111 results about "Industrial Facility" patented technology

An anthropomorphic AI control system including a plurality of sensors configured to collect industrial input data. The control system also includes an artificial intelligence-enabled edge-deployed computing device configured to analyze and fuse multimodal input data and generate an output through anthropomorphic computing. The computing device includes a cognitive module performing real-time decision-making at the sensor edge and a controller to manage industrial process actuators across operational domains. In management of industrial fluid flow, the control system may include AI-enabled fluid characterization modules to calculate the Reynolds number and incorporate compliance with AGA3 and AGA8 standards.

Implementations set forth herein relate to triaging of robots at an industrial facility using generated snapshots that can be based on an operating status of various features of each particular robot. The snapshots can be generated from data generated by each robot and / or data observed for a particular robot, such as images captured of a particular robot. Each snapshot can be utilized to estimate a likelihood or probability that each robot can complete the task without first visiting a service station. When a probability for a particular task is satisfied, the robot predicted to complete the task can be assigned to the particular task. Alternatively, or additionally, when certain estimated probabilities do not satisfy a threshold for completing the task, each robot associated with such an estimated probability can be directed to a service station of the industrial facility.

The invention specifically relates to a facility intelligent operation and maintenance decision-making method based on big data and artificial intelligence, and relates to the technical field of industrial facility operation and maintenance and intelligent decision-making, and the method comprises the steps: simulating the multi-dimensional operation state of a corresponding physical facility in a future preset time period after each candidate decision is executed; according to the method, the dynamic optimization of the operation and maintenance decision is realized by relying on the multi-scheme deduction capability of the digital twin and combining the non-dominated sorting genetic algorithm II and the dynamic weight evaluation system; the maintenance cost, the fault risk and the production efficiency are balanced by generating five types of candidate schemes, simulating the fault probability, the cost and the production efficiency within seven days and dynamically adjusting the weight based on the equipment health index, the production load and the like; a closed-loop feedback mechanism corrects model parameters through actual data, incremental learning and reinforcement learning continuously optimize decision experience, fault shutdown loss is reduced, operation and maintenance requirements in different scenes are met, and practicability and adaptability of the method are enhanced.

In variants, an ML-based control system for an industrial facility can include a remote control plane, a client, a set of agents, data processing modules, an optional data store, a telemetry module, a learning module, and / or any other suitable components. In variants, the set of agents can include a facility agent, a set of local cooling agents, and / or any other suitable agents. In variants, the set of agents can include system state approximators and decision models learned via reinforcement learning. The ML-based control system can control components of the industrial facility by predicting and / or determining component setpoints based on power load and / or heat load of the industrial facility.

This invention is a method for the production and distribution of compressed air in organized industrial zones (OIZs), specifically involving the centralized production of compressed air using energy-efficient turbo compressors and its transmission to industrial facilities via high-pressure conveying lines. The method aims to produce compressed air more efficiently and minimize energy losses during transport, thanks to the low energy consumption and multi-stage compression mechanism of turbo compressors. This method also ensures fair and accurate billing based on the measurement of compressed air consumption supplied to industrial facilities. Each industrial facility is equipped with high-accuracy meters to precisely measure the amount of compressed air consumed. Data from these meters is collected and processed in real time via a central control system. Billing is then based solely on the amount of air consumed. The system aims to reduce operating costs through energy efficiency and low maintenance costs, as well as to ensure environmental sustainability by reducing carbon emissions. The fact that turbo compressors offer lower energy consumption and maintenance requirements compared to screw compressors increases the overall efficiency of this method and provides economic advantages.

The disclosure relates a wind blade (1) segmented by a cutting contour (2, 9, 12) into an inner segment (3) and an outer segment (4). The cutting contour includes straight and circular portions enabling modular assembly. The outer segment comprises an opening (7) configured to receive the inner segment of another wind blade. A building assembly is formed using three wind blades, where the outer segments align and abut each other in a horizontal plane, optionally creating a hollow space for accommodating utilities. The disclosure also provides a method for manufacturing wind blades with specific cutting contours and assembling them into modular structures for versatile applications. This design facilitates cost-effective construction of warehouses, residential, commercial, and industrial facilities, while reusing wind blade components for sustainable architecture.

