Risk monitoring system and method for mining frequency converter

Abstract

The invention discloses a risk monitoring system and method for a mining frequency converter, relates to the technical field of frequency converter monitoring, and solves the technical problems that monitoring data is not comprehensive, fault diagnosis is not timely, and an analysis method is not intelligent enough. Similar records in fault data can be quickly screened with abnormal parameters as standards, preliminary analysis reasons are generated, efficiency and accuracy are improved, abnormal parameter changes are tracked with time as a period and matched with theoretical change results corresponding to the preliminary analysis reasons, the fault reasons are further verified, fault diagnosis is more scientific and reliable, and the fault diagnosis efficiency is improved. For the situation that no analysis signal exists, the system can comprehensively collect fault records and judge fault risks, meanwhile, a fault tree is constructed to calculate the probability of all fault events, a potential risk fault logic gate is defined, the occurrence probability of the potential risk fault logic gate is calculated, fault probability information is generated, and a quantitative basis is provided for risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of frequency converter monitoring, and particularly to a risk monitoring system and method for a frequency converter used in mining industry. Background Art

[0002] In mining production, as a key device, the stable operation of the frequency converter is crucial for production efficiency and safety. With the development of Internet technology, realizing remote and accurate monitoring of the mine-used frequency converter has become an important requirement for improving the automation level of the mining industry.

[0003] According to the patent application with the publication number CN102323806B, a frequency converter monitoring platform and its monitoring method are disclosed. It is installed in a computer and includes a foreground human-computer interaction module, a core parameter list, and a background communication module. The foreground human-computer interaction module includes a parameter list display module, a parameter setting module, a parameter classification module, a parameter list input / output module, an oscilloscope module, a keyboard module, and a communication parameter setting module. The core parameter list contains the parameters of the frequency converter, which are used to control the frequency converter and display its operating status in real time, and serves as a channel connecting the foreground human-computer interaction module and the background communication part. The background communication part includes a data structure initialization module, a communication module, and a parameter monitoring module.

[0004] However, traditional monitoring means for mine-used frequency converters often have problems such as incomplete monitoring data, untimely fault diagnosis, and insufficiently intelligent analysis methods, making it difficult to meet the requirements of real-time understanding and efficient maintenance of the operating status of the frequency converter under complex working conditions. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a risk monitoring system and method for a frequency converter used in mining industry, which solves the problems of incomplete monitoring data, untimely fault diagnosis, and insufficiently intelligent analysis methods.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: A risk monitoring system for a frequency converter used in mining industry, the system includes: A frequency converter status analysis module, which is used to compare the working data transmitted by the sensor data acquisition module with the normal state data, judge the working state of the frequency converter, generate a normal monitoring signal or an abnormal working signal, and transmit both of them respectively; An abnormal state analysis module, which is used to analyze the abnormal working signal, screen the fault data with abnormal parameters as the standard to obtain similar records, and determine the preliminary analysis reason. At the same time, match the abnormal parameter change result with the preliminary analysis reason change result to generate an analysis signal of existence or non-existence; Analyze the existing analysis signal, obtain the corresponding matching result denoted as the result to be analyzed, classify the cause of the fault, generate fault cause classification information, match the associated parameters of the abnormal parameter with the fault classification cause, determine the cause of the fault, and generate monitoring analysis information; Analyze the non-existing analysis signal, obtain the number of faults and the fault process according to historical data, sort out the stage parameters before and during the fault, obtain the fault stage parameter interval, match the real-time stage parameter interval of the abnormal parameter with the working stage parameter interval, generate a potential risk analysis signal, and transmit it to the comprehensive analysis and processing module; The comprehensive analysis and processing module is used to analyze the potential risk analysis signal, establish a fault tree based on the fault records corresponding to the number of faults, calculate the fault time probability, determine the probability of the potential risk fault corresponding to the potential risk analysis signal at the same time, generate fault probability information, generate fault level information according to historical data, and transmit it to the monitoring information output module at the same time.

[0007] As a further solution of the present invention, it further includes a sensor data acquisition module and a monitoring information output module; The sensor data acquisition module is used to collect the working data of the frequency converter through different sensors, transmit the working data to the frequency converter state analysis module, and the working data includes the frequency converter current, the frequency converter voltage, and the frequency converter power; The monitoring information output module is used to display the obtained normal monitoring information, fault probability information, and fault level information to the corresponding operators.

