Fault detection method and system for coal conveying programmed control system of thermoelectric company

Abstract

The invention discloses a fault detection method and system for a coal conveying programmed control system of a thermoelectric company, and relates to the technical field of coal mine fully-mechanized mining, and the method comprises the steps: building a fault intelligent diagnosis model based on historical fault data; collecting field data in real time, and judging whether a fault occurs or not through the fault intelligent diagnosis model; and executing a corresponding strategy according to the type and degree of the fault. According to the method, multi-dimensional feature analysis is carried out according to multiple groups of sensors, comprehensive analysis of coal level height data is realized by extracting basic features, statistical features, time domain features, frequency domain features and other features, and the accuracy of fault detection is remarkably improved. The deep learning network is utilized to train and classify the feature data, so that the fault types of blockage, coal interruption and the like can be identified more accurately, and misinformation and missing report are reduced. The coal level height data is collected in real time, features are rapidly extracted, and the running state of the coal drop pipe is monitored in real time.

Description

Technical Field

[0001] The invention relates to the technical field of coal mine fully mechanized mining, in particular to a fault detection method and system for a coal conveying program control system of a thermal power company. Background Art

[0002] As an important part of the thermal power company, the coal conveying program control system is responsible for efficiently and stably conveying coal from the storage area to the boiler room. The normal operation of the system is of vital importance to ensuring the power generation efficiency of the thermal power company, reducing production costs, improving energy utilization and reducing environmental pollution. Specifically, the coal conveying program control system realizes the continuous and uniform transportation of coal through automatic control, avoids the tediousness and instability of manual operation, and greatly improves the production efficiency of the thermal power company.

[0003] However, once the coal conveying program control system fails, it will cause a serious blow to the thermal power company. First, the failure may cause the interruption of coal transportation, which in turn affects the normal combustion of the boiler and even causes a shutdown accident, resulting in huge economic losses. Secondly, the failure may cause safety accidents, such as friction fire caused by belt deviation and explosion caused by coal drop pipe blockage, which seriously threatens the safety of personnel and the integrity of equipment. In addition, the failure may also cause environmental pollution, such as coal leakage contaminating soil and water sources.

[0004] At present, the existing coal handling program control system fault detection methods mainly rely on manual inspections and simple sensor alarms. These methods have many drawbacks and shortcomings:

[0005] Manual inspections are inefficient and have a high rate of missed inspections: Manual inspections are limited by the number of personnel and inspection frequency, making it difficult to achieve comprehensive, real-time monitoring of the entire coal transportation system and prone to missed inspections of potential faults.

[0006] Sensor alarm function is single and accuracy is poor: Existing sensors mainly alarm for specific faults, such as temperature sensors used to detect belt slippage, but they cannot fully cover all types of faults. At the same time, sensors are easily affected by environmental interference, resulting in poor alarm accuracy.

[0007] Delayed fault diagnosis and slow maintenance response: Existing methods often diagnose and repair faults only after they occur, which makes it impossible to provide early warning and preventive maintenance, resulting in slow maintenance response and affecting system recovery time.

[0008] Serious data silos and difficulty in information sharing: The existing system has isolated data between different parts and lacks a unified data platform and analysis tools, which makes it difficult to share information and achieve global optimization and intelligent decision-making.

[0009] In view of the many drawbacks and shortcomings of the above-mentioned existing coal conveying program control system fault detection method, the present invention aims to provide a fault detection method and system for the coal conveying program control system of a thermal power company, so as to realize comprehensive, real-time and accurate monitoring and early warning of coal conveying program control system faults, improve the production efficiency of the thermal power company, ensure personnel safety and reduce environmental pollution. The present invention constructs an efficient and intelligent fault detection system by introducing advanced sensor technology, data fusion analysis technology and intelligent diagnosis algorithm, which provides a strong guarantee for the stable operation of the thermal power company. Summary of the invention

[0010] In view of the above-mentioned problems, the present invention is proposed.

[0011] Therefore, the problem to be solved by the present invention is that the existing manual inspection is limited by the number of personnel and the inspection frequency, making it difficult to achieve comprehensive and real-time monitoring of the entire coal transportation system, and it is easy to miss potential faults. Existing sensors mainly alarm for specific faults, such as temperature sensors for detecting belt slippage, but they cannot fully cover all types of faults.

[0012] To solve the above technical problems, the present invention provides the following technical solutions: a fault detection method for a coal transportation program control system of a thermal power company, which includes building a fault intelligent diagnosis model based on historical fault data; collecting field data in real time, and judging whether a fault occurs through the fault intelligent diagnosis model; and executing corresponding strategies according to the type and degree of the fault.