In an exemplary embodiment, a method for detecting anomalies associated with a machine includes recording a dataset associated with the machine, determining, with a first machine learning model, a label associated with the dataset, determining whether the label should be reviewed, and if it is determined that the label should be reviewed, subjecting the dataset and label to review and updating the label based on the review.Alternatively or additionally, in an exemplary embodiment, a method for presenting an analysis of a machine included in an industrial facility includes generating a digital twin of the machine, determining at least one property of the digital twin based on a simulation of operation of the machine, and generating a representation of the industrial facility including a visualization of the digital twin and a visual indicator of the at least one property of the digital twin.

The method according to the invention for monitoring an automation system (2), in particular automation devices (4, 5, 10) in an industrial plant (1), comprises the following steps: - receiving operating data (B) from the automation system (2); - assigning the operating data (B) to at least one of the automation devices (4, 5, 10); - monitoring the operating data (B) for the presence of an event by checking whether the operating data (B) fulfils an event condition associated with the event; - counting each case in which the event condition is fulfilled using a counter (69, 71) associated with the event condition; - storing the value of the counter (69, 71) in a data set (D) which is associated with the respective event and the respective automation device (4, 5, 10); - outputting the data set (D) with the value of the counter (69, 71) via a user interface. Thereby, the status of the automation devices can be quickly and easily overviewed even over a longer observation period.

The invention belongs to the technical field of industrial facility safety assessment, and particularly discloses an overdue service pressure pipeline risk assessment method and terminal equipment, and the method comprises the steps: constructing a fault tree with the failure of a pressure pipeline as a top event; obtaining a fuzzy number corresponding to a plurality of expert ratings of each bottom event in the fault tree, and determining a relative consistency degree of the expert ratings according to the fuzzy number; clustering the fuzzy number by using a similarity clustering method, and determining an optimal relaxation factor according to a clustering result; determining the failure probability of each bottom event according to the relative consistency degree of expert rating and the optimal relaxation factor; and determining the failure probability of the top event according to the failure probability of each bottom event. According to the method, the optimal relaxation factor is determined by combining the fuzzy number of expert rating, and the relaxation factor is improved, so that the influence of subjectivity is remarkably reduced, and the reliability of risk assessment of the overdue service pressure pipeline is greatly improved.

The application relates to the technical field of intelligent robot inspection, and discloses a somatic intelligent AI inspection method based on digital twinning, wherein the somatic intelligent AI inspection method based on digital twinning comprises the following steps: a dynamic attention multi-layer feature extraction network is used to analyze multispectral images collected by a robot-mounted sensor to generate a real-time distribution diagram of industrial facility abnormal coverage; a space-time sequence contrast learning model is used to analyze historical abnormal coverage data to generate an abnormal coverage trend prediction; a physically constrained neural differential equation model is used to analyze an abnormal coverage evolution process to generate an accurate abnormal evolution prediction result; a multi-objective optimization algorithm is used to generate an optimal inspection path; a digital twinning system is dynamically updated, and an industrial facility maintenance optimization suggestion is generated; and through the dynamic attention multi-layer feature extraction network and a light condition self-adaptive enhancement algorithm, the detection difficulty in a strong light and high reflection environment is effectively solved.

An industrial structure for wind energy collection includes an industrial facility requiring ventilation and a ventilation opening. An industrial fan associated with the vent discharges exhaust gas forming an exhaust flow zone opposite the vent. The exhaust flow zone is defined by first and second boundaries extending from the industrial structure at first and second sides of the vent. At least one wind turbine is located outside the vent, including a turbine blade portion and a generator. The at least one wind turbine is positioned relative to the vent such that, during each rotational cycle, the blade portion lies partially within the exhaust flow zone across the first boundary and partially outside the exhaust flow zone.

Variants of the system includes an industrial facility, job scheduling services, and a set of control agents configured to manage power and cooling resources. The method includes determining a current system state, optionally predicting a future system state, determining control setpoints, and controlling the facility based on the setpoints. In a data center implementation, the control agents receive physical state information from infrastructure components and job data from the job scheduling services. Using this information, the control agents predict future power and heat loads associated with scheduled compute jobs. The predictions are generated by a state approximator. Based on the predicted resource demands, the control agents determine control setpoints for cooling and power infrastructure components. The job scheduling services can allocate, delay, or reschedule jobs based on predicted thermal and power constraints.

