AI-ENABLED HANDHELD DEVICE WITH MACHINE LEARNING FOR BEHAVIORAL ANALYSIS AND FEEDBACK ON HYDRAULIC MINING DRILL MACHINES USING NRF AND LORA HYBRID NETWORKS

Abstract

AI-ENABLED HANDHELD DEVICE WITH MACHINE LEARNING FOR BEHAVIORAL ANALYSIS AND FEEDBACK ON HYDRAULIC MINING DRILL MACHINES USING NRF AND LORA HYBRID NETWORKS An ai-enabled handheld device with machine learning for behavioral analysis and feedback on hydraulic mining drill machines using nrf and lora hybrid networks comprises HMBATCS Node incorporates several components including a Raspberry Pi processor Board, an nRF module, a Liquid Pressure Sensor, an Accelerometer, a Current Sensor, a Temperature Sensor, a Buzzer, and a Power Supply, to allow for real-time capture of critical operating information and also maintain strong wireless communication that can be used for efficient supervision and predictive maintenance in hydraulic mining drill activities HMBATRCS Node consists of a Raspberry Pi Processor Board, an nRF Module, LoRa RF Module, HMI Touch Display, LED, and a Power Supply, and therefore can be said to enhance system capability, since it includes an on-site display of system information, a long-range communication of system data and an alert system to notify the Operator when harmful machine conditions are detected.

