DESC:FIELD OF DISCLOSURE
The present disclosure relates to digital twin technology for electrical systems, and more particularly to a system and method for real-time 3D holographic digital twin for predictive maintenance and remaining useful life estimations in electrical systems.
BACKGROUND
Digital twin technology has emerged as a powerful tool for monitoring, analyzing, and maintaining complex systems in various industries. This technology creates virtual representations of physical assets, allowing for real-time monitoring, simulation, and predictive analysis. In the field of electrical systems, digital twins have shown great potential for improving maintenance practices and optimizing system performance.
Conventional digital twin implementations often rely on two-dimensional visualizations and static data representations. While these approaches provide valuable insights, they can limit the depth of understanding and interaction with complex electrical systems. Additionally, existing solutions may struggle to provide real-time updates and accurate predictions for system behavior, particularly in dynamic environments where conditions can change rapidly.
Current predictive maintenance techniques for electrical systems typically rely on periodic inspections, historical data analysis, and simple sensor-based monitoring. These methods, while useful, may not capture the full complexity of system interactions or provide sufficiently detailed and timely information for optimal decision-making. Furthermore, estimating the remaining useful life of electrical components often involves significant uncertainties and may not account for real-time operational conditions.
Therefore, there exists a need for a technical solution that solves the aforementioned problems of conventional systems and methods.
SUMMARY
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
In an aspect of the present disclosure, a system for generating a real-time 3D holographic digital twin for predictive maintenance is disclosed. The system includes a plurality of high-resolution cameras configured to capture 3D models of components of an electro-mechanical system. The system also includes a plurality of sensors configured to capture sensor data including voltage, current, temperature, and vibration measurements from the electro-mechanical system. A data processing apparatus includes processing circuitry is configured to process the captured 3D models and sensor data into custom holographic representations. The processing circuitry creates a real-time 3D holographic digital twin based on the custom holographic representations. The processing circuitry continuously updates the holographic representations based on real-time data using artificial intelligence (AI) techniques. The processing circuitry analyzes historical and real-time data for predictive maintenance and remaining useful life (RUL) estimation using machine learning algorithms. The system further includes 3D holographic projection devices configured to display the custom holographic representations on Industrial Internet of Things (IIoT) digital twin dashboards.
In some aspects of the present disclosure, the processing circuitry is further configured to apply mesh simplification, texture mapping, and lighting calculations to optimize the 3D models for holographic display.
In some aspects of the present disclosure, the 3D holographic projection devices utilize volumetric displays, light field displays, or holographic optical elements to render the 3D holographic representations.
In some aspects of the present disclosure, the system further includes augmented reality (AR) or mixed reality (MR) devices configured to provide an immersive experience for interacting with the digital twin.
In some aspects of the present disclosure, the processing circuitry is further configured to implement blockchain technology to ensure integrity and traceability of sensor data and maintenance records.
In an aspect of the present disclosure, a method for generating a real-time 3D holographic digital twin for predictive maintenance is disclosed. The method includes capturing, by a plurality of high-resolution cameras, 3D models of components of an electro-mechanical system. The method also includes capturing, by a plurality of sensors, sensor data including voltage, current, temperature, and vibration measurements from the electro-mechanical system. The method further includes processing, by processing circuitry of a data processing apparatus, the captured 3D models and sensor data into custom holographic representations. The processing circuitry creates a real-time 3D holographic digital twin based on the custom holographic representations. The processing circuitry continuously updates the holographic representations based on real-time data using artificial intelligence (AI) techniques. The processing circuitry analyzes historical and real-time data for predictive maintenance and remaining useful life (RUL) estimation using machine learning algorithms. The method also includes displaying, by 3D holographic projection devices, the custom holographic representations on Industrial Internet of Things (IIoT) digital twin dashboards.
In some aspects of the present disclosure, processing the captured 3D models includes applying mesh simplification, texture mapping, and lighting calculations to optimize the 3D models for holographic display.
In some aspects of the present disclosure, displaying the custom holographic representations includes utilizing volumetric displays, light field displays, or holographic optical elements to render the 3D holographic representations.
In some aspects of the present disclosure, the method further includes providing an immersive experience for interacting with the digital twin using augmented reality (AR) or mixed reality (MR) devices.
In some aspects of the present disclosure, the method further includes implementing, by the processing circuitry, blockchain technology to ensure integrity and traceability of sensor data and maintenance records.