[0013] As a preferred solution of the fault detection method of a thermal power company's coal transportation programmable control system described in the present invention, wherein: the construction of a fault intelligent diagnosis model based on historical fault data includes: marking the historical fault data, clarifying the fault type and state corresponding to each segment of data, using signal processing technology to extract features of the corresponding fault type from the original data, using a feature selection algorithm to screen the extracted features, and retaining the features with the largest contribution to fault diagnosis; dividing the labeled data set into a training set and a test set, and training a deep learning model, and using the test set to evaluate the trained model after training, calculating the accuracy, recall rate and F1 score, optimizing the model according to the evaluation results, using the Bayesian optimization method to find the best model parameter combination, repeating the training and evaluation process until the model performance meets the requirements to output the fault intelligent diagnosis model.

[0014] As a preferred solution of a fault detection method for a thermal power company's coal transportation program control system described in the present invention, the real-time collection of field data includes collecting faults to be detected through sensors, and the faults to be detected include transmission belt faults, electrical control system faults and coal drop pipe blockages. The sensors include vibration sensors, temperature sensors, voltage sensors, current sensors, ultrasonic sensors and pressure sensors; the vibration sensor is installed on the belt drive roller, the redirecting roller and the middle support frame of the belt, and is used to detect the vibration of the belt during operation and identify abnormal vibrations; the temperature sensor is arranged at the contact between the belt and the drive roller and the redirecting roller, and is used to monitor the temperature at the contact between the belt and the roller; the current sensor is installed at the main power supply line, the motor and the input end of the electrical equipment, and is used to monitor the operating current of the electrical equipment and identify overload and short circuit faults; the voltage sensor is arranged at the power input end of the electrical system and the power module of the equipment, and is used to detect voltage fluctuations in the electrical system; the ultrasonic sensor is installed on the top or side wall of the coal drop pipe, aligned with the coal flow direction, and is used to detect the coal level height in the coal drop pipe and identify blockages; the pressure sensor is installed at the bottom or side wall of the coal drop pipe, and is used to detect pressure changes in the coal drop pipe.

[0015] As a preferred solution of a fault detection method for a thermal power company's coal transportation programmable control system described in the present invention, the method comprises: determining whether a fault has occurred through a fault intelligent diagnosis model includes, through a data acquisition and transmission module, transmitting data collected by vibration and temperature sensors to a central processing unit in real time, cleaning the data, removing noise and outliers, supplementing missing data, and maintaining data integrity; performing Fourier transform on the vibration waveform, extracting frequency domain features, smoothing the temperature data, eliminating the influence of short-term fluctuations, extracting characteristic values ​​from the vibration data, including the main frequency and energy distribution, and extracting temperature gradient and temperature change rate characteristics from the temperature data; inputting the extracted data features into an intelligent diagnosis algorithm model, the intelligent diagnosis algorithm model performs real-time analysis on the input data, identifies potential fault modes, and the model outputs fault mode recognition results including loose drive belt, bearing wear, and drive belt slippage, and issues fault warning information in real time based on the recognition results.

[0016] As a preferred solution of the fault detection method of a thermal power company's coal transportation programmable control system described in the present invention, the method further comprises: determining whether a fault has occurred through a fault intelligent diagnosis model also includes, through a data acquisition and transmission module, transmitting the data collected by the voltage and current sensors to a central processing unit in real time, and preprocessing the data; extracting the effective value, peak value and waveform mutation characteristics in the collected voltage and current data, and inputting the extracted data characteristics into an intelligent diagnosis algorithm model, the intelligent diagnosis algorithm model performs real-time analysis on the input data, identifies potential fault modes, and the model outputs fault mode recognition results including voltage overload, short circuit and motor failure, and issues fault warning information in real time according to the recognition results.

[0017] As a preferred scheme of the fault detection method of a thermal power company's coal transportation programmable control system described in the present invention, wherein: the judging whether a fault occurs through the fault intelligent diagnosis model also includes, through the data acquisition and transmission module, transmitting the data collected by the ultrasonic sensor and the pressure sensor to the central processing unit in real time, and pre-processing the data; extracting the pressure average value, pressure fluctuation range and pressure change rate characteristics of the data collected by the pressure sensor, and the coal level height average value, coal level height fluctuation range and coal level height change rate characteristics of the data collected by the ultrasonic sensor; inputting the extracted data characteristics into the intelligent diagnosis algorithm model, the intelligent diagnosis algorithm model performs real-time analysis on the input data, identifies potential fault probabilities, and compares the fault probability output by the model with a threshold value. If it is higher than the threshold value, it indicates that the coal drop pipe is blocked, and fault warning information is issued in real time.

