AI-ENABLED INLINE EDGE VISION DEVICE FOR CONDITION MONITORING OF INDUSTRIAL PROFILE EXTRUSION MACHINES WITH GATEWAY AND CLOUD COMMUNICATION USING NRF AND LORA

Abstract

An ai-enabled inline edge vision device for condition monitoring of industrial profile extrusion machines with gateway and cloud communication using nrf and lora comprises CMEdge Device incorporates an Arduino Tiny Machine Learning Kit, Camera Module, nRF Module, Accelerometer, Temperature Sensor, Pressure Sensor, and Power Supply to monitor extrusion machines, strengthen their reliability and efficiency the DataCapture Device consists of a Raspberry Pi Board, nRF Module, Waberable LoRaWAN Module, Display, and Power Supply which ensures secure edge data capture and transmission to the internet cloud in a fast-moving sensor environment.

Specification

Description:FIELD OF THE INVENTION
This invention relates to ai-enabled inline edge vision device for condition monitoring of industrial profile extrusion machines with gateway and cloud communication using nrf and lora.
BACKGROUND OF THE INVENTION
This new development brings forth a sophisticated technique for the supervision in operation and prediction of malfunction of industrial profile extrusion machines. The technology employs an intelligent edge vision device for the acquisition, treatment and transmission of operational data such as video, environmental factors and performance. Data are collected and pooled together through a strong communication gateway that guarantees easy reach of the data to a cloud based analytics system. The technology works in conjunction with a cloud system that features machine learning and AI programs that are used for duct control, reporting, forecasting and recommending action. The complete system offers the operators and authorized users with the insights on a web dashboard which is easy to use thereby increasing effectiveness, reliability and making management and operational decisions in industrial settings more efficient.
This invention deals with the important problem of making sure that the industrial profile extrusion machines are running efficiently and reliably, while enhancing the operating processes, because they usually have problems with mechanical wear, temperature differences and structural deformity's which results into large downtimes, poor quality of the end product and threats to the safety of the workers. Conventional observation methods mostly involve manual checking or other primitive systems which fail to give up to date information regarding any faults or offer solutions for prevention. This project solves these problems by providing a more advanced way of doing things, which continually gives around the clock monitoring of the system, alerts users of possible breakdowns, and is able to recommend decisive actions that will improve the overall effectiveness of the business, lower the costs of repairs, and avoid surprises of sudden breakdowns.
CN111496109B: The invention discloses intelligent automatic steel pipe multi-combination cold extrusion equipment which comprises a steel pipe feeding mechanism, a main machine automatic material grabbing mechanism, a finished product stacking mechanism, a main machine die sitting mechanism, a steel pipe joint placing mechanism, an equipment rack and a control system. When the steel pipe cold extrusion device is used, steel pipes are conveyed to the steel pipe joint placing mechanism through the steel pipe feeding mechanism, manual or automatic feeding (namely, loading of the connecting device) is carried out in the steel pipe joint placing mechanism, then the steel pipes loaded with the connecting device are conveyed to the main machine die sitting mechanism through the main machine automatic material grabbing mechanism for cold extrusion processing, and after the processing is finished, the steel pipes are unloaded by the finished product stacking mechanism.
RESEARCH GAP: AI-driven inline edge vision system for real-time condition monitoring and predictive maintenance of industrial profile extrusion machines with nRF and LoRa-based cloud communication is the novelty of the system.