The foregoing general description of the illustrative aspects and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.
BRIEF DESCRIPTION OF FIGURES
The following detailed description of the preferred aspects of the present disclosure will be better understood when read in conjunction with the appended drawings. The present disclosure is illustrated by way of example, and not limited by the accompanying figures, in which like references indicate similar elements.
FIG. 1 illustrates a block diagram of a system for real-time 3D holographic digital twin generation and predictive maintenance, according to aspects of the present disclosure;
FIG. 2 illustrates a block diagram of a data processing apparatus of the system of FIG. 1, according to aspects of the present disclosure; and
FIG. 3 illustrates a flowchart of a method for generating and maintaining a real-time 3D holographic digital twin, according to aspects of the present disclosure.
DETAILED DESCRIPTION
The following description sets forth exemplary aspects of the present disclosure. It should be recognized, however, that such description is not intended as a limitation on the scope of the present disclosure. Rather, the description also encompasses combinations and modifications to those exemplary aspects described herein.
The present disclosure provides a system and method for generating a real-time three-dimensional (3D) holographic digital twin for predictive maintenance of electrical systems. This innovative approach leverages advanced camera-based 3D modeling, custom holographic designs, artificial intelligence (AI)-driven dynamic content updates, and machine learning algorithms to create a comprehensive, immersive representation of electrical systems. The system captures detailed 3D models of electro-mechanical system components and collects sensor data, which is then processed into custom holographic representations. These representations are displayed on Industrial Internet of Things (IIoT) digital twin dashboards, providing real-time visualization and interaction capabilities. The system continuously updates the holographic representations using AI techniques and analyzes historical and real-time data for predictive maintenance and remaining useful life estimation. This integration of cutting-edge visualization, data processing, and artificial intelligence technologies represents a significant advancement in the field of predictive maintenance and system management for electrical systems.
The system offers several key advantages, including enhanced visualization and interaction through real-time 3D holographic representations, improved predictive maintenance capabilities, increased accuracy in remaining useful life estimations, seamless integration with IIoT platforms, reduced system downtime and maintenance costs, and a scalable architecture that can incorporate emerging technologies. By providing unprecedented insight into system operations and powerful predictive capabilities, this system enables more comprehensive, accurate, and efficient monitoring and maintenance of complex electro-mechanical systems.
FIG. 1 illustrates a block diagram of a system 100 for real-time 3D holographic digital twin generation and predictive maintenance, according to aspects of the present disclosure. The system 100 includes imaging devices 102, sensors 104, a data processing apparatus 106, and a user device 108, all interconnected through a communication network 110.
The imaging devices 102 may be configured to capture visual data of the system components. The imaging devices 102 may include high-resolution cameras capable of capturing detailed 3D models of the electro-mechanical system components. In some aspects of the present disclosure, the imaging devices 102 may utilize various imaging technologies such as laser scanning, structured light, or photogrammetry to generate accurate 3D models of the components. The imaging devices 102 may be coupled to the data processing apparatus 106 through the communication network 110, enabling the transfer of captured visual data for further processing.
The sensors 104 may be configured to collect various operational parameters from the electro-mechanical system. The sensors 104 may capture data including, but not limited to, voltage, current, temperature, and vibration measurements. In some aspects of the present disclosure, the sensors 104 may include additional sensor types such as acoustic sensors or infrared cameras to capture a wider range of data from the electro-mechanical system components. The sensors 104 may be coupled to the data processing apparatus 106 through the communication network 110, facilitating the transmission of collected sensor data for analysis and processing.
The data processing apparatus 106 may be configured to process and analyze the data collected by the imaging devices 102 and sensors 104. The data processing apparatus 106 may include processing circuitry 112 and a database 114 for processing and storing the collected data. The processing circuitry 112 may be configured to execute various operations associated with the system 100, including data processing, holographic modeling, real-time visualization, AI-driven updates, and predictive analytics.
The data processing apparatus 106 may be a network of computers, a framework, or a combination thereof, that may provide a generalized approach to create a server implementation. In some aspects of the present disclosure, the data processing apparatus 106 may be a server. Examples of the data processing apparatus 106 may include, but are not limited to, personal computers, laptops, mini-computers, mainframe computers, any non-transient and tangible machine that can execute a machine-readable code, cloud-based servers, distributed server networks, or a network of computer systems. The data processing apparatus 106 may be realized through various web-based technologies such as, but not limited to, a Java web-framework, a .NET framework, a personal home page (PHP) framework, or any other web-application framework.
