Abstract: The present disclosure relates to a method for verifying the production process of field devices, including a step of accessing a service platform on which data from field devices, including identification data, the respective type of field device, configuration data, containing application-specific data, environment information of the field devices or parameter data, data relating to the production date of a respective field device and repair or troubleshooting cases of the field devices are stored. The method also includes steps of detecting anomalies by statistically evaluating the repair or troubleshooting cases stored on service platform and creating a notification in the event of a detected anomaly, supplying the data of the field devices and the notifications to a machine learning or prognosis system, and evaluating the data of the field devices and the notifications by means of the machine learning or prognosis system for forecasting series errors of the field devices.

Abstract: The present disclosure relates to a method for the application-specific setting of a field device, which is installed in a defined application in a process in automation engineering and determines or monitors at least one process variable of a medium, comprising: recording a diagnostic message which occurs or has occurred on the field device during a defined interval, analyzing which of the diagnostic messages are tolerable and/or plausible in the defined application, assigning an application-specific notice with regard to tolerability and/or plausibility to each diagnostic message that has arisen in the defined application, clearing the diagnostic message if this is classified as tolerable and/or plausible in the defined application, and using the application-specific setting of the field device, which clears the diagnostic message classified as tolerable and/or plausible.

Abstract: The present disclosure relates to a method for the application-specific setting of a field device, which is installed in a defined application in a process in automation engineering and determines or monitors at least one process variable of a medium, comprising: recording a diagnostic message which occurs or has occurred on the field device during a defined interval, analyzing which of the diagnostic messages are tolerable and/or plausible in the defined application, assigning an application-specific notice with regard to tolerability and/or plausibility to each diagnostic message that has arisen in the defined application, clearing the diagnostic message if this is classified as tolerable and/or plausible in the defined application, and using the application-specific setting of the field device, which clears the diagnostic message classified as tolerable and/or plausible.

Abstract: The present disclosure relates to a method for verifying the production process of field devices, including a step of accessing a service platform on which data from field devices, including identification data, the respective type of field device, configuration data, containing application-specific data, environment information of the field devices or parameter data, data relating to the production date of a respective field device and repair or troubleshooting cases of the field devices are stored. The method also includes steps of detecting anomalies by statistically evaluating the repair or troubleshooting cases stored on service platform and creating a notification in the event of a detected anomaly, supplying the data of the field devices and the notifications to a machine learning or prognosis system, and evaluating the data of the field devices and the notifications by means of the machine learning or prognosis system for forecasting series errors of the field devices.