Specification

Description:FIELD OF THE INVENTION
This invention relates to ai-enabled handheld device with machine learning for behavioral analysis and feedback on hydraulic mining drill machines using nrf and lora hybrid networks.
BACKGROUND OF THE INVENTION
This invention proposes a novel and advanced system that utilizes data and artificial intelligence for the enhancement of monitoring and functioning of hydraulic mining drill systems. The whole system consists of a number of interconnected nodes which are outfitted with sensors and communication modules used for the measurement of parameters such as pressure, acceleration, temperature, and current. Such parameters are communicated through a hybrid communication network to a cloud server tailored to have machine learning where the information is sifted for behaviors and recommendations. The system allows users to receive timely feedback and constructive analysis through a friendly interface available in both a web dashboard and an integrated touch display in order to give operators and authorized users the facts needed for making informed decisions and improving the operations and the reliability of the equipment.
Hydraulic mining drill machines have immense industrial applications, hereto performance of such machines is often low due to unknown breakdowns, non-efficient working, and absence of online monitoring thus resulting in increased downtime and maintenance costs. This invention overcomes these problems by allowing the constant tracking of machine patterns, supervision of failure and predictive maintenance with more understanding through data and AI tools. The instant monitoring and predictive feedback system allows to eliminate unplanned breakdowns, increase performance and increase the safety of the mining process which leads to lowering of costs and improved operational efficiency of all kinds of mining.
CN101705820A: The invention discloses a mining coal machine, in particular a coal drilling machine suitable for mining a thin coal seam, which comprises a main frame, an electric control system (12), a hydraulic system (9), a drilling tool and an auxiliary system (8), wherein one or two sides of the main frame are provided with a four-shaft linkage drilling tool; the drilling tool consists of a drilling rod (29), an air pipe (16), a front transmission gear box (15) with four output shafts and a drilling bit (14); power is transmitted to the drilling bit (14) through the drilling rod (29) and the front transmission gear box (15); and the air pipe (16) is communicated to the drilling bit (14) end. The invention also discloses a coal drilling machine of which the two sides of the main frame are provided with drilling tools. When used for thin and ultrathin coal seam mining, the coal drilling machine realizes an unmanned working face mining mode with convenient and safe operation, and solves the problems that the mining method of conventional equipment cannot mine or can mine but has low efficiency, high cost, bad reliability and poor safety, which is ideal equipment for safe and efficient mining of the thin and ultrathin coal seam.
RESEARCH GAP: AI-enabled behavioral analysis and predictive feedback for hydraulic mining drill machines using a hybrid nRF and LoRa network is the novelty of the system.
CN102654053A: The invention relates to coal-mining tunneling equipment for mines and particularly relates to an efficient block mining machine. The mining machine comprises a machine body, wherein the machine body comprises a machine frame, a guide device and a travelling device. The efficient block mining machine further comprises an impact blanking mechanism, wherein the impact blanking mechanism comprises a shovel head or a chisel head; the impact blanking mechanism further comprises an impact stroke device and an impacting device; the impact stroke device is in slide connection with the guide device; the guide device is arranged on the impacting device and/or the machine frame; the impact stroke device slides forwards and backwards along the guide device; the shovel head or the chisel head is arranged at the front part of the impact travelling device; the impacting device impacts the impact stroke device and the shovel head or the chisel head; the impact blanking mechanism is arranged on the machine body; and the travelling device drives the machine body to travel.
RESEARCH GAP: AI-enabled behavioral analysis and predictive feedback for hydraulic mining drill machines using a hybrid nRF and LoRa network 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 innovation is useful because it functions on a framework that contains specialized nodes that are unique in monitoring and analyzing the behavior of hydraulic mining drill machines. This procedure initiates from the major sensing node whereby pressure, acceleration, temperature, and electrical current emanating from the machine at its working state are captured, such capturing device is automated with sensors. This infrastructure applies sensors to both acquire and measure these parameters to improve the accuracy and effectiveness of the measurement in question. Subsequently, the measured parameters are subsequently sent wirelessly to the intermediate processing node via hybrid short- and long-range networks. This guarantees that data can be transmitted effectively and accurately in an industrial environment. There, the data is processed, aggregated, and later sent to the cloud server for further analysis. The cloud server processes the data using machine learning techniques in embedding to find patterns, anomalies, and strategic actionable steps. This bearer consists of predictive perspective which not only allow predicting the next event but also predicting the events that will lead to the next maintenance or break down event. The cloud server is the logical node where all the data processing and storage takes place as well as where authorized personnel can interpret the data.
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 innovation is useful because it functions on a framework that contains specialized nodes that are unique in monitoring and analyzing the behavior of hydraulic mining drill machines. This procedure initiates from the major sensing node whereby pressure, acceleration, temperature, and electrical current emanating from the machine at its working state are captured, such capturing device is automated with sensors. This infrastructure applies sensors to both acquire and measure these parameters to improve the accuracy and effectiveness of the measurement in question. Subsequently, the measured parameters are subsequently sent wirelessly to the intermediate processing node via hybrid short- and long-range networks. This guarantees that data can be transmitted effectively and accurately in an industrial environment. There, the data is processed, aggregated, and later sent to the cloud server for further analysis. The cloud server processes the data using machine learning techniques in embedding to find patterns, anomalies, and strategic actionable steps. This bearer consists of predictive perspective which not only allow predicting the next event but also predicting the events that will lead to the next maintenance or break down event. The cloud server is the logical node where all the data processing and storage takes place as well as where authorized personnel can interpret the data.