Although FIG. 1 illustrates that the system 100 includes a single data processing apparatus (i.e., the data processing apparatus 106), it will be apparent to a person skilled in the art that the scope of the present disclosure is not limited to it. In various other aspects, the system 100 may include multiple data processing apparatuses without deviating from the scope of the present disclosure. In such a scenario, each data processing apparatus may be configured to perform one or more operations in a manner similar to the operations of the data processing apparatus 106 as described herein.
The user device 108 may be configured to provide an interface for users to interact with the system 100. The user device 108 may include a processing unit 116, a communication interface 118, and an Industrial Internet of Things (IIoT) digital twin dashboards 120 (hereinafter interchangeably referred as “the dashboard 120). The processing unit 116 may be configured to handle local data processing tasks, while the communication interface 118 enables connectivity with the communication network 110. The dashboard 120 may be configured to display the processed information and holographic representations.
The user device 108 may be adapted to facilitate a user to input data, receive data, and/or transmit data within the system 100. In some aspects of the present disclosure, the user may be, but is not limited to, a professional (including engineers or technicians who maintain electrical systems), a researcher (who may use the data generated by the system 100 to inform new studies), policy makers (who are essential in effecting change), or system operators. Aspects of the present disclosure are intended to include and/or otherwise cover any type of user, without deviating from the scope of the present disclosure.
In some aspects of the present disclosure, the user device 108 may include, but is not limited to, a desktop, a notebook, a laptop, a handheld computer, a touch sensitive device, a computing device, a smart phone, a smart watch, and the like. It will be apparent to a person of ordinary skill in the art that the user device 108 may include any device/apparatus that is capable of manipulation by the user.
Although FIG. 1 illustrates that the system 100 includes a single user device (i.e., the user device 108), it will be apparent to a person skilled in the art that the scope of the present disclosure is not limited to it. In various other aspects, the system 100 may include multiple user devices without deviating from the scope of the present disclosure. In such a scenario, each user device may be configured to perform one or more operations in a manner similar to the operations of the user device 108 as described herein.
The communication network 110 may be configured to facilitate data exchange between all components of the system 100. The communication network 110 enables the transfer of data from the imaging devices 102 and sensors 104 to the data processing apparatus 106, and the transmission of processed information to the user device 108 for display on the dashboard 120.
The communication network 110 may include suitable logic, circuitry, and interfaces that may be configured to provide a plurality of network ports and a plurality of communication channels for transmission and reception of data related to operations of various entities in the system 100. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address.
The communication network 110 may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the imaging devices 102, the sensors 104, the data processing apparatus 106, and the user device 108. The communication data may be transmitted or received via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof.
In one aspect, the communication data may be transmitted or received via at least one communication channel of a plurality of communication channels in the communication network 110. The communication channels may include, but are not limited to, a wireless channel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a Metropolitan Area Network (MAN), a Satellite Network, the Internet, a Fiber Optic Network, a Coaxial Cable Network, an Infrared (IR) network, a Radio Frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and/or later developed technologies.
In operation, the system 100 may capture 3D models and sensor data from the electro-mechanical system components using the imaging devices 102 and sensors 104. The data processing apparatus 106 may process this data into custom holographic representations, which are then displayed on the dashboard 120 of the user device 108 via the communication network 110. The system 100 may continuously update the representations based on real-time data and analyze the data for predictive maintenance and remaining useful life estimation. This integrated approach allows for comprehensive monitoring, analysis, and maintenance of complex electro-mechanical systems.
FIG. 2 illustrates a block diagram of the data processing apparatus 106 of the system 100 of FIG. 1, according to aspects of the present disclosure. The data processing apparatus 106 may include processing circuitry 112 that connects to a network interface 200 and an I/O interface 202. The apparatus also connects to a database 114 through a database connection 204.
The network interface 200 may include suitable logic, circuitry, and interfaces that may be configured to establish and enable a communication between the data processing apparatus 106 and different elements of the system 100 (e.g., the imaging devices 102, the sensors 104, and the user device 108), via the communication network 110. The network interface 200 may be implemented by use of various known technologies to support wired or wireless communication of the data processing apparatus 106 with the communication network 110. The network interface 200 may include, but is not limited to, an antenna, a RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a SIM card, and a local buffer circuit.