[0008] As a further solution of the present invention, the specific method for the frequency converter state analysis module to generate a normal monitoring signal or an abnormal working signal is as follows: Obtain the normal state data range of the frequency converter from historical data, compare the real-time working data with it. If the working data is within this range, it is determined that the frequency converter is working normally and a normal monitoring signal is generated. If it is not within the range, it is determined that the work is abnormal and an abnormal working signal is generated.

[0009] As a further solution of the present invention, the specific method for the abnormal state analysis module to analyze the abnormal working signal is as follows: Obtain the abnormal parameters in the abnormal working signal, screen the frequency converter fault data to find similar records, generate a preliminary analysis reason accordingly, monitor the change of the abnormal parameters with time t as the period, obtain the abnormal change result, and match it with the change result corresponding to the preliminary analysis reason. If the match is successful, a presence analysis signal is generated, and if the match fails, a non-presence analysis signal is generated.

[0010] As a further solution of the present invention, the specific method for the abnormal state analysis module to analyze the presence analysis signal is as follows: Obtain the matching result corresponding to the presence analysis signal as the result to be analyzed, generate fault cause classification information classified by fault causes, obtain the associated parameters of the abnormal parameters, then extract the fault parameters in the fault classification information, divide them into main fault parameters and other fault parameters, match the associated parameters with the other fault parameters, and select the matching ones as the preliminary fault causes; Then obtain the change situation of the associated parameters within time t, match it with the change of the other fault parameters in the preliminary fault causes, determine the fault cause, generate monitoring analysis information and transmit it to the monitoring information output module.

[0011] As a further solution of the present invention, the specific manner in which the abnormal state analysis module analyzes the non - existence analysis signal is as follows: Count the number of faults in the historical data and obtain the fault process, sort out the stage parameters before and during the fault to obtain the corresponding parameter intervals, obtain the real - time stage parameter interval of the abnormal parameters, and match it with the parameter interval before the fault; If the match is successful, it indicates that there is a fault risk during operation under the current abnormal parameters, generate a potential risk analysis signal and transmit it to the comprehensive analysis and processing module. If the match is unsuccessful, it indicates that there is no fault risk, generate a normal monitoring signal and transmit it to the monitoring information output module.

[0012] As a further solution of the present invention, the specific manner in which the comprehensive analysis and processing module analyzes the potential risk analysis signal is as follows: Obtain the number of faults i and its corresponding fault records, construct a fault tree, calculate the probabilities of all fault events, obtain the abnormal parameters, determine the potential risk faults and their logic gates according to the potential risk analysis signal, calculate the occurrence probability of the potential risk faults, and generate fault probability information; At the same time, find the same - type faults from the historical data, analyze their fault levels, match the abnormal parameters of the potential risk faults with them, generate fault level information and transmit it to the monitoring information output module.

[0013] A risk monitoring system for a mining - used frequency converter, the method specifically includes the following steps: Step S1: Collect the working data of the frequency converter through different sensors, obtain the normal state data according to the historical data, and at the same time compare the two to generate a normal monitoring signal or an abnormal working signal; Step S2: Obtain the abnormal working signal, screen the fault data with the abnormal parameters as the standard to obtain similar records, and determine the preliminary analysis reason. At the same time, match the change result of the abnormal parameters with the change result of the preliminary analysis reason to generate a presence or non - existence analysis signal; Step S3: Obtain the analysis signals that exist and the corresponding matching results, denoted as the results to be analyzed, classify the causes of faults, generate fault cause classification information, match the associated parameters of the abnormal parameters with the fault classification causes, determine the fault causes, and generate monitoring and analysis information; Step S4: Obtain the analysis signals that do not exist, obtain the number of faults and the fault process according to historical data, sort out the stage parameters before and during the fault, obtain the fault stage parameter interval, and match the real-time stage parameter interval of the abnormal parameters with the working stage parameter interval to generate potential risk analysis signals; Step S5: Obtain the potential risk analysis signals, establish a fault tree based on the fault records corresponding to the number of faults, calculate the probability of the fault events, and at the same time determine the probability of the potential risk faults corresponding to the potential risk analysis signals, generate fault probability information, and generate fault level information according to historical data.

