ONLINE CONDITION MONITORING IOT DEVICE FOR CRACKER MILLS MACHINES IN THE TIRE SHREDDER INDUSTRY USING ML ALGORITHMS WITH AI SUGGESTIONS

Abstract

An Online Condition Monitoring IoT Device for Cracker Mills Machines in the Tire Shredder Industry Using ML Algorithms with AI Suggestions comprises a CLD_CMMCNode (10) that is equipped with Banana Pi Router Board (21), GSM Modem (13), GPS Modem (14), External GPU Board (11), Liquid Temperature Sensor (19), Vibration Sensor (18), Pressure Sensor (17), Accelerometer (20), RTC Module (15), Touch HMI Display (12) and Power Supply (16), is utilized for incorporating a variety of sensors and developed technologies, to enable real-time data collection, analysis, and AI-driven forecasts for proactive maintenance in the Tire Shredder Industry's Cracker Mills Machines. The Banana Pi Router Board, which is integrated into the CLD_CMMCNode, is utilized to enable effective data transfer and integration for the real-time monitoring and maintenance of Cracker Mills Machines in the Tire Shredder Industry; and it does this by promoting seamless connectivity and communication between various sensors, the cloud server, and the internet. To ensure strong and dependable communication, the CLD_CMMCNode's integrated GSM modem provides a cellular network connection, allowing real-time sensor data to be seamlessly transmitted to the customized cloud server and enabling timely AI-driven maintenance predictions for Cracker Mills Machines in the Tire Shredder Industry.

