An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

In order to control a technical system using a control model, a transformation function is provided for reducing and/or obfuscating operating data of the technical system so as to obtain transformed operating data. In addition, the control model is generated by a model generator according to a first set of operating data of the technical system. In an access domain separated from the control model, a second set of operating data of the technical system is recorded and transformed by the transformation function into a transformed second set of operating data which is received by a model execution system. The control model is then executed by the model execution system, by supplying the transformed second set of operating data in an access domain separated from the second set of operating data, control data being derived from the transformed second set of operating data.

The invention relates to a monitoring system for controlling a plant, such as a beverage dispensing plant, said plant being comprising one or more apparatuses connected to one another or to other apparatuses external to said plant by electrical, pneumatic or hydraulic lines. The monitoring system comprises a plurality of detection sensors (S.sub.power, S.sub.CO2, T.sub.ambiente (bevanda), T.sub.ambiente (impianto), S.sub.p_Aria) sensor, positioned on at least one of said electrical, pneumatic or hydraulic lines, and a monitoring unit, provided with transceiver means and connected to said detection sensors, is configured to receive the data detected by said detection sensors, determine the values and trends over time of the respective parameters of said detection sensors by comparing them with average variable values and/or trends to verify the operation of said plant. The invention also relates to a method for detecting and monitoring the sanitization of a beverage dispensing plant.

The invention relates to a monitoring system for controlling a plant, such as a beverage dispensing plant, said plant being comprising one or more apparatuses connected to one another or to other apparatuses external to said plant by electrical, pneumatic or hydraulic lines. The monitoring system comprises a plurality of detection sensors (S.sub.power, S.sub.CO2, T.sub.ambiente (bevanda), T.sub.ambiente (impianto), S.sub.p_Aria) sensor, positioned on at least one of said electrical, pneumatic or hydraulic lines, and a monitoring unit, provided with transceiver means and connected to said detection sensors, is configured to receive the data detected by said detection sensors, determine the values and trends over time of the respective parameters of said detection sensors by comparing them with average variable values and/or trends to verify the operation of said plant. The invention also relates to a method for detecting and monitoring the sanitization of a beverage dispensing plant.

According to one embodiment, a data collection system includes an event data collector, a state machine generator, a state machine list, and a state machine driver. The event data collector collects sense signals respectively as a plurality of event data. The state machine generator generates a state machine as a model corresponding to the workpiece. One of the sense signals is acquired when the workpiece is fed into the processing system. The state machine generator generates the state machine and generates an ID for the state machine when the event data collector collects one of the plurality of event data corresponding to the one of the sense signals. The state machine driver drives the state machine retained in the state machine list by sending, to the state machine retained in the state machine list, an event corresponding to another one of the sense signals.

The present technology addresses the problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, an exploration strategy comprising diverse exploration (DE) is employed, which learns and deploys a diverse set of safe policies to explore the environment. DE theory explains why diversity in behavior policies enables effective exploration without sacrificing exploitation. An empirical study shows that an online policy improvement algorithm framework implementing the DE strategy can achieve both fast policy improvement and safe online performance.

A method of optimizing the operation of a fleet of gas turbine engines is provided. The method comprises the steps of: (a) measuring respective values for plural control actuator settings within each of the gas turbine engines; (b) deriving, based on data external to the operation of the gas turbine engines, a desired performance modification of the gas turbine engines; (c) determining, based on the measured control actuator settings, one or more respective trim signals for varying selected of the control actuator settings to achieve the desired performance modification; and (d) transmitting the trim signals to respective electronic controllers of the engines to vary the selected control actuator settings accordingly.

Apparatuses, systems, and techniques to place one or more objects in a location and orientation. In at least one embodiment, one or more circuits are to use one or more neural networks to cause one or more autonomous devices to place one or more objects in a location and orientation based, at least in part, on one or more images of the location and orientation.

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Systems and methods described herein can involve management of a system having a plurality of sensors, the plurality of sensors observing a plurality of process steps, which can involve selecting a subset of the plurality of sensors for observation; executing anomaly detection from data provided from the subset of the plurality of sensors; for a detection of an anomaly from a sensor from the subset of sensors, selecting ones of the plurality of process steps based on the detected anomaly; estimating a probability of anomaly occurrence for the selected ones of the plurality of process steps; and for the estimated probability of anomaly occurrence meeting a predetermined criteria, selecting ones of the plurality of sensors associated with the selected ones of the plurality of process steps for observation.