WO2025218163A1 - Fault identification method and system based on operating condition of continuous system in open-pit mine - Google Patents

Abstract

Disclosed in the present invention are a fault identification method and system based on the operating condition of a continuous system in an open-pit mine. The method comprises: establishing a simulation model for a continuous system in an open-pit mine, and monitoring device data in real time; pre-processing the data, and performing potential fault identification; on the basis of a system pressure change rate and an adaptive adjustment mechanism of the continuous system in the open-pit mine, optimizing fault identification output; and designing a fault response and real-time adjustment mechanism to prevent fault occurrence. The fault identification method and system based on the operating condition of a continuous system in an open-pit mine provided in the present invention improve the speed and accuracy of fault diagnosis, particularly the rapid processing capability for complex data relationships. A breakthrough is achieved in fault prevention, thus enabling early warning and adaptive adjustment to be implemented before faults occur. Thus, the stability and safety of continuous systems in open-pit mines are significantly improved, and a more efficient technical solution is provided for operation management of modern open-pit mines.

Description

A fault identification method and system based on the operation status of the continuous system of the open-pit mine Technical Field

The present invention relates to the technical field, and in particular to a fault identification method and system based on the operation status of a continuous system in an open-pit mine.

Background Art

Continuous system operation in open-pit mines is a highly complex and integrated process, involving the collaborative work of multiple devices and technologies. These devices often operate in harsh environments and need to operate continuously for long periods of time, resulting in frequent equipment failures, which affect production efficiency and safety. Existing fault diagnosis methods often rely on traditional physical sensor data and subsequent fault analysis, which not only has a long response time but also has difficulty processing complex data relationships and meeting the needs of real-time preventive maintenance. Existing fault diagnosis systems often ignore the impact of environmental factors on equipment and lack targeted optimization in data preprocessing and fault identification algorithms, which limits the system's fault handling capabilities and stability.

Summary of the Invention

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

Therefore, the technical problem addressed by the present invention is: by establishing a simulation model and conducting real-time monitoring, the comprehensiveness and real-time nature of device data are ensured. Highly specialized feature extraction functions and adaptive adjustment mechanisms enable more accurate and efficient fault identification. Fault response and real-time adjustment mechanisms not only enable rapid problem resolution but also proactively adjust system parameters before potential faults occur, thereby preventing them from occurring.

To solve the above technical problems, the present invention provides the following technical solutions: a fault identification method based on the operation status of an open-pit mine continuous system, comprising: establishing an open-pit mine continuous system simulation model and monitoring equipment data in real time.

Preprocess the data and identify potential faults.

Optimize the fault identification output based on the system pressure change rate and the adaptive adjustment mechanism of the open-pit mine continuous system.

Design fault response and real-time adjustment mechanisms to avoid failures.

As a preferred solution of the fault identification method based on the operation status of the open-pit mine continuous system described in the present invention, the establishment of an open-pit mine continuous system simulation model and real-time monitoring of equipment data include creating a system simulation model that comprehensively simulates the operation of the open-pit mine, including all key equipment and their interactions.

Simulations include equipment operation, environmental impacts, and failure scenarios.

Equipment operation simulates the operation of various equipment under normal, high load and potential fault conditions.

Environmental impact simulates the impact of different environmental conditions on equipment performance.

Fault scenarios are intentionally designed fault scenario simulations, including mechanical failures, electrical failures, and operational errors to test the system.

Real-time monitoring equipment data includes equipment status data, operating parameters and environmental monitoring data.

Equipment status data includes equipment vibration data, temperature readings, current and voltage.

Operating parameters include equipment operating speed, load size, and operating frequency.

Environmental monitoring data includes temperature, humidity and wind speed.

As a preferred solution of the fault identification method based on the operation status of the open-pit mine continuous system described in the present invention, the data preprocessing includes data cleaning, data standardization and noise filtering.

The data cleaning objects are invalid, erroneous and incomplete data records. For incomplete data points, the average value of the previous and next data points is used to fill them, and invalid and erroneous data points are directly eliminated.

