Drilling machine electric control fault real-time monitoring and early warning system based on intelligent algorithm

Abstract

The invention belongs to the technical field of electromechanical equipment monitoring, and particularly relates to a drilling machine electric control fault real-time monitoring and early warning system based on an intelligent algorithm, which comprises a data acquisition module, a data processing module, an intelligent algorithm module, a fault early warning module, a real-time communication module, a database module, a user interface module, a self-adaptive adjustment module and a remote monitoring module, the data acquisition module is used for acquiring operation data of the electric control system of the drilling machine, and the data processing module is used for carrying out preprocessing and feature extraction on the data acquired by the data acquisition module. According to the drilling machine electric control fault real-time monitoring and early warning system based on the intelligent algorithm, multi-source data of a drilling machine electric control system are collected through multiple sensors, the data are analyzed and processed through the intelligent algorithms such as deep learning, accurate diagnosis and early warning of faults are achieved, and the real-time monitoring and early warning system is more accurate in fault diagnosis and early warning through the mode of combining multi-sensor fusion and the intelligent algorithm. And the monitoring precision and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electromechanical equipment monitoring, and particularly to a real-time monitoring and early warning system for drilling rig electric control faults based on intelligent algorithms. Background Art

[0002] In modern industrial production, the drilling rig, as a key mechanical equipment, is widely used in fields such as mine exploitation, oil exploration, and geological drilling. The stable operation of the drilling rig is crucial for the smooth progress of production, and the electric control system is the core component of the drilling rig, and its operating state directly affects the performance and safety of the drilling rig.

[0003] Currently, during the use of drilling rigs, most of the existing monitoring methods only simply monitor some key parameters, such as voltage, current, etc. These parameters can only reflect part of the operating state of the electric control system and cannot comprehensively and accurately evaluate the health status of the system. For some complex faults, a single monitoring parameter cannot provide sufficient information for fault diagnosis, and it is easy to have missed diagnosis and misdiagnosis situations. In view of this, a real-time monitoring and early warning system for drilling rig electric control faults based on intelligent algorithms is proposed. Summary of the Invention

[0004] The main purpose of the present invention is to provide a real-time monitoring and early warning system for drilling rig electric control faults based on intelligent algorithms, which can solve the problems raised in the above background art.

[0005] To achieve the above object, the real-time monitoring and early warning system for drilling rig electric control faults based on intelligent algorithms proposed by the present invention includes:

[0006] A data acquisition module for collecting the operating data of the drilling rig electric control system;

[0007] A data processing module for preprocessing and feature extraction of the data collected by the data acquisition module;

[0008] An intelligent algorithm module that uses deep learning algorithms to learn and analyze the feature parameters extracted by the data processing module, and determines whether there are faults in the electric control system, as well as the fault type and severity;

[0009] A fault early warning module that issues corresponding early warning signals according to the diagnosis results of the intelligent algorithm module;

[0010] A real-time communication module responsible for data transmission between modules and sending early warning information to relevant personnel;

[0011] A database module for storing the historical operating data, fault data, sensor parameters, and algorithm models of the drilling rig;

[0012] The user interface module provides an operation interface for users to view the running status, monitor data and warning information, and set system parameters;

[0013] The adaptive adjustment module automatically adjusts the parameters and warning thresholds of the intelligent algorithm module according to the running data and fault conditions of the drilling rig;

[0014] The remote monitoring module realizes remote monitoring of the drilling rig through the Internet.

[0015] Preferably, the data acquisition module includes a voltage sensor, a current sensor, a temperature sensor, and a vibration sensor, which are used to collect the voltage, current, temperature, and vibration data of the drilling rig's electric control system. The voltage sensor can accurately capture the subtle fluctuations of the voltage in the electric control system to ensure the accurate acquisition of voltage data and provide a basis for subsequent analysis. The current sensor can monitor the current changes in real time to provide key data for judging the equipment load. The temperature sensor can quickly and accurately sense the temperature of each part of the equipment to timely detect potential fault hazards caused by overheating. The vibration sensor uses the piezoelectric effect to accurately collect the vibration signals during the operation of the drilling rig and evaluate the mechanical state of the equipment by analyzing the vibration characteristics.

