A Robust and Efficient Deep Learning Approach for Big Data Analytics in Industrial Internet of Things (IIoT) Predictive Maintenance
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P122Keywords:
Industrial Internet of Things (IIoT), Predictive Maintenance, Big Data Analytics, Deep Learning, Sensor Data, Fault DetectionAbstract
Industrial Internet of Things (IIoT) has also revolutionized the traditional industries, making them able to connect in mass and provide continuous sensing and real-time monitoring, which has resulted in new possibilities of predictive maintenance. Nevertheless, the sheer size, great velocity and non-uniformity of sensor-generated data are important issues with regard to accurate and timely fault prediction. To resolve these problems, the proposed research provides a powerful and effective deep learning model based on a Convolutional Neural Network (CNN) that can be used in the analytics of a big data in IIoT setting. The solution proposed utilizes modern elements of preprocessing, such as Min-Max normalization and One-Hot encoding, and then divides the data into categories, which is used to train the models reliably. An architecture of CNN is designed to extract the time-spatial detail features of multimodal sensor data. The evaluation conducted in the form of an experiment has shown a better performance rate of 99.47% accuracy, as well as high precision, recall, and F1 scores, which prove the stability of the model and its ability to generalize. The usefulness of the CNN-based model in predictive maintenance is also confirmed by the comparative analysis with other machine learning models, including MLP, SVM, and GBT. The paper adds a flexible architecture that can be used to scale the operations of industries, minimize downtime, enhance the overall integrity of equipment in IIoT-based smart manufacturing facilities.
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