[0014] The present invention provides a risk monitoring system and method for a frequency converter used in the mining industry. Compared with the prior art, it has the following beneficial effects: Through the abnormal state analysis module, the present invention uses data mining algorithms and expert system knowledge, can quickly screen similar records in the fault data with abnormal parameters as the standard, generate preliminary analysis reasons, improve efficiency and accuracy, track the changes of abnormal parameters with time as a cycle, and match them with the theoretical change results corresponding to the preliminary analysis reasons to further verify the fault causes, making the fault diagnosis more scientific and reliable.

[0015] For the analysis signals that do not exist, the system of the present invention can comprehensively collect fault records, sort out the stage parameter intervals before and during the fault, match them with the real-time stage parameter intervals of the abnormal parameters, judge the fault risks, and the comprehensive analysis and processing module constructs a fault tree according to the potential risk analysis signals to calculate the probabilities of all fault events, clarify the logic gates of the potential risk faults and calculate their occurrence probabilities, and generate fault probability information, providing a quantitative basis for risk assessment.

[0016] Through in-depth analysis of the same type of faults and the corresponding fault levels in historical data, match the abnormal parameters of the potential risk faults with them to determine the fault levels, and dynamically adjust considering the development trend of the faults, making the fault level division more refined and objective, and being able to provide more valuable equipment maintenance information for operators, helping to formulate reasonable maintenance strategies. Description of the Drawings

[0017] Figure 1 It is the system principle block diagram of the present invention; Figure 2 It is the step method diagram of the present invention. Detailed Embodiments

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention 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 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 belong to the scope of protection of the present invention.

[0019] Embodiment 1: Please refer to Figure 1 , this application provides a risk monitoring system for a frequency converter used in the mining industry, including: a sensor data acquisition module, a frequency converter status analysis module, an abnormal status analysis module, a comprehensive analysis and processing module, and a monitoring information output module, and in combination with Figure 1 it can be known that the above functional modules are connected in a one-way electrical manner.

[0020] The sensor data acquisition module is used to collect the working data of the frequency converter through different sensors and transmit the working data to the frequency converter status analysis module, and the working data includes the frequency converter current, the frequency converter voltage, and the frequency converter power, and specifically, it is measured by the corresponding current sensor and voltage sensor.

[0021] The frequency converter status analysis module is used to analyze the working status of the frequency converter according to the obtained working data, and the specific analysis method is as follows: Obtain the normal status data corresponding to the frequency converter according to the historical data, and the normal status data here is a range value, and compare the working data with the normal status data. If the working data exists within the normal status data, it means that the working status of the frequency converter is normal, and a normal monitoring signal is generated. On the contrary, if the working data does not exist within the normal status data, it means that the working status of the frequency converter is abnormal, and an abnormal working signal is generated; Deeply mine the historical data, and use means such as statistical analysis and data modeling to determine the normal status data range of the frequency converter under various working conditions.

[0022] For example, for a certain model of mine-used frequency converter, by analyzing the current data during the stable operation in the past year, it is obtained that the normal working current range under the rated load is [I min , I max , where I min = 50A, I max = 80A; the normal working voltage range is [V min , V max , V min = 370V, V max = 410V; Real-time collect the working data of the frequency converter, such as the current running current I now , voltage V nowetc., and carefully compare it with the normal state data range under the corresponding working conditions. If I now ∈ Imin , I max and V now ∈[V min , V max , it is determined that the inverter is working normally, and a normal working signal is immediately generated; otherwise, once a certain working data exceeds the corresponding range, it is determined that the inverter is working abnormally, and an abnormal working signal is quickly generated.

[0023] For example, when the running current I now = 85A of the inverter is monitored in real time, which exceeds the upper limit of the normal current of 80A under the rated load, the system immediately determines that the working state is abnormal and generates an abnormal working signal.

[0024] Furthermore, the normal monitoring signal is transmitted to the monitoring information output module, and the abnormal working signal is transmitted to the abnormal state analysis module.

