A system 10 for controlling an industrial plant 12 comprises automatic control equipment 14 comprising a plurality of measurement sensors 16 for sensing predetermined variables associated with components of the industrial plant 12. The sensors 16 generate measured data relating to operation of the components of the industrial plant 12. A database 20 contains operational data, including observational data, regarding the industrial plant 12. A processor 18 is in communication with the automatic control equipment 14 and the database 20 for receiving the measured data from the sensors 16 of the automatic control equipment 14 and the operational data from the database 20. The processor 18 manipulates the measured and operational data to provide an evolving description of a process condition of each component over time, along with output information relating to operational control of the industrial plant 12 and for updating the database 20.

A method of optimizing equipment usage in a network includes determining wear of a first machine in the network. A state of a second machine in the network is determined. When the second machine will be available for use with the first machine is determined based on the state of the second machine. A state of the first machine is recorded. Optimal usage criteria for the first machine is determined based on when the second machine will be available, the recorded state of the first machine and the wear of the first machine. The optimal usage criteria is provided to the first machine. The first machine is monitored after its has received the optimal usage criteria.

A method for operating a machine-human interface in an automation environment includes receiving or automatically retrieving, by a machine-human interface computer, sensor data corresponding to a plurality of humans working the automation environment. The machine-human interface computer applies a human model to the sensor data to yield a plurality of human state records, each human state record corresponding to one of the humans working within the automation environment. The machine-human interface computer also identifies automation tasks using a factory state schedule. Based on the plurality of human state records, the machine-human interface computer assigns the automation tasks to the plurality of humans.

A system for cognitive assistance in aircraft assembly includes a system to monitor an aircraft assembly process and acquire physical and physiological data on a human worker performing assembly operations. A cognitive model of the human worker on a data-processing device is coupled to the monitoring system to receive acquired physical and physiological data of the human worker, the cognitive model configured to provide state information on the human worker and prognostic data on expected behavior of the human worker during the aircraft assembly process. The state information and the prognostic data are continuously updated during the aircraft assembly process. A system control is coupled to the monitoring system and the cognitive model to assess current state of the aircraft assembly process based on the monitored aircraft assembly process, the state information of the of the human worker and the prognostic data of the cognitive model.

A task analysis apparatus that determines whether tasks have been performed in a predetermined task process. The task analysis apparatus sets, in an image in which a task state has been captured, a plurality of task regions for which task analysis is to be performed, sets, for the plurality of task regions, an order in which tasks are to be performed, detects an object in the image, and in response to the object being detected in a predetermined task region, determines that a task has been started in the predetermined task region, and in response to the object being detected in a task region that is next in order after the predetermined task region, determines that the task has ended in the predetermined task region.