Predictive Maintenance in Smart Factories Using Iot and Big Data Analytics Enhanced by Long Shortterm Memory Networks

As we are now experiencing the advance of smart factories as a phenomenon of the Industry 4.0 paradigm, predictive maintenance takes the place of critical importance in optimizing the operation of industrial production. This study aims to examine the implementation of the Internet of Things (IoT) and Big Data Analytics with Long Short-Term Memory (LSTM) networks to improve predictive maintainability. IoT devices help in acquiring real time data from industrial machinery equipment including temperature, vibration and cycle details. Such databases, frequently large and integrated with dissimilar formats, are then reviewed and examined through Big Data platforms for symptoms of potential failures. Maintenance schedule predictions are improved with integrated use of LSTM networks because of their efficiency in capturing long term data dependency. Based on past and current data, the model accurately predicts equipment breakdowns and minimizes costly preventable breakdowns. The applicability of the proposed framework is also tested, as case studies from different industries and belonging to different categories of machines are provided. The analysis reveals an increase in the maintenance performance level, the Service Life of the facilities, and safe operation approaches. This presented work highlights the disruptive nature of IoT enabled Predictive Maintenance powered by LSTM networks hence opening up possibilities of smarter and more robust industrial systems.