Electronic ODM manufacturers' factories generally have many heavy-asset, data-intensive equipment. A certain electronic ODM customer has 15 production lines in its SMT workshop, and its three core equipment are all heavy-asset, data-intensive equipment. Currently, the overall equipment effectiveness (OEE) of the workshop is 70%, unable to perform statistical analysis on defective or discarded material stations, nozzles, material numbers. Despite regular maintenance and replacement, unplanned failures still occur causing downtime. Currently, over twenty maintenance engineers perform routine maintenance based on time, using paper records and empirical debugging, urgently hoping to obtain data for analysis and integrate with the equipment maintenance system.
Through the GETECH industrial IoT platform, information such as pressure, station, feeder, material number, speed, parameter settings, end position, nozzle, program version number of the placement machine is captured on the platform for digital modeling, and combined with the equipment's fault tree model, an equipment fault prediction model is built using the MFA multi-factor analysis tool.
