AI-ENHANCED MAINTENANCE RECOMMENDATIONS FOR DOWN-THE-HOLE DRILLS' TORQUE ANALYSIS IN MINING OPERATIONS

Abstract

This invention introduces an AI-driven maintenance system designed for Down-the-Hole Drills (DDMs) in mining operations, utilizing real-time torque analysis to enhance operational reliability. Integrated with a Raspberry Pi Processor Board, Neural Stick, Torque Sensor, Optical Encoder, LED Indicator, and cloud-based analytics, the system provides immediate and remote access to critical torque data. The AI algorithms generate actionable maintenance recommendations based on torque trends, accessible on-site via an interactive display and remotely through a web dashboard. This system empowers maintenance teams with timely insights, reducing downtime and optimizing drill performance in mining environments.

Specification

Description:FIELD OF THE INVENTION
This invention relates to AI-Enhanced Maintenance Recommendations for Down-the-Hole Drills' Torque Analysis in Mining Operations.
BACKGROUND OF THE INVENTION
The Down-the-Hole Drills (DDMs) monitoring and maintenance process in coal mining operations is revolutionized by the MRSDDCMote invention. With this breakthrough, torque data is captured in real time during drill operations by seamlessly integrating artificial intelligence and modern sensors. With an intuitive interface, the system makes it easier to analyze drill performance in detail and provides insightful data. The system makes use of a cloud-based architecture to guarantee centralized storage and simple data accessible. This allows authorized staff to view analytics through a personalized online dashboard, receive AI-generated maintenance recommendations, and remotely monitor torque trends.
Maintaining the operating reliability and efficiency of Down-the-Hole Drills (DDMs) used in coal mining operations is a substantial challenge to the mining industry. During drill operations, current maintenance procedures often do not have instant access to critical torque data, which can lead to inefficiencies, unplanned downtime, and increased maintenance costs. The inability of maintenance staff to proactively address problems and improve drill performance is hampered by the absence of an extensive monitoring and recommendation system.
CA2041254C - A rock bit for a down-the-hole drill includes an auxiliary apparatus for flushing the working face of the drill bit to facilitate the removal of cuttings and debris through peripheral troughs in the drill and significantly reducing the abrasive effect of the cuttings and debris on the bit. The exhaust fluid is supplied to the working face of the drill bit through several delivery channels spaced equidistantly about the periphery of the drill head where a first set of delivery channels are disposed at acute angles to the central axis of the drill bit and a second set of delivery channels are interposed directly between the central bore of the drill bit and the working face. AI based Recommendations and suggestions for Maintenance in Mining operations is the novelty of the system.
AU2010322100B2 - A down-the-hole drill hammer is provided that includes a housing, a solid core piston mounted within the housing, a seal located between the solid core piston and the housing, and a backhead configured to exhaust working fluid volumes about a proximal end of the DHD hammer. The backhead includes a check valve assembly having a plug seal that can be moved to a closed seal position by gravity. A DHD hammer having a segmented chuck assembly is also provided. The segmented chuck assembly includes a plurality of chuck segments forming a cylindrical chuck assembly. AI based Recommendations and suggestions for Maintenance in Mining operations 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.
The MRSDDCMote is used to seamlessly integrate advanced sensors, artificial intelligence, and cloud-based analytics to provide real-time torque analysis, actionable maintenance recommendations, and remote monitoring capabilities for Down-the-Hole Drills in coal mining operations. This device addresses significant challenges in operational efficiency and reliability. It is equipped with a Raspberry Pi Processor Board, Neural Stick, Torque Sensor, Optical Encoder, Led Indicator, and Power Supply.
The MRSDDCMote innovation for improved down-the-hole drill monitoring and maintenance in coal mining operations uses a Raspberry Pi Processor Board to support artificial intelligence capabilities, enable real-time data processing and analysis, and facilitate the seamless integration of advanced sensors.
The MRSDDCMote innovation is empowered by the Neural Stick, which is combined with the MRSDDCMote, to use machine learning algorithms for real-time torque data analysis from down-the-hole drills. This improves the system's capacity to produce practical suggestions and insights for coal mining operations.
The torque sensor built into MRSDDCMote is used to precisely record torque data in real-time from down-the-hole drills. This data is crucial for the analytics of the system, AI-based recommendations, and general drill performance monitoring in coal mining operations.
The optical encoder that is attached to the MRSDDCMote is used to measure down-the-hole drill rotational movement precisely. It also provides vital information for torque analysis in real-time and improves the system's overall capacity to monitor and optimize drill performance in coal mining operations.
To ensure consistent and dependable monitoring of down-the-hole drills in coal mining operations, the Power Supply, which is plugged into the MRSDDCMote, provides the energy required to support the operation of hardware components like the Raspberry Pi Processor Board, Neural Stick, Torque Sensor, Optical Encoder, LED Indicator, and other hardware.
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 MRSDDCMote invention integrates multiple hardware components and sophisticated algorithms to improve the monitoring and maintenance of Down-the-Hole Drills (DDMs) in coal mining operations. Neural Stick, Optical Encoder, Torque Sensor, LED Indicator, Raspberry Pi Processor Board, and Power Supply are important parts. The torque sensor and optical encoder are essential components that record data from DDMs in real time while they are in use. They also measure the torque that the drills apply and give accurate performance data. The Raspberry Pi's computational capacity and the Neural Stick's artificial intelligence capabilities are used to process the gathered data. This processed data, which includes operational parameters and torque measurements, is sent to a dedicated cloud server created specifically for this innovation. The data may be centrally stored, analyzed, and accessed from any location with an internet connection thanks to the cloud-based infrastructure.
