A predictive maintenance model uses sensors and IoT technology to monitor the condition of equipment and predict when it will need repairs or replacements. This equipment may be anything from personal cars to large factory machines.
The machine learning programs used to run these models tend to take either the regression approach or the classification approach. Regression produces more accurate results but relies on more data; classification best predicts equipment failure within a set time period.
Smart factory technology allows factory sensors to communicate with each other, with employee mobile devices, and with management's software platform. This enables employees to monitor equipment 24/7 and receive immediate alerts whenever there is a problem. The technology also enables the equipment itself to automatically adjust operations in order to keep the factory running smoothly.
A smart stadium uses IoT to collect, analyze, and act on data within a stadium. This data may come from the stadium itself or from the mobile phones of visitors. Some stadiums also collect data on surrounding areas, like parking lots.