Machine learning approaches for descriptive, predictive, and directive equipment operation

Abstract

The digital twin of a facility defines the relationships between the different components of the facility and the facility's record system. Information from various monitoring systems of the facility is related to events by the digital twin of the facility. Use the operation history information of the facility to train a machine learning model. The trained machine learning model facilitates operations at the facility by providing descriptive information, predictive information, and / or directive information regarding the operation(s) at the facility.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit of U.S. Provisional Application No. 63 / 338,563, filed on May 5, 2022, entitled "MACHINE LEARNING APPROACH FOR DESCRIPTIVE, PREDICTIVE, AND PRESCRIPTIVE FACILITY OPERATIONS", which is hereby incorporated by reference in its entirety.

[0002] The present disclosure generally relates to the field of using machine - learning approaches to facilitate facility operations.

Background Art

[0003] Various monitoring systems can be used to monitor operations in a facility and solve problems. Data collected by different monitoring systems may be siloed in different databases, and the use of such data to facilitate facility operations can be difficult and time - consuming.

Summary of the Invention

[0004] [[ID=二十五]]Operation history information and / or other information of the facility may be obtained. The operation history information of the facility may be obtained based on a digital twin of the facility and / or other information. The digital twin of the facility may define relationships between components of the facility. A machine - learning model may be trained using the operation history information and / or other information of the facility. The trained machine - learning model can facilitate one or more operations in the facility by outputting descriptive information, predictive information, prescriptive information, and / or other information regarding the operation(s) in the facility. The trained machine - learning model may be stored in a storage medium.

[0005] Equipment scenario information and / or other information may be acquired. Equipment scenario information may define a scenario for a given operation on the equipment. Equipment scenario information may be input into a trained machine learning model. The trained machine learning model may output descriptive information, predictive information, directive information, and / or other information regarding a given operation on the equipment.

[0006] A system for operating equipment may include one or more electronic storage devices, one or more processors, and / or other components. The electronic storage device may store information related to the equipment, operation history information, information related to the operation history on the equipment, information related to the digital twin of the equipment, information related to the components of the equipment, information related to the relationships between the components of the equipment, information related to machine learning models, information related to training machine learning models, information related to the use of machine learning models, and / or other information.

[0007] The processor(s) may consist of machine-readable instructions. The execution of machine-readable instructions may facilitate the operation of the equipment by the processor(s). The machine-readable instructions may include one or more computer program components. The computer program components may include one or more of the following: operation history components, training components, memory components, scenario components, equipment operation components, and / or other computer program components.

[0008] The operation history component may be configured to acquire operation history information and / or other information about the equipment. The equipment's operation history information may be acquired based on the equipment's digital twin and / or other information. The equipment's digital twin may define the relationships between the equipment's components.

[0009] In some embodiments, the digital twin may output operation history information based on the relationships between equipment components and / or other information.

[0010] In some embodiments, the equipment operation history information may include process control information, alarm information, bypass information, safety information, operator action information, and / or other information. Process control information, alarm information, bypass information, safety information, operator action information, and / or other information related to the event may be correlated by a digital twin for machine learning model(s).

[0011] In some embodiments, process control information, alarm information, bypass information, safety information, operator action information, and / or other information related to an event may be correlated based on piping instrumentation diagrams, cause-and-effect diagrams, and / or other information.

[0012] In some embodiments, the correlation of process control information, alarm information, bypass information, safety information, and operator action information related to an event, based on the piping instrumentation diagram, may include the generation of a graph model of the equipment based on the piping instrumentation diagram and / or other information, and the correlation of process control information, alarm information, bypass information, safety information, and operator action information related to an event being performed, based on the graph model of the equipment.

[0013] In some embodiments, the graph model of the equipment may include nodes representing the equipment's physical components and nodes representing the equipment's control components. The graph model of the equipment may include different types of edges between nodes to represent the physical and logical connections between the corresponding components of the equipment. The physical connections between the equipment's components may include process lines between the equipment's components. The logical connections between the equipment's components may include electrical connections and / or input / output connections between the equipment's components.

[0014] The training component may be configured to train one or more machine learning models. The machine learning models may be trained using equipment operation history information and / or other information. The trained machine learning models can facilitate one or more operations on the equipment. The trained machine learning models can facilitate operations on the equipment by outputting descriptive information, predictive information, instructional information, and / or other information about operations on the equipment.

[0015] In some embodiments, the machine learning model(s) may include one or more sequence models.

[0016] The memory component may be configured to store the trained machine learning model(s). The trained machine learning model(s) may be stored in one or more storage media.

[0017] In some embodiments, a trained machine learning model can perform one or more classification tasks.

[0018] In some embodiments, a trained machine learning model can perform one or more regression tasks.

[0019] The scenario component may be configured to retrieve equipment scenario information and / or other information. Equipment scenario information may define a scenario for one or more operations on the equipment.

[0020] The equipment operation component may be configured to input equipment scenario information and / or other information into a trained machine learning model(s). The trained machine learning model(s) may output descriptive, predictive, and / or instructional information regarding equipment operation(s). The trained machine learning model(s) may output descriptive, predictive, and / or instructional information regarding the equipment operation(s) scenario(s).

[0021] In some embodiments, one or more automated operations at a facility may be performed based on instruction information and / or other information regarding the operation(s) at the facility.

[0022] In some embodiments, a facility operation component may be configured to provide a visualization of descriptive information, predictive information, and / or instruction information regarding the operation(s) at the facility. The facility operation component may be configured to provide a visualization of descriptive information, predictive information, and / or instruction information regarding a scenario of the operation(s) at the facility.

[0023] These and other objects, features, and characteristics of the systems and / or methods disclosed herein, as well as the manner of operation and functions of the related elements and components of the structure, and the methods of manufacturing economy, will become more apparent from the following description and the appended claims, when considered in conjunction with the accompanying drawings. All of them form a part of this specification, and like reference numerals in the various figures indicate corresponding parts. However, it should be clearly understood that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the present invention. As used in the specification and claims, the singular forms "a", "an", and "the" include the plural referents unless the context clearly dictates otherwise.

Brief Description of the Drawings

[0024] [Figure 1] An exemplary system for facilitating facility operation is shown. [Figure 2A] An exemplary method for facilitating facility operation is shown. [Figure 2B] An exemplary method for facilitating facility operation is shown. [Figure 3] An exemplary process for facilitating facility operation is shown [Figure 4] An overview of an exemplary instrumentation protection layer and operator actions is shown. [Figure 5] An exemplary graph model of a facility is shown.