The I/O interface 202 may include suitable logic, circuitry, interfaces, and/or code that may be configured to receive inputs and transmit data processing apparatus's outputs (i.e., one or more outputs generated by the data processing apparatus 106) via a plurality of data ports in the data processing apparatus 106. The I/O interface 202 may include various input and output data ports for different I/O devices. Examples of such I/O devices may include, but are not limited to, a touch screen, a keyboard, a mouse, a joystick, a projector audio output, a microphone, an image-capture device, a liquid crystal display (LCD) screen and/or a speaker.
The database connection 204 may be configured to establish and maintain a connection between the processing circuitry 112 and the database 114. This connection enables the processing circuitry 112 to store and retrieve data from the database 114 as needed for various operations of the system 100.
The database 114 may be configured to store logic, instructions, circuitry, interfaces, and/or codes of the processing circuitry 112 to enable the processing circuitry 112 to execute the one or more operations associated with the system 100. The database 114 may be further configured to store therein, data associated with the system 100, and the like. It will be apparent to a person having ordinary skill in the art that the database 114 may be configured to store various types of data associated with the system 100, without deviating from the scope of the present disclosure. Examples of the database 114 may include but are not limited to, a Relational database, a NoSQL database, a Cloud database, an Object oriented database, and the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the database 114 including known, related art, and/or later developed technologies. In some aspects of the present disclosure, a set of centralized or distributed network of peripheral memory devices may be interfaced with the data processing apparatus 106, as an example, on a cloud server.
The processing circuitry 112 may include multiple specialized engines that work together to process and analyze data. These include a data acquisition engine 206, a holographic modeling engine 208, a real-time visualization engine 210, an AI-driven update engine 212, a predictive analytics engine 214, and a digital twin integration engine 216.
The data acquisition engine 206 may be configured to receive and process data from the imaging devices 102 and sensors 104. This engine may be responsible for collecting, organizing, and preparing the raw data for further processing by other engines. The data acquisition engine 206 may implement various data filtering and preprocessing techniques to ensure the quality and consistency of the incoming data.
The holographic modeling engine 208 may be configured to convert the captured 3D models and sensor data into the custom holographic representations suitable for projection. This engine may apply various algorithms to optimize the 3D models for holographic display, such as mesh simplification, texture mapping, and lighting calculations. The holographic modelling engine 208 may also be responsible for integrating sensor data with the 3D models to create comprehensive holographic representations of the electro-mechanical system components. Specifically, the holographic modelling engine 208 may be configured to apply mesh simplification, texture mapping, and lighting calculations to generate the custom holographic representations. In some aspects of the present disclosure, the custom holographic representations may continuously be updated using AI techniques by way of the AI-driven update engine (212), and rendered by 3D holographic projection devices that may be displayed by the dashboard 120 to form a synchronized, real-time digital replica of the electro-mechanical system.
The real-time visualization engine 210 may be configured to generate and manage the display of the holographic representations on the IIoT digital twin dashboards 120. This engine may be responsible for rendering the 3D holographic representations in real-time, handling user interactions with the holographic models, and updating the visual display based on changes in the underlying data.
The AI-driven update engine 212 may be configured to continuously update the custom holographic representations based on real-time data. This engine may utilize machine learning algorithms such as neural networks or deep learning techniques to identify patterns and anomalies in the real-time data. The AI-driven update engine 212 may be responsible for dynamically adjusting the custom holographic representations to reflect the current state of the electro-mechanical system accurately.
In some aspects of the present disclosure, the AI-driven update engine 212 may be configured to employ convolutional neural networks (CNNs) for detecting spatial anomalies in vibration and thermal sensor data that is used to localize faults on 3D models. Further, the AI-driven update engine 212 may be configured to employ Long Short-Term Memory (LSTM) Networks for time-series prediction of sensor trends to identify deviations and predict potential failures. Further, the AI-driven update engine 212 may be configured to employ autoencoders for unsupervised anomaly detection by comparing real-time data with learned normal patterns. Furthermore, in some aspects of the present disclosure, the CNNs, the LSTM networks, and/or the autoencoders may trigger updates to the custom holographic representations, including color changes, deformations, and alerts to reflect the real-time state of the system.