[0018] As a preferred solution of the fault detection method of a thermal power company's coal transportation programmable control system described in the present invention, wherein: the fault warning information includes the fault type, fault location and fault degree; the execution of corresponding strategies according to the type and degree of the fault includes converting the data of the corresponding fault into a waveform diagram for visual display through a visual interface, the operator confirms the fault according to the visual display, and records the confirmed fault information in the system database.

[0019] Another object of the present invention is to provide a fault detection system for a coal transportation program control system of a thermal power company, which can detect the parameters of various parts of the coal program control system in real time and provide early fault warning.

[0020] To solve the above technical problems, the present invention provides the following technical solutions: a system of fault detection method for a coal transportation program control system of a thermal power company, comprising: a model building module, a fault judgment module and an execution module; the model building module builds a fault intelligent diagnosis model based on historical fault data; the fault judgment module collects field data in real time, and determines whether a fault occurs through the fault intelligent diagnosis model; the execution module executes corresponding strategies according to the type and degree of the fault.

[0021] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned fault detection method for a coal transportation program control system of a thermal power company when executing the computer program.

[0022] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a fault detection method for a programmable control system of coal transportation in a thermal power company as described above.

[0023] The beneficial effects of the present invention are as follows: the existing manual inspection is limited by the number of personnel and inspection frequency, making it difficult to achieve comprehensive and real-time monitoring of the entire coal transportation system, and it is easy to miss potential faults. In addition, the existing sensors mainly alarm for specific faults, such as temperature sensors for detecting belt slippage, but cannot fully cover all types of faults.

[0024] The present invention performs multi-dimensional feature analysis based on multiple groups of sensors, and realizes a comprehensive analysis of coal level height data by extracting basic features, statistical features, time domain features, frequency domain features and other features, thereby significantly improving the accuracy of fault detection.

[0025] Using deep learning networks to train and classify feature data can more accurately identify fault types such as blockage and coal shortage, reducing false alarms and missed alarms. Real-time coal level data is collected and features are quickly extracted to achieve real-time monitoring of the operation status of the coal drop pipe. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0027] Figure 1 This is a flow chart of a fault detection method for a coal transportation program control system of a thermal power company in Example 1.

[0028] Figure 2 This is a module structure diagram of a fault detection system of a thermal power company's coal transportation program control system in Example 2. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0031] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and which provides a method for detecting a fault in a program-controlled coal transportation system of a thermal power company, including: Figure 1 As shown:

[0032] S1. Build an intelligent fault diagnosis model based on historical fault data.

[0033] Label the historical fault data, clarify the fault type and status corresponding to each segment of data, use signal processing technology to extract the features of the corresponding fault type from the original data, use feature selection algorithm to screen the extracted features, and retain the features with the greatest contribution to fault diagnosis;

[0034] The labeled data set is divided into a training set and a test set, and a deep learning model is trained. After training, the trained model is evaluated using the test set, and the accuracy, recall rate, and F1 score are calculated. The model is optimized based on the evaluation results, and the Bayesian optimization method is used to find the best parameter combination for the model. The training and evaluation process is repeated until the model performance meets the requirements and the fault intelligent diagnosis model is output.

[0035] S2. Collect on-site data in real time and determine whether a fault occurs through the intelligent fault diagnosis model.

[0036] Real-time collection of field data specifically includes collecting faults to be detected through sensors. The faults to be detected include transmission belt failures, electrical control system failures and coal drop pipe blockages. The sensors include vibration sensors, temperature sensors, voltage sensors, current sensors, ultrasonic sensors and pressure sensors.

[0037] The vibration sensor is installed on the belt drive roller, the redirecting roller and the middle support frame of the belt to detect the vibration of the belt during operation and identify abnormal vibration; the temperature sensor is arranged at the contact point between the belt and the drive roller and the redirecting roller to monitor the temperature of the contact point between the belt and the roller.

[0038] Current sensors are installed at the main power supply line, motor and electrical equipment input terminals to monitor the operating current of electrical equipment and identify overload and short circuit faults; voltage sensors are arranged at the electrical system power input terminal and the equipment power module to detect voltage fluctuations in the electrical system.

