Deep Learning and Anomaly Detection in Predictive Maintenance Platform

With the development of intelligent manufacturing and industrial Internet of Things (IIoT), predictive maintenance has become an important technology to improve product reliability and reduce downtime. Establishing a predictive maintenance platform through deep learning algorithms, providing support for equipment fault prediction and anomaly detection through sensor technology, data collection and cleaning, feature extraction, etc. The architecture methods of deep learning such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders, and Long Term Short Term Memory (LSTM) have been widely adopted in fields such as wind turbine fault prediction, intelligent manufacturing quality inspection, and equipment health assessment, which can improve equipment judgment accuracy, reduce maintenance costs, and ultimately enhance production capacity. This article will further explore the application and prospects of deep learning in predictive maintenance.