The predictive analytics engine 214 may be configured to analyze historical and real-time data for predictive maintenance and remaining useful life (RUL) estimation. This engine may employ various machine learning techniques such as regression analysis, time series forecasting, or anomaly detection to generate accurate predictions and estimations. Specifically, the predictive analytics engine 214 may be configured to employ regression models based on linear regression, random forest regression, or the like to estimate degradation trends and project remaining useful life based on historical sensor data. In some aspect, the predictive analytics engine 214 may be configured to employ time series forecasting such as ARIMA, LSTM, or the like to predict future values of critical parameters like temperature or vibration. In some aspect, the predictive analytics engine 214 may be configured to employ anomaly detection techniques such as isolation forest, one-class SVM to identify early signs of faults by detecting deviations from normal patterns. In some aspects, the regression models, time series forecasting, and the anomaly detection techniques may be configured to analyse both historical trends and live data to generate timely maintenance insights and accurate RUL predictions for each component.
The predictive analytics engine 214 may be responsible for identifying potential failures, estimating component lifespans, and providing insights for proactive maintenance.
The digital twin integration engine 216 may be configured to create and maintain the overall digital twin representation of the electro-mechanical system. This engine may be responsible for integrating data from all other engines to create a cohesive and comprehensive digital replica of the physical system. The digital twin integration engine 216 may also manage the synchronization between the physical system and its digital counterpart, ensuring that the digital twin accurately reflects the current state and behavior of the real-world system.
A communication bus 218 interconnects the various engines within the processing circuitry 112, allowing for data exchange between components. This bus facilitates the seamless flow of information between different engines, enabling efficient processing and analysis of data within the system.
In operation, the data processing apparatus 106 receives data from the imaging devices 102 and sensors 104 through the network interface 200. The data acquisition engine 206 processes this raw data and passes it to the holographic modeling engine 208, which creates custom holographic representations. These representations are then passed to the real-time visualization engine 210 for display on the IIoT digital twin dashboards 120. Simultaneously, the AI-driven update engine 212 continuously updates these representations based on real-time data, while the predictive analytics engine 214 analyzes historical and current data for predictive maintenance insights. The digital twin integration engine 216 integrates all this information to maintain an accurate and comprehensive digital twin of the electro-mechanical system. Throughout this process, data is stored in and retrieved from the database 114 as needed, and results are communicated back to the user device 108 through the network interface 200 and I/O interface 202.
FIG. 3 illustrates a flowchart of a method 300 for generating and maintaining a real-time 3D holographic digital twin, according to aspects of the present disclosure.
At step 302, the system 100 may capture 3D models of electro-mechanical system components. The imaging devices 102 may be configured to capture detailed 3D models of the electro-mechanical system components. In some aspects of the present disclosure, the imaging devices 102 may utilize various imaging technologies such as laser scanning, structured light, or photogrammetry to generate accurate 3D models of the components.
At step 304, the system 100 may capture sensor data from the electro-mechanical system. The sensors 104 may be configured to collect various operational parameters from the electro-mechanical system, including but not limited to voltage, current, temperature, and vibration measurements. In some aspects of the present disclosure, additional sensor types such as acoustic sensors or infrared cameras may be used to capture a wider range of data from the electro-mechanical system components.
At step 306, the system 100 may process the captured 3D models and sensor data. The data processing apparatus 106, specifically the data acquisition engine 206 and the holographic modeling engine 208, may be configured to process the captured data. This processing may involve data filtering, preprocessing, and conversion of the 3D models and sensor data into custom holographic representations suitable for projection.
At step 308, the system 100 may create a real-time 3D holographic digital twin based on the processed data. The digital twin integration engine 216 may be configured to integrate the processed data to create a comprehensive digital replica of the physical system. This step involves combining the 3D models, sensor data, and any other relevant information to create an accurate and detailed digital representation of the electro-mechanical system.
At step 310, the system 100 may continuously update the holographic representations using AI. The AI-driven update engine 212 may be configured to process incoming sensor data and modify the holographic representations accordingly. This step ensures that the digital twin remains synchronized with the physical system in real-time, reflecting any changes or fluctuations in the system's state or behavior.
At step 312, the system 100 may analyze data for predictive maintenance and remaining useful life (RUL) calculations. The predictive analytics engine 214 may be configured to process large volumes of historical and real-time data to identify trends, predict potential failures, and estimate the remaining useful life of system components. This analysis may employ various machine learning techniques such as regression analysis, time series forecasting, or anomaly detection.