Data standardization eliminates the effects of different dimensions and magnitudes, making the data at the same magnitude, expressed as:

Among them, X represents the original data, μ represents the mean value of the data, and σ represents the standard deviation of the data, which indicates the fluctuation size of the data.

Use a low-pass filter to remove noise from the data and improve data quality.

As a preferred embodiment of the fault identification method based on the operation of the open-pit mine continuous system of the present invention, the potential fault identification includes: in the open-pit mine system, due to the complexity of the equipment, a single data source may not be sufficient to fully predict the fault. By integrating the characteristics of multiple data, a highly specialized feature extraction function is established, and the vibration signal analysis, temperature change trend, speed periodicity and current anomaly index extraction are combined to fully reflect the operating status of the equipment. The feature extraction formula is expressed as follows:

Where T represents time, ω represents the adjustment coefficient, V(t) represents the device vibration data, α represents the device vibration index parameter, T(t) represents the device temperature data, β represents the device temperature adjustment coefficient, λ represents the device speed adjustment coefficient, S(t) represents the device speed data, μ represents the current adjustment coefficient, C(t) represents the current data, and ν represents the current index parameter.

The occurrence of faults is not only related to the intrinsic characteristics of the system, but also affected by the external environment and operating conditions. Dynamic threshold determination is used to adjust the threshold according to real-time environment and operating data to respond to changes in system status, which is expressed as:

Where ζ represents the baseline threshold, which is set based on the system's historical operating data. κ represents the adjustment factor, which adjusts the threshold based on the deviation of the eigenvalue. ξ represents the exponential adjustment parameter.

Combining the results of feature extraction and dynamic threshold adjustment, the fault identification formula is obtained:

Among them, Φ(x(t)) represents the feature value extracted from real-time data, and Θ(Φ) represents the threshold value dynamically determined according to the current feature.

As a preferred solution of the fault identification method based on the operation status of the open-pit mine continuous system described in the present invention, wherein: the system pressure change rate and the adaptive adjustment mechanism of the open-pit mine continuous system include: in the open-pit mine continuous system, heavy mechanical equipment such as crushers and conveyor belts are subjected to huge mechanical pressure. The pressure state of the system is not only affected by the operating conditions of the equipment, but also changes due to environmental factors and external factors such as material accumulation. Rapid changes in system pressure often indicate abnormal equipment load or potential failure.

Abnormal changes in system pressure can trigger the adaptive adjustment mechanism to automatically adjust the conveyor belt speed and crusher working intensity to reduce wear on the equipment and prevent potential failures.

First, calculate the system pressure change rate, which is expressed as:

Where ΔP(t) represents the pressure difference within a specified time period, and Δt represents the time interval.

The system pressure change rate is used in the adaptive adjustment mechanism to help the system automatically adjust the fault identification parameters according to the current operating status. The adaptive adjustment function is expressed as:

Where μ _RCSP represents the mean of the historical system pressure change rate and is used for baseline adjustment. σ _RCSP represents the standard deviation of the historical system pressure change rate and is used for normalization and sensitivity adjustment. ω represents the weighting factor used to adjust the sensitivity of adaptive control.

A(RCSP) is introduced into the fault identification formula as a regulating factor to dynamically adjust the sensitivity of fault identification and optimize the fault identification output. The final integrated fault identification formula is expressed as:

Where Θ(Φ) represents the original fault identification threshold based on the feature extraction value Φ(x(t)), and the threshold is adjusted according to the real-time status of the system by multiplying it by the adaptive adjustment factor A(RCSP(t)).

As a preferred solution of the fault identification method based on the operation of the open-pit mine continuous system of the present invention, the fault response and real-time adjustment mechanism includes first comprehensively evaluating the accuracy of the fault identification output result, and defining a theoretical expected result model based on the normal operating parameters of the system and known faults, expressed as:

Where _ai represents the intensity of the impact of different fault types on the system; _bi represents the speed at which different fault types affect the system; and c represents the baseline offset, which represents the normal operating level of the system in a fault-free state.

