A natural language processing system includes a storage device and a processor. The storage device is configured to preload records of failure histories of semiconductor equipment, and the records of the failure histories of the semiconductor equipment include natural language. The processor is electrically connected to the storage device and is configured to perform a natural language process on the records of the failure histories of the semiconductor equipment to generate an abnormal model classification table.

A natural language processing system includes a storage device and a processor. The storage device is configured to preload records of failure histories of semiconductor equipment, and the records of the failure histories of the semiconductor equipment include natural language. The processor is electrically connected to the storage device and is configured to perform a natural language process on the records of the failure histories of the semiconductor equipment to generate an abnormal model classification table.

Systems and methods for controlling industrial an industrial plant comprise: inputting an engineering diagram for a unit of the industrial plant, the engineering diagram including symbols representing assets of the industrial plant; extracting one or more assets from the engineering diagram using machine learning to recognize the one or more assets, the one or more assets including equipment, instruments, connectors, and lines, the lines relating the equipment, instruments, and connectors to one another; determining one or more relationships between the equipment, instruments, connectors, and lines to one another using machine learning to recognize the one or more relationships; and creating a flow graph from the equipment, instruments, connectors, and lines and the relationships between the equipment, instruments, connectors, and lines.

Robotic process automation (RPA) systems with improved user access enable a user to interact with an RPA system by way of a communication platform. The communication platform can support text messaging and/or speech communication with a virtual agent that in turn is able to interface with an RPA system. In this way, a user of the communication platform is able to conveniently interact with the RPA system, such as in a conversational manner. By analyzing and interpreting the conversation, the user's intent or desire can be determined and then carried out by the RPA system. Thereafter, results from the RPA system can be formatted and returned to the user. In one embodiment, to better understand the user's intent or desire from the text messages or natural language communications (i.e., voice or speech communications), artificial intelligence can be used.

A recommendation method (200) includes: querying a knowledge base (130) of maintenance cases using the text query to retrieve a ranked list (138) of maintenance cases relating to a text query; performing topic modeling (140) on the maintenance cases of the ranked list of maintenance cases to group the maintenance cases into N topics; for each topic, ranking the replaced part identifiers associated to the maintenance cases grouped into that topic to generate a ranked replaced part identifiers list (142) for that topic; displaying summarization (144) of the N topics on the display of the user interface and receiving a selection of a selected topic of the N topics via the user interface device; and displaying a list (150) of recommended parts for replacement comprising at least a top portion of the ranked replaced part identifiers list for the selected topic on the display of the user interface device.

A telemetry master (TM) system provides automatic monitoring and maintenance of equipment by automatically generating predictive actions of a certain confidence threshold to backend systems which maintain a full duplex communication channel with the TM system. Data entries including context information that are generated by applications corresponding to the backend systems during the functioning of the backend systems are collected, analyzed and actionable items such as alerts, alarms of particular messages cause the telemetry master system to generate a predictive action of a minimum confidence threshold using a pre-trained first data model. A second data model is employed to identify particular equipment from the backend systems that is to implement the action. Results from implementing the action are collected and again used to train the data models. Certain applications can include intelligent agents which enable automatic execution of the actions.

Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

Systems and methods for detecting and predicting faults in an industrial process automation system use trend data to forecast alerts and allow action to be taken before a problem occurs. The systems and methods provide fault/failure predictions that improve over time as more empirical data is collected for a related set of system components. The systems and methods may identify relationships among the components of a process automation system; identify and collect changes to system configuration; identify and collect data to inform reliability and predictive models; develop a domain-specific predictive model for one or more components that allows for component-based failure or degradation prediction; develop a system-predictive model that leverages reliability and criticality relationships, component-based predictions and operating parameters to predict the health of a part of or the entire process automation system; deliver a prioritized alert system; and identify root-cause failures of a component.

Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

A computer-implemented method for transforming a sequence comprising multiple words from a natural language to a machine executable sequence in real-time to control a machine. The sequence constituted by multiple characters forming words from the natural language is preprocessed by comparing the sequence to data from a database comprising classes. The sequence is searched for simple expressions and GI expressions known to be an upstream function class or a downstream function class. The sequence is dichotomized until all of the function classes contained in the sequence that are capable of resulting in dichotomies have been dichotomized. The first to the last classes of the sequence are iterated and each executable class is executed by a machine.