Best Mode for Carrying Out the Invention

[0025] The present disclosure relates to facilitating facility operation. A digital twin of a facility defines the relationships between different components of the facility and the facility's record system. Information from various monitoring systems of the facility is related to events by the digital twin of the facility. Machine learning models are trained using the operation history information of the facility. The trained machine learning models facilitate the operation of the facility by providing descriptive information, predictive information, and / or instructional information regarding the operation(s) in the facility.

[0026] The methods and systems of the present disclosure may be implemented by and / or in a system such as system 10 shown in FIG. 1. System 10 may include one or more of a processor 11, an interface 12 (e.g., a bus, a wireless interface), an electronic storage device 13, a display 14, and / or other components. Operation history information and / or other information of the facility may be obtained by processor 11. The operation history information of the facility may be obtained based on the digital twin of the facility and / or other information. The digital twin of the facility may define the relationships between the components of the facility and the facility's record system. The machine learning model may be trained by processor 11 using the operation history information and / or other information of the facility. The trained machine learning model can facilitate one or more operations in the facility by outputting descriptive information, predictive information, instructional information, and / or other information regarding the operation(s) in the facility. The trained machine learning model may be stored in a storage medium by processor 11.

[0027] Facility scenario information and / or other information may be obtained by processor 11. The facility scenario information may define a scenario of a given operation in the facility. The facility scenario information may be input by processor 11 to the trained machine learning model. The trained machine learning model may output descriptive information, predictive information, instructional information, and / or other information regarding a given operation in the facility.

[0028] The electronic storage device 13 may be configured to include an electronic storage medium for electronically storing information. The electronic storage device 13 may store software algorithms, information determined by the processor 11, information received remotely, and / or other information that enables the system 10 to function properly. For example, the electronic storage device 13 may store information related to the equipment, operation history information, information related to the operation history on the equipment, information related to the digital twin of the equipment, information related to the components of the equipment, information related to the relationships between the components of the equipment, information related to the recording system of the equipment, information related to machine learning models, information related to training machine learning models, information related to the use of machine learning models, and / or other information.

[0029] Display 14 may refer to an electronic device that provides a visual presentation of information. Display 14 may include a color display and / or a non-color display. Display 14 may be configured to visually present information. Display 14 may present information using / within one or more graphical user interfaces. For example, Display 14 may present information related to equipment, operation history information, information related to operation history on equipment, information related to a digital twin of equipment, information related to equipment components, information related to relationships between equipment components, information related to the equipment's recording system, information related to a machine learning model, information related to training a machine learning model, information related to the use of a machine learning model, and / or other information.

[0030] A facility may refer to a location where one or more specific activities occur. A facility may include equipment for performing one or more activities. A facility may include equipment for achieving one or more functions. For example, a facility may include an oil platform, oil rig, offshore platform, refinery, or oil and / or gas production platform that extracts and / or processes resources (e.g., hydrocarbons) from underground rock formations. Other types of facilities are also contemplated.

[0031] Process disturbances may refer to operational disturbances in the equipment (e.g., interruptions, failures, deviations, malfunctions). The equipment may be equipped with automated and / or manual tools to deal with process disturbances in the equipment. For example, the equipment may be equipped with process control loops for automatically responding to process disturbances and / or mitigating operational instability caused by process disturbances. If a process disturbance is not adequately addressed, a process alarm may prompt the equipment operator to take action. If the process disturbance is not addressed by manual action by the operator, other alarms may be triggered, and automatic safety device actions may be activated to partially and / or completely shut down the equipment (e.g., to prevent a catastrophic failure). Such shutdown events can be costly and destructive.

[0032] The facility may have multiple monitoring systems for monitoring processes, equipment, operator actions, status, and / or other aspects of operation on the facility. Different monitoring layers may be used to monitor and troubleshoot different aspects of operation on the facility. For example, the facility may include separate monitoring of process control, alarms, bypass actions, and instrumentation protection systems. The information collected by these separate monitoring systems may be maintained separately and used separately for different purposes.

[0033] This disclosure provides a machine learning-based tool that provides descriptive, predictive, and / or directive information regarding equipment operation. The machine learning-based tool can improve equipment reliability and reduce downtime by connecting information from separate monitoring systems (siloed systems) to enable more efficient prioritization and decision-making. Information from separate monitoring systems may be contextualized and correlated using a digital twin of the equipment. Contextualization and correlation of information enables the use of machine learning models for process automation and operator responses to process disturbances. The machine learning model may be trained using equipment operation history information. The machine learning model may digitize operator knowledge / experience from operation history information. The machine learning model may be used to describe what is happening in the equipment, predict what will happen in the equipment (e.g., predict equipment responses), and / or instruct the operator on what actions to take. The machine learning model may be used to automate actions in the equipment.

[0034] Figure 3 shows an exemplary process 300 for facilitating operation on equipment. In process 300, a machine learning model 312 may be trained using equipment operation history information 302. Operation history information 302 may include operation history information from process control 322, alarms 324, instrumentation protection systems 326, bypasses 328, and / or operator actions 330. Different parts of operation history information 302 may be monitored, tracked, and / or stored separately. Different parts of operation history information 302 may not be correlated.

[0035] A digital twin 340 of the equipment may be used to contextualize and correlate different parts of the operation history information 302. Information from process control 322, alarms 324, instrumentation protection system 326, bypass 328, and / or operator actions 330 may be contextualized and correlated by the digital twin 340 of the equipment. The digital twin 340 may identify, extract, and package information related to events from control 322, alarms 324, instrumentation protection system 326, bypass 328, and / or operator actions 330 for use in training a machine learning model 312. The machine learning model 312 may be trained using the operation history information 302 provided by the digital twin to perform classification and / or regression tasks.

[0036] The digital twin 340 may identify, extract, and package information related to events from control 322, alarm 324, instrumentation protection system 326, bypass 328, and / or operator action 330 based on the relationships between components of the equipment and / or the equipment's recording system(s). The digital twin 340 may include piping and instrumentation diagram (P&ID) information digitized in the form of equipment and instrumentation ontology maps. The P&ID may include diagrams showing piping and process equipment together with instrumentation equipment (measuring instruments used to indicate, measure, and record physical quantities) and control devices. The P&ID may include diagrams showing the interconnections between process equipment and instrumentation equipment used to control the process. The equipment ontology map may specify which components are associated with a particular component and / or which components are associated with a particular event. The equipment ontology may provide information about the connections, interactions, dependencies, and / or the strength of the hierarchy between components of the equipment. Using an instrument ontology map, information related to events from control 322, alarm 324, instrumentation protection system 326, bypass 328, and / or operator action 330 may be identified, extracted, and packaged for use in training a machine learning model 312.

