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An overview of the application of machine learning in predictive maintenance:
The literature review on predictive maintenance using machine learning focuses on forecasting the remaining useful life (RUL) of machinery. Models like artificial neural networks, support vector machines, and Gaussian process regression are used for RUL prediction, providing high accuracy. However, challenges include computational complexity, parameter optimization, and the need for standardized datasets, limiting real-world applicability.


Comparative Analysis of Machine Learning Models for
Predictive Maintenance of Ball Bearing SystemsL:

The literature review discusses the use of different machine learning models for predictive maintenance of ball bearing systems. The central idea is to optimize machine safety and reduce maintenance costs in industrial analysis. The algorithms and models evaluated and compared include Random Forest, Linear Regression, Support Vector Machine, Extreme Gradient Boost, and long short-term memory (LSTM). The models are assessed based on their accuracy, precision, recall, F1 scores, and computation requirement. The comparison results show that Extreme Gradient Boost performs the best in terms of overall performance and computation time. However, Logistic Regression underperforms for the given dataset. The study concludes that the use of machine learning models can improve the efficiency of industrial systems by enabling early fault detection.
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