A Fault Monitoring Method for Pump Units Based on Multidimensional Perception Fusion Recognition

Abstract

The present invention relates to the technical field of fault monitoring, and discloses a fault monitoring method for pump units based on multi-dimensional perception fusion recognition. The method includes: obtaining the operation stage information of a key monitoring area, and determining the fault monitoring threshold of the pump unit to be monitored based on the operation stage information; analyzing the historical operation records, and judging whether it is necessary to optimize the fault monitoring threshold based on the analysis results. If so, setting an optimization coefficient corresponding to the fault monitoring threshold, and obtaining an optimized fault monitoring threshold; analyzing the abnormal working condition data, and judging whether it is necessary to compensate the optimized fault monitoring threshold based on the analysis results. If so, calculating the abnormal working condition influence index of the abnormal working condition data, and setting a compensation coefficient for the optimized fault monitoring threshold based on the abnormal working condition influence index, and obtaining a compensated fault monitoring threshold; determining the fault warning level of the pump unit to be monitored according to the compensated fault monitoring threshold. The present invention ensures the accuracy and effectiveness of monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of fault monitoring, and more particularly, to a fault monitoring method for pump units based on multi-dimensional perception fusion recognition. Background Art

[0002] With the rapid development of industrial technology, pump units, as key equipment in various industrial processes, their operating status directly affects the efficiency and safety of the entire production line. However, due to the complex and changeable working environment of pump units, they are often affected by various factors such as fluid media, temperature, and pressure, resulting in frequent failures. Traditional fault monitoring methods often rely on single sensor data and are difficult to comprehensively and accurately reflect the operating status of pump units.

[0003] Therefore, it is necessary to design a fault monitoring method for pump units based on multi-dimensional perception fusion recognition to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes a fault monitoring method for pump units based on multi-dimensional perception fusion recognition, aiming to solve the problem that traditional fault monitoring methods in the current technology often rely on single sensor data and are difficult to comprehensively and accurately reflect the operating status of pump units.

[0005] The present invention proposes a fault monitoring method for pump units based on multi-dimensional perception fusion recognition, including the following steps:

[0006] S100: Determine the pump unit to be monitored, divide the monitoring area of the pump unit to be monitored to obtain a key monitoring area and a general monitoring area; obtain the operation stage information of the key monitoring area, and determine the fault monitoring threshold of the pump unit to be monitored based on the operation stage information;

[0007] S200: Extract the corresponding historical operation records from the historical operation record library based on the key monitoring area, general monitoring area, and the fault monitoring threshold, and analyze the historical operation records. Based on the analysis results, determine whether it is necessary to optimize the fault monitoring threshold. If so, set the optimization coefficient corresponding to the fault monitoring threshold and obtain the optimized fault monitoring threshold;

[0008] S300: Collect the abnormal working condition data of the general monitoring area, parse the abnormal working condition data, and determine whether it is necessary to compensate the optimized fault monitoring threshold based on the parsing results. If so, calculate the abnormal working condition influence index of the abnormal working condition data, and set the compensation coefficient of the optimized fault monitoring threshold based on the abnormal working condition influence index, and obtain the compensated fault monitoring threshold;

[0009] S400: Determine the fault warning level of the pump unit to be monitored according to the compensation fault monitoring threshold.

[0010] Further, when analyzing the historical operation records and judging whether it is necessary to optimize the fault monitoring threshold based on the analysis results, it includes:

[0011] Extract the historical operation records of the key monitoring area respectively, denoted as key historical operation records; extract the historical operation records of the ordinary monitoring area, denoted as ordinary historical operation records;

[0012] Analyze the key historical operation records to obtain key historical normal operation records and key historical abnormal operation records;

[0013] Obtain the abnormal occurrence frequency of the key monitoring area according to the key historical normal operation records and key historical abnormal operation records, denoted as key abnormal frequency;

[0014] Analyze the ordinary historical operation records to obtain ordinary historical normal operation records and ordinary historical abnormal operation records;

[0015] Obtain the abnormal occurrence frequency of the ordinary monitoring area according to the ordinary historical normal operation records and ordinary historical abnormal operation records, denoted as ordinary abnormal frequency;

[0016] Judge whether it is necessary to optimize the fault monitoring threshold according to the key abnormal frequency and ordinary abnormal frequency.

[0017] Further, when judging whether it is necessary to optimize the fault monitoring threshold according to the key abnormal frequency and ordinary abnormal frequency, it includes:

[0018] Obtain the ratio of the key abnormal frequency and the ordinary abnormal frequency, denoted as abnormal frequency ratio;

[0019] Compare the abnormal frequency ratio with the abnormal frequency ratio threshold, and judge whether it is necessary to optimize the fault monitoring threshold according to the comparison result;

[0020] When the abnormal frequency ratio is greater than the abnormal frequency ratio threshold, it is determined to optimize the fault monitoring threshold;

[0021] When the abnormal frequency ratio is less than or equal to the abnormal frequency ratio threshold, it is determined not to optimize the fault monitoring threshold.