[0037] The digital twin 340 may improve the efficiency of equipment monitoring by facilitating the integration of information from separate monitoring systems. Process 300 may facilitate operations on the equipment by reducing the risk of equipment downtime, improving equipment lifespan, and / or facilitating maintenance prioritization. By using the digital twin, Process 300 may digitize operator knowledge / experience by contextualizing operator actions, relating control loops, equipment, alarms, and bypass systems to reflect real-world relationships, enabling the identification of root causes of problems on the equipment, and / or facilitating operations on the equipment in other ways.

[0038] Equipment scenario information 314 may be input to the machine learning model 312. Equipment scenario information 314 may define a scenario for one or more operations on the equipment. The machine learning model 312 outputs descriptive information, predictive information, and / or directive information regarding the operation(s) on the equipment. That is, the machine learning model 312 outputs descriptive information, predictive information, and / or directive information regarding the operation(s) scenario defined by the equipment scenario information 314. Descriptive information regarding operations may include information describing the operations (for example, the machine learning model 312 identifies the operation(s) and / or event(s) occurring on the equipment). Predictive information regarding operations may include information predicting what will happen on the equipment (for example, the machine learning model 312 predicts the outcome of an operation(s) on the equipment, and predicts events(s) that follow / occur as a result of an operation(s)). Operational instruction information may include information recommending / requesting what steps / actions should be taken on the equipment (for example, the machine learning model 312 recommends how to change the operation(s) on the equipment, or the machine learning model 312 is used to automate the change(s) on the equipment). Other uses of the machine learning model 312 to facilitate equipment operation are envisioned.

[0039] Returning to Figure 1, the processor 11 may be configured to provide information processing capabilities in the system 10. Thus, the processor 11 may include one or more of the following: a digital processor, an analog processor, a digital circuit designed to process information, a central processing unit, a graphics processing unit, a microcontroller, an analog circuit designed to process information, a state machine, and / or other mechanisms for electronically processing information. The processor 11 may be configured to execute one or more machine-readable instructions 100 to facilitate equipment operation. The machine-readable instructions 100 may include one or more computer program components. The machine-readable instructions 100 may include an operation history component 102, a training component 104, a memory component 106, a scenario component 108, an equipment operation component 110, and / or other computer program components.

[0040] The operation history component 102 may be configured to acquire operation history information and / or other information about the equipment. The operation history information of the equipment may include information about operations performed on the equipment. The operation history information of the equipment may include information about operations that occurred on the equipment. The operation history information of the equipment may include time-series data related to operations performed on the equipment. The operation history information of the equipment may include a set of operational characteristics measured, sensed, detected, and / or recorded on the equipment at different times. For example, the operation history information of the equipment may include timestamped information about the occurrence of events, sensor readings, operator actions, tolerances, and / or bypass and safety actions.

[0041] Equipment operation history information can characterize operations that have occurred on the equipment. Operation history information may include information about the operational characteristics of the equipment. Equipment operational characteristics may refer to the characteristics of the equipment during operation (e.g., characteristics within and / or around the equipment, characteristics of the equipment's components). Equipment operational characteristics may refer to the attributes, quality, configuration, parameters, and / or other characteristics of objects / equipment inside, within, and / or around the equipment during operation.

[0042] Equipment operation history information can characterize operations on equipment by including information that defines, describes, defines, identifies, associates, quantizes, reflects, specifies, and / or otherwise characterizes the values ​​of attributes, quality, configuration, parameters, and / or other characteristics of objects / equipment inside, inside, and / or around the equipment during operation. Equipment operation history information can characterize operations on equipment by including information that can determine the values ​​of attributes, quality, configuration, parameters, and / or other characteristics of objects / equipment inside, inside, and / or around the equipment during operation. Other types of operation history information are conceivable.

[0043] Acquiring operation history information may include one or more of the following: accessing, obtaining, analyzing, determining, inspecting, generating, identifying, loading, discovering, measuring, opening, receiving, searching, reviewing, selecting, storing, and / or otherwise obtaining operation history information. Operation history component 102 may acquire operation history information from one or more locations. For example, operation history component 102 may acquire operation history information from storage locations such as electronic storage device 13, electronic storage devices of devices accessible via a network, and / or other locations. Operation history component 102 may acquire operation history information from one or more hardware components (e.g., computing devices, sensors) and / or one or more software components (e.g., software running on a computing device). Operation history component 102 may acquire operation history information from multiple databases / storage locations. For example, different types of operation history information may be stored in different databases / storage locations, and some operation history information related to a particular event(s) may be acquired from different databases / storage locations.

[0044] Equipment operation history information may be obtained based on the equipment's digital twin and / or other information. The equipment's digital twin may refer to a virtual representation or digital model of the equipment. The digital twin can function as a real-time digital counterpart to the equipment and / or operations / processes occurring in the equipment. The equipment's digital twin may define relationships between the equipment's components. Equipment components may refer to equipment within the equipment, materials used in the equipment, and / or other components of the equipment. Relationships between equipment components may include connections, interactions, dependencies, hierarchies, and / or other relationships between components. Equipment operation history information may be obtained using the relationships between equipment components. The equipment's digital twin may define one or more recording systems for the equipment. The equipment's recording systems may refer to information storage and retrieval systems that are reliable sources of data related to the equipment. The equipment's recording systems may refer to a collection of relevant and / or contextualized information about the equipment, and / or connections between them, such as design documents, equipment databases, time-series data, inspection records, maintenance records, turnaround information, change management, and / or other information about the equipment. Information may be stored in different systems / databases, and information stored in different systems / databases may be interconnected via a digital twin. Information stored in different systems / databases may be accessed via a digital twin.

[0045] In some embodiments, the equipment operation history information may include process control information, alarm information, bypass information, safety information, operator action information, and / or other information. Process control information may refer to information from a process control system. Process control information may refer to information that defines and / or characterizes processes in the equipment (e.g., operations, parts of operations) and / or the control of processes in the equipment. Alarm information may refer to information from an alarm management system. Alarm information may refer to information that defines and / or characterizes alarms triggered in the equipment and / or the operation of alarms in the equipment. Bypass information may refer to information from a bypass management system. Bypass information may refer to information that defines and / or characterizes bypasses in the equipment (e.g., bypass location, equipment affected by the bypass, process under bypass). Safety information may refer to information from an instrumentation protection system (IPS). Safety information may refer to information that defines and / or characterizes safety conditions, triggers / activations of safety conditions, and / or the operation of SIS equipment. Operator action information may refer to information about operational actions in the equipment. Operator action information may refer to information that defines and / or characterizes actions taken by one or more operators on the equipment.

