A SYSTEM FOR PREDICTIVE MAINTENANCE OF HORIZONTAL BORING MILLS IN BOREHOLE DIGGING

Abstract

The present invention provides an edge computing solution for predictive maintenance of horizontal boring mills in borehole digging operations. It comprises a Raspberry Pi Processor Board, GSM Modem, Temperature Sensor, Vibration Sensor, Accelerometer Board, RTC Module, SD Card Module, Touch HMI Display, and Power Supply. The device collects real-time data on acceleration, vibration, and temperature, which is then transmitted to a dedicated cloud server for analysis using machine learning algorithms. The system generates recommendations for maintenance and operational adjustments based on friction analysis, displayed on a user-friendly interface accessible through a secure web dashboard and a local touch screen. This invention enables proactive maintenance, reduces downtime, and optimizes machine performance in borehole digging operations.

Specification

Description:FIELD OF THE INVENTION
The present invention relates to predictive maintenance and condition monitoring of horizontal boring mills used in borehole digging operations. It addresses the challenges of traditional maintenance methods by providing a real-time, data-driven solution for optimizing machine performance and preventing costly downtime.
BACKGROUND OF THE INVENTION
Horizontal boring mills are essential in borehole digging operations, requiring continuous operation and high reliability. However, these machines are susceptible to wear and tear, and unexpected failures can lead to significant delays and financial losses.
Traditional maintenance approaches often rely on scheduled inspections or reactive repairs, which can be inefficient and lead to unnecessary downtime. These methods may fail to identify potential issues before they escalate into major problems, resulting in costly repairs and production delays.
Therefore, there is a need for a proactive, data-driven solution that provides real-time insights into the condition of horizontal boring mills, enabling timely maintenance and preventing costly downtime in borehole digging operations.
Followings are some prior arts to the present invention:
CN204344040U The utility model of prior art discloses the combination of a kind of continuous tubing drill mill horizontal segment cement plug downhole tool, comprise the coiled tubing, coiled tubing connector, spy plug tool string or the brill mill tool string that connect successively, described spy plug tool string can determine position, cement plug face, and described brill mill tool string can carry out brill mill to described cement plug face. During the continuous tubing drill mill horizontal segment cement plug downhole tool combination that application the utility model provides, can according to the size of well, the size of variation tool string flexibly, thus can meet the brill mill of slim hole well. Meanwhile, tool string can effectively large by dog-leg degree, the well section that has step, and the cement plug that can be applicable to long horizontal sections bores mill, and thus applicability is more extensive. And by coiled tubing, bore without the need to termination of pumping in honed journey, can realize creeping into continuously, and then effectively reduce the bit freezing risk of boring in honed journey, improve operating efficiency. Meanwhile, adopt coiled tubing, can carry out being with kill-job especially to drill through the brill mill operation of the higher well of rear pressure.
CN107378028B The said prior art discloses a kind of radial drilling machines convenient for adjusting, including pedestal, rotating device, horizontally moving device, drill bit fixes device, lifting device, workbench, the rotating device is fixedly mounted on base top, horizontally moving device is fixedly connected on the spinning device, rotating device drives horizontally moving device rotation, the fixed device of drill bit is fixed on horizontally moving device, horizontally moving device drives the fixed device of drill bit to move in the horizontal direction, lifting device is set on the base, lifting device control workbench moves up and down, rotating device moves up and down for workbench and provides support. The present invention is capable of quick and stable the drill bit in radial drilling machine is horizontally moved and is rotated, and can quickly adjust height of table, is not only convenient for operator's use, and can be improved finishing precision.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed.
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 present invention provides an innovative edge computing solution that addresses the limitations of traditional maintenance methods for horizontal boring mills in borehole digging. The device comprises a Raspberry Pi Processor Board, GSM Modem, Temperature Sensor, Vibration Sensor, Accelerometer Board, RTC Module, SD Card Module, Touch HMI Display, and Power Supply.
TDFA_EACNode collects real-time data on acceleration, vibration, and temperature from the horizontal boring mill. This data is transmitted to a dedicated cloud server where machine learning algorithms analyze it to provide insights into machine health and predict potential issues. The system generates recommendations for maintenance and operational adjustments based on friction analysis.
The results are displayed on a user-friendly interface accessible through a secure web dashboard and a local touch screen, enabling both on-site and remote monitoring of the machine's condition. This real-time data analysis and predictive maintenance capability allows operators to address potential problems proactively, minimizing downtime and optimizing machine performance in borehole digging operations.
To further clarify advantages and features of the present invention, a more particular description of the invention is rendering by reference to specific embodiments thereof, which is illustrated in the appended drawing.
It is appreciated that the drawing depicts only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention is being described and explained with additional specificity and detail with the accompanying drawing.
BRIEF DESCRIPTION OF DRAWINGS
The foregoing detailed description of embodiments is better understood when read in conjunction with the attached drawing. For better understanding, each component is represented by a specific number which is further illustrated as a reference number for the components used with the figures.
Figure 1 represents block diagram of present system
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the system, one or more components of the system may have been represented in the drawing by conventional symbols, and the drawing may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawing with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
DETAILED DESCRIPTION OF THE INVENTION
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawing and specific language will be used to describe the same.
It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skilled in the art to which this invention belongs.
Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.
The present invention provides an innovative edge computing solution that addresses the limitations of traditional maintenance methods for horizontal boring mills in borehole digging. The device comprises a Raspberry Pi Processor Board, GSM Modem, Temperature Sensor, Vibration Sensor, Accelerometer Board, RTC Module, SD Card Module, Touch HMI Display, and Power Supply.
TDFA_EACNode collects real-time data on acceleration, vibration, and temperature from the horizontal boring mill. This data is transmitted to a dedicated cloud server where machine learning algorithms analyze it to provide insights into machine health and predict potential issues. The system generates recommendations for maintenance and operational adjustments based on friction analysis.
The results are displayed on a user-friendly interface accessible through a secure web dashboard and a local touch screen, enabling both on-site and remote monitoring of the machine's condition. This real-time data analysis and predictive maintenance capability allows operators to address potential problems proactively, minimizing downtime and optimizing machine performance in borehole digging operations.
TDFA_EACNode operates through the following key components and processes:
Data Acquisition: The device collects real-time data on acceleration, vibration, and temperature from the horizontal boring mill using its integrated sensors.
Data Transmission: The GSM Modem transmits the collected sensor data to a dedicated cloud server for analysis.
Machine Learning Analysis: The cloud server utilizes machine learning algorithms trained on historical data to analyze the sensor data and predict potential issues.
Friction Analysis: The system performs friction analysis based on the sensor data and machine learning predictions to recommend operational and maintenance adjustments.
Real-time Monitoring and Alerts: The results, including real-time data, trend analysis, and maintenance recommendations, are displayed on a Touch HMI Display and a secure web dashboard. This allows for continuous monitoring of the machine's condition and enables timely interventions to prevent potential problems.
While specific language has been used to describe the present invention, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment.
ADVANTAGES OF THE PRESENT INVENTION
1. This creative approach relies heavily on the TDFA_EACNode, which allows real-time data collecting from various sensors on Horizontal Boring Mills. By sending this data to a specialized cloud server, it makes machine learning-based predictive maintenance recommendations easier to implement, which eventually improves the operational effectiveness of Borehole Digging operations.
2. The GSM Modem is an essential communication channel that facilitates the seamless transfer of sensor data from the TDFA_EACNode to the cloud server. For Horizontal Boring Mills used in Borehole Digging operations, this technology facilitates machine learning-driven predictive maintenance and allows real-time monitoring.
3. Together, the Accelerometer Board, Vibration Sensor, and Temperature Sensor give vital real-time information on the state of operation for Horizontal Boring Mills. With the use of this data, the TDFA_EACNode is better equipped to keep an eye on and evaluate their health, which enhances the efficiency of Borehole Digging operations through machine learning-based predictive maintenance.
4. The Touch HMI Display serves as the user-friendly interface, giving operators real-time data from machine learning analysis and friction-based recommendations. As a result, they are better able to make decisions and maximize the efficiency of Horizontal Boring Mills in Borehole Digging operations.

