AI-EQUIPPED EDGE DEVICE FOR PREDICTIVE HEALTH MONITORING OF STEEL GANTRY MILLING MACHINES USING NRF AND XBEE IOT NETWORK

Abstract

An ai-equipped edge device for predictive health monitoring of steel gantry milling machines using nrf and xbee iot network comprises ControlRxNode (100) is an advanced device consisting of a control board, wireless communication module, vibration sensor (105), environmental sensor, location module and power supply (104), it is used to acquire data upon different operational parameters of the steel gantry milling machines with a view to transmitting the data to other networks for analytical purposes and predictive maintenance thereby improving functionality and efficiency the MiddelWaveNode that is equipped with a control board, dual wireless communication modules, LED indicator, and power supply is employed to increase the distance that can be maintained between ControlRxNode and the EndGatewayNode for effective data communication, this improves the performance of predictive health monitoring systems by enabling reliable data transmission over long distances.

Specification

Description:FIELD OF THE INVENTION
This invention relates to ai-equipped edge device for predictive health monitoring of steel gantry milling machines using nrf and xbee iot network.
BACKGROUND OF THE INVENTION
This innovation proposes a new AI-based health condition monitoring capability using IoT communication intentionally embedded within the edge milling machine of the gantry structure. More specifically, such a setup collects operational and environmental data like vibration, temperature, and location of the machine always, thus enabling the evaluation of structural health in real-time. Data is then transferred through a multi-node network; there are intermediate relay and final gateway interfaces to ensure that the signals are strong over long distances and connect all nodes to one another. The edge computing minimizes supervisor attention directly from stakeholders considering AI algorithms built within the framework of the system, which is constantly monitoring and informs in advance about the threat of malfunctions machines for repairing. This configuration improves fault tolerance, increases reliability, and optimizes exploitation in any industrial processes, making it a contemporary scalable technology for predictive maintenance and health situation assessment of heavy machines.
This invention responds to the urgent requirement for continuous and predictive health assessment and monitoring of steel gantry milling machines which are important but prone to degradation in machine performance due to wear, environmental factors, heat, and operational stress. In traditional maintenance strategies, regular inspections and unscheduled breakdown maintenance, which is conducted after equipment fails to operate, are common, resulting in high lost production time, wastage of resources, and risks to safety. This system consists of a continuous automatic learning algorithm that monitors the equipment and predicts the probability of failure based on time and conditions. This helps avoid unexpected failures, decreases the need for machine repair, and enhances the general machine maintenance performance. Machine reliability is raises, which increases the availability and efficiency of maintenance and the industrial processes as a whole.
CN210360590U: The utility model relates to the technical field of detection, in particular to a three-head engraving and milling machine with a rotary table, which comprises a base, wherein a Y-axis mechanism is bolted at the top of the base and comprises a Y-axis motor, the bottom of the Y-axis motor is bolted with the top of the base, two Y-axis guide rails are welded at the top of the base, and a Y-axis lead screw is rotatably connected at the middle part of the base; the utility model discloses a base, Y axle mechanism, the operation platform, the revolving stage, the work piece setting element, the longmen, X axle mechanism, the slide, Z axle mechanism, Z axle slide, the electricity main shaft, the mounting panel, the slip table cylinder, the setting of probe and tool setting appearance, make this three head cnc engraving and milling machine with the revolving stage, possess existing gauge head and improve the machining precision, there is the revolving stage to improve machining efficiency's advantage again, the current conventional double-end of universal adoption or single-end cnc engraving and milling machine has been solved, or the brill is attacked the center and is processed, do not adopt the structure of combination formula, influence the problem of machining efficiency and precision.
RESEARCH GAP: AI-driven predictive health monitoring of steel gantry milling machines using a multi-node nRF RF and XBee IoT network for real-time data acquisition and remote diagnostics is the novelty of the system.