CN206047252U: The utility model discloses a kind of aluminum profile extrusion cutting machine, including workbench, cutting chamber is provided with the upside of workbench, hydraulic cylinder is provided with the downside of cutting chamber, gripper shoe is provided with the downside of hydraulic cylinder, gripper shoe or so two ends are provided with limited post, cutting motor is provided with the downside of gripper shoe, cutting blade is provided with the right side of cutting motor, on the downside of supporting table, left end is provided with storage box, dividing plate is provided with inside storage box, dust exhaust apparatus is provided with the downside of dividing plate, workbench right-hand member is provided with the second opening, second open interior is provided with connecting rod, connecting seat is provided with the upside of connecting rod, fixed cover is provided with the upside of connecting seat, slide block is provided with the downside of connecting rod, screw mandrel is provided with inside slide block, screw mandrel left end is provided with motor, on the downside of workbench, left and right corner is provided with support base, vibration absorber is provided with the downside of support base.This utility model is effectively improved work efficiency and cut quality, it is ensured that the cleaning of work surface by limited post, dust exhaust apparatus, conveying arrangement and vibration absorber.
RESEARCH GAP: AI-driven inline edge vision system for real-time condition monitoring and predictive maintenance of industrial profile extrusion machines with nRF and LoRa-based cloud 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 system consists of three interrelated elements: an edge system for gathering data, a gateway for integration and distribution of data and a cloud-based hosting system for analysis. These components collectively provide a complete solution for continuous monitoring and predictive maintenance of the asset. The edge device is placed adjacent to the industrial profile extrusion machines to capture operational data. This includes visual supervision of the extrusion machine, monitoring operational environment, structural vibrations, temperature and pressure parameters as well as dislocation of the functioning equipment. The device uses imaged data to perform such filtering operations on the data in the cloud to ensure low volumes of information are sent for processing. Such important processing is carried out at the edge ensuring only relevant data is sent for further processing in the next stage. The data sent is also said to have low latency which improves efficiency of the bandwidth used. The gateway is positioned in the center as a connecting node that accepts the data from various edge devices positioned throughout the industrial sites. The gateway also helps transmit data to the cloud without being interrupted or impeded by the surrounding environment due to the availability of long-range communication protocols. It is also a local offsite backup system, storing data and preventing it from being lost in case of interruptions in the network. Thanks to the implementation of modern communication technologies, the system can efficiently function in a variety of industrial environment.
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 system consists of three interrelated elements: an edge system for gathering data, a gateway for integration and distribution of data and a cloud-based hosting system for analysis. These components collectively provide a complete solution for continuous monitoring and predictive maintenance of the asset. The edge device is placed adjacent to the industrial profile extrusion machines to capture operational data. This includes visual supervision of the extrusion machine, monitoring operational environment, structural vibrations, temperature and pressure parameters as well as dislocation of the functioning equipment. The device uses imaged data to perform such filtering operations on the data in the cloud to ensure low volumes of information are sent for processing. Such important processing is carried out at the edge ensuring only relevant data is sent for further processing in the next stage. The data sent is also said to have low latency which improves efficiency of the bandwidth used. The gateway is positioned in the center as a connecting node that accepts the data from various edge devices positioned throughout the industrial sites. The gateway also helps transmit data to the cloud without being interrupted or impeded by the surrounding environment due to the availability of long-range communication protocols. It is also a local offsite backup system, storing data and preventing it from being lost in case of interruptions in the network. Thanks to the implementation of modern communication technologies, the system can efficiently function in a variety of industrial environment.