[0039] The ultrasonic sensor is installed on the top or side wall of the coal drop pipe, aligned with the direction of coal flow, and is used to detect the coal level in the coal drop pipe and identify blockage conditions; the pressure sensor is installed at the bottom or side wall of the coal drop pipe and is used to detect pressure changes in the coal drop pipe.

[0040] When judging the transmission belt fault, the data collected by the vibration and temperature sensors are transmitted to the central processing unit in real time through the data acquisition and transmission module to clean the data, remove noise and outliers, supplement missing data, and maintain data integrity.

[0041] Perform Fourier transform on the vibration waveform to extract frequency domain features, smooth the temperature data to eliminate the impact of short-term fluctuations, extract characteristic values ​​from the vibration data, including the main frequency and energy distribution, and extract temperature gradient and temperature change rate characteristics from the temperature data.

[0042] The extracted data features are input into the intelligent diagnosis algorithm model. The intelligent diagnosis algorithm model analyzes the input data in real time and identifies potential fault modes. The model outputs fault mode recognition results including loose transmission belt, bearing wear and transmission belt slippage. According to the recognition results, fault warning information is issued in real time.

[0043] When the electrical control system fails, the data collected by the voltage and current sensors are transmitted to the central processing unit in real time through the data acquisition and transmission module to pre-process the data.

[0044] The effective value, peak value and waveform mutation features in the collected voltage and current data are extracted, and the extracted data features are input into the intelligent diagnosis algorithm model. The intelligent diagnosis algorithm model analyzes the input data in real time and identifies potential fault modes. The model outputs fault mode recognition results including voltage overload, short circuit and motor failure. According to the recognition results, fault warning information is issued in real time.

[0045] When judging whether the coal drop pipe is blocked, the data collected by the ultrasonic sensor and the pressure sensor are transmitted to the central processing unit in real time through the data acquisition and transmission module to pre-process the data.

[0046] The pressure average value, pressure fluctuation range and pressure change rate characteristics of the data collected by the pressure sensor, and the coal level height average value, coal level height fluctuation range and coal level height change rate characteristics of the data collected by the ultrasonic sensor are extracted.

[0047] The extracted data features are input into the intelligent diagnosis algorithm model. The intelligent diagnosis algorithm model analyzes the input data in real time, identifies potential fault probabilities, and compares the fault probability output by the model with the threshold. If it is higher than the threshold, it indicates that the coal drop pipe is blocked, and fault warning information is issued in real time.

[0048] S3. Execute corresponding strategies according to the type and severity of the failure.

[0049] Fault warning information includes fault type, fault location and fault severity. The corresponding fault data is converted into a waveform graph for visual display through a visual interface. The operator confirms the fault based on the visual display and records the confirmed fault information in the system database.

[0050] Example 2, reference Figure 2, which is the second embodiment of the present invention, and is different from the first embodiment in that: a system of a fault detection method for a coal transportation program control system of a thermal power company, comprising a model building module 100, a fault judgment module 200 and an execution module 300; the model building module 100 builds a fault intelligent diagnosis model based on historical fault data; the fault judgment module 200 collects field data in real time, and determines whether a fault occurs through the fault intelligent diagnosis model; the execution module 300 executes a corresponding strategy according to the type and degree of the fault.

[0051] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0052] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0053] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0054] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for fault detection of a program-controlled coal handling system of a thermal power company, characterized in that: include, Build intelligent fault diagnosis model based on historical fault data; Collect field data in real time and determine whether a fault has occurred through the intelligent fault diagnosis model; Execute corresponding strategies according to the type and severity of the failure.

2. A method for detecting faults in a program-controlled coal handling system of a thermal power company as claimed in claim 1, characterized in that: The construction of the intelligent fault diagnosis model based on historical fault data includes labeling the historical fault data, clarifying the fault type and state corresponding to each segment of data, extracting features corresponding to the fault type from the original data using signal processing technology, and screening the extracted features using a feature selection algorithm to retain the features with the greatest contribution to fault diagnosis; The labeled data set is divided into a training set and a test set, and a deep learning model is trained. After training, the trained model is evaluated using the test set, and the accuracy, recall rate, and F1 score are calculated. The model is optimized based on the evaluation results, and the Bayesian optimization method is used to find the best parameter combination for the model. The training and evaluation process is repeated until the model performance meets the requirements and the fault intelligent diagnosis model is output.

