CONDITION MONITORING AND AI PREDICTION THROUGH SENSOR INTEGRATION AND IOT TECHNOLOGY IN RUBBER CRUMB GRANULATORS FOR THE MANUFACTURING OF RUBBER INDUSTRY

Abstract

This invention introduces an IoT-enabled condition monitoring and predictive maintenance system for Rubber Crumb Granulators used in rubber manufacturing. By integrating sensors such as an Accelerometer, Vibration Sensor, Pressure Sensor, and DHT Sensor, the system continuously collects data on machine operations and environmental conditions. Equipped with a Raspberry Pi and GPU Processor Board, data is processed locally and transmitted to a cloud server for analysis by machine learning algorithms, generating AI-driven predictions and maintenance insights. The HMI Display provides operators with real-time data and AI recommendations, while a web dashboard allows for remote monitoring. This solution enhances operational efficiency and reduces downtime in rubber manufacturing through proactive maintenance strategies.

Specification

Description:FIELD OF THE INVENTION
This invention relates to Condition Monitoring and AI Prediction through Sensor Integration and IoT Technology in Rubber Crumb Granulators for the Manufacturing of Rubber Industry.
BACKGROUND OF THE INVENTION
Rubber sector manufacturing processes are revolutionized by this ground-breaking solution for condition monitoring and maintenance prediction in Rubber Crumb Granulators. The system continuously collects and assesses real-time data about vibrations, environmental conditions, and machine parameters thanks to the smooth integration of cutting-edge sensors and cutting-edge technologies. After the data has been examined, it is sent to a specific cloud server, where a machine learning algorithm generates AI-powered forecasts and suggestions. Operators and authorities can then remotely monitor the performance of granulators and make well-informed decisions thanks to the user-friendly interface that presents this actionable knowledge.
The rubber production industry has significant issues with regard to Rubber Crumb Granulator maintenance and operational efficiency. Since many modern systems are not capable of real-time monitoring, it can be challenging to anticipate such problems and take prompt action to resolve them. Increased downtime, unanticipated maintenance costs, and a general drop in production are the outcomes of this.
CN212021300U - The utility model relates to a reclaimed rubber makes technical field, and discloses a high strength reclaimed rubber granulator, including the granulator body. This high strength reclaimed rubber granulator, pour rubber raw materials into the conveyer pipe through the feeder hopper in, it rotates to drive the right side bull stick through first motor, under driving belt's transmission effect, two bull sticks drive two puddlers in step and rotate, the raw materials that drop between two puddlers receive stirring extrusion reentrant granulator originally internal, can effectually extrude away the inside bubble of raw materials before the pelletization, and it is inseparable to increase the degree that the raw materials bonded, then setting through constant temperature heater, heat the heating copper pipe, the heating copper pipe gives out the heat and heats the movable rod, the spiral extrusion pole that makes the movable rod outside keeps constant temperature, and rubber raw materials is when being extruded by the spiral extrusion pole, also obtain the heating, prevent that rubber raw materials cooling from solidifying and causing the extrusion difficulty, the effectual purpose of granulation has been reached. AI based prediction and recommendations for Rubber Crumb Granulators in Industrial environments is the novelty of the system.
CN214982371U - The utility model provides a rubber granulator, includes the granulator body, the granulator body set up to cylindric cavity spare, the internal fixed electric heating layer that is equipped with of granulator body inner chamber lateral wall, the granulator body about rotate between the wall body and be equipped with the transport axle, the epaxial fixedly connected with helical blade of transport, transport axle left end follow granulator body left end stretch out and power is connected with the conveying motor, the fixed feed inlet that is equipped with in granulator body left side top, the right side wall body of granulator body in link up and be equipped with annular output groove, the output inslot array be equipped with three connecting block. The utility model discloses a be equipped with left granulation board, right granulation board, big granulation hole and little granulation hole, the rubber grain of
wo kinds of different particle diameters of production that can be quick reduces the limitation of using. AI based prediction and recommendations for Rubber Crumb Granulators in Industrial environments 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 purpose of the APITRCNode invention is to improve the effectiveness of predictive maintenance and condition monitoring in Rubber Crumb Granulators used in the manufacturing processes of the Rubber Industry. This is accomplished by methodically integrating cutting-edge technology with a variety of hardware parts. The GPU Processor Board, HMI Display, Vibration Sensor, DHT Sensor, Pressure Sensor, Accelerometer, and Power Supply are some of the essential parts. The procedure include attaching sensors to the Rubber Crumb Granulators, such as the Accelerometer, Pressure Sensor, DHT Sensor (temperature and humidity), and Vibration Sensor. Real-time data on operational parameters, ambient variables, and physical vibrations are continuously collected by these sensors. After that, the collected data is sent to the APITRCNode, which is outfitted with a GPU Processor Board and Raspberry Pi Board for processing and analysis.
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 purpose of the APITRCNode invention is to improve the effectiveness of predictive maintenance and condition monitoring in Rubber Crumb Granulators used in the manufacturing processes of the Rubber Industry. This is accomplished by methodically integrating cutting-edge technology with a variety of hardware parts. The GPU Processor Board, HMI Display, Vibration Sensor, DHT Sensor, Pressure Sensor, Accelerometer, and Power Supply are some of the essential parts. The procedure include attaching sensors to the Rubber Crumb Granulators, such as the Accelerometer, Pressure Sensor, DHT Sensor (temperature and humidity), and Vibration Sensor. Real-time data on operational parameters, ambient variables, and physical vibrations are continuously collected by these sensors. After that, the collected data is sent to the APITRCNode, which is outfitted with a GPU Processor Board and Raspberry Pi Board for processing and analysis.
Using Internet of Things technologies, the APITRCNode connects to a dedicated cloud server. The gathered sensor data is analyzed and stored on this cloud server, which acts as a central hub. The data is analyzed using a pre-programmed machine learning algorithm to produce AI-based predictions about the state of the Rubber Crumb Granulators. Two channels are used to distribute the AI analysis results, including maintenance recommendations. The HMI Display provides operators with a local interface via which they can examine real-time data and instantly receive alerts. Concurrently, authorized personnel-like operators and management-are able to receive detailed insights remotely thanks to a customized online dashboard. The web dashboard has an intuitive user interface that shows historical data, the machine's current state, and recommendations and predictions produced by artificial intelligence.
BEST METHOD OF WORKING
A condition monitoring and AI prediction system for Rubber Crumb Granulators comprising a Raspberry Pi Board and GPU Processor Board for data processing and analysis, enabling real-time condition monitoring and predictive maintenance within the rubber manufacturing sector.
A condition monitoring and AI prediction system with integrated Accelerometer, Vibration Sensor, DHT Sensor, and Pressure Sensor to collect real-time data on machine operational parameters and environmental conditions, facilitating AI-driven predictive maintenance.
A condition monitoring and AI prediction system with a dedicated cloud server where sensor data is stored and analyzed through machine learning algorithms, providing AI-based predictions and maintenance recommendations.
A condition monitoring and AI prediction system incorporating an HMI Display that presents real-time data, AI predictions, and maintenance recommendations to operators, enhancing decision-making capabilities for preventative maintenance.
A condition monitoring and AI prediction system including an internet-enabled connectivity module that allows for remote monitoring and data transfer to authorized personnel via a web dashboard.
A condition monitoring and AI prediction system integrating a Power Supply to ensure the continuous operation of condition monitoring and predictive maintenance functions within the Rubber Crumb Granulators.
ADVANTAGES OF THE INVENTION
1. The APITRCNode, which seamlessly combines cutting-edge sensors and AI-driven data processing, is a key component of this creative solution. Through this integration, Rubber Crumb Granulators can benefit from real-time condition monitoring and predictive maintenance, which maximizes operational effectiveness and reduces downtime in the rubber production sector.
2. The GPU Processor Board greatly increases the system's computational capacity, enabling the faster processing of large amounts of data and the running of intricate algorithms. This improvement helps the rubber manufacturing industry's Rubber Crumb Granulators perform optimal condition monitoring and predictive maintenance through real-time analysis and AI-driven forecasts.
3. Comprehensive real-time data on machine operational characteristics and environmental conditions are provided by the Accelerometer, Pressure Sensor, DHT Sensor (which measures temperature and humidity), and Vibration Sensor combined. These sensors are key elements of the innovation that improves efficiency in the rubber production sector by enabling accurate condition monitoring and AI-driven predictive maintenance in Rubber Crumb Granulators.
4. The HMI Display serves as an easy-to-use interface that provides operators with real-time information on the state of Rubber Crumb Granulators. It presents predictions and recommendations created by AI, enabling prompt decision-making for preventive maintenance and maximizing operational effectiveness in the rubber manufacturing sector.