Compare the actual output of F(t) with the theoretical value of Θ(t) to evaluate the performance and deviation of F(t) in actual situations, expressed as:

Where T represents the evaluation time window, D(t) represents the overall deviation between F(t) and Θ(t), and the smaller D(t) is, the more accurate F(t) is.

Convert the deviation into an accuracy score and map the deviation value to [0,1] through accuracy evaluation, which is expressed as:

Where D _max represents the theoretical maximum deviation value.

As a preferred solution of the fault identification method based on the operation status of the open-pit mine continuous system described in the present invention, the fault response and real-time adjustment mechanism also includes that when A(t) is lower than 0.5, the fault identification accuracy is considered insufficient.

Initiate a comprehensive system review, including a hardware check and software audit, to determine if there are any undetected faults and data acquisition issues.

Re-evaluate the dataset used to train the model, checking its completeness, accuracy, and timeliness. Clean outliers and noise from the data to ensure that the data quality supports accurate fault prediction.

Adjust the parameters of the fault identification formula, increase the number of fault case samples by increasing the number of fault scenario simulations, increase data sampling in poorly performing areas, and recalculate based on the new data.

When A(t) is between 0.5 and 0.75, the fault identification accuracy is good, and the data re-collection and model retraining procedures are initiated to collect more data from the inaccurately predicted areas and recalculate based on the new data.

When A(t) is higher than 0.75, the model performs well.

A fault identification system based on the operation status of the continuous system of an open-pit mine, characterized by comprising:

The data acquisition module establishes a continuous system simulation model for open-pit mines and monitors equipment data in real time.

The preprocessing module preprocesses the data and identifies potential faults.

The fault identification module optimizes the fault identification output according to the system pressure change rate and the adaptive adjustment mechanism of the open-pit mine continuous system.

Fault response module, designs fault response and real-time adjustment mechanisms to avoid faults.

A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

The beneficial effects of this invention include improved speed and accuracy of fault diagnosis, particularly the ability to rapidly process complex data relationships. It also achieves a breakthrough in fault prevention, enabling early warning and adaptive adjustments before a fault occurs. It significantly improves the stability and safety of continuous systems in open-pit mines, providing a more efficient technical solution for modern open-pit mine operations and management.

BRIEF DESCRIPTION OF THE DRAWINGS

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. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

FIG1 is an overall flow chart of a method and system provided by the first embodiment of the present invention.

DETAILED DESCRIPTION

To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

Example 1

1 , an embodiment of the present invention provides a method, including:

S1: Establish a continuous system simulation model for open pit mines and monitor equipment data in real time.

Establish a continuous system simulation model for open pit mines and monitor equipment data in real time, including creating a system simulation model that fully simulates open pit mine operations, including all key equipment and their interactions.

Simulations include equipment operation, environmental impacts, and failure scenarios.

Equipment operation simulates the operation of various equipment under normal, high load and potential fault conditions.

Environmental impact simulates the impact of different environmental conditions on equipment performance.

Fault scenarios are intentionally designed fault scenario simulations, including mechanical failures, electrical failures, and operational errors to test the system.

Real-time monitoring equipment data includes equipment status data, operating parameters and environmental monitoring data.

Equipment status data includes equipment vibration data, temperature readings, current and voltage.

Operating parameters include equipment operating speed, load size, and operating frequency.

Environmental monitoring data includes temperature, humidity and wind speed.

S2: Preprocess the data and identify potential faults.

Preprocessing includes data cleaning, data standardization and noise filtering.

The data cleaning objects are invalid, erroneous and incomplete data records. For incomplete data points, the average value of the previous and next data points is used to fill them, and invalid and erroneous data points are directly eliminated.

Data standardization eliminates the impact of different dimensions and magnitudes, making the data at the same magnitude, expressed as:

Among them, X represents the original data, μ represents the mean value of the data, and σ represents the standard deviation of the data, which indicates the fluctuation size of the data.

Use a low-pass filter to remove noise from the data and improve data quality.

