VISION-EQUIPPED EDGE DEVICE WITH HEALTH MONITORING AND RECOMMENDATIONS USING MACHINE LEARNING ANALYTICS FOR TEXTURE FLOCK PRINT DESIGN MACHINES WITH XBEE AND LORA GATEWAY COMMUNICATION

Abstract

A vision-equipped edge device with health monitoring and recommendations using machine learning analytics for texture flock print design machines with xbee and lora gateway communication comprises TFTXLTXN furnished with an STM32 Board, an XBee RF module, a vibration sensor, a temperature sensor, a current sensor, a buzzer and a power supply permits accurate and instantaneous measurement as well as wireless data transmission effecting an uninterrupted supervision of the working parameters of the machines of the specific texture flock printing design the Node TFTXLTRXN equipped with an STM32 Board, XBee RF Module, LoRaWPAN Module, LED indicator and a power supply acts like an intermediate relay allowing data transmission between edge devices and the gateway through long range communication.

Specification

Description:FIELD OF THE INVENTION
This invention relates to vision-equipped edge device with health monitoring and recommendations using machine learning analytics for texture flock print design machines with xbee and lora gateway communication.
BACKGROUND OF THE INVENTION
This implementation brings about the establishment of a holistic framework for automated assessment, real time monitoring and predictive modeling of a texture flock print designing machines. It relies on an edge device network that has communication modules for data collection and transfer, ensuring good integration and monitoring of vibration, temperature, and current parameters. The architecture incorporates a central node which controls operations as well as the monitoring of all devices connected to the custom cloud platform for the purpose of data collection and dissemination. Machine Learning and AI technology capabilities embedded in the cloud platform aids in analytics based on knowledge and recommendations to accomplish maximum productivity while preventing future occurrences of similar processes. There is a structured web smart dashboard and an interactive display through which the operators and users authorized by the design managers login to retrieve data, visualize the information needed and take the necessary actions to maintain the tool and prevent failure from occurring.
This invention fulfills an important unsatisfied demand - that of monitoring and maintenance of the textile flock printing machine designs which must be frequently repaired due to heavy use. In traditional methods, the focus is put on the operator what comprises of a manual inspection and maintenance procedures which are undertaken when the breakdown occurs. This results in unplanned halts, high operating costs, and reduced standards of finished goods. Predictive maintenance is difficult as there is no real-time information or estimates on machine conditions that can be utilized prior to breakdowns and resulting in loss of production and opportunities. This advancement addresses these problems by enabling the user to automate the collection of performance and maintenance information and thus adopt a predictive strategy. This will enhance system performance, lower operational expenses and boost overall product reliability.
JP2017533119A: The present invention relates to a sheet printing machine (10), which is a sheet feeding device (11) for feeding a printing sheet to be printed to the sheet printing machine, At least one printing unit (12) and / or varnishing unit (13) for printing the same printed image for all printing sheets, static on the printing sheet, for printed printing A paper discharge device (14) for delivering the sheet from the sheet printer and a sheet printer (10) for printing a particularly dynamic and variable print image on the printing sheet. ) At least one printing plateless printing device (1) integrated therein. According to the present invention, the printing device (1) has a sheet-fed printing machine (10) in the area of the feed table (19) that sends the misaligned stream (S) to the first printing unit of the printing unit (12). Integrated within.
RESEARCH GAP: The ZigBee RF and Cloud innovation for automation of Flock Printing Machine for Texture Design is the novelty of the system.
CN112497920B: The invention provides a printing device, a printing control device and a control method of the printing device, which can reduce the working time before printing is started so as to quickly start printing. A printer (1) is provided with: a printing unit (101); a conveyor belt that conveys a printing medium; a camera (7) that detects both ends in a cross direction that intersects a conveyance direction of the print medium, among the ends of the print medium placed on the conveyance belt; and a control unit (100) which causes the printing unit (101) to execute printing based on the print data, wherein one of the two ends detected by the camera (7) is a print start position, and the distance from the one end to the other end is the width of the print medium.
RESEARCH GAP: The ZigBee RF and Cloud innovation for automation of Flock Printing Machine for Texture Design 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 new concept functions via a specialized mesh of equipment nodes that are specifically tailored for the acquisition, dissemination and assessment to observe and maintain the health of the texture flock print design machine. The first node is placed close to the machine and it incorporates the most significant parameters such as vibration level, temperatures and current usage among others. Such parameters enables to gauge the functionality of the particular machine. This data is afterwards transmitted to the intermediate node using wireless interface and strong antennas. This intermediate node serves as the forwarding unit that aids in relaying the information to the central gateway node in a better way. The control room's centralized gateway node is the main processing and communication processing node. It combines the received information with data from all contact points and structures it in readiness for various forms of analyses. This node is more advanced as it has access to through the custom cloud designed for it due analyses through machine learning algorithms the patterns in the data and the incoming data to identify trends and anticipate problems. Such a system based on artificial intelligence capabilities will be able to provide suggestions on what actions to take to maintain the machine or improve its performance as well as making it possible to effectively manage the machine health.
