VISION-BASED INLINE EDGE DEVICE FOR CRITICAL HEALTH MONITORING OF INDUSTRIAL HORIZONTAL DIE CASTER MACHINES WITH XBEE AND NRF GATEWAY AND AI RECOMMENDATIONS

Abstract

A vision-based inline edge device for critical health monitoring of industrial horizontal die caster machines with xbee and nrf gateway and ai recommendations comprises VSxcapt Mote containing an Arduino Tiny Machine Learning Kit, Camera Module, XBee RF Module, Accelerometer, Current Sensor, Temperature Sensor, Actuator, and Power Supply, the machine is capable of performing real-time anomaly analysis and shutting down the equipment in critical conditions, this way, safety and operation stability are assured on industrial die caster machines the VSbeenrf Mote enhances operational effectiveness through the provision of a 32-Bit output, nRF Module, XBee RF Module, Display of HMI, and Supply of Power, virtually eliminating downtime by reducing the distance and time needed for operators to control and visualize machinery and act on alerts.

Specification

Description:FIELD OF THE INVENTION
This invention relates to vision-based inline edge device for critical health monitoring of industrial horizontal die caster machines with xbee and nrf gateway and ai recommendations.
BACKGROUND OF THE INVENTION
This advancement provides an inline edge device system that is based on computer vision concepts for monitoring the health of horizontal die-casting machines. It includes a wide range of interconnected modules for data acquisition, data processing, and predictive analysis. It has sufficient means for diagnosing critical conditions and will stop the machine on its own to avoid damage. The data from the system is sent wirelessly to a self-developed cloud server and analytics are performed through machine learning and AI monitoring and suggestions are provided. It allows the user to make use of the rich functionalities of the system via an intuitive web interface while performing such operations as machine health status monitoring, performance enhancement, and maintenance forecasting.
The present invention resolves the effective demand for real time assessment and maintenance of industrial horizontal die caster machines which are susceptible to operational ineffectiveness, unforeseen breakdowns, and safety hazards. Most methods are said to be effective because they depend on scheduled machinery diagnosis or use of rudimentary sensors that do not deliver preventive actionable solutions nor address emerging issues in a timely manner. These shortcomings may lead to unproductive downtime, poor quality of the finished products, and escalating safety risks. This project, by employing advanced data acquisition, machine learning-based analytics, and AI-based recommendations, guarantees constant oversight, quick recognition of irregularities, and predictive maintenance, eventually improving operational effectiveness and reducing machine and operator downtime and safety risks respectively.
CN218574926U: The utility model relates to the field of die casting machines, in particular to a cooling device for a die casting machine, which comprises a conveying mechanism, a sprinkling module, an air cooling module and a material tray for bearing die castings; the material tray is provided with a plurality of material trays which are all horizontally placed on the conveying mechanism, and a plurality of accommodating cavities which are uniformly distributed are formed at the upper end of the material tray; the sprinkling module is fixedly arranged on one side of the left end part of the conveying mechanism, and the execution part of the sprinkling module is positioned above the conveying mechanism; the air cooling module is positioned above the right end part of the conveying mechanism; can the efficient to the die casting cooling through the combined action of watering mechanism and forced air cooling module, improve work efficiency.
RESEARCH GAP: Vision-based real-time health monitoring and AI-driven predictive maintenance for industrial horizontal die caster machines using XBee and nRF communication is the novelty of the system.
CN107971470A: The invention discloses a kind of protection type vertical die-casting machine, Including base, Mold die casting mechanism and protection mechanism, The molding die casting mechanism is arranged on base, The protection mechanism is arranged to four, And four protection mechanisms are separately positioned on the top of four sides of base, The molding die casting mechanism includes top plate, Convex template, Recessed template and die casting cylinder, The top plate is arranged on the other end of four guide rods, The present invention has reasonable in design, By the structure design for increasing protection mechanism on the basis of traditional vertical die-casting machine, It can not only avoid in the die casting machine course of work, Its movement parts may cause pinching to the person, Shearing, Collision, The danger such as winding, And its loosening, Loosen, Drop, Fracture, Fragmentation, Throw away etc. may caused by it is dangerous, But also it can hurt sb.'s feelings to avoid mold parting surface contact position and anti-metal liquid splash at cast gate, Protective plate also has dust-proof at the same time, Mute and protection function.
RESEARCH GAP: Vision-based real-time health monitoring and AI-driven predictive maintenance for industrial horizontal die caster machines using XBee and nRF communication 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.
The shared vision is achieved through the usage of a modular multi-component system which is capable of controlling the operational state of horizontal die caster machines in an automatic mode. The first module, which is located near the machines, registers certain parameters like visual pictures, resonance, electricity used, and temperature. The data gathered are subsequently analyzed on-site to detect abnormal trends or trends that could be the source of a problem. If an emergency situation occurs, this hardware can turn on an actuator that will automatically stop the machine so that there are no hazards or further damage. Afterward, all data is sent wirelessly and then it goes to the processing module to be ready for communication with users. An operator, by means of this module, obtains a local interface displaying machine health parameters and warnings, which are constantly updated on the screen embedded in the unit. Simultaneously, that information is provided from the local network to the remote cloud server through the communication gateway.
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.
