AI-ENABLED LIVE DATA MONITORING AND RAPID TRANSFORMATION ANALYTICS FOR PRODUCTION ENHANCEMENT IN INDUSTRIAL FACTORIES WITH MACHINE LEARNING AND RECOMMENDATIONS

Abstract

A system of ai-enabled live data monitoring and rapid transformation analytics for production enhancement in industrial factories with machine learning and recommendations comprises EdgeCom Mote (100) is a versatile device equipped with multiple advanced sensors such as Raspberry Pi Board (110), Camera (120), Temperature Sensor (140), Accelerometer (150), Current Sensor (160), Touch HMI display panel (130), Buzzer (170), Actuator (180), and Power Supply (170), it enhances the safety response and operational performance through automated safety measures and remote monitoring for maintenance in the industrial setting the embedded Touch HMI within the EdgeCom Mote display enables the operator to visualize live images and alerts of the status of the machine right from where he or she is stationed and thus intervene when need be.

Specification

Description:FIELD OF THE INVENTION
This invention relates to ai-enabled live data monitoring and rapid transformation analytics for production enhancement in industrial factories with machine learning and recommendations.
BACKGROUND OF THE INVENTION
This advancement presents a complete AI-enabled platform for the improvement of production performance and safety in industry thanks to his or her ability to handle data in real time and perform rapid data analytics. Due to the constant monitoring of the environmental and operational variables including temperature, vibration, and current, the system allows for real time monitoring of machine status and performance. The data gets processed on-site and in the cloud server, which uses machine learning models to identify patterns and provide prescriptive and predictive analytics. On the field, operators get instantaneous communication about their tasks through an integrated display. There are built-in devices to halt machine activity automatically under given conditions which reduce danger and save equipment from getting damaged. Furthermore, the employed personnel are able to log into their dashboard to view trends and historical recommendations so that the processes of production are carried out in an efficient manner without any delays.
This innovation addresses the need for efficient decision-making, predictive maintenance, and real-time monitoring in industrial production environments where even minor disruptions can result in economic loss due to both plant downtime and safety risks. Most of the older conventional approaches to monitoring systems do not allow for the immediate information and responsive action to avert the malfunctioning of a machine or its work performance. Heavy industrial machinery is used under severe conditions, and without prolonged insights based on data, the operators fall behind in recognizing the initial signs of machinery aging, operational inefficiencies, or excessive exposure to unfavorable conditions. This invention provides real-time monitoring of critical parameters and timely initiation of automated safety actions while offering predictive insights, thereby lowering operational risks, improving the efficiency of machines, and promoting effective production processes, resulting in a safe and robust industrial setting.
US11402826B2: Systems and methods for data collection in an industrial production line are disclosed. A systems may include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and a data acquisition and analysis circuit for receiving the collected data and analyzing the received collected data using a neural network to determine an occurrence of an anomalous condition of at least one component.
RESEARCH GAP: AI-enabled live data monitoring and rapid transformation analytics with automated safety responses for production enhancement in industrial factories is the novelty of the system.
CN111562769B: The present disclosure relates to AI extension and intelligent model validation for industrial digital twins. Industrial intelligence data tags that conform to structured data types are used as a basis for creating a digital twin of industrial assets. A digital twin may include an automated model and a mechanical model or other type of non-automated model, both models referencing smart tags with respect to digitally modeling industrial assets. The structured data topology provided by the smart tag allows digital twins to easily interface with Artificial Intelligence (AI) systems. AI analytics can utilize smart tags to discover new relationships between key performance indicators and other variables of an asset, and encode these relationships in the smart tags themselves. These enhanced smart tags may also be used to perform AI-based digital twin verification. The additional contextualization provided by the enhanced smart tags can simplify AI analysis and help quickly converge on the desired analysis results.
RESEARCH GAP: AI-enabled live data monitoring and rapid transformation analytics with automated safety responses for production enhancement in industrial factories is the novelty of the system.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This concept functions as a smart, smart, and smart connected solution that captures industrial machines key parameters and highlights whenever automation is triggered. A complex data collection module supports the system and gathers a number of active and passive parameters (temperature, acceleration, current, and images). The parameters that are monitored and managed include the performance of the equipment, the state of the environment, and the level of stress and malfunction, etc. A camera's visual data is relied upon to provide sensor information on the machine's health with an emphasis on detecting any environmental or machine behavioral changes.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
This concept functions as a smart, smart, and smart connected solution that captures industrial machines key parameters and highlights whenever automation is triggered. A complex data collection module supports the system and gathers a number of active and passive parameters (temperature, acceleration, current, and images). The parameters that are monitored and managed include the performance of the equipment, the state of the environment, and the level of stress and malfunction, etc. A camera's visual data is relied upon to provide sensor information on the machine's health with an emphasis on detecting any environmental or machine behavioral changes.
The system incorporates the necessary components to allow data captured by the local components of the system to reach a basic computation level and thus provide timely patient care. Response situations of interest with a clearly defined cause and an operating threshold, for example the occurrence of a threshold temperature have also been programmed into the system. With such a programmable function, the response of the machine can be a call to action where a machine may be immediately switched off forbidden any significant damage or emergency. This also allows for a timely intervention, which is needed in an industry where operations are carried out in very quick succession.
Apart from local processing, the data is also sent to a specialized cloud server where it is subject to further analysis using machine learning models. Here, analysts look at past information and current data to find relationships and find forecasts. Such forecasts contain advice on keeping the machine working optimally, suggestions for improving its efficiency, and reports on leanness defects that might arise. Each of the algorithms being deployed within the model learns with new data in an incremental manner, making the model's predictions more precise over time and thus enhancing the operational capabilities of the system.