In open-pit mine systems, due to the complexity of equipment, a single data source may not be sufficient to fully predict faults. By integrating the characteristics of multiple data, a highly specialized feature extraction function is established. This function combines vibration signal analysis, temperature change trends, speed periodicity, and current anomaly indicators to fully reflect the operating status of the equipment. The feature extraction formula is expressed as:

Where T represents time, ω represents the adjustment coefficient, V(t) represents the device vibration data, α represents the device vibration index parameter, T(t) represents the device temperature data, β represents the device temperature adjustment coefficient, λ represents the device speed adjustment coefficient, S(t) represents the device speed data, μ represents the current adjustment coefficient, C(t) represents the current data, and ν represents the current index parameter.

It should be noted that the design of the feature extraction formula not only enhances the specificity and accuracy of the fault identification model, but also significantly improves the model's practicality and predictive reliability by incorporating specific operating parameters into the calculation. By combining multi-source data and fusing features through complex mathematical operations, this fault identification method can comprehensively consider multiple aspects of equipment information and detect potential faults early, providing a scientific basis and a powerful tool for open-pit mine maintenance.

The occurrence of faults is not only related to the intrinsic characteristics of the system, but also affected by the external environment and operating conditions. Dynamic threshold determination is used to adjust the threshold according to real-time environment and operating data to respond to changes in system status, which is expressed as:

Where ζ represents the baseline threshold, which is set based on the system's historical operating data. κ represents the adjustment factor, which adjusts the threshold based on the deviation of the eigenvalue. ξ represents the exponential adjustment parameter.

Combining the results of feature extraction and dynamic threshold adjustment, the fault identification formula is obtained:

Among them, Φ(x(t)) represents the feature value extracted from real-time data, and Θ(Φ) represents the threshold value dynamically determined according to the current feature.

S3: Optimize the fault identification output based on the system pressure change rate and the adaptive adjustment mechanism of the open-pit mine continuous system.

In open-pit mine continuous systems, heavy machinery such as crushers and conveyor belts are subject to enormous mechanical pressure. The system's pressure state is not only affected by the equipment's operating conditions, but also by external factors such as environmental factors and material accumulation. Rapid changes in system pressure often indicate abnormal equipment load or potential failure.

Abnormal changes in system pressure can trigger the adaptive adjustment mechanism to automatically adjust the conveyor belt speed and crusher working intensity to reduce wear on the equipment and prevent potential failures.

It should be noted that abnormal changes in system pressure can trigger adaptive regulation mechanisms, such as automatically adjusting the conveyor belt speed or crusher working intensity, to reduce wear on the equipment and prevent potential failures.

By monitoring RCSP in real time, the system can adjust maintenance plans in advance and prioritize equipment parts that may have problems due to abnormal pressure.

First, calculate the system pressure change rate, which is expressed as:

Where ΔP(t) represents the pressure difference within a specified time period, and Δt represents the time interval.

The system pressure change rate is used in the adaptive adjustment mechanism to help the system automatically adjust the fault identification parameters according to the current operating status. The adaptive adjustment function is expressed as:

Where μ _RCSP represents the mean of the historical system pressure change rate and is used for baseline adjustment. σ _RCSP represents the standard deviation of the historical system pressure change rate and is used for normalization and sensitivity adjustment. ω represents the weighting factor used to adjust the sensitivity of adaptive control.

It should be noted that introducing RCSP as a parameter for optimizing fault identification makes the system more sensitive to pressure-related faults and dynamically adjusts the fault identification mechanism, improving diagnostic accuracy. This adaptive optimization approach ensures that the open-pit mine system maintains optimal operating efficiency under varying operating conditions, responds promptly to potential fault risks, and minimizes unplanned downtime.

A(RCSP) is introduced into the fault identification formula as a regulating factor to dynamically adjust the sensitivity of fault identification and optimize the fault identification output. The final integrated fault identification formula is expressed as:

Where Θ(Φ) represents the original fault identification threshold based on the feature extraction value Φ(x(t)), and the threshold is adjusted according to the real-time status of the system by multiplying it by the adaptive adjustment factor A(RCSP(t)).

S4: Design fault response and real-time adjustment mechanisms to avoid failures.

