Summary of Advancing Machine Learning in Industry 4.0: Benchmark Framework For Rare-event Prediction in Chemical Processes, by Vikram Sudarshan and Warren D. Seider
Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes
by Vikram Sudarshan, Warren D. Seider
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel benchmark framework for predicting rare events in industrial processes using machine learning algorithms. The authors compare the performance of various ML models, including Linear Support-Vector Regressor, k-Nearest Neighbors, Random Forests, XGBoost, LightGBM, CatBoost, Dense Neural Networks, and TabNet, to identify optimal strategies for predicting abnormal rare events. The evaluation uses comprehensive performance metrics such as RMSE, training and testing times, hyperparameter tuning time, deployment time, and alarm efficiency. The goal is to enable operators to obtain safer and more reliable plant operations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict rare bad things that can happen in big factories. It compares lots of different computer programs (machine learning models) to see which one works best for finding these problems before they happen. The models tested include simple ones like Linear Support-Vector Regressor, and more complex ones like Random Forests and Neural Networks. To see how well each model worked, the authors used special measures like accuracy, speed, and time it took to train the model. This helps find the best way to predict rare bad events and make sure factories run safely. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning » Xgboost