[0025] The abnormal state analysis module is used to analyze the obtained abnormal working signal, obtain the abnormal parameters corresponding to the abnormal working signal, then obtain the fault data of the inverter, and screen the fault data based on the abnormal parameters to obtain similar records. At the same time, a preliminary analysis reason is generated based on the similar records; For example, in the past fault records, find all cases of faults caused by abnormal current. These cases constitute similar records. Suppose the current abnormal current value is 120A. Through screening, it is found that in 3 past records, the current values at the time of fault were 115A, 122A, and 118A respectively. These records are similar records. Based on these similar records, combined with the situation of the fault occurrence and relevant data characteristics, use data mining algorithms and expert system knowledge to generate a preliminary analysis reason for this abnormality.

[0026] Then, taking time t as the period, obtain the change situation of the abnormal parameters, denoted as the abnormal change result. At the same time, match the abnormal change result with the change result corresponding to the preliminary analysis reason. If the two match, a presence analysis signal is generated; otherwise, if the two do not match, a non - presence analysis signal is generated, and both are analyzed respectively; Set a monitoring period with time \(t\) (e.g., \(t = 10\) minutes), continuously track the changes of abnormal parameters within this time period, and form the abnormal change results. For example, within these 10 minutes, the abnormal current value gradually rises from 120A to 130A. At the same time, retrieve the theoretical change results corresponding to the preliminary analysis reasons (such as a sudden increase in motor load) from the knowledge base, that is, when the motor load increases, the current should show a continuous upward trend. Accurately match the actual abnormal change results with the theoretical change results. If the trends of the two are consistent, such as the continuous increase in abnormal current is consistent with the theoretical change trend of current when the motor load increases, then generate an existence analysis signal; conversely, if the abnormal current suddenly drops or shows irregular fluctuations within the monitoring period, which does not match the expected change trend of current when the motor load increases, then generate a non-existence analysis signal. Conduct subsequent in-depth analysis for these two signals respectively.

[0027] Process the generated existence analysis signal, deeply analyze the specific matching results corresponding to this signal, and mark it as the result to be analyzed.

[0028] For example, the result to be analyzed clearly shows a strong correlation between current abnormality and an increase in motor load. Then, systematically classify these results to be analyzed according to the cause of the fault, and construct the classification information of the cause of the fault.

[0029] For example, classify the cause of the fault into motor-related (such as abnormal motor load, short circuit of motor windings, etc.), faults of the components of the frequency converter itself (such as damage to the power module, aging of capacitors, etc.), and external line problems (such as line short circuit, poor contact, etc.).

[0030] Next, determine the associated parameters closely related to the abnormal parameter (such as current abnormality). For current abnormality, the associated parameters may include motor speed, output voltage of the frequency converter, etc.

[0031] Using the associated parameters as a new matching basis, conduct in-depth matching with the classification information of the cause of the fault again. The specific operations are as follows: Traverse all the classification information of the cause of the fault, extract the fault parameters in each cause of the fault, and further divide these parameters into main fault parameters and other fault parameters. The main fault parameter is the core factor causing the fault; For example, in the case of a short circuit of the motor windings, the sharply decreased resistance value of the short-circuited winding is the main fault parameter; the other fault parameters are the related parameter changes accompanying the main fault. For example, when the motor windings are short-circuited, the current will increase sharply, and this increase in current is the other fault parameter.

[0032] In this case, the motor speed and the output voltage of the frequency converter are used as associated parameters, and are matched one by one with the remaining fault parameters obtained by classification. Suppose in the fault classification reason information of increased motor load, the remaining fault parameters include a decrease in motor speed (because as the load increases, the motor speed will decrease accordingly). When it is monitored that the motor speed does gradually decrease during the abnormal current period and matches the remaining fault parameters in this fault classification reason information, then this fault classification reason information is selected as the preselected fault reason.

[0033] Further in-depth analysis is carried out to obtain the detailed change of the associated parameter (motor speed) within the time period t. For example, the motor speed drops from 1500 revolutions per minute to 1300 revolutions per minute within 10 minutes. Taking this change as a reference standard, a secondary matching verification is carried out on the change of the remaining fault parameters (such as current) in the preselected fault reason (increased motor load). If the rising trend of the current and the falling trend of the motor speed both conform to the theoretical change model when the motor load increases in terms of time and amplitude, it is finally determined that the fault reason is a sudden increase in the motor load. Based on this, detailed monitoring and analysis information is generated.