[0046] Process control information, alarm information, bypass information, safety information, operator action information, and / or other information related to an event may be correlated for training one or more machine learning models using a digital twin. Correlation of different information may involve establishing / determining relationships between different pieces of information. An event may refer to the occurrence of one or more things. An event may refer to one or more changes in equipment. For example, an event may include changes in pressure, temperature, and / or flow rate, starting / stopping some equipment, triggering an alarm, operator action, change in operation, and / or other changes in equipment. Information correlated with events may be used to train machine learning models. A digital twin may be used to determine which information is related to an event and which information is not related to an event.

[0047] For example, a digital twin may be used to identify, extract, and / or package some of the operational history information of an event based on the relationships between components of the equipment and / or the equipment's recording system(s). The digital twin may include an equipment ontology map that defines which components are associated with a particular component and / or which components are associated with a particular event. The equipment ontology map may be used to determine which information is relevant to an event, and the event-related information may be used as equipment operational history information to train a machine learning model.

[0048] In some embodiments, process control information, alarm information, bypass information, safety information, operator action information, and / or other information related to an event may be correlated based on piping and instrumentation diagrams, cause-and-effect diagrams, and / or other information. Piping and instrumentation diagrams and cause-and-effect diagrams may be used to establish relationships between different types of information. For example, different information may be correlated to an event using the interconnections of process equipment and instrumentation equipment used to control the process in a piping and instrumentation diagram. A cause-and-effect diagram may define specific sequential relationships between components, such as how equipment may be affected / changed based on the occurrence of a particular event (e.g., which components can be activated in response to an alarm that shuts down equipment / facilities), and these sequential relationships between components may be used to correlate different information to an event.

[0049] Figure 4 shows an overview of an exemplary instrumentation protection layer and operator action 400. As shown in overview 400, piping and instrumentation diagrams (P&ID) and cause-and-effect diagrams (C&E) may be used to establish relationships between information from different monitoring systems. Between process disturbances (e.g., undesirable operating conditions such as pressure spikes or equipment malfunctions) and process safety incidents, there may be the following different monitoring applications: (1) automated process control to help regulate and stabilize the process, (2) alarms to warn operators, (3) operator actions to manage process disturbances and return the equipment to normal operation, and (4) activation of instrumentation protection systems to shut down the equipment if safety conditions are violated. Piping and instrumentation diagrams may be used to establish relationships between information from process control and alarms (e.g., associating alarms with process control loop performance), and cause-and-effect diagrams may be used to establish relationships between information from alarms and instrumentation protection systems. Other information (e.g., inspection records, design documents) may be used to establish relationships between information from different monitoring systems.

[0050] In some embodiments, the digital twin may output equipment operation history information based on the relationships between equipment components, equipment recording systems, and / or other information. The digital twin itself can determine which parts of the operation history information are relevant to events, and those relevant parts of the operation history information may be output by the digital twin for use in training a machine learning model.

[0051] In some embodiments, the correlation of different parts of equipment operation history information (e.g., process control information, alarm information, bypass information, safety information, operator action information, and / or other information) related to a particular event being performed, based on the piping and instrumentation diagram, may include (1) generating a graph model of the equipment based on the piping and instrumentation diagram and / or other information, and (2) correlating different parts of equipment operation history information related to a particular event being performed, based on the graph model of the equipment and / or other information. That is, a graph model of the equipment may be generated to determine whether different parts of operation history information are related to a particular event (e.g., for training a machine learning model), and the graph model may be used to determine which parts of operation history information are related to a particular event.

[0052] A graph model may refer to a model that represents the components of a facility using nodes and the connections between components using edges between nodes. A graph model of a facility may be generated based on the facility's piping and instrumentation diagrams and / or other information. For example, a facility's piping and instrumentation diagram may be converted into a graph model of the facility. For example, the facility's piping and instrumentation diagram may be scanned, the component blocks of the piping and instrumentation diagram may be converted into nodes, and the lines between component blocks may be converted into edges. Other generation of graph models is contemplated.

[0053] A graph model of equipment may include nodes for the equipment's physical components, control components, and / or other components. The graph model may include different types of nodes for different types of components of equipment. Physical components of equipment may refer to parts, devices, and / or other components of equipment that are operated and / or operated on in order to process, contain, move, and / or otherwise interact with materials in the equipment. Physical components of equipment may refer to parts, devices, and / or other components of equipment that receive signals from the equipment's control components and operate according to the received signals (e.g., commands transmitted by the received signals). For example, physical components of a fluid equipment may include pumps, actuators, motors, valves, doors, and / or other physical components that can control the flow rate of fluid.

[0054] A control component of equipment may refer to a part, device, and / or other component of equipment that is operated and / or operated on in order to control the operation of the physical components of the equipment. A control component of equipment may refer to a part, device, and / or other component of equipment that transmits signals to the physical components of the equipment and controls the operation of the physical components of the equipment. A control component of equipment may refer to a part, device, and / or other component of equipment that receives signals from sensors of the equipment to monitor the operation of the physical components of the equipment. For example, a control component of a fluid system may include a logic block that performs sensor measurements of the equipment and, in response to deviations in the fluid flow rate within the equipment from normal operating conditions, transmits signals to pumps, actuators, and / or motors to control the fluid flow rate within the equipment.

[0055] The equipment may have separate, independent control components to maintain safe operation. For example, the equipment may have a distributed control system and an instrumentation protection system that operate independently of each other to correct deviations in the operation of the equipment and to stop the operation as necessary. Different types of control components may be represented by the same or different types of nodes in the graph model.

[0056] A graph model of equipment may include different types of edges between nodes to represent different connections between corresponding components of the equipment. Connections between components of equipment may include physical connections, logical connections, and / or other connections. Physical connections may be represented by one type of edge, and logical connections may be represented by another type of edge.

[0057] Physical connections between components of equipment may refer to connections that transport physical materials between different components of equipment. For example, physical connections between components of a fluid system may include one or more process lines between components of the fluid system. Process lines may include interconnected piping components such as tubes, pipes, pressure hoses, valves, separators, traps, flanges, fittings, gaskets, strainers, and / or other components. Physical connections between components of equipment may refer to connections that physically link components of equipment. For example, physical connections between components of a fluid system may include a physical link between an actuator and a valve of the equipment.