At step 314, the system 100 may display custom holographic representations on IIoT digital twin dashboards 120. The real-time visualization engine 210 may be configured to render and display the 3D holographic representations on the IIoT digital twin dashboards 120. This step provides users with an intuitive and immersive interface for interacting with the digital twin, allowing them to visualize and analyze the electro-mechanical system in real-time.
In some aspects of the present disclosure, the method 300 may incorporate additional steps or variations of the described steps. For example, the method 300 may include steps for user interaction with the holographic representations, integration of additional data sources, or implementation of specific maintenance actions based on the predictive analytics results. The exact implementation of the method 300 may vary depending on the specific requirements and constraints of each application, without deviating from the scope of the present disclosure.
Thus, the system 100 and the method 300 provide several significant technical advantages. The real-time 3D holographic visualization offers unprecedented insight into complex electro-mechanical systems, enabling more accurate and intuitive analysis. The integration of AI-driven updates ensures the digital twin remains synchronized with the physical system, providing up-to-the-minute accuracy. The advanced predictive analytics capabilities, powered by machine learning algorithms, allow for early detection of potential failures and more precise estimation of component lifespans. The system's ability to seamlessly integrate with IIoT platforms enhances its utility across various industrial applications. Furthermore, the use of custom holographic representations and immersive interfaces significantly improves user interaction and decision-making processes. Lastly, the scalable architecture of the system allows for easy incorporation of emerging technologies, ensuring its long-term relevance and adaptability to evolving industrial needs.
Aspects of the present disclosure are discussed here with reference to flowchart illustrations and block diagrams that depict methods, systems, and apparatus in accordance with various aspects of the present disclosure. Each block within these flowcharts and diagrams, as well as combinations of these blocks, can be executed by computer-readable program instructions. The various logical blocks, modules, circuits, and algorithm steps described in connection with the disclosed aspects may be implemented through electronic hardware, software, or a combination of both. To emphasize the interchangeability of hardware and software, the various components, blocks, modules, circuits, and steps are described generally in terms of their functionality. The decision to implement such functionality in hardware or software is dependent on the specific application and design constraints imposed on the overall system. Person having ordinary skill in the art can implement the described functionality in different ways depending on the particular application, without deviating from the scope of the present disclosure.
The flowcharts and block diagrams presented in the figures depict the architecture, functionality, and operation of potential implementations of systems, methods, and apparatus according to different aspects of the present disclosure. Each block in the flowcharts or diagrams may represent an engine, segment, or portion of instructions includes one or more executable instructions to perform the specified logical function(s). In some alternative implementations, the order of functions within the blocks may differ from what is depicted. For instance, two blocks shown in sequence may be executed concurrently or in reverse order, depending on the required functionality. Each block, and combinations of blocks, can also be implemented using special-purpose hardware-based systems that perform the specified functions or tasks, or through a combination of specialized hardware and software instructions.
Although the preferred aspects have been detailed here, it should be apparent to those skilled in the relevant field that various modifications, additions, and substitutions can be made without departing from the scope of the disclosure. These variations are thus considered to be within the scope of the disclosure as defined in the following claims.
Features or functionalities described in certain example aspects may be combined and re-combined in or with other example aspects. Additionally, different aspects and elements of the disclosed example aspects may be similarly combined and re-combined. Further, some example aspects, individually or collectively, may form components of a larger system where other processes may take precedence or modify their application. Moreover, certain steps may be required before, after, or concurrently with the example aspects disclosed herein. It should be noted that any and all methods and processes disclosed herein can be performed in whole or in part by one or more entities or actors in any manner.
Although terms like "first," "second," etc., are used to describe various elements, components, regions, layers, and sections, these terms should not necessarily be interpreted as limiting. They are used solely to distinguish one element, component, region, layer, or section from another. For example, a "first" element discussed here could be referred to as a "second" element without departing from the teachings of the present disclosure.
The terminology used here is intended to describe specific example aspects and should not be considered as limiting the disclosure. The singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "includes," "comprising," and "including," as used herein, indicate the presence of stated features, steps, elements, or components, but do not exclude the presence or addition of other features, steps, elements, or components.