[0022] Further, when setting the optimization coefficient corresponding to the fault monitoring threshold and obtaining the optimized fault monitoring threshold, it includes:

[0023] Set an optimization coefficient range, where the optimization coefficient range includes a first optimization coefficient, a second optimization coefficient, and a third optimization coefficient;

[0024] Perform a weighted sum of the critical abnormal frequency and the normal abnormal frequency to obtain a comprehensive abnormal frequency;

[0025] Compare the comprehensive abnormal frequency with a first comprehensive abnormal frequency and a second comprehensive abnormal frequency, and determine the optimization coefficient corresponding to the fault monitoring threshold according to the comparison result; wherein, the first comprehensive abnormal frequency is less than the second comprehensive abnormal frequency;

[0026] When the comprehensive abnormal frequency is less than the first comprehensive abnormal frequency, determine that the optimization coefficient corresponding to the fault monitoring threshold is the first optimization coefficient, and use the product value of the first optimization coefficient and the fault monitoring threshold as the optimized fault monitoring threshold;

[0027] When the comprehensive abnormal frequency is greater than or equal to the first comprehensive abnormal frequency and less than the second comprehensive abnormal frequency, determine that the optimization coefficient corresponding to the fault monitoring threshold is the second optimization coefficient, and use the product value of the second optimization coefficient and the fault monitoring threshold as the optimized fault monitoring threshold;

[0028] When the comprehensive abnormal frequency is greater than or equal to the second comprehensive abnormal frequency, determine that the optimization coefficient corresponding to the fault monitoring threshold is the third optimization coefficient, and use the product value of the third optimization coefficient and the fault monitoring threshold as the optimized fault monitoring threshold.

[0029] Further, when collecting the abnormal condition data of the ordinary monitoring area and analyzing the abnormal condition data, and determining whether to compensate the optimized fault monitoring threshold based on the analysis result, it includes:

[0030] Extract features from the abnormal condition data to obtain abnormal condition feature data; wherein, the abnormal condition feature data includes abnormal sound feature data, abnormal vibration feature data, and abnormal temperature feature data;

[0031] Obtain the abnormal condition feature values of each abnormal condition feature data and the corresponding abnormal condition standard values of each abnormal condition feature value;

[0032] Compare all the abnormal condition feature values with the corresponding abnormal condition standard values, and determine whether to compensate the optimized fault monitoring threshold according to the comparison result.

[0033] Further, when comparing all the abnormal condition feature values with the corresponding abnormal condition standard values and determining whether to compensate the optimized fault monitoring threshold according to the comparison result, it includes:

[0034] When all the abnormal condition characteristic values are less than or equal to the corresponding abnormal condition standard values, it is determined that there is no need to compensate the optimized fault monitoring threshold;

[0035] When there is an abnormal condition characteristic value greater than the corresponding abnormal condition standard value, it is determined that the optimized fault monitoring threshold needs to be compensated.

[0036] Further, when calculating the abnormal condition influence index of the abnormal condition data, it includes:

[0037] Perform weighted processing on each abnormal condition characteristic value greater than the corresponding abnormal condition standard value to obtain a weighted abnormal condition characteristic value;

[0038] Sum all the weighted abnormal condition characteristic values to obtain the abnormal condition influence index.

[0039] Further, when setting the compensation coefficient of the optimized fault monitoring threshold based on the abnormal condition influence index and obtaining the compensated fault monitoring threshold, it includes:

[0040] Compare the abnormal condition influence index with historical data, set the compensation coefficient of the optimized fault monitoring threshold according to the comparison result, and obtain the compensated fault monitoring threshold;

[0041] When there is a historical abnormal condition influence index identical to the abnormal condition influence index in the historical data, use the historical optimization coefficient corresponding to the historical abnormal condition influence index as the compensation coefficient, and use the product value of the compensation coefficient and the optimized fault monitoring threshold as the compensated fault monitoring threshold;

[0042] When there is no historical abnormal condition influence index identical to the abnormal condition influence index in the historical data, calculate the difference between the abnormal condition influence index and the historical data one by one, record it as the abnormal difference, construct an abnormal difference set, set the compensation coefficient of the optimized fault monitoring threshold according to the abnormal difference set, and obtain the compensated fault monitoring threshold.

[0043] Further, when setting the compensation coefficient of the optimized fault monitoring threshold according to the abnormal difference set and obtaining the compensated fault monitoring threshold, it includes:

[0044] Obtain the minimum value of the abnormal differences in the abnormal difference set, and record it as the minimum abnormal difference;

[0045] Compare the minimum abnormal difference with the first minimum abnormal difference and the second minimum abnormal difference, and determine the compensation coefficient of the optimized fault monitoring threshold according to the comparison result; wherein, the first minimum abnormal difference is less than the second minimum abnormal difference;

[0046] When the minimum abnormal difference is less than the first minimum abnormal difference, determine that the compensation coefficient corresponding to the optimized fault monitoring threshold is the first compensation coefficient, and use the product value of the first compensation coefficient and the optimized fault monitoring threshold as the compensated fault monitoring threshold;

[0047] When the minimum abnormal difference is greater than or equal to the first minimum abnormal difference and less than the second minimum abnormal difference, determine that the compensation coefficient corresponding to the optimized fault monitoring threshold is the second compensation coefficient, and use the product value of the second compensation coefficient and the optimized fault monitoring threshold as the compensated fault monitoring threshold;

[0048] When the minimum abnormal difference is greater than or equal to the second minimum abnormal difference, determine that the compensation coefficient corresponding to the optimized fault monitoring threshold is the third compensation coefficient, and use the product value of the third compensation coefficient and the optimized fault monitoring threshold as the compensated fault monitoring threshold.