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 MRSDDCMote invention integrates multiple hardware components and sophisticated algorithms to improve the monitoring and maintenance of Down-the-Hole Drills (DDMs) in coal mining operations. Neural Stick, Optical Encoder, Torque Sensor, LED Indicator, Raspberry Pi Processor Board, and Power Supply are important parts. The torque sensor and optical encoder are essential components that record data from DDMs in real time while they are in use. They also measure the torque that the drills apply and give accurate performance data. The Raspberry Pi's computational capacity and the Neural Stick's artificial intelligence capabilities are used to process the gathered data. This processed data, which includes operational parameters and torque measurements, is sent to a dedicated cloud server created specifically for this innovation. The data may be centrally stored, analyzed, and accessed from any location with an internet connection thanks to the cloud-based infrastructure.
The Neural Stick's machine learning algorithms allow it to produce AI-based insights and recommendations. A real-time overview of torque trends, statistics, and AI-generated recommendations are provided by an interactive TFT display on-site, which authorized personnel-including operators and maintenance teams-can access. Moreover, the information may be accessed via a customized web dashboard that is linked to the cloud server, giving customers the ability to remotely monitor drill performance and get timely maintenance and optimization recommendations. In order to provide operators with rapid visual feedback on-site and to enable them to promptly handle issues that are noticed, the system additionally includes an LED indicator. Furthermore, email can be used to convey AI-generated alerts and recommendations, allowing the maintenance staff to stay informed and take preventative action based on the insights offered.
BEST METHOD OF WORKING
An AI-enhanced maintenance system for Down-the-Hole Drills (DDMs) in mining operations, comprising a Raspberry Pi Processor Board for real-time data processing and integration of torque analysis with cloud-based analytics for proactive maintenance recommendations.
An AI-enhanced maintenance system with a Neural Stick to enable machine learning algorithms to analyze torque data, generating actionable insights and recommendations for optimized drill performance.
An AI-enhanced maintenance system including a Torque Sensor that captures real-time torque data during drill operations, supporting accurate performance monitoring and AI-based analysis.
An AI-enhanced maintenance system incorporating an Optical Encoder that monitors rotational movement of the drill, providing essential data for comprehensive torque analysis and performance optimization.
An AI-enhanced maintenance system with an LED Indicator providing immediate visual feedback to operators on-site, ensuring timely response to detected issues.
An AI-enhanced maintenance system with cloud-based storage for centralized data access, enabling authorized personnel to remotely monitor and receive AI-driven recommendations via a web dashboard.
An AI-enhanced maintenance system including a Power Supply to maintain consistent and reliable operation of all components, supporting uninterrupted torque analysis and maintenance recommendations for Down-the-Hole Drills in mining operations.
ADVANTAGES OF THE INVENTION
1. The MRSDDCWhen it comes to solving important problems with operational dependability and efficiency in coal mining operations, moto innovation plays a crucial role. It accomplishes this by fusing artificial intelligence, cloud-based analytics, and sophisticated sensors in a seamless manner to provide real-time torque analysis, practical maintenance advice, and remote monitoring capabilities for down-the-hole drills. This integrated strategy addresses important problems and offers a complete fix to improve drilling operations' overall effectiveness.
2. The MRSDDCMote invention places a strong emphasis on the Neural Stick as a crucial element for AI processing. With the help of this component, the system is able to analyze torque data from down-the-hole drills in real-time using machine learning algorithms, which greatly enhances the system's capacity to produce useful suggestions and insights for coal mining operations. The Neural Stick adds to the innovation's efficiency in sustaining and enhancing drill performance by integrating cutting-edge AI capabilities.
3. The Torque Sensor, which precisely records torque data from down-the-hole drills in real time, is an essential part of the MRSDDCMote invention. The analytics, AI-based suggestions, and general drill performance monitoring in coal mining operations depend heavily on this data. The Torque Sensor guarantees the accuracy and dependability of the data collected, which makes it easier to produce insightful findings that enhance maintenance procedures and operational effectiveness.
4. The Optical Encoder plays a crucial part in the MRSDDCMote invention by accurately detecting the Down-the-Hole Drills' rotational movement. For real-time torque analysis to be performed, this exact data is necessary. This improves the system's overall capacity to monitor and optimize drill performance in coal mining operations. Because of the Optical Encoder's assistance, the drill's movements can be accurately assessed, which promotes proactive maintenance and better decision-making.
, Claims:1. An AI-enhanced maintenance system for Down-the-Hole Drills (DDMs) in mining operations, comprising a Raspberry Pi Processor Board for real-time data processing and integration of torque analysis with cloud-based analytics for proactive maintenance recommendations.
2. The maintenance system as claimed in Claim 1, wherein a Neural Stick enables machine learning algorithms to analyze torque data, generating actionable insights and recommendations for optimized drill performance.
3. The maintenance system as claimed in Claim 1, further comprising a Torque Sensor that captures real-time torque data during drill operations, supporting accurate performance monitoring and AI-based analysis.
4. The maintenance system as claimed in Claim 1, incorporating an Optical Encoder that monitors rotational movement of the drill, providing essential data for comprehensive torque analysis and performance optimization.
5. The maintenance system as claimed in Claim 1, wherein an LED Indicator provides immediate visual feedback to operators on-site, ensuring timely response to detected issues.
6. The maintenance system as claimed in Claim 1, with cloud-based storage for centralized data access, enabling authorized personnel to remotely monitor and receive AI-driven recommendations via a web dashboard.
7. The maintenance system as claimed in Claim 1, including a Power Supply to maintain consistent and reliable operation of all components, supporting uninterrupted torque analysis and maintenance recommendations for Down-the-Hole Drills in mining operations.

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