, Claims:1. A system for predictive maintenance of horizontal boring mills in borehole digging, comprising:
a Raspberry Pi Processor Board as the central processing unit;
a GSM Modem for transmitting data to a cloud server;
a plurality of sensors, including a Temperature Sensor, a Vibration Sensor, and an Accelerometer Board, for collecting operational data from the horizontal boring mill;
an RTC Module for accurate timestamping of data;
an SD Card Module for local data storage;
a Touch HMI Display for on-site visualization of data and recommendations; and
a Power Supply for powering the system.
2. The system as claimed in claim 1, wherein the cloud server is configured to:
receive the operational data transmitted by the GSM Modem;
analyze the operational data using machine learning algorithms to predict potential issues; and
generate recommendations for maintenance and operational adjustments based on friction analysis.
3. The system as claimed in claim 1, wherein the Touch HMI Display provides a user interface for:
displaying real-time operational data from the sensors;
visualizing trend analysis of the operational data; and
presenting the maintenance and operational recommendations generated by the cloud server.
4. The system as claimed in claim 1, further comprising a secure web dashboard accessible via the internet, enabling remote monitoring of the horizontal boring mill and access to the maintenance and operational recommendations.
5. The system as claimed in claim 1, wherein the machine learning algorithms are trained on historical operational data to predict potential issues and generate friction-based recommendations for optimizing machine performance and preventing downtime.

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