Fault Aware Data Prediction in Wireless Sensor Networks Using Machine Learning

This research study examines the difficult task of fault prediction in industry processes by integrating autonomous and intelligent Cyber-Physical System within the context of Industry 4.0& Industrial Internet of Things (IIoT). The researchers put out a comprehensive deep learning strategy that makes use of wireless sensors to gather sensory data from chemical plants in real-time. This approach makes it possible to use autoregression on skewed time series data to make accurate predictions, and this can be achieved by using a cutting-edge recursive design with several historical inputs. To enhance fault prediction accuracy while reducing uncertainty, this study has specifically integrated a Recursive Gradient Descent (RGD) framework. In addition to the initial fault prediction, the classification model that makes use of decaying temporal convolutions across several channels to identify and pinpoint the origin of multi- class faults has also been used. The resulting architecture, called as the cumulative uncertainty reduction networks (CURNet), exhibits improved capability in mitigating the accumulated prediction uncertainties across several stages. The research using CURNet clearly demonstrates a promising and effective solution for fault prediction in process industries. It outshines existing methods, particularly in terms of accuracy, precision, and uncertainty reduction, showcasing its potential to revolutionize fault detection and classification in the era of intelligent CPS and IIoT technologies. Overall, the CURNet proves to be a promising and effective solution for fault prediction in process industries by integrating the intelligent CPS and IIoT technologies, showcasing its potential to significantly enhance fault detection and classification capabilities when compared to traditional methods.