A method for controlling operation of a fluid transport system by applying a self-learning control process. The method includes: receiving obtained values of input signals during operation of the system during a first period of time, which is controlled by a predetermined control process, automatically selecting a subset of the input signals based on the received obtained values of the input signals, receiving obtained values of at least the selected subset of input signals during a second period of time, which is controlled by applying the self-learning control process, which is configured to control operation based only on the selected subset of input signals, and wherein applying the self-learning control process includes updating the self-learning control process based on the received obtained values of the selected subset of the input signals and based on at least an approximation of a performance indicator function.

A method and a system for performing stochastic sequential assignment of jobs with random arrival times is provided. The method includes receiving a first plurality of jobs in a sequence; and sequentially applying, to each respective job from among the first plurality of jobs, a non-parametric sequential allocation algorithm in order to determine whether to accept the respective job or to decline the respective job. The application of the non-parametric sequential allocation algorithm includes calculating, for each respective job, a corresponding reward value that relates to a reward that is gained when the respective job is accepted; and maximizing an expected cumulative reward value based on the calculated reward values.

An Industrial Internet of Things (IIoT) agent module or device preferably used as or in place of a Supervisory Control and Data Acquisition (SCADA) data node system and/or conventional SCADA system, which is operatively coupled and in communication with an IIoT cloud platform, so as to perform control and data acquisition operations and exchange data and commands automatically or in response to Inputs from the IIoT cloud platform; wherein all production details/settings/parameters, including process logic, control methodology, product recipe, and data point setup, can be dynamically changed based on decision/input/command of the IIoT cloud platform, such that the IIoT cloud platform might completely “re-configure/re-program” a software portion of the IIoT agent module or device governing its working behavior or characteristics by sending a reconfiguration/reprogramming information.

A method includes identifying at least one of historical data associated with historical substrate lots processed by substrate processing tools in a substrate processing facility or simulated data for simulated substrate lots processed by simulated substrate processing tools. The method further includes generating features from the at least one of the historical data for the historical substrate lots or the simulated data for the simulated substrate lots. The method further includes training a machine learning model with data input comprising the features to generate a trained machine learning model. The trained machine learning model is capable of generating one or more outputs indicative of one or more corrective actions to be performed in the substrate processing facility.

Systems and methods are provided for detecting events in industrial processes. An acquisition system may include one of a camera and an audio recorder to acquire monitoring data in the form of one of imaging data and acoustic data, respectively. A computer system, may include a machine learning engine and may be programmed to classify the monitoring data under a classifier, quantify, based on the classifier, the monitoring data with at least one quantifier, and detect an event when the at least one quantifier satisfies a predetermined rule corresponding to the at least one quantifier.

Systems and methods are provided for detecting events in industrial processes. An acquisition system may include one of a camera and an audio recorder to acquire monitoring data in the form of one of imaging data and acoustic data, respectively. A computer system, may include a machine learning engine and may be programmed to classify the monitoring data under a classifier, quantify, based on the classifier, the monitoring data with at least one quantifier, and detect an event when the at least one quantifier satisfies a predetermined rule corresponding to the at least one quantifier.

Methods, apparatus, systems, and articles of manufacture are disclosed for automatically updating artificial intelligence models operating on data of a first factory production line, the apparatus comprising, an intelligent trigger circuitry to trigger an automated model update process, an automated model search circuitry to, in response to a model update, generate a plurality of candidate artificial intelligence models, and an intelligent model deployment circuitry to output a prediction of an artificial intelligence model combination to improve prediction performance over time.

A state management system according to an aspect of the present invention includes: a generation unit that generates a state element representing a modification request to change a first state element to a second state element, the first state element representing a state of an element, in which a first setting value is set, of a system of before modification, the second state element representing a state of the element, in which a second setting value is set, of the system of after modification, wherein a current state indicated by the first state element is a current state indicated by the second state element, and the modification request indicates that the current state is a request state, a state that depends on difference between the first setting value and the second setting value is the current state, and the second setting value is a setting value after modification.

A programmable controller includes a time allocation setting section for setting execution time allocation, a stage analysis section for analyzing the operation stage of a machining system, a measurement section for measuring cycle time, and a machine learning device for learning the changing of the execution time allocation to sequence programs. The machine learning device includes: a state observation section for observing execution time allocation data, operation stage data, and machine operation pattern data as a state variable; a determination data acquisition section for acquiring, as determination data, cycle time determination data for determining whether cycle time taken to execute the operation stage is appropriate; and a learning section for learning the changing of the execution time allocation in relation to the operation stage of the machining system and an operation pattern of the machine.

A programmable controller includes a time allocation setting section for setting execution time allocation, a stage analysis section for analyzing the operation stage of a machining system, a measurement section for measuring cycle time, and a machine learning device for learning the changing of the execution time allocation to sequence programs. The machine learning device includes: a state observation section for observing execution time allocation data, operation stage data, and machine operation pattern data as a state variable; a determination data acquisition section for acquiring, as determination data, cycle time determination data for determining whether cycle time taken to execute the operation stage is appropriate; and a learning section for learning the changing of the execution time allocation in relation to the operation stage of the machining system and an operation pattern of the machine.