First, the accuracy of the fault identification output needs to be comprehensively evaluated. Based on the normal operating parameters of the system and the theoretical expected result model of known fault definitions, it can be expressed as:

Where _ai represents the intensity of the impact of different fault types on the system; _bi represents the speed at which different fault types affect the system; and c represents the baseline offset, which represents the normal operating level of the system in a fault-free state.

Compare the actual output of F(t) with the theoretical value of Θ(t) to evaluate the performance and deviation of F(t) in actual situations, expressed as:

Where T represents the evaluation time window, D(t) represents the overall deviation between F(t) and Θ(t), and the smaller D(t) is, the more accurate F(t) is.

Convert the deviation into an accuracy score and map the deviation value to [0,1] through accuracy evaluation, which is expressed as:

Where D _max represents the theoretical maximum deviation value.

When A(t) is lower than 0.5, the fault identification accuracy is considered insufficient.

Initiate a comprehensive system review, including a hardware check and software audit, to determine if there are any undetected faults and data acquisition issues.

Re-evaluate the dataset used to train the model, checking its completeness, accuracy, and timeliness. Clean outliers and noise from the data to ensure that the data quality supports accurate fault prediction.

Adjust the parameters of the fault identification formula, increase the number of fault case samples by increasing the number of fault scenario simulations, increase data sampling in poorly performing areas, and recalculate based on the new data.

When A(t) is between 0.5 and 0.75, the fault identification accuracy is good, and the data re-collection and model retraining procedures are initiated to collect more data from the inaccurately predicted areas and recalculate based on the new data.

When A(t) is higher than 0.75, the model performs well.

The above embodiment also includes a fault identification system based on the operation status of the open-pit mine continuous system, specifically:

The data acquisition module establishes a continuous system simulation model for open-pit mines and monitors equipment data in real time.

The preprocessing module preprocesses the data and identifies potential faults.

The fault identification module optimizes the fault identification output according to the system pressure change rate and the adaptive adjustment mechanism of the open-pit mine continuous system.

Fault response module, designs fault response and real-time adjustment mechanisms to avoid faults.

The computer device may be a server. The computer device includes a processor, a memory, an input/output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected via a system bus, and the communication interface is connected to the system bus via the input/output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data cluster data of the power monitoring system. The input/output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method is implemented.

Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided in this application may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc., but are not limited to these.

Example 2

One embodiment of the present invention provides a fault identification method and system based on the operation status of a continuous system in an open-pit mine. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation comparative experiments.

The fault identification method was tested in an open-pit mine. First, a detailed simulation model of the open-pit mine's continuous system was built, including all key equipment and their interactions. Various normal, high-load, and potential fault conditions were simulated. Sensors and data acquisition technology were used to monitor equipment status, operating parameters, and environmental data in real time. This data included vibration sensors, temperature sensors, and current and voltage monitors.

The collected data was cleaned and standardized through normalization, missing value filling, and low-pass filtering to reduce noise and inconsistencies. By integrating vibration, temperature, speed periodicity, and current anomaly data, feature extraction functions were established that can reflect the operating status of the equipment and identify potential faults.

Using feature extraction and a dynamic threshold adjustment formula, potential faults were identified. An adaptive adjustment mechanism was implemented based on the system pressure change rate and real-time environmental data. The effectiveness of the optimization method was evaluated by comparing the fault responses of the experimental model with those of existing technologies. The experimental results are shown in Table 1.

Table 1 Experimental results

The table shows that the invention in the examples demonstrates significant improvements in both response time and fault identification rate. In the area of equipment vibration monitoring, the invention reduces response time from 10 seconds to 2 seconds and increases fault identification rate from 80% to 95%. This significant improvement is primarily attributed to the application of real-time monitoring technology and highly specialized feature extraction formulas, which enable faster and more accurate fault identification.

In terms of temperature change monitoring, the response time is significantly reduced and the fault recognition rate is increased by 20 percentage points. In addition, the invention in the embodiment significantly increases the frequency of preventive adjustments through the adaptive adjustment mechanism, thereby achieving timely adjustments before faults occur.