Described are techniques for identifying a design of an infrastructure on an industrial floor to service a user. A knowledge corpus of information to be used as a sample data set for training a machine learning model is generated, which includes information, such as mobility patterns of users on the industrial floor for various manufacturing process flows, and interaction of users on the industrial floor with different services at different locations on the industrial floor for various manufacturing process flows. A machine learning model is then trained to identify the designs of the infrastructure on the industrial floor based on the knowledge corpus. Upon receiving the requirements of a service level agreement and the current manufacturing process flow, the trained machine learning model is used to identify a particular design of the infrastructure of cloud and edge computing resources on the industrial floor that optimally services the user.

A multi-modal combustion interruption system comprises a detection subsystem configured to identify combustion events, an electromagnetic emission subsystem having pulsed energy source and directional emission structure, a control processor implementing adaptive modulation based on measured combustion response, and a safety enforcement subsystem configured to control emission activation based on presence detection. The system may be configured in various modes from baseline electromagnetic emission through enhanced configurations incorporating optional ionization enhancement, particle generation, and acoustic energy subsystems. Adaptive modulation enables parameter selection based on measured combustion response rather than predetermined values. The modular architecture enables configuration selection based on deployment type and application requirements. Fixed installations may utilize grid power for industrial facility protection. Mobile platforms may employ onboard power for emergency response or wildfire intervention. Configuration selection may consider environmental compatibility including omission of particle generation for water-sensitive equipment environments or incorporation of multiple enhancement subsystems for demanding applications.

Methods, systems, apparatus, and computer program products for implementing machine learning within a control system are disclosed. An industrial facility settings board can be received from a machine learning system, and a determination can be made as to whether to adopt settings in the industrial facility settings board. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning, to predict data settings boards that are predicted to optimize efficiency of a data center.

A drone navigation method based on optical distortion and a drone using the same are provided. The method includes: capturing images of an environment using a drone's camera with high optical distortion properties; detecting a reference object and reference lines in the images; determining cells formed by the reference lines and monitoring curvatures of the lines; determining a reference cell adjacent to the drone; determining the drone's projected position in the reference cell based on curvatures of cell boundaries; determining a target motion direction according to a navigation task and controlling the drone's motion; and continuously updating the drone's projected position based on monitored boundary curvatures to manage the drone's motion. This method enables precise indoor navigation without relying on GPS or external markers, significantly enhancing drone operability in complex indoor environments such as warehouses and industrial facilities.

An adjustable fire extinguishing and explosion suppression bottle clamp belongs to the technical field of mounting and fixing of fire fighting equipment and safety devices, and in special equipment and industrial facilities, a fire extinguishing bottle of a fire extinguishing system and an explosion suppression system needs to be reliably fixed on a preset base through a special clamp; in the prior art, the holding force of a hoop on a bottle body is insufficient due to tolerance accumulation, and hidden dangers are buried in safe operation in a long-term vibration environment; the invention provides an adjustable fire-extinguishing explosion-suppression bottle hoop, and the core purpose of the adjustable fire-extinguishing explosion-suppression bottle hoop is to realize step-by-step independent operation in a mounting process through structural design, namely, a positioning block is independently fixed on a base firstly, and then a bottle body is independently locked, so that a size chain accumulative error is effectively compensated, and two key connecting points can reach an optimal fastening state; and the installation efficiency, reliability and consistency are improved.

A gas leak detection system includes a plant subsystem including a gas leak detection sensor disposed at an industrial facility and at least one vehicle subsystem including a vehicle operable in and around the industrial facility. The vehicle subsystem further includes at least one vehicle gas leak detection sensors carried by the vehicle and operable to detect the presence and concentration of gasses in the atmosphere in and around the vehicle, a controller carried by the vehicle and operably coupled to the at least one vehicle gas leak detection sensor to identify a gas leak and a communication device carried by the vehicle and communicatively coupled to the controller and the plant subsystem. The communication device is operable to transmit a alarm signal to the plant subsystem in response to the controller identifying a gas leak and to receive an alarm signal from the plant subsystem.