[0034] Process the generated non-existence analysis signal, comprehensively collect all fault records related to the current frequency converter from historical data, and accurately count the total number of faults that occurred, denoted as j. For the orderly management of historical fault data, assign a unique number i to each fault, and i = 1, 2,..., j. For each fault numbered i, obtain its complete fault process in detail. This process includes a period of time before the fault (pre-fault stage) and the actual moment when the fault occurs (fault stage); In the pre-fault stage, closely monitor and record multiple key parameters, such as current, voltage, temperature, frequency, etc. These parameters reflect the operating state of the frequency converter just before the fault occurs. Systematically organize these parameters, calculate the maximum value, minimum value, and average value of each parameter, so as to determine the parameter interval in the pre-fault stage. At the actual moment when the fault occurs, also record the above key parameters and perform similar sorting operations on these parameters to obtain the parameter interval in the fault stage; Then obtain the real-time stage parameter interval corresponding to the abnormal parameter and match it with the fault stage parameter interval. Here, it is matched with the pre-fault stage parameter interval. If the two match successfully, it means that working with the current abnormal parameter has a fault risk, and a potential risk analysis signal is generated and transmitted to the comprehensive analysis and processing module. On the contrary, if the two do not match successfully, it means that working with the current abnormal parameter has no fault risk, and a normal monitoring signal is generated and transmitted to the monitoring information output module.

[0035] The monitoring information output module is used to perform real-time monitoring of the frequency converter according to the obtained normal monitoring information.

[0036] Example 2: As the second example of the present invention, it is implemented on the basis of Example 1, and the differences from Example 1 are as follows: The comprehensive analysis and processing module is used to process the obtained potential risk analysis signals, obtain the number of failures i, and at the same time obtain the failure records corresponding to the number of failures i, and establish a corresponding fault tree. Then, according to the obtained number of failures, calculate the probabilities of all fault events. For example, the bearing wear of the cooling fan (failure rate = 0.005 times / hour), dust accumulation on the heat sink (determined by the environmental dust concentration, assuming the occurrence probability = 0.1), capacitor failure of the power supply module ( = 0.002 times / hour), obtain abnormal parameters, and determine the corresponding potential risk faults according to the potential risk analysis signals. At the same time, determine the logic gates of the potential risk faults. The specific logic gates include AND gates (indicating that the output event will occur only when all input events occur) and OR gates (indicating that the output event will occur as long as one input event occurs). Then, calculate the occurrence probabilities of the potential risk faults according to the logic gates, and generate fault probability information; At the same time, determine the corresponding fault levels according to the potential risk faults, and the determination method of the fault levels is as follows: Obtain historical data, obtain the same type of faults in the historical data, and at the same time analyze the fault levels corresponding to the same type of faults. Then, match the abnormal parameters of the potential risk faults with the same type of faults, generate corresponding fault level information, and transmit it to the monitoring information output module.

[0037] Combined with actual examples, all the fault data of the past year were collected from the maintenance records, fault logs and operation detection databases of the frequency converter. After screening, it was found that there were 20 records in the historical data showing that the output current of the frequency converter increased abnormally; Analyze these 20 records of the same type of faults. It is found that 5 of the records correspond to minor faults. The abnormal current values of these faults are between 120 - 130A, and the current rising rate is slow, with little impact on the operation of the equipment; 8 records correspond to general faults, the abnormal current values are between 130 - 140A, the current rising rate is moderate, and some performances of the equipment are affected to a certain extent; 4 records correspond to serious faults, the abnormal current values are between 140 - 150A, the current rising rate is fast, resulting in equipment shutdown, but it can be repaired in a short time; 3 records correspond to major faults, the abnormal current values exceed 150A, the current rising rate is very fast, causing serious damage to the equipment and long-term interruption of production; Match the abnormal parameters of the current potential risk failure (abnormal current value of 150 A, current rising rate of 10 A / minute) with the abnormal parameters of the same type of failure. It is found that the abnormal parameters are closest to those of a severe failure. However, considering that the current is still rising continuously and the rising rate is relatively fast, it may develop into a major failure; Taking into account the matching result and the development trend of the current failure comprehensively, determine the failure level of the current potential risk failure as a severe failure, but it is necessary to pay close attention. If the current continues to rise rapidly, it may be upgraded to a major failure; Monitoring information output module, which is used to display the obtained failure probability information and failure level information to the corresponding operators.

[0038] Embodiment 3: As Embodiment 3 of the present invention, the key lies in combining the implementation processes of Embodiment 1 and Embodiment 2.

