There is described a system and method for managing control performance of a field device receiving variable data. Variable and setpoint references corresponding to a control loop of the field device are identified. A time delay normal period based on expected oscillations of the variable reference and settling limits associated with the setpoint reference are also identified. An offnormal timestamp is generated based on the variable reference relative to one or more second pre-settling limits associated with the setpoint reference. A normal timestamp is generated based on the variable reference relative to the settling limits. A settling time of the control performance is determined based on the normal timestamp, the offnormal timestamp, and the time delay normal period. One or more performance features of the field device are modified based on the determined settling time.

A diagnosis device stores a model used for diagnosing the condition of an industrial machine in a storage unit, acquires data related to the condition of the industrial machine, and based on the acquired data, determines the condition of the industrial machine by using the model stored in the storage unit. Then, in response to detecting that a component of the industrial machine has been replaced based on the acquired data and the data related to the determined condition of the industrial machine, the diagnosis device adapts the model stored in the storage unit to the condition of the industrial machine whose component has been replaced.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

A method and a system determine a change of state of an autonomous device, such as an autonomous vehicle. A plurality of performance parameter values obtained by monitoring at least one performance parameter during the autonomous operation of the device is received. A performance quantity quantifying the quality of autonomous operation of the device, in particular the quality of driving of the autonomous vehicle, is determined based on the obtained performance parameter values and information associated with a flux of software and/or hardware related to the autonomous operation of the device. Further, a change of state value for the device is determined based on the performance quantity.

Disclosed is a method for monitoring and/or controlling a chemical plant (12) with multiple assets via a distributed computing system (10) with more than two deployment layers (14, 16, 30, 32, 34), wherein the deployment layers (14, 16, 30, 32, 34) comprise at least two of a first processing layer (14), a second processing layer (16, 32, 34) and an external processing layer (30), the method comprising the steps of: providing (60) a containerized application (48, 50) including an asset or plant template specifying input data, output data and an asset or plant model, deploying (62) the containerized application (48, 50) to execute on at least one of the deployment layers (14, 16, 30, 32, 34), wherein the deployment layer (14, 16, 30, 32, 34) is assigned based on the input data, a load indicator, or a system layer tag, and executing the containerized application (46, 52, 54) on the assigned deployment layer(s) (14, 16, 30, 32, 34) to generate output data for controlling and/or monitoring the chemical plant (12), providing (66) the generated output data for controlling and/or monitoring the chemical plant (12).

The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.

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.

One example includes a rotary electric machine. The device includes at least one sensor. Each of the at least one sensor can be configured to provide a sensor signal in a first data format, the sensor signal providing an indication of a respective one of a plurality of operational characteristics of the rotary electric machine. The device also includes a programmable interface configured to receive the sensor signal from each of the at least one sensor and to translate the first data format associated with each of the at least one sensor into a second data format associated with a respective type of sensor corresponding to the respective at least one sensor and to provide at least one output signal in the second data format. Each of the at least one output signal can correspond to at least one of the operational characteristics.

A machine learning apparatus that learns an alarm factor in a motor drive device includes a state observation unit that obtains a feature amount as a state variable from the motor drive device and an alarm factor as label data, the alarm factor corresponding to the feature amount, and a learning unit that generates a learning model for inferring a new alarm factor corresponding to a new feature amount, from a dataset created on a basis of a combination of the state variable and the label data. The feature amount includes at least one of a detected current value detected from the motor, a speed command value specifying a rotational speed of the motor, an output voltage value output to the motor, an estimated speed value of the motor, and a detected speed value of the motor.

A shape data group that is shape data of each of a predetermined number or more of winding bodies correlated with time information is acquired, a first defect ratio before maintenance is calculated on the basis of each shape data group of the predetermined number of winding bodies read before the maintenance, a second defect ratio after the maintenance is calculated on the basis of each shape data group of the predetermined number of winding bodies read after the maintenance, and a facility state diagnosis model is generated by using each shape data group of the predetermined number of winding bodies read before the maintenance in a case where a difference between the first defect ratio and the second defect ratio is greater than or equal to a predetermined value.