Compared to existing technologies, the present invention not only improves the speed and accuracy of fault diagnosis, but also significantly enhances system stability and safety through a real-time adjustment mechanism. For example, while existing technologies only perform preventative adjustments to current anomalies annually, the present invention enables monthly adjustments, which is crucial for preventing faults caused by current anomalies.

Overall, the application of the invention significantly improves the fault diagnosis capabilities of continuous open-pit mine systems, reduces system downtime, and improves overall operational efficiency. These advantages provide a more reliable and efficient fault diagnosis method for continuous open-pit mine system operation, demonstrating the innovation and superiority of the invention in practical applications.

It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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 (10)

A fault identification method based on the operation status of a continuous system in an open-pit mine is characterized by comprising: Establish a continuous system simulation model for open pit mines and monitor equipment data in real time; Pre-process data and identify potential faults; Optimize fault identification output based on the system pressure change rate and the adaptive adjustment mechanism of the open-pit mine continuous system; Design fault response and real-time adjustment mechanisms to avoid failures. The fault identification method based on the operation of an open-pit mine continuous system according to claim 1 is characterized in that: said establishing a simulation model of the open-pit mine continuous system and monitoring equipment data in real time comprises creating a system simulation model that comprehensively simulates the operation of the open-pit mine, including all key equipment and their interactions; Simulation operations include equipment operation, environmental impacts, and failure scenarios; Equipment operation simulates the operation of various equipment under normal, high-load and potential fault conditions; Environmental impact is to simulate the impact of different environmental conditions on equipment performance; Fault scenarios are intentionally designed fault scenario simulations, including mechanical failures, electrical failures, and operational errors to test the system; Real-time monitoring of equipment data including equipment status data, operating parameters and environmental monitoring data; Equipment status data includes equipment vibration data, temperature readings, current and voltage; Operating parameters include equipment operating speed, load size, and operating frequency; Environmental monitoring data includes temperature, humidity and wind speed. The fault identification method based on the operation status of the open-pit mine continuous system according to claim 2 is characterized in that: the preprocessing of the data includes data cleaning, data standardization and noise filtering; The data cleaning targets invalid, erroneous and incomplete data records. For incomplete data points, the average value of the previous and next data points is used to fill them. For invalid and erroneous data points, they are directly removed. Data standardization eliminates the effects of different dimensions and magnitudes, making the data at the same magnitude, expressed as:
Among them, X represents the original data, μ represents the data mean, σ represents the standard deviation of the data, and represents the fluctuation of the data; Use a low-pass filter to remove noise from the data and improve data quality. The fault identification method based on the operation status of the open-pit mine continuous system according to claim 3 is characterized in that: the potential fault identification includes: in the open-pit mine system, due to the complexity of the equipment, a single data source may not be sufficient to fully predict the fault, by integrating the characteristics of multiple data to establish a highly specialized feature extraction function, combined with vibration signal analysis, temperature change trend, speed periodicity and current anomaly index extraction to fully reflect the operating status of the equipment, the feature extraction formula is expressed as:
Where T represents time, ω represents the adjustment coefficient, V(t) represents the device vibration data, α represents the device vibration index parameter, T(t) represents the device temperature data, β represents the device temperature adjustment coefficient, λ represents the device speed adjustment coefficient, S(t) represents the device speed data, μ represents the current adjustment coefficient, C(t) represents the current data, and ν represents the current index parameter; The occurrence of faults is not only related to the intrinsic characteristics of the system, but also affected by the external environment and operating conditions. Dynamic threshold determination is used to adjust the threshold according to real-time environment and operating data to respond to changes in system status, which is expressed as:
Among them, ζ represents the baseline threshold, which is set according to the system's historical operation data; κ represents the adjustment factor, which adjusts the threshold according to the deviation of the characteristic value; ξ represents the exponential adjustment parameter; Combining the results of feature extraction and dynamic threshold adjustment, the fault identification formula is obtained:
