A computer-implemented method for determining an operational state of an industrial plant includes acquiring alarms raised within the plant and adding them to a pool of important alarms, determining whether a physical state of the plant indicated by a first alarm causes a second alarm or meets a predetermined state-dependent condition and, if so, moving the first alarm to a pool of informative alarms; and determining the operational state of the plant and/or a corrective action for improving this operational state based on the alarms in the pool of important alarms.

An alarm rationalization system receiving and responsive to industrial process information collected from a process control system for identifying one or more alarms and executing an artificial intelligence (AI) alarm engine. The AI alarm engine builds a process/domain model based on the received industrial process information and historized alarm information to evaluate the alarms in accordance with a predefined alarm philosophy. The AI alarm engine then generates a plurality of alarm definitions based on the model to optimize the alarms. The AI alarm engine automatically populates a Master Alarm Database (MADB) with the alarm definitions. The alarms are then rationalized based on the alarm definitions stored in the MADB.

Regarding each of a plurality of operation patterns virtually generated in regard to the operation performed by a worker with respect to an actual plant, an information processing device uses plant data related to the operation of the actual plant and uses a virtual plant which follows the actual plant, and predicts the state transition of the actual plant in the case of implementing each operation pattern. Then, the information processing device outputs each operation pattern in a corresponding manner to the state transition of the actual plant as obtained by the virtual plant.

An information processing device uses models generated under different conditions, and predicts alarms occurring in the case in which each of a plurality of operation patterns, which is virtually generated in relation to the operation of an actual plant by a worker, is implemented with respect to the actual plant. Then, the information processing device sets the degree of reliability of the prediction result regarding each of the plurality of operation patterns. Subsequently, based on the degree of reliability of the prediction result, the information processing device performs display control with respect to the alarms.

A data collection and analysis system includes a communication unit and a data acquisition unit. The communication unit collects data indicating an internal state or operation state of an equipment item. The data acquisition unit acquires, from the communication unit, the data synchronized for each of groups according to setting information, the setting information being information in which the groups, a data acquisition timing, and a storage location of the data are set for a channel allocated to the data to be acquired from the equipment item, each of the groups bringing together a plurality of the channels, the data acquisition timing being a timing at which the data are acquired from the communication unit.

A method includes receiving first metrology data associated with first substrates produced by manufacturing equipment. The method further includes training a first machine learning model with data input including the first metrology data to generate a first trained machine learning model. The first trained machine learning model is capable of reducing dimensionality of second metrology data associated with second substrates produced by second manufacturing equipment to perform corrective actions associated with the second manufacturing equipment.

Methods, devices, and systems for space conditioning based on weather information are described herein. One device includes a memory, and a processor to execute executable instructions stored in the memory to receive forecasted weather information, determine, based on the forecasted weather information, future weather conditions, determine based on the future weather conditions and historical setpoint data, whether conditioning of a space is expected, and generate an alert in response to conditioning of the space being expected.

A data collection and analysis system includes a communication unit and a data acquisition unit. The communication unit collects data indicating an internal state or operation state of an equipment item. The data acquisition unit acquires, from the communication unit, the data synchronized for each of groups according to setting information, the setting information being information in which the groups, a data acquisition timing, and a storage location of the data are set for a channel allocated to the data to be acquired from the equipment item, each of the groups bringing together a plurality of the channels, the data acquisition timing being a timing at which the data are acquired from the communication unit.

An AI-based smart asset health surveillance system for a connected system is presented. The connected system includes a plurality of production and/or process lines, wherein each of the plurality of production and/or process lines includes a plurality of assets. The smart asset health surveillance system includes a memory having computer-readable instructions stored therein; and a processor configured to execute the computer-readable instructions. The processor is configured to execute the computer-readable instructions to monitor the plurality of assets and to automatically predict one or more downtime and/or anomalous events for the plurality of assets. An AI-based smart asset health surveillance method is also presented.

An AI-based smart asset health surveillance system for a connected system is presented. The connected system includes a plurality of production and/or process lines, wherein each of the plurality of production and/or process lines includes a plurality of assets. The smart asset health surveillance system includes a memory having computer-readable instructions stored therein; and a processor configured to execute the computer-readable instructions. The processor is configured to execute the computer-readable instructions to monitor the plurality of assets and to automatically predict one or more downtime and/or anomalous events for the plurality of assets. An AI-based smart asset health surveillance method is also presented.