[0039] Embodiment 4: Please refer to Figure 2 , this application provides a risk monitoring system for a mining frequency converter. The method specifically includes the following steps: Step S1: Collect the working data of the frequency converter through different sensors, obtain the normal state data according to the historical data, and compare the two at the same time to generate a normal monitoring signal or an abnormal working signal. The specific processing method is the same as that of the frequency converter state analysis module in Embodiment 1; Step S2: Obtain the abnormal working signal, screen the failure data based on the abnormal parameters to obtain similar records, and determine the preliminary analysis reason. At the same time, match the abnormal parameter change result with the preliminary analysis reason change result to generate an analysis signal of existence or non-existence. The specific processing method is the same as that of the abnormal state analysis module in Embodiment 1; Step S3: Obtain the analysis signal of existence and the corresponding matching result, record it as the result to be analyzed, and conduct failure cause classification to generate failure cause classification information. Match the associated parameters of the abnormal parameters with the failure classification reasons to determine the failure cause and generate monitoring analysis information. The specific processing method is the same as that of the abnormal state analysis module in Embodiment 1 for the analysis signal of existence; Step S4: Obtain the analysis signal of non-existence, and obtain the number of failures and the failure process according to the historical data. Sort out the stage parameters before and during the failure to obtain the failure stage parameter interval. Match the real-time stage parameter interval of the abnormal parameters with the working stage parameter interval to generate a potential risk analysis signal. The specific processing method is the same as that of the abnormal state analysis module in Embodiment 1 for the analysis signal of non-existence; Step S5: Obtain potential risk analysis signals, establish a fault tree based on the fault records corresponding to the number of faults, calculate the probability of fault events, and at the same time determine the probability of potential risk faults corresponding to the potential risk analysis signals, generate fault probability information, and generate fault level information based on historical data. The specific processing method is the same as that of the comprehensive analysis and processing module in Embodiment 2.

[0040] For some data in the above formula, only their numerical values are taken for calculation, and the parameter units are not substituted for calculation. At the same time, the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0041] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A risk monitoring system for a frequency converter used in the mining industry, characterized in that, The system includes: A frequency converter status analysis module, which is used to compare the working data transmitted by the sensor data acquisition module with the normal status data, judge the working status of the frequency converter, generate a normal monitoring signal or an abnormal working signal, and transmit both of them respectively; An abnormal status analysis module, which is used to analyze the abnormal working signal, screen the fault data based on the abnormal parameters to obtain similar records, and determine the preliminary analysis reason. At the same time, match the abnormal parameter change result with the preliminary analysis reason change result to generate an analysis signal indicating existence or non-existence; Analyze the analysis signal indicating existence, obtain the corresponding matching result denoted as the result to be analyzed, and conduct fault cause classification to generate fault cause classification information. Match the associated parameters of the abnormal parameters with the fault classification reasons to determine the fault cause and generate monitoring analysis information; Analyze the analysis signal indicating non-existence, obtain the number of faults and the fault process according to historical data, sort out the stage parameters before and during the fault, obtain the fault stage parameter interval, and match the real-time stage parameter interval of the abnormal parameters with the working stage parameter interval to generate a potential risk analysis signal and transmit it to the comprehensive analysis and processing module; A comprehensive analysis and processing module, which is used to analyze the potential risk analysis signal, establish a fault tree based on the fault records corresponding to the number of faults, calculate the fault time probability, and determine the probability of the potential risk fault corresponding to the potential risk analysis signal to generate fault probability information. At the same time, generate fault level information according to historical data and transmit it to the monitoring information output module.

2. The risk monitoring system of a frequency converter for mining according to claim 1, characterized in that, It also includes a sensor data acquisition module and a monitoring information output module; The sensor data acquisition module is used to collect the working data of the frequency converter through different sensors, transmit the working data to the frequency converter status analysis module, and the working data includes the frequency converter current, the frequency converter voltage, and the frequency converter power; The monitoring information output module is used to display the obtained normal monitoring information, fault probability information, and fault level information to the corresponding operators.

3. The risk monitoring system of a frequency converter for mining according to claim 1, characterized in that, The specific way for the frequency converter status analysis module to generate a normal monitoring signal or an abnormal working signal is as follows: Obtain the normal status data range of the frequency converter from historical data, compare the real-time working data with it. If the working data is within this range, it is determined that the frequency converter is working normally and a normal monitoring signal is generated. If it is not within the range, it is determined that the work is abnormal and an abnormal working signal is generated.