Among them, Φ(x(t)) represents the feature value extracted from real-time data, and Θ(Φ) represents the threshold value dynamically determined according to the current feature. The fault identification method based on the operation of an open-pit mine continuous system according to claim 4 is characterized in that: the system pressure change rate and the adaptive adjustment mechanism of the open-pit mine continuous system include: in the open-pit mine continuous system, heavy machinery such as crushers and conveyor belts are subjected to huge mechanical pressure, and the pressure state of the system is not only affected by the operating conditions of the equipment, but also changes due to environmental factors and external factors such as material accumulation. Rapid changes in system pressure often indicate abnormal equipment load or potential failure; Abnormal changes in system pressure can trigger the adaptive adjustment mechanism to automatically adjust the conveyor belt speed and crusher working intensity to reduce wear on the equipment and prevent potential failures; First, calculate the system pressure change rate, which is expressed as:
Where ΔP(t) represents the pressure difference within a specified time period, and Δt represents the time interval; The system pressure change rate is used in the adaptive adjustment mechanism to help the system automatically adjust the fault identification parameters according to the current operating status. The adaptive adjustment function is expressed as:
Wherein, μ _RCSP represents the mean of the historical system pressure change rate, which is used for baseline adjustment; σ _RCSP represents the standard deviation of the historical system pressure change rate, which is used for normalization and sensitivity adjustment; ω represents the weight factor, which is used to adjust the sensitivity of adaptive regulation; A(RCSP) is introduced into the fault identification formula as a regulating factor to dynamically adjust the sensitivity of fault identification and optimize the fault identification output. The final integrated fault identification formula is expressed as:
Where Θ(Φ) represents the original fault identification threshold based on the feature extraction value Φ(x(t)), and the threshold is adjusted according to the real-time status of the system by multiplying it by the adaptive adjustment factor A(RCSP(t)). The fault identification method based on the operation status of the open-pit mine continuous system according to claim 5 is characterized in that: the fault response and real-time adjustment mechanism includes first comprehensively evaluating the accuracy of the fault identification output result, and defining a theoretical expected result model based on the normal operating parameters of the system and known faults, expressed as:
Where _ai represents the intensity of the impact of different fault types on the system; _bi represents the speed at which different fault types affect the system; c represents the baseline offset, which represents the normal operating level of the system in a fault-free state; Compare the actual output of F(t) with the theoretical value of Θ(t) to evaluate the performance and deviation of F(t) in actual situations, expressed as:
Where T represents the evaluation time window, D(t) represents the overall deviation between F(t) and Θ(t), and the smaller D(t) is, the more accurate F(t) is. Convert the deviation into an accuracy score and map the deviation value to [0,1] through accuracy evaluation, which is expressed as:
Where D _max represents the theoretical maximum deviation value. The fault identification method based on the operation status of the open-pit mine continuous system according to claim 6 is characterized in that: the fault response and real-time adjustment mechanism further includes: when A(t) is lower than 0.5, it is considered that the fault identification accuracy is insufficient; Initiate a comprehensive system review, including hardware inspection and software audit, to determine if there are any undetected faults and data acquisition issues; Re-evaluate the dataset used to train the model, checking its completeness, accuracy, and timeliness; clean up outliers and noise in the data to ensure that the data quality can support accurate fault prediction; Adjust the parameters of the fault identification formula to increase the number of fault case samples by increasing the number of fault scenario simulations, increase data sampling in poorly performing areas, and recalculate based on the new data; When A(t) is between 0.5 and 0.75, the fault identification accuracy is good, and the data re-collection and model retraining procedures are initiated to collect more data from the inaccurately predicted areas and recalculate based on the new data; When A(t) is higher than 0.75, the model performs well. A fault identification system based on the operation status of a continuous system in an open-pit mine using the method according to any one of claims 1 to 7, characterized in that: Data acquisition module, building open pit mine continuous system simulation model, and real-time monitoring of equipment data; Preprocessing module, which preprocesses data and identifies potential faults; The fault identification module optimizes the fault identification output based on the system pressure change rate and the adaptive adjustment mechanism of the open-pit mine continuous system; Fault response module, designs fault response and real-time adjustment mechanisms to avoid faults. A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.