The platform incorporates a touch screen interface that is straightforward for operators to use as they show real time metrics and statistics themselves allowing for immediate reaction to alerts concerning machine parameters. The interface consolidates the main indicators of the current state of the machine and received messages, which improves the situational awareness of the staff present on the manufacturing facility. In addition, such users can also obtain access through the Internet to a web dashboard with a description of a machine status, its dynamics, and suggestions for its maintenance. Such remote access narrows down an array of locations that the management is to mount the machine in order to evaluate the production level and adds more control of what transpires inside the factory.
This system enhances the efficiency of production by incorporating continuous monitoring, predictive analysis, and automated responses which prevents machine performance from becoming unsafe, supports preventive maintenance, and decreases the chances of unplanned downtimes. Emphasizing the use of AI and automation, it contributes to a more resilient and flexible industrial environment.
BEST METHOD OF WORKING
The EdgeCom Mote is a versatile device equipped with multiple advanced sensors such as Raspberry Pi Board, Camera, Temperature Sensor, Accelerometer, Current Sensor, Touch HMI display panel, Buzzer, Actuator, and Power Supply. It enhances the safety response and operational performance through automated safety measures and remote monitoring for maintenance in the industrial setting.
The embedded Touch HMI within the EdgeCom Mote display enables the operator to visualize live images and alerts of the status of the machine right from where he or she is stationed and thus intervene when need be.
The EdgeCom Mote comprises an Actuator which serves the function of emergency control measures that enable shutting down of machine operations immediately at the time critical parameters are sensed.
The inside camera that is embedded inside the EdgeCom Mote acts as a valuable resource in improving the monitoring aspect by augmenting the real time analysis of machine health status thus increasing the efficiency of diagnostics and anomaly detection.
Having in view the fact that the Temperature Sensor, Accelerometer, and Current Sensor included in the EdgeCom Mote are able to constantly track basic parameters of the machine such as heat, vibrations, and electric load, it allows to detect faults and implement maintenance interventions before faults actually occur.
ADVANTAGES OF THE INVENTION
1. In each unit's core, a Raspberry Pi, upper and lower environmental parameters, a current, and an accelerometer, a temperature sensor in the system ensuring dynamic machine health is preserved, environmental disturbances, and faults can be addressed in advance.
2. A high-risk environment operating under critical conditions can be dangerous for an operator, hence the system's capability to stop the machine with the use of an actuator and a buzzer when warranted helps minimize exposure.
3. With the help of an HMI touchscreen display, the operator can obtain necessary data without going through the internet by physically being on a particular site that snaps alerts whenever conditions change. This optimizes decision making since key parameters and alerts are always present where decisions are made.
4. In this case, the data is acquired, and the result is used through remote access web dashboards to have flexibility in operations via predictive data analysis acquired from this custom cloud. This guarantees security for authorized users.
5. Using the camera part therefore helps monitor processes visually hence facilitating desired machine operations and promoting the overall lifespan of equipment by limiting chances of unanticipated failures and associated downtimes.
, Claims:1. A system of ai-enabled live data monitoring and rapid transformation analytics for production enhancement in industrial factories with machine learning and recommendations comprises EdgeCom Mote (100) is a versatile device equipped with multiple advanced sensors such as Raspberry Pi Board (110), Camera (120), Temperature Sensor (140), Accelerometer (150), Current Sensor (160), Touch HMI display panel (130), Buzzer (170), Actuator (180), and Power Supply (170), it enhances the safety response and operational performance through automated safety measures and remote monitoring for maintenance in the industrial setting.
2. The system as claimed in claim 1, wherein the embedded Touch HMI within the EdgeCom Mote display enables the operator to visualize live images and alerts of the status of the machine right from where he or she is stationed and thus intervene when need be.
3. The system as claimed in claim 1, wherein the EdgeCom Mote comprises an Actuator which serves the function of emergency control measures that enable shutting down of machine operations immediately at the time critical parameters are sensed.
4. The system as claimed in claim 1, wherein the inside camera that is embedded inside the EdgeCom Mote acts as a valuable resource in improving the monitoring aspect by augmenting the real time analysis of machine health status thus increasing the efficiency of diagnostics and anomaly detection.
5. The system as claimed in claim 1, wherein having in view the fact that the Temperature Sensor, Accelerometer, and Current Sensor included in the EdgeCom Mote are able to constantly track basic parameters of the machine such as heat, vibrations, and electric load, it allows to detect faults and implement maintenance interventions before faults actually occur.

Documents