Scalable Deep Learning Algorithms with Big Data for Predictive Maintenance in Industrial IoT
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I1P109Keywords:
Predictive maintenance, Industrial IoT, DNN, Sensor data, Fault, Machine learning (ML), Deep learning (DL)Abstract
Predictive maintenance for industrial machinery makes use of cutting-edge methods and data analysis to predict when machinery may break down. Planning ahead can reduce downtime and maximize operating efficiency via scheduled maintenance. Achieving accurate, consistent, and well-integrated IoT sensor data is difficult, as poor data quality can result in inaccurate predictions and false alerts. In this study, a scalable approach utilizing large data derived from a variety of industrial sensors, a deep learning (DL) framework is suggested for use in predictive maintenance within Industrial IoT settings. Using data gathered from current, non-contact temperature, humidity, and three-axis accelerometers, the method trains a Deep Neural Network (DNN) model. Various fault states, including normal, overcurrent, stop rotation, misalignment, and excessive load, are included in the dataset. These circumstances were recorded in both online and offline settings. Normalization, one-hot encoding, and data division into training and testing sets (70/30) were part of the preparation steps. The DNN model is fine-tuned using graph cut methods to improve performance, leading to a low loss value of 0.0014, a high recall of 99.29%, a precision of 99.07%, and a classification accuracy of 99.45%. The use of fog computing also helps with IIoT system latency and interoperability problems when handling data promptly. Proof that the product works suggested a model for early, non-destructive fault detection, contributing to the goals of smart, efficient, and sustainable industrial operations
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