[0016] Preferably, the preprocessing of the data processing module includes removing noise and interference signals through a filtering algorithm and normalizing the data. In the preprocessing link, the filtering algorithm can effectively remove the noise and interference signals in the data, and through establishing a system state model and an observation model, perform optimal estimation on the data to improve the quality and reliability of the data. At the same time, by using the normalization method, the data collected by different types of sensors are standardized to make the data in the same dimension, which is convenient for the subsequent intelligent algorithm module to analyze.

[0017] Preferably, the intelligent algorithm module uses a convolutional neural network (CNN) or a recurrent neural network (RNN) for fault diagnosis and prediction. For faults caused by component aging in the electric control system, the convolutional neural network (CNN) can accurately judge whether such faults exist in the current system by learning the fault characteristics in historical data. The recurrent neural network (RNN) is particularly suitable for processing data with time series characteristics. The running data of the drilling rig's electric control system often has time correlation. The recurrent neural network (RNN) can use its internal recurrent structure to model the time series data, capture the long-term dependence relationship in the data, and realize the prediction of faults.

[0018] Preferably, the fault warning module sets different warning levels according to the severity of the fault and issues warning signals through means such as sound, light, text message, and email. This module sets different warning levels according to the severity of the fault. For example, a first-level warning indicates a minor fault that may not temporarily affect the operation of the equipment, but requires close attention; a second-level warning indicates a moderate fault where the equipment performance may be affected to a certain extent and maintenance needs to be arranged as soon as possible; a third-level warning indicates a serious fault where the equipment may face the risk of shutdown and immediate measures need to be taken.

[0019] Preferably, the real-time communication module uses wireless communication technology and encrypts the transmitted data. To ensure the security of data transmission, advanced encryption technologies such as the AES encryption algorithm are used for data encryption and decryption to prevent data from being stolen or tampered with during transmission and to ensure the secure and reliable transmission of system data.

[0020] Preferably, the database module adopts an efficient storage structure and management system for easy data storage, query, and update. Relational databases such as MySQL or non-relational databases such as MongoDB are used. For structured historical operation data and fault data, relational databases can achieve efficient storage, query, and update operations by establishing a perfect data table structure.

[0021] Preferably, the user interface module adopts a graphical design, which is simple and convenient to operate and easy for users to use. The interface displays the changes in key parameters such as voltage, current, and temperature in real time through dynamic charts, enabling users to clearly understand the operating status of the equipment at a glance. In terms of monitoring data display, a combination of lists and bar charts is used to clearly present the specific values and trend comparisons of various types of data. For warning information, it is prompted to the user in the form of a prominent pop-up window, which details the warning level, fault type, and recommended measures. System parameters can also be set through the interface, such as adjusting the warning threshold and setting the data acquisition frequency.

[0022] Preferably, the adaptive adjustment module automatically adjusts the sensitivity of the sensor and the parameters of the fault diagnosis model according to the changes in the operating conditions of the drilling rig.

[0023] Preferably, the remote monitoring module allows managers to view the operating status of the drilling rig in real time at a remote terminal, receive warning information, and perform remote control and management. Managers can receive warning information. When the system issues a fault warning, the remote terminal will immediately receive a notification, including text messages, emails, or APP push notifications, etc., to ensure that managers can promptly grasp the fault situation. In addition, the remote monitoring module supports remote control and management functions. When necessary, managers can perform some operations on the drilling rig through the remote terminal, such as remotely starting and stopping the equipment and adjusting the operating parameters of the equipment.

[0024] The present invention provides a real-time monitoring and early warning system for drilling rig electric control faults based on intelligent algorithms, which has the following beneficial effects:

[0025] (1) The real-time monitoring and early warning system for drilling rig electric control faults based on intelligent algorithms collects multi-source data of the drilling rig electric control system through multiple sensors, and uses intelligent algorithms such as deep learning to analyze and process the data, realizing accurate diagnosis and early warning of faults. This way of combining multi-sensor fusion and intelligent algorithms improves the monitoring accuracy and reliability of the system.

[0026] (2) The real-time monitoring and early warning system for drilling rig electric control faults based on intelligent algorithms can automatically adjust the parameters and early warning thresholds of the intelligent algorithms according to the operating status and fault conditions of the drilling rig, realizing adaptive adjustment and optimization. This adaptive ability enables the system to better adapt to different working conditions and individual differences of drilling rigs, improving the adaptability and accuracy of the system.

