Deep Learning Algorithms for Predictive Maintenance in Data Centers

Due to the quick development of cloud services and enterprise information., the dependability and opening of information area foundation has turned out to be mission critical. Deep learning algorithms in predictive maintenance help data centers run components longer and avoid system interruptions. The aim of this study is to propose a deep learning-based predictive maintenance framework using real-time sensor data collected from Internet of Things (IoT) devices embedded in equipment used in data centers. The system uses a CNN-LSTM model to examine equipment spatial and temporal data patterns to estimate failure events before they happen. The local features can be extracted from multivariate sensor inputs by CNN., and the LSTM is able to effectively capture the dynamically sequential behavior in time to estimate the Remaining Useful Life (RUL) and detect anomaly. Such an approach results in a lower number of unplanned outages., better utilization of resources and better overall operation of the data center. Future directions are extended to edge environments and integration with BIM systems for full digital twin application.