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Enhancing Industrial Equipment Reliability Through an Optimized ANN-Powered Predictive Maintenance System
Abstract
Maintaining industrial equipment ensures efficiency, reduces downtime, and prevents costly failures. Routine inspections or equipment's reactive response breakdowns may not be efficient and it can cause unexpected failures. This chapter presents an automated framework for predictive maintenance using ANN. The independent parameters including air temperature, torque, rotational speed and tool wear are used to estimate the failure of equipment. The proposed ANN network is initially optimized by tuning its hyperparameters i.e. hidden layers, learning rate and regularization parameter. Later optimized ANN is validated using quantitative parameters i.e. accuracy, precision, recall and F1-score. The optimized ANN succeeded with 98% accuracy in equipment failure prediction. This real-time predictive maintenance can improve equipment reliability with reduction in maintenance cost and boost industrial efficiency. The framework can be customized and integrated with a maintenance management system further to meet the demand of various industrial equipment and to prevent a shutdown of machinery.
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