4. The risk monitoring system of a frequency converter for mining according to claim 1, wherein The specific way for the abnormal status analysis module to analyze the abnormal working signal is as follows: Obtain the abnormal parameters in the abnormal working signal, screen the frequency converter fault data to find similar records, and generate a preliminary analysis reason accordingly. Monitor the change of the abnormal parameters with time t as the period to obtain the abnormal change result, and match it with the change result corresponding to the preliminary analysis reason. If the match is successful, an analysis signal indicating existence is generated. If the match fails, an analysis signal indicating non-existence is generated.

5. The risk monitoring system of a frequency converter for mining according to claim 1, characterized in that, The specific way for the abnormal status analysis module to analyze the analysis signal indicating existence is as follows: Obtain the matching result corresponding to the presence analysis signal as the result to be analyzed, generate fault cause classification information by classifying according to fault causes, obtain the associated parameters of the abnormal parameters, then extract the fault parameters in the fault classification information, divide them into main fault parameters and other fault parameters, match the associated parameters with the other fault parameters, and select the matching ones as the preliminary fault causes; Then obtain the change situation of the associated parameters within time t, match it with the change of the other fault parameters in the preliminary fault causes, determine the fault cause, generate monitoring analysis information and transmit it to the monitoring information output module.

6. The risk monitoring system of a frequency converter for mining according to claim 1, characterized in that, The specific way for the abnormal state analysis module to analyze the non-presence analysis signal is as follows: Count the number of faults in the historical data and obtain the fault process, sort out the stage parameters before and during the fault, obtain the corresponding parameter intervals, obtain the real-time stage parameter intervals of the abnormal parameters, and match them with the stage parameter intervals before the fault; If the match is successful, it indicates that there is a fault risk during operation under the current abnormal parameters, generate a potential risk analysis signal and transmit it to the comprehensive analysis and processing module. If the match is unsuccessful, it indicates that there is no fault risk, generate a normal monitoring signal and transmit it to the monitoring information output module.

7. The risk monitoring system of a frequency converter for mining according to claim 1, characterized in that, The specific way for the comprehensive analysis and processing module to analyze the potential risk analysis signal is as follows: Obtain the number of faults i and its corresponding fault records, construct a fault tree, calculate the probabilities of all fault events, obtain the abnormal parameters, determine the potential risk faults and their logic gates according to the potential risk analysis signal, calculate the occurrence probability of the potential risk faults, and generate fault probability information; At the same time, find the same type of faults from the historical data, analyze their fault levels, match the abnormal parameters of the potential risk faults with them, generate fault level information and transmit it to the monitoring information output module.

8. A risk monitoring method for a frequency converter used in mining, which is executed by the risk monitoring system of the frequency converter for mining according to any one of claims 1-7, characterized in that, This method specifically includes the following steps: Step S1: Collect the working data of the frequency converter through different sensors, obtain the normal state data according to the historical data, and at the same time compare the two to generate a normal monitoring signal or an abnormal working signal; Step S2: Obtain the abnormal working signal, screen the fault data with the abnormal parameters as the standard, obtain the similar records, and determine the preliminary analysis reason. At the same time, match the change result of the abnormal parameters with the change result of the preliminary analysis reason to generate a presence or non-presence analysis signal; Step S3: Obtain the presence analysis signal and its corresponding matching result as the result to be analyzed, and conduct fault cause classification to generate fault cause classification information. Match the associated parameters of the abnormal parameters with the fault classification reasons to determine the fault cause and generate monitoring analysis information; Step S4: Obtain the non-presence analysis signal, obtain the number of faults and the fault process according to the historical data, sort out the stage parameters before and during the fault to obtain the fault stage parameter intervals, and match the real-time stage parameter intervals of the abnormal parameters with the working stage parameter intervals to generate a potential risk analysis signal; Step S5: Obtain the potential risk analysis signal, establish a fault tree based on the fault records corresponding to the number of faults, calculate the fault time probability, and at the same time determine the probability of the potential risk faults corresponding to the potential risk analysis signal to generate fault probability information, and generate fault level information according to the historical data.

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