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 new concept functions via a specialized mesh of equipment nodes that are specifically tailored for the acquisition, dissemination and assessment to observe and maintain the health of the texture flock print design machine. The first node is placed close to the machine and it incorporates the most significant parameters such as vibration level, temperatures and current usage among others. Such parameters enables to gauge the functionality of the particular machine. This data is afterwards transmitted to the intermediate node using wireless interface and strong antennas. This intermediate node serves as the forwarding unit that aids in relaying the information to the central gateway node in a better way. The control room's centralized gateway node is the main processing and communication processing node. It combines the received information with data from all contact points and structures it in readiness for various forms of analyses. This node is more advanced as it has access to through the custom cloud designed for it due analyses through machine learning algorithms the patterns in the data and the incoming data to identify trends and anticipate problems. Such a system based on artificial intelligence capabilities will be able to provide suggestions on what actions to take to maintain the machine or improve its performance as well as making it possible to effectively manage the machine health.
Once the data is processed, operators and authorized personnel can access this information and recommendations through a web dashboard and an interactive display embedded into the control room. The dashboard allows visualizations of machine performance metrics in real time and issues notifications in case any operational discrepancies occur as well as forecast maintenance needs. The automated nature of data acquisition, its analysis, and reporting reduces the number of interruptions in production processes, simplifies and lowers the expenses required for maintenance, and guarantees the stable productivity of the enterprise.
BEST METHOD OF WORKING
The Node TFTXLTXN furnished with an STM32 Board, an XBee RF module, a vibration sensor, a temperature sensor, a current sensor, a buzzer and a power supply permits accurate and instantaneous measurement as well as wireless data transmission effecting an uninterrupted supervision of the working parameters of the machines of the specific texture flock printing design.
The Node TFTXLTRXN equipped with an STM32 Board, XBee RF Module, LoRaWPAN Module, LED indicator and a power supply acts like an intermediate relay allowing data transmission between edge devices and the gateway through long range communication.
The Node TFTXLRXGN which has embedded within it the Arduino Tiny Machine Learning Kit, LoRaWAN Module, GSM Modem, LED indicator, HMI display and power supply goes on to amalgamate most of the common processes of centralised control and data aggregation as well as AI concepts which enables HTTPS based secure cloud communication and predictive decision-making.
As part of the Node TFTXLRXGN, the HMI display interface enables operators to quickly and intuitively interface with the machine in order to visualize the real time machine parameters, warnings, and suggestive actions on the machine, increasing their situational awareness and control over the operations.
The communication in both nodes, placement of the XBee RF Module in the TFTXLTXN and TFTXLTRXN is satisfying and the data is free from interruption as it is sent wirelessly across different operations locations in the field."
In case the LoRaWAN Module is present, let the TFTXLTRXN and TFTXLRXGN Nodes use it for low-powered and long-distance data transmission to support large scale and remote areas' connectivity."
The incorporation of GSM Modem in the TFTXLRXGN Node facilitates cloud connection in network deficient regions to ensure that there is an uninterrupted uploading of data for machine learning and monitoring purposes.
ADVANTAGES OF THE INVENTION
1. STM32 based units fitted with vibration, temperature, and current sensors allow the system to automatically collect data and thus it makes possible to control machine operation in real time in terms deviation from the normal.
2. Data transmission from one node to another takes place without interruptions thanks to robust and long range wireless communication provided by XBee RF Modules and LoRa modules.
3. The concentrated node accumulates information and passes it on to the cloud with security system while easing the management of the whole system.
4. The system encourages AI predictions by incorporating data processed through the custom cloud platform which helps to mitigates on unplanned downtimes and avoid expensive breakdowns.
5. Authorized employees and operators can control the performance of the machine, get notifications, alerts, and actionable insights through HMI display or a web dashboard.
6. The incorporation of hardware devices such as the GSM modem and the LoRaWAN modules aids in the automation of data collection and analysis which in turn minimizes the need for manual inspecting of the systems and performing reactive maintenance hence decreasing the cost of operations.
7. The combined use of advanced hardware equipment along with machine learning analytics helps improve the performance of the machines and makes better use of the available resources enhancing productivity.
, Claims:1. A vision-equipped edge device with health monitoring and recommendations using machine learning analytics for texture flock print design machines with xbee and lora gateway communication comprises TFTXLTXN furnished with an STM32 Board, an XBee RF module, a vibration sensor, a temperature sensor, a current sensor, a buzzer and a power supply permits accurate and instantaneous measurement as well as wireless data transmission effecting an uninterrupted supervision of the working parameters of the machines of the specific texture flock printing design.
2. The device as claimed in claim 1, wherein the Node TFTXLTRXN equipped with an STM32 Board, XBee RF Module, LoRaWPAN Module, LED indicator and a power supply acts like an intermediate relay allowing data transmission between edge devices and the gateway through long range communication.
3. The device as claimed in claim 1, wherein the Node TFTXLRXGN which has embedded within it the Arduino Tiny Machine Learning Kit, LoRaWAN Module, GSM Modem, LED indicator, HMI display and power supply goes on to amalgamate most of the common processes of centralised control and data aggregation as well as AI concepts which enables HTTPS based secure cloud communication and predictive decision-making.
4. The device as claimed in claim 1, wherein as part of the Node TFTXLRXGN, the HMI display interface enables operators to quickly and intuitively interface with the machine in order to visualize the real time machine parameters, warnings, and suggestive actions on the machine, increasing their situational awareness and control over the operations.
5. The device as claimed in claim 1, wherein the communication in both nodes, placement of the XBee RF Module in the TFTXLTXN and TFTXLTRXN is satisfying and the data is free from interruption as it is sent wirelessly across different operations locations in the field.
6. The device as claimed in claim 1, wherein case the LoRaWAN Module is present, let the TFTXLTRXN and TFTXLRXGN Nodes use it for low-powered and long-distance data transmission to support large scale and remote areas connectivity.
7. The device as claimed in claim 1, wherein the incorporation of GSM Modem in the TFTXLRXGN Node facilitates cloud connection in network deficient regions to ensure that there is an uninterrupted uploading of data for machine learning and monitoring purposes.

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