[0058] Logical connections between equipment components may refer to connections that logically link the equipment components. Logical connections between equipment components may refer to connections that transmit information between different components of the equipment. For example, local connections between components of a fluid equipment may include one or more electrical connections (e.g., transmitting sensor signals, command signals, information signals) and / or one or more input / output connections (e.g., facilitating communication) between the equipment components.

[0059] Figure 5 shows an exemplary graph model 500 of the equipment. Graph model 500 may represent a portion of a larger graph model of the equipment. Graph model 500 may include nodes 502, 512, 514, 522, 524, and 526 of the equipment's physical components, and nodes 532, 534, 536, and 542 of the equipment's control components. Solid edges between nodes in graph model 500 may represent physical connections between corresponding components. Dashed edges between nodes in graph model 500 may represent logical connections between corresponding components. For example, node 502 may represent a heat exchanger, node 514 may represent a valve for the heat exchanger, and node 526 may represent an actuator for the valve. Nodes 532, 534, and 536 may represent a control system / block that can control the actuator (by sending a signal to the actuator) to operate the valve. The control systems / blocks represented by nodes 532 and 534 can communicate with each other. The control system / block represented by node 536 may transmit signals to and / or receive signals from the heat exchanger represented by node 502. The control systems / blocks represented by nodes 532, 534, and 536 may be part of a distributed control system for the facility to keep the heat exchanger and other components of the facility running.

[0060] The equipment may include an instrumentation protection system represented by node 542. The instrumentation protection system can operate independently of the distributed control system to prevent the equipment from operating in a dangerous condition. For example, if the distributed control system fails to return an operational deviation to normal operating conditions (e.g., operation approaches or exceeds a safety limit), the instrumentation protection system may shut down operation on the equipment. The instrumentation protection system may provide an automatic shutdown response on the equipment in the event of a safety violation.

[0061] In some embodiments, different types of physical connections and / or different types of logical connections may be represented by different types of edges. For example, a logical connection to an instrumentation protection system may be represented by one type of edge, while a logical connection to a distributed control system may be represented by another type of edge.

[0062] Equipment operation history information can contain a large amount of data about operations performed on the equipment. For example, operation history information may contain information about actions taken by multiple operators on the equipment. When an event occurs on the equipment, one or more operators may take action to address the event, and one or more operators may take action unrelated to that event. For example, when an alarm is triggered, one or more operators may take action to address the alarm, while one or more other operators may take action unrelated to the alarm. To use operation history information for machine learning training, it may be necessary to correlate information related to events. That is, different parts of history information that are related to each other may need to be correlated for use in training a machine learning model.

[0063] A graph model may be used to correlate information related to events (e.g., changes in operating parameters, alarm triggers). Equipment operation history information may be filtered by time to identify information that may be related to an event. For example, operation history information may be filtered to identify information about actions taken after an alarm was triggered and before the alarm (e.g., operation derailment / deviation) was resolved. Such filtering can identify operation history information that may be temporally related to an event.

[0064] After time filtering, a graph model may be used to identify operational history information associated with an event. An event may be associated with a specific node, and operational history information related to equipment components located a certain distance (e.g., hops) from the node associated with the event may be identified as related to the event. For example, referring to Figure 5, an alarm may be triggered by a component (e.g., control system / block, sensor) represented by node 536. Nodes within a threshold distance from node 536 may be identified, and operational history information of actions performed on the corresponding components may be identified as related to the alarm. Using such identification of operational history information, a repository of actions taken in response to various events on the equipment may be generated. Actions taken by one or more operators may be correlated with specific events on the equipment.

[0065] In some embodiments, different types of edges between nodes in a graph model may be treated the same in distance (e.g., hop) calculations. For example, whether the traversed edge represents a physical connection or a logical connection may not be important when determining the distance traversed. In some embodiments, different types of edges between nodes in a graph model may be treated differently in distance calculations. For example, traversals of edges representing physical connections may be weighted more or less than traversals of edges representing logical connections. As another example, traversals of edges representing a particular type of physical or logical connection may be weighted more or less than traversals of edges representing other types of physical or logical connections. For example, traversals of edges representing a logical connection to an instrumentation protection system may be weighted more or less than traversals of edges representing a logical connection to a distributed control system. In some embodiments, edge weights may be customized. For example, edges representing a particular connection may be assigned different weights than other edges.

[0066] The results of actions performed on the equipment may be analyzed and correlated with specific actions performed on the equipment. For example, for individual actions and / or combinations of actions taken in response to a particular type of event, the results of the action(s) may be correlated with the action(s). The results of different actions / combinations of actions may be compared to determine which actions should be suggested / recommended to the operator and / or which actions should be performed automatically. For example, actions(s) may be ranked using the time spent by the action to resolve operational derailment / deviation and the magnitude of the change in operational parameters caused by the action. For example, actions(s) may be ranked based on their results using how quickly the operational derailment / deviation was resolved and the impact of the action on the operation of the equipment (e.g., the amount by which temperature, flow rate, pressure, etc., changed due to the action(s)). One or more machine learning models may be trained using the correlation between actions taken on the equipment and the results of those actions, and these machine learning models(s) may be used to suggest / recommend or automate operator actions based on events occurring on the equipment.

[0067] The training component 104 may be configured to train one or more machine learning models. The machine learning models may be trained using equipment operation history information and / or other information. The machine learning models may be trained to perform classification tasks and / or regression tasks. That is, the trained machine learning models may perform one or more classification tasks or one or more regression tasks and produce outputs. The trained machine learning models may facilitate one or more operations on the equipment. The trained machine learning models may facilitate operations on the equipment by outputting descriptive information, predictive information, instructional information, and / or other information about operations on the equipment. Descriptive information may include information about what is happening on the equipment (e.g., identification of operations / events occurring on the equipment). Predictive information may include information about what will happen on the equipment (e.g., prediction of events that will occur on the equipment). Instructional information may include information about what steps / actions should be taken on the equipment (e.g., recommendations on how operators should change operations, or automatically changing operations on the equipment to prevent equipment shutdown).

[0068] Training a machine learning model may involve facilitating the model's learning by processing examples through the model. Pairing operation history information with information about desired output types may be provided to the machine learning model as examples of inputs and desired results, respectively. The operation history information may be used as the type of input received by the trained machine learning model, and the information about desired output types paired with the operation history information may be used as the type of output generated by the machine learning model.

[0069] For example, to train a machine learning model to output descriptive information, operation history information related to an operation / event may be paired with the identification / description of the operation / event for training the machine learning model. Operation history information related to an operation / event may be paired with the identification / description of what is happening at the facility for training the machine learning model. By processing such pairings of information through the machine learning model, the machine learning model may be able to learn patterns of operation history information that correlate with specific operations / events at the facility.

