Predictive maintenance is an important factor in ensuring stable production capacity to identify potential problems that may arise in advance. Data from sensors, devices, and operating systems can be used to accurately predict asset errors.
For planners and operators, predictive maintenance timing provides a comprehensive insight into asset risk to maintain a higher level of asset availability, providing service-based differentiation, and helping reduce maintenance costs. Using data such as customer's collected equipment information (number of breakdowns, number of repairs, and purchase date), it is possible to predict the maintenance date to prevent equipment failure and to prevent property damage.
|Data||Data type||Content||Use mode|
|Input data||CSV||Equipment information (number of failures, number of repairs, date of purchase, etc.)||API|
|Output data||CSV||Repair date prediction||API|
||Attached file upon application
||Customer data required for model creation
|API Setting||$1,800 ~|