Maintenance Enhancement of Smart Manufacturing Units In Industry 4.0

摘要

The fourth industrial revolution has had a significant impact on the modern world. The fourth industrial revolution is primarily driven by the intelligent edge systems. Industry 4.0 is the integration of Big Data, Cloud Computing, Advanced Robotics, Internet of Things, Digital twin, and several other new technologies to enhance production processes that result in better products in a shorter amount of time at a cheaper cost. Tracking of machine health, maintenance through prediction, and scheduling of production are the three main issues facing any industrial sector. Smart manufacturing can address the aforementioned issues by using data-driven deep learning techniques. The model to effectively address the aforementioned difficulties is developed using the deep convolutional LSTM encoder-decoder architecture. LSTM auto-encoder, a feed-forward neural network whose input signal is equal to its output signal, can be used to predict the machine's speed. To put it another way, an auto encoder is an individual research algorithm that uses input data without a labeled target dataset to extract features. Some performance metrics, such as RMSE, MAE, MSE, and SMAPE, are revealed by the experimental results.