Fault Diagnosis Using Data Fusion with Ensemble Deep Learning Technique in IIoT

Abstract

Detecting the breakdown of industrial IoT devices is a major challenge. Despite these challenges, real-time sensor data from the industrial internet of things (IIoT) present several advantages, such as the ability to monitor and respond to events in real time. Sensor statistics from the IIoT can be processed, fused with other data sources, and used for rapid decision-making. The study also discusses how to manage denoising, missing data imputation, and outlier discovery using preprocessing. After that, data fusion techniques like the direct fusion technique are used to combine the cleaned sensor data. Fault detection in the IIoT can be accomplished by using a variety of deep learning models such as PropensityNet, deep neural network (DNN), and convolution neural networks-long short term memory network (CNS-LSTM). According to various outcomes, the suggested model is tested with Case Western Reserve University (CWRU) data. The results suggest that the method is viable and has a good level of accuracy and efficiency.