, Claims:1. A condition monitoring and AI prediction system for Rubber Crumb Granulators comprising APITRCNode (100), which is outfitted with a Raspberry Pi Board, GPU Processor Board, Vibration Sensor, DHT Sensor, Pressure Sensor, Accelerometer, HMI Display, and Power Supply; which maximizes operational efficiency and reduces downtime in the rubber manufacturing sector.
2. The system as claimed in Claim 1, wherein an Accelerometer, Vibration Sensor, DHT Sensor, and Pressure Sensor are integrated to collect real-time data on machine operational parameters and environmental conditions, facilitating AI-driven predictive maintenance.
3. The system as claimed in Claim 1, further comprising a dedicated cloud server where sensor data is stored and analyzed through machine learning algorithms, providing AI-based predictions and maintenance recommendations.
4. The system as claimed in Claim 1, incorporating an HMI Display that presents real-time data, AI predictions, and maintenance recommendations to operators, enhancing decision-making capabilities for preventative maintenance.
5. The system as claimed in Claim 1, including an internet-enabled connectivity module that allows for remote monitoring and data transfer to authorized personnel via a web dashboard.
6. The system as claimed in Claim 1, further integrating a Power Supply to ensure the continuous operation of condition monitoring and predictive maintenance functions within the Rubber Crumb Granulators.

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