As used herein, the term "or" is intended to be inclusive, meaning that "X employs A or B" would be satisfied by X employing A, B, or both A and B. Unless specified otherwise or clearly understood from the context, this inclusive meaning applies to the term "or."
Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the relevant art. Terms should be interpreted consistently with their common usage in the context of the relevant art and should not be construed in an idealized or overly formal sense unless expressly defined here.
The terms "about" and "substantially," as used herein, refer to a variation of plus or minus 10% from the nominal value. This variation is always included in any given measure.
In cases where other disclosures are incorporated by reference and there is a conflict with the present disclosure, the present disclosure takes precedence to the extent of the conflict, or to provide a broader disclosure or definition of terms. If two disclosures conflict, the later-dated disclosure will take precedence.
The use of examples or exemplary language (such as "for example") is intended to illustrate aspects of the disclosure and should not be seen as limiting the scope unless otherwise claimed. No language in the specification should be interpreted as implying that any non-claimed element is essential to the practice of the disclosure.
While many alterations and modifications of the present disclosure will likely become apparent to those skilled in the art after reading this description, the specific aspects shown and described by way of illustration are not intended to be limiting in any way.
A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the scope of the disclosure. Accordingly, other implementations are within the scope of the following claims. ,CLAIMS:1. A system (100) for generating a real-time 3D holographic digital twin for predictive maintenance, the system comprising:
a plurality of imaging devices (102) configured to capture 3D models of components of an electro-mechanical system;
a plurality of sensors (104) configured to capture sensor data including voltage, current, temperature, and vibration measurements from the electro-mechanical system;
processing circuitry (112) that is coupled to the configured to:
process the captured 3D models and sensor data into custom holographic representations;
create a real-time 3D holographic digital twin based on the custom holographic representations;
continuously update the holographic representations based on real-time data using artificial intelligence (AI) techniques;
analyze historical and real-time data for predictive maintenance and remaining useful life (RUL) estimation using machine learning algorithms; and
3D holographic projection devices configured to display the custom holographic representations on Industrial Internet of Things (IIoT) digital twin dashboards (120).
2. The system of claim 1, wherein the processing circuitry (112) is further configured to apply mesh simplification, texture mapping, and lighting calculations to optimize the 3D models for holographic display.

3. The system of claim 1, wherein the 3D holographic projection devices utilize volumetric displays, light field displays, or holographic optical elements to render the 3D holographic representations.

4. The system of claim 1, further comprising augmented reality (AR) or mixed reality (MR) devices configured to provide an immersive experience for interacting with the real-time 3D holographic digital twin.

5. The system of claim 1, wherein the processing circuitry (112) is further configured to implement blockchain technology to ensure integrity and traceability of sensor data and maintenance records.

6. A method (300) for generating a real-time 3D holographic digital twin for predictive maintenance, the method comprising:
capturing, by way of a plurality of imaging devices (102), 3D models of components of an electro-mechanical system;
capturing, by way of a plurality of sensors (104), sensor data including voltage, current, temperature, and vibration measurements from the electro-mechanical system;
processing, by way of processing circuitry (112) of a data processing apparatus (106), the captured 3D models and sensor data into custom holographic representations;
creating, by the processing circuitry (112), a real-time 3D holographic digital twin based on the custom holographic representations;
continuously updating, by the processing circuitry (112), the holographic representations based on real-time data using artificial intelligence (AI) techniques;
analyzing, by the processing circuitry (112), historical and real-time data for predictive maintenance and remaining useful life (RUL) estimation using machine learning algorithms; and
displaying, by 3D holographic projection devices, the custom holographic representations on Industrial Internet of Things (IIoT) digital twin dashboards (120).
7. The method (300) as claimed in claim 6, wherein processing the captured 3D models includes applying mesh simplification, texture mapping, and lighting calculations to optimize the 3D models for holographic display.

8. The method (300) as claimed in claim 6, wherein displaying the custom holographic representations includes utilizing volumetric displays, light field displays, or holographic optical elements to render the 3D holographic representations.

9. The method (300) as claimed in claim 6, further comprising providing an immersive experience for interacting with the real-time 3D holographic digital twin using augmented reality (AR) or mixed reality (MR) devices.

10. The method (300) as claimed in claim 6, further comprising implementing, by the processing circuitry (112), blockchain technology to ensure integrity and traceability of sensor data and maintenance records.