The invention discloses an industrial facility automation control system and method based on multi-dimensional information fusion, and relates to the technical field of industrial facility automation control, and the method comprises the steps: collecting the operation state signal, production power data and equipment basic parameters of industrial equipment, calculating the operation health degree and production adaptation degree of the equipment, and calculating the operation state signal of the industrial equipment; and inputting the operation health degree and the production adaptation degree into a comprehensive operation index evaluation model, calculating a comprehensive operation index, obtaining a real-time health degradation rate according to the historical operation health degree and the production time, and determining a suggested operation duration according to the real-time health degradation rate and the comprehensive operation index. According to the invention, through multi-dimensional information fusion and comprehensive consideration of the matching degree of the equipment health state and the production demand, accurate evaluation and predictive maintenance of the equipment operation state are realized, overuse or premature shutdown of the equipment is effectively avoided, and the operation reliability and the production efficiency of industrial facilities are improved.

A method for generating a map for augmented reality devices in an industrial facility that includes a plurality of subspaces, wherein each subspace from the plurality of subspaces includes a plurality of anchors, where the method includes obtaining position information of a first subspace from the plurality of subspaces, determining a coordinate system for the first subspace based on the obtained position information and a plurality of anchors of the first sub space, and includes calculating a relative position and an orientation of at least one neighboring subspace based on the determined coordinate system to generate the map based on the position information of the first subspace and the relative position of the at least one neighboring subspace, where the at least one neighboring subspace includes at least one anchor in an area overlapping the first subspace.

The present invention relates to a computer-implemented method and apparatus for determining a load profile of a building and / or an industrial facility. The apparatus includes at least one interface circuit configured to receive first data indicative of a geographic location and static information about structural conditions and usage of the building and / or industrial facility, and to receive second data indicative of a weather profile of the indicated geographic location. Further, the apparatus includes processing circuitry configured to determine a load profile of the building and / or industrial facility based on the first data and the second data. Furthermore, the invention relates to the use of such a load profile, a computer program and a computer readable medium.

The invention can be used in the creation of devices for producing hydrogen as a fuel, inter alia, at energy-intensive industrial facilities. What is proposed is a system for producing hydrogen from superheated steam comprising the following units: a generating unit consisting of the following elements arranged coaxially in a direction from the centre to the periphery: a central electrode, a cathode, a tube sealed at one end and made of a solid oxide electrolyte with oxygen ion conductivity, an anode, and permanent magnets; an electric power unit for supplying a voltage to the cathode, the anode and the central electrode; a control unit; and a gas measuring unit. The control unit receives data from the gas measuring unit and also engages in two-way communication with the electric power unit. The electric power unit, the control unit and the gas measuring unit are combined into a single unit that engages in two-way communication with the generating unit. The gas measuring unit is comprised of a system of sensors. Also proposed is a method for producing hydrogen using the claimed system. The group of inventions makes it possible to simplify the structure of a system for producing hydrogen, to regulate and automate the process, to conduct monitoring, to obtain controlled and efficient feedback, and to expand the existing range of energy-efficient means and methods for producing hydrogen.

The invention relates to a superheating device comprising a combustion chamber (10) including a fuel inlet, an oxidizer inlet, and a heat inlet for receiving heat at an average temperature (Tm) between 150°C and 500°C, the combustion chamber being adapted to burn the fuel to heat and produce heat (Th) at a high temperature exceeding 600°C. Application to the construction of industrial installations requiring high-temperature thermal energy. Figure 1

We provide the system. [Solution] It has natural language processing capabilities and means for analyzing the content of work documents, A means for selecting an appropriate machine control tool based on the analyzed work documents, Means for generating control codes for selected machine control tools, A means for applying the generated control code to the execution environment and reporting the results, Means for automating the functions of industrial facilities, A system that includes this.