[0027] (3) The real-time monitoring and early warning system for drilling rig electric control faults based on intelligent algorithms realizes real-time data transmission and remote monitoring by adopting advanced wireless communication technology and Internet technology. Operators and managers can obtain the operating information of the drilling rig at any time and place, and handle faults in a timely manner, improving production efficiency and management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0029] Figure 1 It is the system block diagram of the present invention;

[0030] Figure 2 It is the data acquisition and processing flow chart of the present invention;

[0031] Figure 3 It is the working flow chart of the intelligent algorithm module of the present invention;

[0032] Figure 4 It is the working flow chart of the fault early warning module of the present invention;

[0033] Figure 5 It is the working flow chart of the adaptive adjustment module of the present invention;

[0034] Figure 6 It is the working flow chart of the remote monitoring module of the present invention.

[0035] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0036] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] Please refer to Figures 1-6 , the present invention provides a real-time monitoring and early warning system for drilling rig electric control faults based on intelligent algorithms, including a data acquisition module, a data processing module, an intelligent algorithm module, a fault early warning module, a real-time communication module, a database module, a user interface module, an adaptive adjustment module, and a remote monitoring module. The data acquisition module is used to collect the operation data of the drilling rig electric control system. The data processing module is used to preprocess and extract features from the data collected by the data acquisition module. The intelligent algorithm module uses deep learning algorithms to learn and analyze the feature parameters extracted by the data processing module to determine whether there are faults in the electric control system, the type and severity of the faults. The fault early warning module can issue corresponding early warning signals according to the diagnosis results of the intelligent algorithm module. The real-time communication module is responsible for data transmission between modules and sending early warning information to relevant personnel. The database module is used to store the historical operation data, fault data, sensor parameters, and algorithm models of the drilling rig. The user interface module can provide an operation interface for users to view the operation status, monitoring data, and early warning information, and set system parameters. The adaptive adjustment module can automatically adjust the parameters and early warning thresholds of the intelligent algorithm module according to the operation data and fault conditions of the drilling rig. The remote monitoring module can realize remote monitoring of the drilling rig through the Internet.

[0038] In an embodiment of the present invention, the data acquisition module includes a voltage sensor, a current sensor, a temperature sensor, and a vibration sensor, which are used to collect voltage, current, temperature, and vibration data of the drilling rig's electric control system. The voltage sensor can accurately capture the subtle fluctuations of the voltage in the electric control system, ensuring the precise acquisition of voltage data and providing a basis for subsequent analysis. The current sensor can monitor the current changes in real time and provide key data for judging the load condition of the equipment. The temperature sensor can quickly and accurately sense the temperature of various parts of the equipment and timely detect potential fault hazards caused by overheating. The vibration sensor uses the piezoelectric effect to accurately collect the vibration signals during the operation of the drilling rig and evaluate the mechanical state of the equipment by analyzing the vibration characteristics. The preprocessing of the data processing module includes removing noise and interference signals through a filtering algorithm and normalizing the data using a normalization method. In the preprocessing stage, the filtering algorithm can effectively remove noise and interference signals in the data, and through establishing a system state model and an observation model, perform optimal estimation on the data, thereby improving the quality and reliability of the data. At the same time, by using the normalization method, the data collected by different types of sensors are standardized, making the data in the same dimension, which is convenient for the subsequent intelligent algorithm module to analyze. For example, by using the maximum-minimum normalization method, the data is mapped to the [0,1] interval to eliminate the influence caused by different dimensions of the data and lay a foundation for the accurate learning and analysis of the intelligent algorithm. In the feature extraction stage, various methods such as time-domain analysis and frequency-domain analysis are used to extract key feature parameters reflecting the operating state of the equipment, such as mean value, variance, frequency peak, etc. These feature parameters will be used as the input of the intelligent algorithm module to judge whether there are faults in the electric control system, as well as the type and severity of the faults. The intelligent algorithm module uses a convolutional neural network (CNN) or a recurrent neural network (RNN) for fault diagnosis and prediction. For faults caused by component aging in the electric control system, the convolutional neural network (CNN) can accurately judge whether there are such faults in the current system by learning the fault characteristics in the historical data. The recurrent neural network (RNN) is particularly suitable for processing data with time-series characteristics. The operating data of the drilling rig's electric control system often has time correlation. The recurrent neural network (RNN) can use its internal recurrent structure to model the time-series data, capture the long-term dependence relationship in the data, and achieve the prediction of faults. In this way, multiple sensors can collect multi-source data of the drilling rig's electric control system and use intelligent algorithms such as deep learning to analyze and process the data, realizing the accurate diagnosis and early warning of faults. This way of combining multi-sensor fusion and intelligent algorithms improves the monitoring accuracy and reliability of the system.