Specification

Description:FIELD OF THE INVENTION
This invention relates to Online Condition Monitoring IoT Device for Cracker Mills Machines in the Tire Shredder Industry Using ML Algorithms with AI Suggestions.
BACKGROUND OF THE INVENTION
Properly maintaining and monitoring Cracker Mill Machines is a major difficulty faced by the Tire Shredder Industry. Uptime, increased repair costs, and operational inefficiencies are the outcomes of current maintenance methods, which usually rely on planned interventions or reactive efforts in response to failures. It is more difficult for the sector to proactively address possible problems before they escalate when real-time insights on the state of the machinery are lacking.
US11753530B2 discloses Crumb rubber obtained from recycled tires is subjected to an interlinked substitution process. The process utilizes a reactive component that interferes with sulfur bonds. The resulting treated rubber exhibits properties similar to those of the virgin composite rubber structure prior to being granulated, and is suitable for use in fabricating new tires, engineered rubber articles, and asphalt rubber for use in waterproofing and paving applications.
Research Gap: A Predictive maintenance based on real time data using AI for the Cracker Mills Machines within Tire Shredder Industry is the novelty of the system.
AU2018226498B2 discloses herein are methods for increasing the extractable rubber content of non-Hevea plant matter. The methods comprise the use of particular forms of hammer milling and/or roller milling and result in an increase in the amount of rubber that can be extracted from the resulting plant matter such as by organic solvent extraction or aqueous extraction.
Research Gap: A Predictive maintenance based on real time data using AI for the Cracker Mills Machines within Tire Shredder Industry 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.
This ingenious method uses a sophisticated blend of IoT, machine learning (ML), and artificial intelligence (AI) to change the way the tire shredder industry approaches maintenance for Cracker Mills machines. Through the use of an embedded sensor network within the machinery, real-time data on a variety of operating parameters is continuously collected and sent to a cloud server. With the help of pre-trained machine learning algorithms, the server examines incoming data to spot trends and abnormalities that help the system understand typical operating behavior. Then, using this knowledge, the embedded AI component makes forecasts and suggestions about possible maintenance needs. The decision-making process is streamlined by providing maintenance authorities with these insights via email and presenting them on an intuitive touch interface.
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.
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 CLD_CMMCNode operates by continuously gathering data from a variety of sensors that are integrated into the tire shredder industry's Cracker Mills Machines. These sensors, which include the accelerometer, pressure, vibration, liquid temperature, and others, record data on the machine's working parameters in real time. The collected data includes a range of parameters that are essential to comprehending the condition and functionality of the equipment. Subsequently, the gathered sensor data is sent to a cloud server created especially for this innovation. The core center for receiving, processing, and analyzing data is this cloud-based architecture. In order to discover patterns, anomalies, and trends in the sensor data, prepared machine learning algorithms are run on the server and applied to the datasets. This allows the algorithms to learn the typical behavior and performance benchmarks of the Cracker Mills Machines.
The artificial intelligence part of the system activates once the machine learning algorithms are taught and continuously learn from incoming data. After processing the examined data, AI algorithms forecast and recommend future maintenance requirements for the equipment. These forecasts are shown in real-time on the Touch HMI Display that is integrated into the system, in addition to being sent to the maintenance authority via email, offering a proactive method of resolving possible problems. Moreover, an online dashboard makes the AI-based forecasts available, allowing for remote management and monitoring. The maintenance staff can make fast decisions and take necessary action because of this connectivity, which makes critical information accessible from any location with an internet connection. This invention's use of IoT, ML, and AI technologies guarantees a thorough and perceptive approach to condition monitoring, which eventually improves the effectiveness and dependability of the Cracker Mills Machines in the Tire Shredder Industry.
BEST METHOD OF WORKING
1. The CLD_CMMCNode that is equipped with Banana Pi Router Board, GSM Modem, GPS Modem, External GPU Board, Liquid Temperature Sensor, Vibration Sensor, Pressure Sensor, Accelerometer, RTC Module, Touch HMI Display and Power Supply, is utilized for incorporating a variety of sensors and developed technologies, to enable real-time data collection, analysis, and AI-driven forecasts for proactive maintenance in the Tire Shredder Industry's Cracker Mills Machines.
2. The Banana Pi Router Board, which is integrated into the CLD_CMMCNode, is utilized to enable effective data transfer and integration for the real-time monitoring and maintenance of Cracker Mills Machines in the Tire Shredder Industry. It does this by promoting seamless connectivity and communication between various sensors, the cloud server, and the internet.
3. To ensure strong and dependable communication, the CLD_CMMCNode's integrated GSM modem provides a cellular network connection, allowing real-time sensor data to be seamlessly transmitted to the customized cloud server and enabling timely AI-driven maintenance predictions for Cracker Mills Machines in the Tire Shredder Industry.
4. The GPS modem connected to the CLD_CMMCNode is utilized to provide accurate location information, improve the contextual knowledge of Cracker Mills Machines in the Tire Shredder Sector, and facilitate effective tracking and monitoring for upkeep and operational optimization.
5. The External GPU Board, which is also integrated into the CLD_CMMCNode, is utilized to greatly increase computational power. This allows for the effective processing of intricate machine learning algorithms and helps to accurately analyze sensor data for predictions of proactive maintenance in tire shredder industry cracker mill machines.
6. The CLD_CMMCNode is equipped with a Liquid Temperature Sensor, Vibration Sensor, Pressure Sensor, and Accelerometer. These sensors work together to provide extensive real-time data on the operational parameters of Cracker Mills Machines in the Tire Shredder Industry. This makes it possible to apply machine learning algorithms for predictive maintenance and to accurately monitor the machines' conditions.
7. The CLD_CMMCNode's Touch HMI Display functions as an intuitive user interface for presenting AI-driven forecasts and real-time machine condition updates, guaranteeing maintenance staff in charge of Cracker Mills Machines in the Tire Shredder Industry have quick access to pertinent information.
8. The Power Supply, which plugs into the CLD_CMMCNode, is used to monitor and maintain Tire Shredder Industry Cracker Mill Machines with smooth efficiency. It does this by supplying enough electrical energy to assure the continuous and dependable operation of all components.

