Predictive Maintenance in Industries Using Deep Learning Models: Reducing Downtime and Increasing Efficiency

Condition monitoring is an essential concept in the current industrial employment strategy of assembling forecasting, to decrease time loss and increase the organizational efficiency by anticipating failure of equipment. Predictive maintenance is the focus of this study with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks evaluated for deep learning models. The given methodology entails development of these models for training with historical sensor data of industrial machinery for failure prognosis. Similarly, the accuracy, precision, recall, F1 score, and AUC-ROC are computed to compare the performance with Random Forest and SVM showing that the proposed CNN-based achieves superior predictive accuracy and a lower false positive rate. Furthermore, prediction time, training time and resources consumption are also compared, which confirms that although with the higher computational need deep learning models offer the significant increase of the prediction accuracy. In addition, both cost and time remain substantialised and thus showing clear economic improvement through deep learning-based predictive maintainability. Therefore, the research supports the possibilities of CNN and LSTM models in overhauling industrial maintenance approaches through increased reliability, lowering the operational costs, and improved asset management make them a realistic solution for industries that would want to adopt strong maintenance structures and improve on the lifespan of their equipment.