After the data gets transmitted to the cloud platform, the data undergoes transformation and processing via machine learning and AI algorithms. These algorithms perform data pattern analysis and seek to find some abnormalities or inefficiencies and predictive analysis generation. The system monitors ducts and provides maintenance, operational or component replacement recommendations. Processed data and insights are then provided to operators and authorized personnel through a web based user interface. This interface is used to monitor machine operations, view past data and trends, and receive maintenance information. The most important and most widely spread views delivered by the system allow operators to control the machines, to reduce the downtime of the machines quite substantially. The designs on the gateway device and the web applicant enable users to access information easily and address problems very quickly. The predictive feature of the system helps users schedule maintenance in anticipation so users experience less downtime and greater efficiency.
BEST METHOD OF WORKING
With real-time data collection, the CMEdge Device incorporates an Arduino Tiny Machine Learning Kit, Camera Module, nRF Module, Accelerometer, Temperature Sensor, Pressure Sensor, and Power Supply to monitor extrusion machines, strengthen their reliability and efficiency.
The DataCapture Device consists of a Raspberry Pi Board, nRF Module, Waberable LoRaWAN Module, Display, and Power Supply which ensures secure edge data capture and transmission to the internet cloud in a fast-moving sensor environment.
The Cloud Device integrates a Jetson Nano Board, LoRaWAN Module, Gsm modems, Indicator led, and Power Supply that allow advanced duct surveillance analytics and maintenance prediction in the cloud and subsequently push the system to a web based dashboard and monitor for operators.
nRF and LoRaWAN Modules were mounted on CMEdge Device & DataCapture Device & Cloud Device, which enables efficient data transmission through long range communication even in the Adopted Industrial Environment with poor connectivity.
Through the included Camera Module found in the CMEdge Device, visual monitoring enhances data provided to conduct remote anomaly detection, and such a data assists in improving machine condition assessment and the control of operational processes.
The Novatel OM410 and other GSM Modems built into the Cloud Device provide strong data transfers to the custom cloud server which allows for insights and recommendations to be made with regard to maintenance before it is performed thus enhancing machine efficiency.
Data obtained from the DataCapture Device has a neon crucible retort holder, which is a display useful for on-site operators to monitor the critical performance aspects of a machine in real time and respond to developing problems while improving the overall context awareness.
ADVANTAGES OF THE INVENTION
1. Camera Module and Accelerometer's capability over the edge device continuously oversee machine functioning and help in early failure detection, thus maintaining the quality of the product.
2. By means of Arduino Tiny ML Kit and Jetson Nano Board, the system applies AI and machine learning algorithms to forecast possible failures which improves maintenance schedules and decreases unforeseen downtimes.
3. nRF Module and the LoRaWAN Module work hand in hand to allow effective communication of a long distance even when in an industrial setting that has communication limitations.
4. Edge devices combines Temperature sensor, pressure sensor and Camera Module to exhibit different perspectives of critical information to assist in machine condition monitoring in an integrated manner.
5. Dashboards for operators and authorized personnel may access through the Display on the box and a web dashboard, making machine performance and predictive features simple to visualize.
, Claims:1. An ai-enabled inline edge vision device for condition monitoring of industrial profile extrusion machines with gateway and cloud communication using nrf and lora comprises CMEdge Device incorporates an Arduino Tiny Machine Learning Kit, Camera Module, nRF Module, Accelerometer, Temperature Sensor, Pressure Sensor, and Power Supply to monitor extrusion machines, strengthen their reliability and efficiency.
2. The device as claimed in claim 1, wherein the DataCapture Device consists of a Raspberry Pi Board, nRF Module, Waberable LoRaWAN Module, Display, and Power Supply which ensures secure edge data capture and transmission to the internet cloud in a fast-moving sensor environment.
3. The device as claimed in claim 1, wherein the Cloud Device integrates a Jetson Nano Board, LoRaWAN Module, Gsm modems, Indicator led, and Power Supply that allow advanced duct surveillance analytics and maintenance prediction in the cloud and subsequently push the system to a web based dashboard and monitor for operators.
4. The device as claimed in claim 1, wherein nRF and LoRaWAN Modules were mounted on CMEdge Device & DataCapture Device & Cloud Device, which enables efficient data transmission through long range communication even in the Adopted Industrial Environment with poor connectivity.
5. The device as claimed in claim 1, wherein through the included Camera Module found in the CMEdge Device, visual monitoring enhances data provided to conduct remote anomaly detection, and such a data assists in improving machine condition assessment and the control of operational processes.
6. The device as claimed in claim 1, wherein the Novatel OM410 and other GSM Modems built into the Cloud Device provide strong data transfers to the custom cloud server which allows for insights and recommendations to be made with regard to maintenance before it is performed thus enhancing machine efficiency.
7. The device as claimed in claim 1, wherein data obtained from the DataCapture Device has a neon crucible retort holder, which is a display useful for on-site operators to monitor the critical performance aspects of a machine in real time and respond to developing problems while improving the overall context awareness.

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