[0039] Furthermore, the fault warning module sets different warning levels according to the severity of the fault and issues warning signals through means such as sound, light, text messages, and emails. This module sets different warning levels according to the severity of the fault. For example, a first-level warning indicates a minor fault that may not affect the equipment operation temporarily but requires close attention; a second-level warning indicates a medium-level fault where the equipment performance may be affected to a certain extent and maintenance needs to be arranged as soon as possible; a third-level warning indicates a severe fault where the equipment may face the risk of shutdown and immediate measures need to be taken. The database module adopts an efficient storage structure and management system, facilitating data storage, query, and update. For example, a relational database such as MySQL or a non-relational database such as MongoDB can be used. For structured historical operation data and fault data, a relational database can achieve efficient storage, query, and update operations by establishing a perfect data table structure. The user interface module adopts a graphical design, which is simple and convenient to operate and easy for users to use. The interface displays the changes in key parameters such as voltage, current, and temperature in real time through dynamic charts, enabling users to clearly understand the operation status of the equipment at a glance. In terms of monitoring data display, a combination of lists and bar charts is used to clearly present the specific values and trend comparisons of various types of data. For warning information, it is prompted to users in the form of eye-catching pop-up windows, which display the warning level, fault type, and recommended measures in detail. System parameters can also be set through the interface, such as adjusting the warning threshold and setting the data acquisition frequency. In this way, the parameters of the intelligent algorithm and the warning threshold can be automatically adjusted according to the operation status and fault conditions of the drill rig, achieving adaptive adjustment and optimization. This adaptive ability enables the system to better adapt to different working conditions and individual differences of drill rigs, improving the adaptability and accuracy of the system.

[0040] Furthermore, the real-time communication module adopts wireless communication technology and encrypts the transmitted data. To ensure the security of data transmission, advanced encryption technologies such as the AES encryption algorithm are used for data encryption and decryption, preventing data from being stolen or tampered with during transmission and ensuring the secure and reliable transmission of system data. The adaptive adjustment module automatically adjusts the sensitivity of the sensors and the parameters of the fault diagnosis model according to the changes in the operating conditions of the drill rig. The remote monitoring module allows management personnel to view the operating status of the drill rig in real time at a remote terminal, receive warning messages, and perform remote control and management. Management personnel can receive warning messages. When the system issues a fault warning, the remote terminal will immediately receive a notification, including methods such as text messages, emails, or APP push notifications, ensuring that management personnel can promptly grasp the fault situation. In addition, the remote monitoring module supports remote control and management functions. When necessary, management personnel can perform some operations on the drill rig through the remote terminal, such as remotely starting and stopping the equipment, adjusting the operating parameters of the equipment, etc. By adopting advanced wireless communication technology and Internet technology, real-time data transmission and remote monitoring are achieved. Operators and management personnel can obtain the operating information of the drill rig at any time and place, promptly handle faults, and improve production efficiency and management level.

[0041] In the present invention, devices such as voltage sensors, current sensors, temperature sensors, and vibration sensors need to be installed at key positions of the drill rig's electric control system. According to the structure of the drill rig and the layout of the electric control system, the installation positions of the sensors are reasonably selected to ensure that key data can be accurately collected. The sensors are connected to the data processing module through data transmission lines to ensure stable and reliable line connections. Configure the hardware devices of the data processing module, intelligent algorithm module, fault warning module, real-time communication module, database module, and user interface module, such as servers, computers, communication base stations, etc. Ensure that the performance of the hardware devices meets the operating requirements of the system and has sufficient computing power, storage capacity, and communication capacity.