[0070] To train a machine learning model to output predictive information, operation history information related to an operation / event may be paired with an identification / description of a subsequent event occurring on the equipment. Operation history information related to an operation / event may be paired with an identification / description of what will happen on the equipment to train the machine learning model. By processing such pairing of information through the machine learning model, the machine learning model may be able to learn patterns of operation history information that correlate with specific future events on the equipment.

[0071] To train a machine learning model to output instruction information, operation history information related to an operation / event may be paired with the identification / description of steps / actions performed on the equipment (e.g., automatically by an operator). Operation history information related to an operation / event may be paired with the identification / description of steps / actions taken on the equipment to train a machine learning model. By processing such pairing of information through a machine learning model, the machine learning model may be able to learn patterns of operation history information that correlate with specific steps / actions that should be taken on the equipment.

[0072] In some embodiments, pairing operation history information with specific steps / actions to be performed on the equipment can digitize the operator's historical actions. For example, operator actions in response to various operations / events may be recorded along with the results of those actions. Operation history information may be paired with specific steps / actions based on desired results; that is, the results of previous operator actions may be used to guide how to train a machine learning model. Operation history information, paired with the results of previous operator actions, can enable the machine learning model to provide probable outcomes for the steps / actions it outputs. For example, in addition to recommending specific steps / actions for the equipment operator, the machine learning model may output the probability(s) of the outcome resulting from the operator performing the recommended steps / actions. The machine learning model may output multiple steps / actions that the operator can take, along with the possible outcomes of different steps / actions.

[0073] In some embodiments, the machine learning model(s) may include one or more sequence models. A sequence model may refer to a machine learning model that receives a data sequence as input and / or outputs a data sequence. For example, a sequence model may refer to a machine learning model that receives time series data as input and / or outputs time series data. For example, the machine learning model(s) may include a Markov model. The use of other types of machine learning models is intended.

[0074] The memory component 106 may be configured to store trained machine learning models (or more). The trained machine learning models (or more) may be stored in one or more non-temporary storage media and / or other storage media. For example, the memory component 106 may store the trained machine learning models (or more) / information defining the trained machine learning models (or more) in storage locations such as the electronic storage device 13, the electronic storage device of a network-accessible device, and / or other locations. The trained machine learning models (or more) may be stored for use in facilitating operations on the equipment. The trained machine learning models (or more) may be stored for use in (1) identifying what is happening on the equipment (e.g., output descriptive information that identifies operations / events occurring on the equipment), (2) predicting what will happen in the system (e.g., output predictive information that identifies events occurring on the equipment), and / or (3) recommending / guiding what actions should be taken on the equipment (e.g., output instruction information that recommends specific actions to be taken by an operator, output instruction information that controls how automated operations on the equipment are controlled). Trained machine learning models(s) may be stored for retrieval / execution when facilitating operations on the equipment.

[0075] Scenario component 108 may be configured to retrieve equipment scenario information and / or other information. Equipment scenario information may define a scenario for one or more operations on the equipment. A scenario for one or more operations on the equipment may refer to an instance in which one or more operations are occurring on the equipment. A scenario for one or more operations may include a specific moment or duration. A scenario for one or more operations may include the occurrence of one or more events during the operation. Equipment scenario information may define a scenario for one or more operations on the equipment by including information that defines one or more content, quality, attributes, characteristics, and / or other aspects of the scenario for one or more operations on the equipment.

[0076] For example, equipment scenario information may define a scenario of operation(s) performed on equipment by including information that characterizes the operation(s) being performed on the equipment. Equipment scenario information may include information about the operating characteristics of the equipment at a specific time (moment, duration). Equipment scenario information may include real-time equipment scenario information. Real-time equipment scenario information may refer to equipment scenario information that defines the current operation(s) on the equipment. For example, real-time equipment scenario information may characterize the operating characteristics of the equipment currently reported by one or more sensors, and / or the operating characteristics of the equipment measured within a threshold time (e.g., operating characteristics measured within past minutes / hours / days). Equipment scenario information for equipment may include the same type of information as equipment operation history information. Other types of equipment scenario information are intended.

[0077] Acquiring equipment scenario information may include one or more of the following: accessing, obtaining, analyzing, determining, inspecting, generating, identifying, loading, discovering, measuring, opening, receiving, searching, reviewing, selecting, storing, and / or otherwise obtaining equipment scenario information. Scenario component 108 may acquire equipment scenario information from one or more locations. For example, scenario component 108 may acquire equipment scenario information from storage locations such as electronic storage device 13, electronic storage devices of devices accessible via a network, and / or other locations. Scenario component 108 may acquire equipment scenario information from one or more hardware components (e.g., computing devices, sensors) and / or one or more software components (e.g., software running on a computing device). Scenario component 108 may acquire equipment scenario information from multiple databases / storage locations. For example, different types of equipment scenario information may be stored in different databases / storage locations, and some equipment scenario information related to a particular event / scenario may be acquired from different databases / storage locations. A digital twin of the equipment may be used to retrieve relevant portions of information from different monitoring systems as equipment scenario information.

[0078] The equipment operation component 110 may be configured to input equipment scenario information and / or other information to a trained machine learning model(s). The trained machine learning model(s) may use the equipment scenario information to generate output. The trained machine learning model(s) may output descriptive information, predictive information, and / or instructional information, and / or other information regarding the equipment(s) (operation(s) scenarios).

[0079] For example, a trained machine learning model may output descriptive information about operations on the equipment by outputting information describing operations, events, and / or other states on the equipment. For example, based on equipment scenario information input to the trained machine learning model, the trained machine learning model may output that column flooding is / has occurred on the equipment. The trained machine learning model may output details about the column flooding, such as the location and / or timing of the column flooding, the equipment components / devices affected by the column flooding, and / or the source / root cause of the column flooding.

[0080] A trained machine learning model may output predictive information about the operation(s) of a facility by outputting information that predicts what will happen in the facility. For example, based on facility scenario information input to the trained machine learning model, the trained machine learning model may output a prediction about the time until a facility component / equipment fails due to column flooding. The trained machine learning model may output details about the prediction, such as which component / equipment will fail, the predicted timing of the failure, and / or the predicted extent of the failure.

