LSTM, and Monte Carlo simulation to model electricity consumption, rainfall and tempe…☆45Updated 10 months ago
- The objective of the Project is to predict ‘Full Load Electrical Power Output’ of a Base load operated combined cycle power plant using P…☆10Updated 6 years ago
- This machine learning model (LSTM Time Series model) helps us to forecast demand of a supply chain business problem. This model uses Kera…☆27Updated 6 years ago
- Config files for my GitHub profile.☆15Updated last year
- Development of a machine learning application for IoT platform to predict electric energy consumption in smart building environment in re…☆47Updated 4 years ago
- The goal of this notebook is to implement and compare different approaches to predict item-level sales at different store locations.☆36Updated 3 years ago
- Solar energy power generation, we need to predict the production of solar photovoltaic(PV). And the dataset contains attributes like temp…☆16Updated 2 years ago
- This is my thesis work on renewable energy detection which compares state of art models using Machine Learning and Deep Learning adapted …☆12Updated 3 years ago