This study examines the feasibility of machine failure detection using deep learning approaches in a bid to improve predictive maintenance approaches. A deep-learning model has been created using the AI4I 2020 Predictive Maintenance dataset in order to effectively predict equipment failures. The model is built using two deep learning algorithms Long – Short Term Memory (LSTM), and Convolutional Neural Networks (CNNs). The preprocessing of the data that encompasses data cleaning, feature engineering, and normalization is applied to guarantee data quality. The metrics used to evaluate model performance are accuracy, ROC and AUC. Empirical findings show that the proposed LSTM-CNN model has a high predictive accuracy and significantly better results compared to the other traditional Support Vector Machine (SVM) models, especially when it comes to predicting complex patterns and dependence of operational data. In spite of the benefits, there are still issues of data quality, architecture, hyperparameter choice, and model interpretability. In general, the research validates the high potential of deep learning in reliable machine failure detection and specifies the main directions of future studies.

Machine failure diagnosis, LSTM, CNN, SVM, Data preprocessing.

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