CN203636037U: The utility model discloses a combined gantry milling machine which comprises vertical columns and a transverse beam above the vertical columns, wherein a gang cutter is arranged at the front ends of the vertical columns; a left side surface milling cutter, a left side process boss surface milling cutter, a right side process boss surface milling cutter and a right side surface milling cutter are sequentially fixedly arranged on the gang cutter; two forked tail vertical milling heads which can transversely move are arranged on the front side of the transverse beam; an end surface vertical milling head which can transversely move is arranged on the rear side of the transverse beam; auxiliary beams are further arranged at the rear ends of the two vertical columns and positioned behind the transverse beam; saw blade milling heads which can transversely move are arranged on the auxiliary beams. According to the saddle gantry milling machine, machining on the upper part of a saddle is achieved through one machine; in the saddle machining process, one-time clamping and positioning is needed, the machining can be performed rapidly and conveniently, the working intensity is greatly alleviated, and the efficiency is improved; meanwhile, the machining precision is well ensured. In addition, as the machining on the upper part of the saddle is achieved through one machine, requirements on a machining field and machining equipment are reduced, and the field cost, the equipment cost and the labor cost are lowered.
RESEARCH GAP: AI-driven predictive health monitoring of steel gantry milling machines using a multi-node nRF RF and XBee IoT network for real-time data acquisition and remote diagnostics 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 invention is an IoT and AI-driven predictive health monitoring edge device system for steel gantry milling machines. The system includes a multi-node network developed for capturing and analyzing the vibration level, temperature, as well as location data of the machine. In the network designed there are nodes, each providing a certain functionality: the primary one receives sensor data, the relay one secures over a long distance, the gateway one shows information and provides alerting messages. These nodes transmit sensor data through a wireless communication medium using nRF RF and XBee technologies in order to ensure that sensor data is received by the gateway in a short time. In other systems, operators have to check on operations manually or respond to breakdowns, leading to time and cost inefficiencies. This innovation makes the procedures easier by maintaining a constant machine health reporting system that is capable of accurately following the state of the machine and reporting on its wear and tear. In other words, the device is capable of anticipating failure by continuously analyzing environmental factors in conjunction with real time operational activities to prevent minor problems from escalating into larger ones.
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 invention is an IoT and AI-driven predictive health monitoring edge device system for steel gantry milling machines. The system includes a multi-node network developed for capturing and analyzing the vibration level, temperature, as well as location data of the machine. In the network designed there are nodes, each providing a certain functionality: the primary one receives sensor data, the relay one secures over a long distance, the gateway one shows information and provides alerting messages. These nodes transmit sensor data through a wireless communication medium using nRF RF and XBee technologies in order to ensure that sensor data is received by the gateway in a short time. In other systems, operators have to check on operations manually or respond to breakdowns, leading to time and cost inefficiencies. This innovation makes the procedures easier by maintaining a constant machine health reporting system that is capable of accurately following the state of the machine and reporting on its wear and tear. In other words, the device is capable of anticipating failure by continuously analyzing environmental factors in conjunction with real time operational activities to prevent minor problems from escalating into larger ones.
The AI block of the system makes it possible to process the data at the edge identifying anomalies and patterns providing the indication of the milling machine. Thanks to the implementation of Fast Fourier Transform (FFT) and other machine learning algorithms, the system is capable of analysing complex vibration frequency and thermal data in order to notice minor changes that might indicate impending wear and failure. Such predictive health information is then applied to reschedule maintenance activities and inform the operators for timely actions. The surge in demand for such systems has also resulted in the modular design which is scalable and thus can be incorporated into previously existing IoT networks or enhanced by additional sensors and or analytics. Those can be installed in terminals transmitting over a wide range to provide a maximum coverage area to ensure that all machines, including those stationed at remote locations, are effectively monitored over time. Not only the AI provided recommendations are very precise but they can also be viewed by operators on the local display with an alert, thus prompt action can be initiated. With great prospects, this innovation presents a fast and efficient industrial maintenance strategy that is scalable and has a positive impact on companies' operational efficiency by improving safety and reducing downtime and maintenance costs while optimum performance of machines is being sustained.