The virtual data obtained from the analysis is presented to users in comprehensible forms on the touch screen or web-based dashboard. These interfaces enable the operators and managers who use the system to see live events, follow past actions and future recommendations. The system enables users to respond to critical situations through the indication of visual and sound alarms. The invention then raises efficiency, dependability, and safety concerning the operation of hydraulic mining drill machine by incorporating real-time monitoring with AI analysis and easy to use interfaces.
BEST METHOD OF WORKING
The The HMBATCS Node incorporates several components including a Raspberry Pi processor Board, an nRF module, a Liquid Pressure Sensor, an Accelerometer, a Current Sensor, a Temperature Sensor, a Buzzer, and a Power Supply, to allow for real-time capture of critical operating information and also maintain strong wireless communication that can be used for efficient supervision and predictive maintenance in hydraulic mining drill activities.
The HMBATRCS Node consists of a Raspberry Pi Processor Board, an nRF Module, LoRa RF Module, HMI Touch Display, LED, and a Power Supply, and therefore can be said to enhance system capability, since it includes an on-site display of system information, a long-range communication of system data and an alert system to notify the Operator when harmful machine conditions are detected.
The HMBAGRCS Node consists of a Raspberry Pi Processor Board, a LoRa rf module, a GSM modem, an Indicator LED and a rechargeable battery. Sustained long-range transmission of data and information to the custom cloud server is guaranteed and normal operations are even during outages to allow constant availability of predictive analytics and machine status.
The HMI touch display panel located in the HMBATRCS node has the ability to display data accurately and allow feedbacks in real time, hence, allowing the machine operator to supervise the operation of machines and the output of alerts while on location thereby enhancing situational awareness and operational performance.
The LoRa RF Module is integrated into the constructor of the HMBATRCS and HMBAGRCS Nodes and provides a strong long range wireless link between the nodes and the cloud server which allows steady data flow under severe distance and location conditions.
The nRF Module, also incorporated in the units of HMBATCS and HMBATRCS nodes, provide the possibility of practical communication in short distances for timely data transmission increase the reliability of intra systems connection and performance monitoring.
ADVANTAGES OF THE INVENTION
1. Continuous and accurate surveillance of critical parameters is settled by the harnessing of sensors such as liquid pressure sensor, accelerometer, current sensor and lastly temperature sensor designed within the system, so that no operational anomalies are left exposed.
2. With the use of nRF modules as well as LoRa RF modules coupling, the system has high interoperability for the broad area networks and can be transmitted with great ease with regards to the short range networks as well which makes the system use in different operational scope and settings.
3. The machine learning power customized cloud server does quite a good job in sifting through the data and spotting trends, looking out for irregularities and predicting maintenance which in turn helps reduce the instances of unplanned downtime maintenance expenses.
4. The HMI touch display and the web based dashboard are able to help the operators and supervisors efficiently understand and interpret real time as well as look back into the past data and even trends so that they don't turn the other way upon receiving short and incisive actionable insights into the when and how to take action.
5. It is certain that there would be no power outages or connectivity issues since the HMBAGRCS Node has a built-in GSM modem and recharge bands as well as effective communication and system operation. 
, Claims:1. An ai-enabled handheld device with machine learning for behavioral analysis and feedback on hydraulic mining drill machines using nrf and lora hybrid networks comprises HMBATCS Node incorporates several components including a Raspberry Pi processor Board, an nRF module, a Liquid Pressure Sensor, an Accelerometer, a Current Sensor, a Temperature Sensor, a Buzzer, and a Power Supply, to allow for real-time capture of critical operating information and also maintain strong wireless communication that can be used for efficient supervision and predictive maintenance in hydraulic mining drill activities.
2. The device as claimed in claim 1, wherein the HMBATRCS Node consists of a Raspberry Pi Processor Board, an nRF Module, LoRa RF Module, HMI Touch Display, LED, and a Power Supply, and therefore can be said to enhance system capability, since it includes an on-site display of system information, a long-range communication of system data and an alert system to notify the Operator when harmful machine conditions are detected.
3. The device as claimed in claim 1, wherein the HMBAGRCS Node consists of a Raspberry Pi Processor Board, a LoRa rf module, a GSM modem, an Indicator LED and a rechargeable battery. Sustained long-range transmission of data and information to the custom cloud server is guaranteed and normal operations are even during outages to allow constant availability of predictive analytics and machine status.
4. The device as claimed in claim 1, wherein the HMI touch display panel located in the HMBATRCS node has the ability to display data accurately and allow feedbacks in real time, hence, allowing the machine operator to supervise the operation of machines and the output of alerts while on location thereby enhancing situational awareness and operational performance.
5. The device as claimed in claim 1, wherein the LoRa RF Module is integrated into the constructor of the HMBATRCS and HMBAGRCS Nodes and provides a strong long range wireless link between the nodes and the cloud server which allows steady data flow under severe distance and location conditions.
6. The device as claimed in claim 1, wherein the nRF Module, also incorporated in the units of HMBATCS and HMBATRCS nodes, provide the possibility of practical communication in short distances for timely data transmission increase the reliability of intra systems connection and performance monitoring.

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