To provide a device state monitoring system capable of monitoring an operating status of a device in detail. The device state monitoring system includes: a collection unit that acquires, from a device which executes a series of processes, operating information about the device in a time-series manner; and a process determination unit that performs matching of the operating information acquired by the collection unit with matching data obtained by modeling the operating information acquired from the device when the device is in each of the processes, and determines process information concerning the process which the device is executing.

A processor may analyze a physical environment. One or more portions of the physical environment may have an edge computing resource requirement. The processor may determine, based on the analyzing, one or more additional edge computing resources to be placed in a surrounding area associated with the one or more portions of the physical environment. The processor may situate, automatically, the one or more additional edge computing resources on a material handling device in the surrounding area and that is directed toward the one or more portions of the physical environment.

A processor may analyze a physical environment. One or more portions of the physical environment may have an edge computing resource requirement. The processor may determine, based on the analyzing, one or more additional edge computing resources to be placed in a surrounding area associated with the one or more portions of the physical environment. The processor may situate, automatically, the one or more additional edge computing resources on a material handling device in the surrounding area and that is directed toward the one or more portions of the physical environment.

A valve state grasping system, a display device and a rotary valve, a valve state grasping program, a recording medium, and a valve state grasping method that enable efficient valve system monitoring and accumulation of information. The system includes a valve V, a sensor unit, a server including a database, a terminal device including a display unit, and a system control unit. The database includes a position information unit, a history information unit, and an inference information unit. The position information unit includes unique information and pipe attachment information, and the history information unit includes at least measurement information and diagnosis information. The system control unit accumulates information of the position information unit and information of the history information unit in association with each other and outputs predetermined inference information from the inference information unit based on information of the position information unit and information of the history information unit.

The present invention comprises: a wearable sensor that is worn by a worker, the wearable sensor having a sensor that receives sensor data from a sensing object, and a transmitter that transmits to a terminal the sensor data received by the sensor; and a computer that determines operation content for the worker on the basis of the sensor data received from the wearable sensor, and outputs the result of the determination to a display unit.

The present invention comprises: a wearable sensor that is worn by a worker, the wearable sensor having a sensor that receives sensor data from a sensing object, and a transmitter that transmits to a terminal the sensor data received by the sensor; and a computer that determines operation content for the worker on the basis of the sensor data received from the wearable sensor, and outputs the result of the determination to a display unit.

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.

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.

A computing system is configured to analyze both measurement data and indicator data as big data aggregated in measurement database and indicator database by deep learning for each lot of a part or for each lot of a finished product and a part pre-associated with each other, and also for each consolidation target between bases subordinate to the same start point corresponding to identification information that specifies a business user of the computing system. Analysis target layers by the deep learning are a three-layer serial hierarchical structure containing a production condition layer and an environment condition layer as a start point for analysis of a part layer, or a four-layer serial hierarchical structure containing a part layer, a production condition layer, and an environment condition layer as a start point for analysis of a finished product layer.

A computing system is configured to analyze both measurement data and indicator data as big data aggregated in measurement database and indicator database by deep learning for each lot of a part or for each lot of a finished product and a part pre-associated with each other, and also for each consolidation target between bases subordinate to the same start point corresponding to identification information that specifies a business user of the computing system. Analysis target layers by the deep learning are a three-layer serial hierarchical structure containing a production condition layer and an environment condition layer as a start point for analysis of a part layer, or a four-layer serial hierarchical structure containing a part layer, a production condition layer, and an environment condition layer as a start point for analysis of a finished product layer.