3. A method for detecting a fault in a program-controlled coal handling system of a thermal power company as claimed in claim 2, characterized in that: The real-time collection of field data includes collecting faults to be detected through sensors, the faults to be detected include transmission belt faults, electrical control system faults and coal drop pipe blockage, and the sensors include vibration sensors, temperature sensors, voltage sensors, current sensors, ultrasonic sensors and pressure sensors; The vibration sensor is installed on the belt drive roller, the redirection roller and the middle support frame of the belt to detect the vibration of the belt during operation and identify abnormal vibration; the temperature sensor is arranged at the contact point between the belt and the drive roller and the redirection roller to monitor the temperature at the contact point between the belt and the roller; The current sensor is installed at the main power supply line, motor and electrical equipment input end to monitor the operating current of the electrical equipment and identify overload and short circuit faults; the voltage sensor is arranged at the electrical system power input end and the equipment power module to detect the voltage fluctuation of the electrical system; The ultrasonic sensor is installed on the top or side wall of the coal drop pipe, aligned with the direction of coal flow, and is used to detect the coal level height in the coal drop pipe and identify blockage conditions; the pressure sensor is installed at the bottom or side wall of the coal drop pipe and is used to detect pressure changes in the coal drop pipe.

4. A method for detecting faults in a program-controlled coal handling system of a thermal power company as claimed in claim 3, characterized in that: Determining whether a fault occurs by using the fault intelligent diagnosis model includes transmitting data collected by the vibration and temperature sensors to the central processing unit in real time through the data acquisition and transmission module, cleaning the data, removing noise and abnormal values, supplementing missing data, and maintaining data integrity; Perform Fourier transform on the vibration waveform to extract frequency domain features, smooth the temperature data to eliminate the impact of short-term fluctuations, extract characteristic values ​​from the vibration data, including the main frequency and energy distribution, and extract temperature gradient and temperature change rate characteristics from the temperature data; The extracted data features are input into the intelligent diagnosis algorithm model. The intelligent diagnosis algorithm model analyzes the input data in real time and identifies potential fault modes. The model outputs fault mode recognition results including loose transmission belt, bearing wear and transmission belt slippage. According to the recognition results, fault warning information is issued in real time.

5. A method for detecting faults in a program-controlled coal handling system of a thermal power company as claimed in claim 4, characterized in that: The determining whether a fault occurs by the fault intelligent diagnosis model further includes, through the data acquisition and transmission module, transmitting the data collected by the voltage and current sensors to the central processing unit in real time to pre-process the data; The effective value, peak value and waveform mutation features in the collected voltage and current data are extracted, and the extracted data features are input into the intelligent diagnosis algorithm model. The intelligent diagnosis algorithm model analyzes the input data in real time and identifies potential fault modes. The model outputs fault mode recognition results including voltage overload, short circuit and motor failure. According to the recognition results, fault warning information is issued in real time.

6. A method for detecting faults in a program-controlled coal handling system of a thermal power company as claimed in claim 5, characterized in that: The determining whether a fault occurs by the fault intelligent diagnosis model further includes, through the data acquisition and transmission module, transmitting the data collected by the ultrasonic sensor and the pressure sensor to the central processing unit in real time to pre-process the data; Extract the pressure average value, pressure fluctuation range and pressure change rate characteristics of the data collected by the pressure sensor, and the coal level height average value, coal level height fluctuation range and coal level height change rate characteristics of the data collected by the ultrasonic sensor; The extracted data features are input into the intelligent diagnosis algorithm model. The intelligent diagnosis algorithm model analyzes the input data in real time, identifies potential fault probabilities, and compares the fault probability output by the model with the threshold. If it is higher than the threshold, it indicates that the coal drop pipe is blocked, and fault warning information is issued in real time.

7. A method for detecting faults in a program-controlled coal handling system of a thermal power company as claimed in claim 6, characterized in that: The fault warning information includes the fault type, fault location and fault degree; The executing of corresponding strategies according to the type and degree of the fault includes converting the corresponding fault data into a waveform diagram for visual display through a visual interface, the operator confirming the fault according to the visual display, and recording the confirmed fault information in the system database.

8. A system using the fault detection method of a thermal power company coal transportation program control system as claimed in any one of claims 1 to 7, characterized in that: It comprises a model building module (100), a fault judgment module (200) and an execution module (300); The model building module (100) builds a fault intelligent diagnosis model based on historical fault data; The fault judgment module (200) collects on-site data in real time and judges whether a fault occurs through a fault intelligent diagnosis model; The execution module (300) executes corresponding strategies according to the type and degree of the fault.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a fault detection method for a coal transportation program control system of a thermal power company described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a fault detection method for a coal transportation program control system of a thermal power company described in any one of claims 1 to 7 are implemented.

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