[0081] A trained machine learning model may output instructions for operation(s) on equipment by outputting information detailing which steps / actions should be taken on the equipment. For example, based on equipment scenario information input to the trained machine learning model (e.g., a pump trip), the trained machine learning model may output the optimal steps / actions that one or more operators should take to prevent further disruption to the equipment and restore it to a normal operating state. In some embodiments, one or more automated operations on the equipment may be performed based on instructions and / or other information regarding operation(s) on the equipment. That is, instead of outputting instructions to the operator to guide actions in restoring normal operation of the equipment, operations on the equipment may be automatically modified according to the instructions.

[0082] In some embodiments, the inputs and outputs of multiple trained machine learning models may be chained together. For example, equipment scenario information may be input to a machine learning model trained to output descriptive and / or predictive information. The descriptive and / or predictive information output by the trained machine learning model may be used as equipment scenario information input to a machine learning model trained to output instructional information. The machine learning model trained to output instructional information may use the information output by other machine learning models to instruct what steps / actions should be taken in the equipment.

[0083] In some embodiments, the equipment operation component 110 may be configured to provide visualizations of descriptive, predictive, and / or instructional information relating to (or scenarios of) operations on the equipment. These visualizations may include visual / graphic representations of the descriptive, predictive, and / or instructional information. The visualizations may be provided on a display 14 (for example, within one or more graphical user interfaces for one or more operators). For example, the descriptive, predictive, and / or instructional information may be modeled in three-dimensional space along with a three-dimensional model of the equipment to visualize what is happening on the equipment, what is expected to happen on the equipment, and / or what steps / actions should be taken on the equipment. The use of other visualizations is also contemplated.

[0084] Embodiments of the Disclosure may be in the form of hardware, firmware, software, or any suitable combination thereof. Embodiments of the Disclosure may be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. The machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, tangible computer-readable storage media may include read-only memory, random-access memory, magnetic disk storage media, optical storage media, flash memory devices, etc., and machine-readable transmission media may include propagating signals in the form of carrier waves, infrared signals, digital signals, etc. Firmware, software, routines, or instructions may be described herein in terms of specific exemplary embodiments and models of the Disclosure and the execution of specific actions.

[0085] In some embodiments, some or all of the functions attributed to System 10 in this specification may be provided by external resources not included in System 10. These external resources may include hosts / sources of information, computing, and / or processing, and / or other providers of information, computing, and / or processing outside of System 10.

[0086] The processor 11, the electronic storage device 13, and the display 14 are shown connected to interface 12 in Figure 1, but any communication medium may be used to facilitate interaction between any components of system 10. One or more components of system 10 may communicate with each other through wired communication, wireless communication, or both. For example, one or more components of system 10 may communicate with each other through a network. For example, the processor 11 may communicate wirelessly with the electronic storage device 13. In non-limiting examples, wireless communication may include one or more of radio communication, Bluetooth communication, Wi-Fi communication, cellular communication, infrared communication, or other wireless communication. Other types of communication are also contemplated in this disclosure.

[0087] The processor 11, the electronic storage device 13, and the display 14 are shown as a single entity in Figure 1, but this is for illustrative purposes only. One or more components of system 10 may be contained within a single device or across multiple devices. For example, the processor 11 may contain multiple processing units. These processing units may be physically located within the same device, or the processor 11 may represent a processing function in which multiple devices work together. The processor 11 may be separate from and / or part of one or more components of system 10. The processor 11 may be configured to execute one or more components by software, hardware, firmware, any combination of software, hardware, and / or firmware, and / or other mechanisms for configuring processing capabilities on the processor 11.

[0088] Although the computer program components are shown in Figure 1 as being located in the same place within a single processing unit, it should be understood that one or more computer program components may be located remotely from other computer program components. While computer program components are described as performing or being configured to perform actions, they may contain instructions that the processor 11 and / or system 10 can program to perform those actions.

[0089] Although computer program components are described herein as being implemented via the processor 11 through machine-readable instructions 100, this is merely for the sake of reference and is not intended to limit them. In some embodiments, one or more functions of the computer program components described herein may be implemented via hardware (e.g., a dedicated chip, a field-programmable gate array) rather than software. One or more functions of the computer program components described herein may be implemented in software, in hardware, or in both software and hardware.

[0090] The descriptions of the functions provided by the various computer program components described herein are for illustrative purposes only and are not intended to limit any of the computer program components, as any of them may provide more or fewer functions than those described. For example, one or more computer program components may be removed, and some or all of their functions may be provided by other computer program components. As another example, the processor 11 may be configured to run one or more additional computer program components that can perform some or all of the functions belonging to one or more of the computer program components described herein.

[0091] The electronic storage medium of the electronic storage device 13 may be provided integrally with (i.e., substantially inremovably) one or more components of the system 10, and / or as a removable storage device that can be connected to one or more components of the system 10, for example, via a port (e.g., a USB port, a FireWire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storage device 13 may include one or more of the following: optically readable storage media (e.g., optical discs, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drives, floppy drives, etc.), charge-based storage media (e.g., EPROM, EEPROM, RAM, etc.), solid-state storage media (e.g., flash drives, etc.), and / or other electronically readable storage media. The electronic storage device 13 may be a separate component within the system 10, or the electronic storage device 13 may be provided integrally with one or more other components of the system 10 (e.g., a processor 11). In Figure 1, the electronic storage device 13 is shown as a single entity, but this is for illustrative purposes only. In some embodiments, the electronic storage device 13 may include multiple storage units. These storage units may be physically located within the same device, or the electronic storage device 13 may represent the storage functions of multiple devices working together.

[0092] Figures 2A and 2B illustrate methods 200 and 250 for facilitating the operation of the equipment. The actions of methods 200 and 250 described below are for illustrative purposes only. In some embodiments, methods 200 and 250 can be achieved using one or more additional actions not described and / or without using one or more of the actions described. In some embodiments, two or more of the actions may be performed substantially simultaneously.

[0093] In some embodiments, Methods 200, 250 may be implemented in one or more processing units (e.g., digital processors, analog processors, digital circuits designed to process information, central processing units, graphics processing units, microcontrollers, analog circuits designed to process information, state machines, and / or other mechanisms for electronically processing information). One or more processing units may include one or more devices that perform some or all of the operations of Method 200 in response to instructions electronically stored on one or more electronic storage media. One or more processing units may include one or more devices configured through hardware, firmware, and / or software to be specifically designed to perform one or more of the operations of Method 200.

[0094] Referring to 2A and Method 200, operation 202 may acquire operation history information and / or other information about the equipment. The equipment operation history information may be acquired based on a digital twin of the equipment and / or other information. The digital twin of the equipment may define the relationships between the equipment components and the equipment's recording system. In some embodiments, operation 202 may be performed by the same or similar processor component as the operation history component 102 (shown in Figure 1 and described herein).