Techniques for autonomous maintenance management (AMM) of an industrial facility are described. In one aspect, an AMM system monitors operational data values and an event associated with an equipment to determine key performance indicators and trend patterns associated with the equipment, where the event is characterized by deviations in the operational data values from a pre-determined threshold value, and where the trend pattern is indicative at least of operational history of the equipment. The KPIs and trend patterns are analysed to predict a behaviour of the equipment, based on which a dynamic maintenance schedule is generated, where the dynamic maintenance schedule includes a task to be executed for maintenance of the equipment. Further, the system obtains personnel information of the industrial facility to identify personnel to perform the task and accordingly issues a maintenance work order in correspondence to the dynamic maintenance schedule for maintenance of the equipment.

Implementations are described herein for inspecting industrial facilities using remote sensor packages and wireless access points (WAPs) carried by mobile robots. In various implementations, aspect(s) of an industrial process facility may be monitored using sensor package(s). The sensor package(s) may generate one or more sensor signals, and based on the one or more sensor signals, detect anomal(ies) of the monitored aspect(s) of the industrial facility. Responsive to detecting anomal(ies), the sensor package(s) may modulate a long-range low-bandwidth (LR-LB) signal to indicate detection of the anomal(ies) in the monitored aspect(s) of the industrial process facility. Another component of the industrial facility may identify LR-LB signal(s) representing detection of anomal(ies) by sensor package(s) and instruct mobile robot(s) to travel to area(s) of the industrial facility to receive sensor data carried by a short-range high-bandwidth (SR-HB) signal emanating from that area.

The disclosure relates to a system for monitoring methane emissions at industrial facilities. The system includes a network of methane emission detectors at different locations within at least one industrial facility; a gateway device operably coupled to the network of methane emission detectors; and a cloud computing environment operably coupled to the gateway device; wherein the network of methane emission detectors is configured to perform time-series measurements at different locations within the at least one industrial facility and wirelessly communicate time-series sensor data representing such measurements to the gateway device; wherein the gateway device is configured to process the time-series sensor data to derive time-series operational data and communicate the time-series operational data to the cloud computing environment; and wherein the cloud computing environment is configured to receive and process the time-series operational data to detect and characterize methane emission at the at least one industrial facility.

This exhaust gas treatment system utilizes biomass combustion gas to efficiently remove harmful substances from exhaust gases, thereby reducing energy consumption and lowering environmental impact. [Solution] The system includes a biomass combustion chamber 2 for burning biomass, a combustion gas path 3 for releasing combustion gas G1, an exhaust gas path 4 for releasing exhaust gas G2 discharged from industrial facilities, a filter unit 5 positioned on the exhaust gas path 4 for adsorbing and decomposing harmful substances, a first branch 6 for supplying combustion gas G1 upstream of the filter unit 5, and a second branch 7 for supplying combustion gas G1 to the filter unit 5. Furthermore, a heat exchanger 8 for recovering heat from the combustion gas G1 is positioned downstream of the second branch 7 of the combustion gas path 3, and an ash recovery device 9 for recovering ash generated after biomass combustion is positioned at or immediately after the connection point between the combustion gas path 3 and the biomass combustion chamber 2.

Training data is generated over a plurality of trials. In each trial, training information about a training fluid from sensors having at least two different sensing modalities is obtained. After generating training data, an artificial intelligence engine is trained on the training data. After training the artificial intelligence engine, the artificial intelligence engine infers a composition of a fluid based at least in part on deployed sensors. The artificial intelligence engine can be further trained using legacy sensors at an industrial facility.

The embodiment of the invention discloses a pseudo fire point elimination method and device, electronic equipment and a computer program product. The method comprises the following steps: acquiring historical multi-temporal thermal infrared fire point data from a remote sensing data platform; performing density clustering on the historical multi-temporal thermal infrared fire point data; performing time stability analysis on each fire point cluster obtained by clustering, and determining a fixed heat source class; a fixed heat source database is constructed, the fixed heat source database stores a heat source data structure corresponding to each fixed heat source class, and the heat source data structure comprises class identifiers of the fixed heat source classes, a heat source observation time range and a heat source activity range; and eliminating pseudo fire points in the real-time remote sensing fire point data based on the fixed heat source database. According to the technical scheme, the problem that in the prior art, an infrared heat source recognition system often depends on a static blacklist mechanism and cannot respond to industrial facility updating or urban construction changes in time, and recognition lag or misjudgment is caused can be solved.