[0042] Meanwhile, under the simulated operating environment of the drill rig, the system is comprehensively tested. The test contents include the accuracy of data collection, the effect of data processing, the diagnostic accuracy of intelligent algorithms, the timeliness and accuracy of fault warnings, the stability of real-time communication, etc. According to the test results, the system is optimized by adjusting the installation positions and parameters of the sensors, optimizing the data processing algorithm and the parameters of the intelligent algorithm model, and improving the performance and accuracy of the system. A trial operation is carried out at the actual drill rig operation site to further verify the reliability and practicality of the system, collect feedback from on-site operators and management personnel, and further improve and perfect the system.

[0043] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural transformation made under the inventive concept of the present invention by using the content of the specification and drawings of the present invention, or any direct / indirect application in other related technical fields shall be included within the patent protection scope of the present invention.

Claims

1. A real-time monitoring and early warning system for drilling rig electric control failure based on intelligent algorithm, characterized in that: include: Data acquisition module, used to collect operating data of the drilling rig electronic control system; A data processing module, which performs preprocessing and feature extraction on the data collected by the data collection module; An intelligent algorithm module uses a deep learning algorithm to learn and analyze the characteristic parameters extracted by the data processing module to determine whether there is a fault in the electronic control system and the type and severity of the fault; A fault warning module, which issues a corresponding warning signal according to the diagnosis result of the intelligent algorithm module; Real-time communication module, responsible for data transmission between modules and sending early warning information to relevant personnel; Database module, used to store historical operation data, fault data, sensor parameters and algorithm models of the drilling rig; The user interface module provides users with an operation interface for viewing operating status, monitoring data and warning information, and setting system parameters; Adaptive adjustment module, which automatically adjusts the parameters and warning thresholds of the intelligent algorithm module according to the operation data and fault conditions of the drilling rig; Remote monitoring module, which enables remote monitoring of the drilling rig through the Internet.

2. According to claim 1, a real-time monitoring and early warning system for electric control failure of drilling rigs based on intelligent algorithm is characterized by: The data acquisition module includes a voltage sensor, a current sensor, a temperature sensor and a vibration sensor, which are used to collect voltage, current, temperature and vibration data of the drilling rig electronic control system.

3. The real-time monitoring and early warning system for electric control failure of drilling rigs based on intelligent algorithm according to claim 1 is characterized by: The preprocessing of the data processing module includes filtering algorithm to remove noise and interference signals, and normalization method to standardize the data.

4. The real-time monitoring and early warning system for electric control failure of drilling rigs based on intelligent algorithm according to claim 1 is characterized by: The intelligent algorithm module uses a convolutional neural network (CNN) or a recurrent neural network (RNN) to perform fault diagnosis and prediction.

5. The real-time monitoring and early warning system for electric control failure of drilling rigs based on intelligent algorithm according to claim 1 is characterized by: The fault warning module sets different warning levels according to the severity of the fault and sends out warning signals through sound, light, text message, email, etc.

6. The real-time monitoring and early warning system for electric control failure of drilling rigs based on intelligent algorithm according to claim 1 is characterized by: The real-time communication module adopts wireless communication technology and performs encryption processing on the transmission data.

7. The real-time monitoring and early warning system for electric control failure of drilling rigs based on intelligent algorithm according to claim 1 is characterized by: The database module adopts an efficient storage structure and management system to facilitate data storage, query and update.

8. The real-time monitoring and early warning system for electric control failure of drilling rigs based on intelligent algorithm according to claim 1 is characterized by: The user interface module adopts a graphical design, which is simple and convenient to operate and easy for users to use.

9. The real-time monitoring and early warning system for electric control failure of drilling rigs based on intelligent algorithm according to claim 1 is characterized by: The adaptive adjustment module automatically adjusts the sensitivity of the sensor and the parameters of the fault diagnosis model according to the changes in the operating conditions of the drilling rig.

10. The real-time monitoring and early warning system for electric control failure of drilling rigs based on intelligent algorithm according to claim 1 is characterized by: The remote monitoring module allows managers to view the operating status of the drilling rig in real time, receive warning information and perform remote control and management at the remote terminal.

Images