Enhancing Industrial Automation Through IIoT-Enabled Cloud Computing and LSTM for Scalable and Real-Time Data Analytics

In this thesis, we investigate the combined application of the Industrial Internet of Things (IIoT), cloud computing and Long Short-Term Memory (LSTM) networks to facilitate advanced industrial automation through the industrial analytics enabled by scalable and real time data processing. IIoT is an industrial approach to generating very large amounts of data from connected industrial devices that can be parsed through cloud computing to yield immediate insights into production processes, equipment health, and operational efficiency. Since this is a time series data, we employ LSTM (A type of recurrent neural network) in this architecture which is able to handle time series data well and is a good fit for predictive maintenance and anomaly detection problems in industrial setting. The proposed system involves three main components: Second, the IoT collection of data from IIoT devices, storage of data in the cloud, and processing of data are stored in the cloud; and finally, real time analysis of data using LSTM models. The LSTM models train on historical datasets containing sensor readings and machine logs, to make accurate equipment failure and process optimization predictions. For large scale data processing we use cloud computing platforms like AWS or Azure to manage where these big databases are located. Evaluation of the system performance is presented in the study with regard to prediction accuracy, latency and a comparison of LSTM with other ML techniques. We can see that that there's strong potential for these technologies to have a powerful impact on industrial automation, downtime, and operational efficiency. Yet, there are still data security and system scalability issues to explore. This research shows that IIoT implants and cloud computing, in conjunction with LSTM, can be designed to transform existing production processes.