[0095] In operation 204, a machine learning model may be trained using operation history information and / or other information about the equipment. The trained machine learning model can facilitate one or more operations on the equipment by outputting descriptive information, predictive information, instructional information, and / or other information about the operation(s) on the equipment. In some embodiments, operation 204 may be performed by the same or similar processor component as component 104 (shown in Figure 1 and described herein).

[0096] In operation 206, the trained machine learning model may be stored in a storage medium. In some embodiments, operation 206 may be performed by a processor component that is the same as or similar to the storage component 106 (shown in Figure 1 and described herein).

[0097] Referring to 2B and Method 250, operation 252 may acquire equipment scenario information and / or other information. Equipment scenario information may define a scenario for a given operation on the equipment. In some embodiments, operation 252 may be performed by the same or similar components as scenario component 108 (shown in Figure 1 and described herein).

[0098] In operation 254, equipment scenario information may be input to a trained machine learning model. The trained machine learning model may output descriptive information, predictive information, directive information, and / or other information regarding a given operation on the equipment. In some embodiments, operation 254 may be performed by the same or similar processor component as the equipment operation component 110 (shown in Figure 1 and described herein).

[0099] While the systems and / or methods of this disclosure have been described in detail for illustrative purposes, based on what is currently considered to be the most practical and preferred embodiments, please understand that such details are for illustrative purposes only, and that this disclosure is not limited to the disclosed embodiments, but rather intended to include modifications and equivalent configurations within the spirit and scope of the appended claims. For example, please understand that this disclosure is intended, wherever possible, to combine one or more features of any embodiment with one or more features of any other embodiment.

Claims

1. A system for facilitating equipment operation, It comprises one or more physical processors, and the one or more physical processors, by machine-readable instructions, The acquisition of operation history information of the equipment based on a digital twin of the equipment, wherein the digital twin of the equipment defines the relationships between the components of the equipment and the recording system of the equipment, and the acquisition of such information. Training a machine learning model using the operation history information of the equipment, wherein the trained machine learning model facilitates one or more operations on the equipment by outputting descriptive information, predictive information, and / or directive information relating to one or more operations on the equipment, the descriptive information including information about what is happening on the equipment, the predictive information including information about what will happen on the equipment, and the directive information including information about what action should be taken on the equipment. The trained machine learning model is stored in a storage medium. It is configured to do the following: The operation history information of the aforementioned equipment includes different types of information, The aforementioned different types of information related to the event are Generation of a graph model of the equipment based on the piping and instrumentation diagram, and Correlation of the different types of information related to the event, based on the graph model of the equipment. The system is correlated for the machine learning model based on the piping and instrumentation diagram, control system, and safety system via the system.

2. The one or more physical processors, by means of the machine-readable instructions, The acquisition of equipment scenario information, wherein the equipment scenario information defines a scenario for a given operation on the equipment, and the acquisition of the equipment scenario information, Inputting the equipment scenario information into the trained machine learning model, wherein the trained machine learning model outputs the descriptive information, the predictive information, and / or the instruction information relating to a given operation in the equipment. The system according to claim 1, further configured to perform the following:

3. The system according to claim 1, wherein the trained machine learning model performs a classification task.

4. The system according to claim 1, wherein the trained machine learning model performs a regression task.

5. The system according to claim 1, wherein the one or more physical processors are further configured to provide a visualization of the descriptive information, the predictive information, and / or the instruction information relating to one or more operations on the equipment by machine-readable instructions.

6. The system according to claim 1, wherein one or more automated operations in the equipment are performed based on the instruction information relating to the one or more operations in the equipment.

7. The system according to claim 1, wherein the digital twin outputs the operation history information of the equipment based on the relationships between the components of the equipment and the recording system of the equipment.

8. The system according to claim 1, wherein the machine learning model includes a sequence model.

9. The operation history information of the equipment includes process control information, alarm information, bypass information, safety information, and operator action information, and The process control information, alarm information, bypass information, safety information, and operator action information related to the event are correlated by the digital twin for a machine learning model. The alarm information includes information that defines and / or characterizes alarms triggered by the equipment and / or the operation of alarms in the equipment. The bypass information includes information defining and / or characterizing the bypass in the equipment. The safety information includes information that defines and / or characterizes safety conditions, triggers or activations of safety conditions, and / or the operation of SIS (Safety Instrumented System) equipment. The system according to claim 1.

10. The system according to claim 9, wherein the process control information, alarm information, bypass information, safety information, and operator action information related to the event are correlated based on the piping instrumentation diagram and cause-and-effect diagram.

11. The system according to claim 10, wherein the correlation of the process control information, alarm information, bypass information, safety information, and operator action information related to the event, based on the piping instrumentation diagram, includes the correlation of the process control information, alarm information, bypass information, safety information, and operator action information related to the event being executed, based on the graph model of the equipment.

12. The system according to claim 11, wherein the graph model of the equipment includes nodes for the physical components of the equipment and the control components of the equipment.

13. The system according to claim 12, wherein the graph model of the equipment includes different types of edges between nodes to represent the physical and logical connections between corresponding components of the equipment.

14. The physical connections between the components of the equipment include process lines between the components of the equipment, The logical connections between the components of the equipment include electrical connections and / or input / output connections between the components of the equipment. The system according to claim 13.

15. A method for facilitating equipment operation, The acquisition of operation history information of the equipment based on a digital twin of the equipment, wherein the digital twin of the equipment defines the relationships between the components of the equipment and the recording system of the equipment, and the acquisition of operation history information of the equipment. Training a machine learning model using the operation history information of the equipment, wherein the trained machine learning model facilitates one or more operations on the equipment by outputting descriptive information, predictive information, and / or directive information relating to one or more operations on the equipment, the descriptive information including information about what is happening on the equipment, the predictive information including information about what will happen on the equipment, and the directive information including information about what action should be taken on the equipment. The trained machine learning model is stored in a storage medium, The acquisition of equipment scenario information, wherein the equipment scenario information defines a scenario for a given operation on the equipment, Inputting the equipment scenario information into the trained machine learning model, wherein the trained machine learning model outputs the descriptive information, the predictive information, and / or the instruction information relating to a given operation in the equipment. Includes, The operation history information of the aforementioned equipment includes different types of information, The aforementioned different types of information related to the event are Generation of a graph model of the equipment based on the piping and instrumentation diagram, and Correlation of the different types of information related to the event, based on the graph model of the equipment. The method, which is correlated for the machine learning model based on the piping and instrumentation diagrams, control systems, and safety systems via the method.

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