ADVANTAGES OF THE INVENTION
1. With a variety of sensors and state-of-the-art technologies, the CLD_CMMCNode is an advanced integrated IoT device. Its goal is to make preventative maintenance in the Cracker Mills Machines of the Tire Shredder Industry easier by facilitating real-time data collecting, analysis, and AI-driven forecasts.
2. The CLD_CMMCNode's embedded GSM modem guarantees dependable and strong communication via a cellular network connection. This technology makes it possible for real-time sensor data to be seamlessly transmitted to a specially designed cloud server, allowing for timely AI-driven maintenance recommendations for Tire Shredder Industry Cracker Mills Machines.
3. The CLD_CMMCNode's GPS Modem fosters creativity by offering accurate position information. This feature facilitates effective tracking and monitoring for maintenance and operational optimization by adding to our expertise of Cracker Mills Machines in the Tire Shredder Industry.
4. The CLD_CMMCNode's incorporation of an External GPU Board greatly increases processing power. This improvement makes it possible to perform sophisticated machine learning algorithms more quickly and accurately, which helps with sensor data analysis and predictive maintenance forecasting for Cracker Mills Machines in the Tire Shredder Industry.
5. The CLD_CMMCNode's integrated Liquid Temperature Sensor, Vibration Sensor, Pressure Sensor, and Accelerometer provide extensive real-time data on the operational parameters of Tire Shredder Industry Cracker Mill Machines. This makes it easier to precisely monitor conditions and makes it possible to use machine learning algorithms to anticipate preventative maintenance needs.
6. The CLD_CMMCNode's Touch HMI Display serves as an intuitive user interface. It is intended to provide real-time machine condition updates and AI-driven predictions, giving maintenance staff in charge of Tire Shredder Industry Cracker Mills Machines instant access to pertinent information.

, Claims:1. An Online Condition Monitoring IoT Device for Cracker Mills Machines in the Tire Shredder Industry Using ML Algorithms with AI Suggestions comprises a CLD_CMMCNode (10) that is equipped with Banana Pi Router Board (21), GSM Modem (13), GPS Modem (14), External GPU Board (11), Liquid Temperature Sensor (19), Vibration Sensor (18), Pressure Sensor (17), Accelerometer (20), RTC Module (15), Touch HMI Display (12) and Power Supply (16), is utilized for incorporating a variety of sensors and developed technologies, to enable real-time data collection, analysis, and AI-driven forecasts for proactive maintenance in the Tire Shredder Industry's Cracker Mills Machines.
2. The device as claimed in claim 1, wherein the Banana Pi Router Board, which is integrated into the CLD_CMMCNode, is utilized to enable effective data transfer and integration for the real-time monitoring and maintenance of Cracker Mills Machines in the Tire Shredder Industry; and it does this by promoting seamless connectivity and communication between various sensors, the cloud server, and the internet.
3. The device as claimed in claim 1, wherein to ensure strong and dependable communication, the CLD_CMMCNode's integrated GSM modem provides a cellular network connection, allowing real-time sensor data to be seamlessly transmitted to the customized cloud server and enabling timely AI-driven maintenance predictions for Cracker Mills Machines in the Tire Shredder Industry.
4. The device as claimed in claim 1, wherein the GPS modem connected to the CLD_CMMCNode is utilized to provide accurate location information, improve the contextual knowledge of Cracker Mills Machines in the Tire Shredder Sector, and facilitate effective tracking and monitoring for upkeep and operational optimization.
5. The device as claimed in claim 1, wherein the External GPU Board, which is also integrated into the CLD_CMMCNode, is utilized to greatly increase computational power; and this allows for the effective processing of intricate machine learning algorithms and helps to accurately analyze sensor data for predictions of proactive maintenance in tire shredder industry cracker mill machines.
6. The device as claimed in claim 1, wherein the CLD_CMMCNode is equipped with a Liquid Temperature Sensor, Vibration Sensor, Pressure Sensor, and Accelerometer; and these sensors work together to provide extensive real-time data on the operational parameters of Cracker Mills Machines in the Tire Shredder Industry; and this makes it possible to apply machine learning algorithms for predictive maintenance and to accurately monitor the machines' conditions.
7. The device as claimed in claim 1, wherein the CLD_CMMCNode's Touch HMI Display functions as an intuitive user interface for presenting AI-driven forecasts and real-time machine condition updates, guaranteeing maintenance staff in charge of Cracker Mills Machines in the Tire Shredder Industry have quick access to pertinent information.
8. The device as claimed in claim 1, wherein the Power Supply, which plugs into the CLD_CMMCNode, is used to monitor and maintain Tire Shredder Industry Cracker Mill

Machines with smooth efficiency; and it does this by supplying enough electrical energy to assure the continuous and dependable operation of all components.

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