BEST METHOD OF WORKING
The ControlRxNode is an advanced device consisting of a control board, wireless communication module, vibration sensor, environmental sensor, location module and power supply. It is used to acquire data upon different operational parameters of the steel gantry milling machines with a view to transmitting the data to other networks for analytical purposes and predictive maintenance thereby improving functionality and efficiency.
The MiddelWaveNode that is equipped with a control board, dual wireless communication modules, LED indicator, and power supply is employed to increase the distance that can be maintained between ControlRxNode and the EndGatewayNode for effective data communication. This improves the performance of predictive health monitoring systems by enabling reliable data transmission over long distances.
The EndGatewayNode that is equipped with a control interface, wireless communication module, display and alarming unit as well as a power source is used in the health surveillance system as the last data interface. It collects and presents real time machine health parameters for the operators and allows timely alerts which helps in improving the maintenance and operational efficiency of the steel gantry milling machines.
To facilitate high data transmission at long distances in the different industrial environments, this innovation has the Dual Wireless Communication Modules consisting of two discrete communication protocols which ensures seamless connection across the nodes.
Real time machine health status is visually and audibly relayed to the operators through the alerted utilizing the display and a capacitive screen enabling the operators to act before any problems arise and ensure the machine works as intended.
ADVANTAGES OF THE INVENTION
1. Because of the system's AI algorithms, early signs of upcoming mechanical issues are picked and maintained so that there are no unwanted downtimes. This encourages timely maintenance and extends the longevity of the machines thus reducing repair costs.
2. The innovation allows for uninterrupted monitoring of important doctors that include vibrations and temperature which in turn provide a full overview of the health of the machine and enables the operators to react fast to any arising possibilities.
3. Having nRF and XBee modules, the system provides effective data exchange between many nodes located within a distant area even in huge facilities therefore being amenable to the industrial configuration and its future development.
4. Reduced labor, repair Castanets and machine downtime an overall reduces the system cost and increases optimal resource utilization by minimizing unpredicted breakdowns and adjusting maintenance intervals appropriately.
5. Targeted health monitoring with prompt notifications also curtails manual checks and machine failures are unlikely due to the system and hence safer and more productive working environment is created.
, Claims:1. An ai-equipped edge device for predictive health monitoring of steel gantry milling machines using nrf and xbee iot network comprises ControlRxNode is an advanced device consisting of a control board, wireless communication module, vibration sensor, environmental sensor, location module and power supply, it is used to acquire data upon different operational parameters of the steel gantry milling machines with a view to transmitting the data to other networks for analytical purposes and predictive maintenance thereby improving functionality and efficiency.
2. The device as claimed in claim 1, wherein the MiddelWaveNode that is equipped with a control board, dual wireless communication modules, LED indicator, and power supply is employed to increase the distance that can be maintained between ControlRxNode and the EndGatewayNode for effective data communication, this improves the performance of predictive health monitoring systems by enabling reliable data transmission over long distances.
3. The device as claimed in claim 1, wherein the EndGatewayNode that is equipped with a control interface, wireless communication module, display and alarming unit as well as a power source is used in the health surveillance system as the last data interface, it collects and presents real time machine health parameters for the operators and allows timely alerts which helps in improving the maintenance and operational efficiency of the steel gantry milling machines.
4. The device as claimed in claim 1, wherein to facilitate high data transmission at long distances in the different industrial environments, this innovation has the Dual Wireless Communication Modules consisting of two discrete communication protocols which ensures seamless connection across the nodes.
5. The device as claimed in claim 1, wherein real time machine health status is visually and audibly relayed to the operators through the alerted utilizing the display and a capacitive screen enabling the operators to act before any problems arise and ensure the machine works as intended.

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