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How to apply AI for predictive maintance? 

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Predictive maintenance using AI involves applying artificial intelligence technology to forecast and prevent equipment failures in the manufacturing industry. Several approaches have been explored in the papers. Netisopakul and Phumee developed a system that uses deep learning models, such as multilayer perceptron (MLP) and bidirectional LSTM (Bi-LSTM), to forecast temperature and insulation values of motors . Upasane et al. proposed a type-2 fuzzy-based Explainable AI (XAI) system optimized with Big-Bang Big-Crunch (BB-BC) to predict faults in water pumping equipment . Bundasak and Wittayasirikul used logistic regression to develop a model for predicting motor abnormalities with high accuracy . These papers highlight the importance of using AI techniques, such as deep learning and fuzzy logic, to improve predictive maintenance in the manufacturing industry.

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The paper provides a system called PdMA AI System that uses AI algorithms, specifically logistic regression, to predict motor abnormalities and enable predictive maintenance.
The paper does not provide specific details on how to apply AI for predictive maintenance. It focuses on the development of a motor health prediction system using AI algorithms.
The paper proposes a type-2 fuzzy-based Explainable AI system for predictive maintenance in the water pumping industry, which is optimized using Big-Bang Big-Crunch.
The paper discusses applying AI technology to forecast temperature and insulation values of motors in a manufacturing industry, specifically using deep learning models such as MLP, LSTM, LSTM autoencoder, and Bi-LSTM. The best model, Bi-LSTM, is further optimized using a genetic algorithm.
The paper discusses applying AI technology to forecast temperature and insulation values of motors in manufacturing processes using deep learning models.

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