The shared vision is achieved through the usage of a modular multi-component system which is capable of controlling the operational state of horizontal die caster machines in an automatic mode. The first module, which is located near the machines, registers certain parameters like visual pictures, resonance, electricity used, and temperature. The data gathered are subsequently analyzed on-site to detect abnormal trends or trends that could be the source of a problem. If an emergency situation occurs, this hardware can turn on an actuator that will automatically stop the machine so that there are no hazards or further damage. Afterward, all data is sent wirelessly and then it goes to the processing module to be ready for communication with users. An operator, by means of this module, obtains a local interface displaying machine health parameters and warnings, which are constantly updated on the screen embedded in the unit. Simultaneously, that information is provided from the local network to the remote cloud server through the communication gateway.
The cloud server is the main component of the system as it uses machine learning techniques to examine the incoming data, and get AI-generated insights like trend detection, maintenance prediction, and corrective action take. These insights are made available to authorized users through a web dashboard as well as a local display. Operators are able to manage the machines' health status in real time, analyze their work in depth, and obtain timely suggestions to perform maintenance and boost the efficiency of the processes.
BEST METHOD OF WORKING
The VSxcapt Mote containing an Arduino Tiny Machine Learning Kit, Camera Module, XBee RF Module, Accelerometer, Current Sensor, Temperature Sensor, Actuator, and Power Supply, the machine is capable of performing real-time anomaly analysis and shutting down the equipment in critical conditions. This way, safety and operation stability are assured on industrial die caster machines.
The VSbeenrf Mote enhances operational effectiveness through the provision of a 32-Bit output, nRF Module, XBee RF Module, Display of HMI, and Supply of Power, virtually eliminating downtime by reducing the distance and time needed for operators to control and visualize machinery and act on alerts.
The VSnRF Mote, owing to its Raspberry Pi Board, nRF Module, GPRS Modem, Indicator LED and Power Supply ensures uninterrupted wireless transfer of data to a custom cloud server that allows analysis utilizing machine learning, giving AI powered suggestions, and predictive maintenance features for profitability.
VSNrFMote includes HMI Display that helps interlink the device with users allowing them to see the status of the machine on an interface immediately, threats, and actual state of the machine in real time; a feature that increases awareness of the current situation minimizing chances of erroneous actions by on site operators.
The XBee RF Module found in VSxcapt Mote and VSbeenrf Mote provides dependable and trusted wireless communication for inter-module data transfer, facilitating continuous data tracking and evaluation of essential parameters of the machine.
The Camera Module in VSxcapt Mote allows the acquisition of images in order to improve understanding of machine behaviors and increase adverse anomaly detection through vision-based monitoring.
The nRF Module integrated in VSbeenrf Mote and VSnRF Mote can maintain low-power messages over longer ranges ensuring cloud connectivity and inter-module data transmission in the industrial settings.
ADVANTAGES OF THE INVENTION
1. With the introduction of the Arduino Tiny Machine Learning Kit and Camera Module, operational activities of machines can be monitored continuously and with high sensitivity which ensures anomalies or faults are detected immediately.
2. The system designed in this manner utilizes the data inputs generated from Accelerometer Current Sensor, and Temperature Sensor to establish a pattern and anticipate problems that are likely to arise, thus reducing unforeseen downtimes and improving the time for maintenance.
3. Due to the intergrated Actuator, an electrical machine in a working environment will instantly stop whenever threats exceed the preset limit, thus averting damage to the equipment and improving the safety of the workplace.
4. For the purpose of wireless communication between modules, XBee RF Module and nRF Module have been adopted which shall help in uninterrupted wireless communication of data to cloud storage and local displays.
5. Using an STM32 Board with an HMI Display gives the operator the ability to view the status of the equipment in the area real-time and the machine alerts thereby enhancing the productivity of the operations and decision making.
6. The use of a Raspberry Pi Board and GPRS Modem assists in data transmission to a personal cloud server where the machine learning models conduct further analysis using the algorithms and produce AI based recommendations that can be acted upon.
7. By employing web dashboard and HMI display, operators as well as authorized personnel are in a position to have a complete overview of the state of the machine and make decisions to avert problems.
, Claims:1. A vision-based inline edge device for critical health monitoring of industrial horizontal die caster machines with xbee and nrf gateway and ai recommendations comprises VSxcapt Mote containing an Arduino Tiny Machine Learning Kit, Camera Module, XBee RF Module, Accelerometer, Current Sensor, Temperature Sensor, Actuator, and Power Supply, the machine is capable of performing real-time anomaly analysis and shutting down the equipment in critical conditions, this way, safety and operation stability are assured on industrial die caster machines.
2. The device as claimed in claim 1, wherein the VSbeenrf Mote enhances operational effectiveness through the provision of a 32-Bit output, nRF Module, XBee RF Module, Display of HMI, and Supply of Power, virtually eliminating downtime by reducing the distance and time needed for operators to control and visualize machinery and act on alerts.
3. The device as claimed in claim 1, wherein the VSnRF Mote, owing to its Raspberry Pi Board, nRF Module, GPRS Modem, Indicator LED and Power Supply ensures uninterrupted wireless transfer of data to a custom cloud server that allows analysis utilizing machine learning, giving AI powered suggestions, and predictive maintenance features for profitability.
4. The device as claimed in claim 1, wherein VSNrFMote includes HMI Display that helps interlink the device with users allowing them to see the status of the machine on an interface immediately, threats, and actual state of the machine in real time; a feature that increases awareness of the current situation minimizing chances of erroneous actions by on site operators.
5. The device as claimed in claim 1, wherein the XBee RF Module found in VSxcapt Mote and VSbeenrf Mote provides dependable and trusted wireless communication for inter-module data transfer, facilitating continuous data tracking and evaluation of essential parameters of the machine.
6. The device as claimed in claim 1, wherein the Camera Module in VSxcapt Mote allows the acquisition of images in order to improve understanding of machine behaviors and increase adverse anomaly detection through vision-based monitoring.
7. The device as claimed in claim 1, wherein the nRF Module integrated in VSbeenrf Mote and VSnRF Mote can maintain low-power messages over longer ranges ensuring cloud connectivity and inter-module data transmission in the industrial settings.

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