Provided is a production system including: a first industrial machine configured to control a second industrial machine; and circuitry configured to acquire data relating to an operation of at least one of the first industrial machine or the second industrial machine, wherein the first industrial machine comprises a synchronous area regularly subjected to synchronization and an asynchronous area different from the synchronous area, and wherein the first industrial machine is configured to: write the data into the asynchronous area; and transmit the data written in the asynchronous area to an external device.

The invention comprises a method for analyzing malfunctions and/or changes of device statuses in a system of automation technology, wherein the system has a plurality of field devices communicating with one another directly via a communication network and being designed to issue an appropriate diagnostic notice depending on a malfunction or a change of a device status in the system. The diagnostic notices are transmitted to a data bank and stored in same. The method comprises: reading the diagnostic notices from the data bank; filtering the read diagnostic notices using at least one selection criterion; linking the filtered diagnostic notices using time stamps; defining time intervals; grouping the diagnostic notices linked using the time stamps into the defined time intervals which correspond to their respective time stamps; and evaluating the grouped diagnostic notices with regard to defined abnormalities.

A system for monitoring potential failure in a machine or a component thereof, the system including: at least one optical sensor configured to be fixed on or in vicinity of the machine or the component thereof, at least one processor in communication with the sensor, the processor being executable to: receive signals from the at least one optical sensor, obtain data associated with characteristics of at least one mode of failure of the machine or the component thereof, identify at least one change in the received signals, for an identified change in the received signals, apply the at least one identified change to an algorithm configured to analyze the identified change in the received signals and to classify whether the identified change in the received signals is associated with a mode of failure of the machine or the component thereof, thereby labeling the identified change as a fault, based, at least in part, on the obtained data, and for an identified change is classified as being associated with a mode of failure, outputting a signal indicative of the identified change associated with the mode of failure.

A method for providing process data of a device in an industrial automation environment to a computer system. In one embodiment, the method includes the following steps: executing a process data model on the device for generating estimated process data; determining that the estimated process data deviates from the real process data by more than a threshold value; and only if the estimated process data deviates from the real process data by more than the threshold value: transmitting information representing the real process data from the device to the computer system.

A human flow estimation system comprises: a sensor network comprising a plurality of sensors arranged in a to-be-estimated region for detecting the human flow; a model building module configured to build a human flow state model based on arrangement positions of the sensors, and build a sensor network model based on data of the sensors; and a human flow estimation module configured to estimate the human flow and provide a data weight of the estimated human flow based on the human flow state model and the sensor network model. The human flow estimation system further comprises a failure detection module configured to detect whether each sensor in the sensor network is abnormal, and the model building module is further configured to adjust the human flow state model and the sensor network model when an exception exists on the sensor.

A control device performs logging of information related to communication with an instrument and logging of information related to control of the logging operation. The control device includes: a first connector that connects a first network to which a control target belongs; a second connector that connects a second network to which an external instrument belongs; a control arithmetic unit that executes control arithmetic processing using data related to the control target, a communication unit that exchanges the data with the external instrument by secure communication through the second network; a first logging unit that logs information related to the secure communication performed by the communication unit; and a second logging unit that logs information related to control of a logging operation of the first logging unit.

A power distribution system and method can include a controller and a set of power-using devices. Each power-using device in the set can include a sensor configured to measure a parameter and transmit a sensor signal representing the parameter to the controller, and the controller can respond to the transmitted sensor signal.

A system is provided for measurement data management in a distributed environment. The system comprises at least one storage system adapted to obtain raw measurement data or intermediate results from at least one measurement site via a network. In addition, the system further comprises a database, operatively connected to the said storage system, adapted to be accessed remotely by the measurement site via the network. The storage system or the measurement site is further adapted to perform successive processing steps on the raw measurement data along a process chain in order to generate measurement results, whereby associating metadata with the raw measurement data and with the measurement results. In this context, the metadata associated with each measurement result of the successive processing steps is provided with a new reference as well as a reference to the reference of the measurement result from the preceding processing step.

To improve prediction accuracy of a process including a reaction in a chemical plant. A prediction apparatus includes a process data processing unit that performs a predetermined processing process on process data obtained from a chemical plant, and a prediction model generation unit that generates a prediction model having learned features of the process data obtained from the chemical plant, on the basis of causality information that defines a combination of first process data and second process data or a value corresponding to the second process data among the process data obtained from the chemical plant or the process data processed by the process data processing unit. The first process data is used as an explanatory variable. The second process data or the value corresponding to the second process data is used as a response variable. Furthermore, the process data processing unit obtains a value corresponding to a reaction rate of a processing target in a predetermined period by using the process data.

A regression model reflecting causality between variation of an explanatory variable and variation of a response variable is constructed. A prediction apparatus predicts characteristic values of a product by using process data obtained from a production facility. The prediction apparatus includes a process data acquisition unit that reads the process data from a storage device that stores the process data obtained from the production facility, and a prediction model generation unit that generates a prediction model on the basis of causality information that defines a combination of first process data and second process data or a value corresponding to the second process data. The first process data and the second process data or the value corresponding to the second process data are included in the read process data. The first process data is used as a predetermined explanatory variable. The second process data or the value corresponding to the second process data is used as response variable. The prediction model has learned features of the process data obtained from the production facility. The prediction model generation unit generates the prediction model and determines a positive/negative variation direction of the response variable in accordance with a positive/negative variation direction of the predetermined explanatory variable.