Summary of Comprehensive Study Of Predictive Maintenance in Industries Using Classification Models and Lstm Model, by Saket Maheshwari et al.
Comprehensive Study Of Predictive Maintenance In Industries Using Classification Models And LSTM Model
by Saket Maheshwari, Sambhav Tiwari, Shyam Rai, Satyam Vinayak Daman Pratap Singh
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed study aims to investigate various machine learning classification techniques, including Support Vector Machine (SVM), Random Forest, Logistic Regression, and Convolutional Neural Network LSTM-Based, for predicting and analyzing machine performance. The goal is to assess the algorithms’ performance in terms of accuracy, precision, recall, and F1 score. The findings will aid maintenance experts in selecting the most suitable algorithm for effective prediction and analysis of machine performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers use AI to improve predictive maintenance and diagnostics in machines. They compare four different machine learning techniques: SVM, Random Forest, Logistic Regression, and Convolutional Neural Network LSTM-Based. The goal is to find the best way to predict when a machine might fail or need repair. This can help reduce costs and prevent accidents. |
Keywords
» Artificial intelligence » Classification » F1 score » Logistic regression » Lstm » Machine learning » Neural network » Precision » Random forest » Recall » Support vector machine
