A system, a method, and a computer program for monitoring or controlling a field instrument in a pipeline, including receiving a plurality of process readings which originated from a plurality of remote terminal units that are distributed along a portion of the pipeline, analyzing the received plurality of process readings to determine a plurality of influencing process readings and a target process reading, assessing whether the target process reading includes bad data, and generating an estimated process reading or updating a parameter of a system model based on the plurality of influencing process readings and based on whether the target process reading includes bad data.

An industrial development hub (IDH) supports industrial development and testing capabilities that are offered as a cloud-based service. The IDH comprises an enhanced storage platform and associated design tools that serve as a repository on which customers can store control project code, device configurations, and other digital aspects of an industrial automation project. The IDH system can facilitate discovery and management of digital content associated with control systems, and can be used for system backup and restore, code conversion, and version management. The IDH also supports simulation-based design and testing tools.

There is provision of a control device for outputting an operation amount of a controlled object so as to cause a process value of the controlled object to track a target value. The control device acquires a look-ahead target value within a time series of target values; calculates a look-ahead target value deviation which is a difference between the look-ahead target value and a current process value of the controlled object; calculates an adjusted target value deviation, by calculating a difference between the look-ahead target value and a predicted value of the process value after a look-ahead time length, based on a response model of the controlled object and past change amounts of the operation amount; performs reinforcement learning based on the adjusted target value deviation; and calculates an updated operation amount based on the adjusted target value deviation.

Systems and methods are provided for monitoring and controlling one or more environmental conditions of a building. The method comprises obtaining feedback from a first sensor at a first location within the building and feedback from a second sensor at a second location within the building. The method further comprises combining the feedback from the first sensor with the feedback from the second sensor in a weighted manner to generate a combined feedback, wherein a weight of the feedback from the first sensor relative to a weight of the feedback from the second sensor is based on a time of day. The method finally comprises operating HVAC equipment to drive the combined feedback toward a setpoint for the building.

Disclosed is a computer-implemented method of generating a time-series of data sets for predictive analysis from data comprising input variables and an output variable recorded at sequential time points, the method comprising: dividing the data into a collection of observations, each observation comprising: a subset of sequential time points; associated input variables; and an output variable recorded at a forecasting time point beyond the latest sequential time point of the subset; and using the collection of observations in a convolution neural network to predict the output at the forecasting time point of a streaming data set.

An electronic device and method for predicting whether a manufactured product will exhibit a potential defect by providing measurement information of a home appliance as input to a first learning network model and a second learning network model trained to predict whether the home appliance will exhibit a potential defect, applying a first weight to first prediction information output from the first learning network model and a second weight to second prediction information output from the second learning network model, identifying a probability that the home appliance will exhibit the potential defect based on weighted first prediction information of the first prediction information to which the first weight is applied and second prediction information of the second prediction information to which the second weight is applied. The first learning network model is a supervised learning network model and the second learning network model is an unsupervised learning network model.

An environmental control system of a building including a first building device operable to affect environmental conditions of a zone of the building by providing a first input to the zone. The system includes a second building device operable to independently affect a subset of the environmental conditions by providing a second input to the zone and further includes a controller including a processing circuit. The processing circuit is configured to perform an optimization to generate control decisions for the building devices. The optimization is performed subject to constraints for the environmental conditions and uses a predictive model that predicts an effect of the control decisions on the environmental conditions. The processing circuit is configured to operate the building devices in accordance with the control decisions.

The present disclosure provides a system and a method for controlling semiconductor manufacturing equipment. The system includes a sensor, a sensor interface, and an analysis unit. The sensor provides a sensor signal. The sensor interface receives the sensor signal and generates an input signal for a database server. A front-end subsystem receives the input signal from the database server and performs a comparison process to generate a data signal. A calculation subsystem performs an artificial intelligence analytical process to generate an optimal parameter set and a simulated result map according to the data signal. A message and tuning subsystem generates an alert signal and a feedback signal according to the optimal parameter set and the simulated result map, and the message and tuning subsystem transmits the alert message to a user of the semiconductor manufacturing equipment.

The present disclosure relates to estimation methods of actuator faults based on Bayesian learning. An actuator fault is modeled based on a random walking model, and a joint posterior probability distribution of a system state variable and the actuator fault is represented using two mutually independent hypothesis distributions based on a variational Bayesian theory; a system state variable and an actuator fault of a system at a moment are predicted at a moment; and a predicted system state variable and a predicted actuator fault are iteratively updated at the moment according to the Bayesian theory to output an estimated value of the system state variable at the moment, a variance of the estimated value and an estimated value of the actuator fault at the moment and a variance of the estimated value.