[0049] Further, when determining the fault warning level of the pump unit to be monitored according to the compensated fault monitoring threshold, it includes:

[0050] Compare the compensated fault monitoring threshold with the first compensated fault monitoring threshold and the second compensated fault monitoring threshold, and determine the fault warning level of the pump unit to be monitored according to the comparison result; wherein, the first compensated fault monitoring threshold is less than the second compensated fault monitoring threshold;

[0051] When the compensated fault monitoring threshold is less than the first compensated fault monitoring threshold, determine that the fault warning level of the pump unit to be monitored is the first warning level;

[0052] When the compensated fault monitoring threshold is greater than or equal to the first compensated fault monitoring threshold and less than the second compensated fault monitoring threshold, determine that the fault warning level of the pump unit to be monitored is the second warning level;

[0053] When the compensated fault monitoring threshold is greater than or equal to the second compensated fault monitoring threshold, determine that the fault warning level of the pump unit to be monitored is the third warning level;

[0054] Wherein, the first warning level is less than the second warning level, and the second warning level is less than the third warning level.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows: The pump unit fault monitoring method based on multi-dimensional perception fusion recognition provided by the present invention realizes the comprehensive control of the operation state of the pump unit by carefully dividing and monitoring the key monitoring areas and ordinary monitoring areas of the pump unit; according to the characteristics and operation stage information of the pump unit, reasonable fault monitoring thresholds are set to ensure the accuracy and effectiveness of the monitoring; through the analysis of historical operation records, the dynamic optimization of the fault monitoring thresholds is realized, improving the sensitivity and adaptability of the monitoring; at the same time, the in-depth analysis and compensation processing of abnormal working condition data further enhance the accuracy and reliability of the fault warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0057] Figure 1 It is a flowchart of the pump unit fault monitoring method based on multi-dimensional perception fusion recognition provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in conjunction with the embodiments.

[0059] Refer to Figure 1 As shown, in some embodiments of the present application, the present embodiment provides a pump unit fault monitoring method based on multi-dimensional perception fusion recognition, including the following steps:

[0060] S100: Determine the pump unit to be monitored, divide the monitoring area of the pump unit to be monitored to obtain a key monitoring area and an ordinary monitoring area; obtain the operation stage information of the key monitoring area, and determine the fault monitoring threshold of the pump unit to be monitored based on the operation stage information;

[0061] S200: Extract corresponding historical operation records from the historical operation record library based on the key monitoring area, general monitoring area, and the fault monitoring threshold, analyze the historical operation records, and based on the analysis result, determine whether it is necessary to optimize the fault monitoring threshold. If so, set an optimization coefficient corresponding to the fault monitoring threshold and obtain an optimized fault monitoring threshold;

[0062] S300: Collect abnormal condition data of the general monitoring area, parse the abnormal condition data, and based on the parsing result, determine whether it is necessary to compensate the optimized fault monitoring threshold. If so, calculate an abnormal condition impact index of the abnormal condition data, and based on the abnormal condition impact index, set a compensation coefficient for the optimized fault monitoring threshold and obtain a compensated fault monitoring threshold;

[0063] S400: Determine the fault warning level of the pump unit to be monitored according to the compensated fault monitoring threshold.

[0064] In this embodiment, the key monitoring area includes core components such as the bearings, seals, and motors of the pump unit; the general monitoring area includes the inlet and outlet pipelines, connectors, and auxiliary equipment of the pump unit.

[0065] In this embodiment, the operation stage information includes the start-up stage, stable operation stage, and shutdown stage of the pump unit.

[0066] In this embodiment, the fault monitoring threshold in the start-up stage is preferably 1.5 times the vibration amplitude of the pump set, the fault monitoring threshold in the stable operation stage is preferably 1.2 times the vibration amplitude of the pump set, and the fault monitoring threshold in the shutdown stage is set to 1 times the vibration amplitude of the pump set. Such settings are based on the characteristics and fault occurrence probabilities of the pump unit in different operation stages, aiming to more accurately capture potential fault signals and improve the monitoring efficiency.

[0067] It can be understood that the pump unit fault monitoring method based on multi-dimensional perception fusion recognition provided in this embodiment realizes the comprehensive control of the operation state of the pump unit by carefully dividing and monitoring the key monitoring area and general monitoring area of the pump unit; sets reasonable fault monitoring thresholds according to the characteristics and operation stage information of the pump unit to ensure the accuracy and effectiveness of the monitoring; realizes the dynamic optimization of the fault monitoring threshold through the analysis of historical operation records, improving the sensitivity and adaptability of the monitoring; at the same time, the in-depth parsing and compensation processing of abnormal condition data further enhance the accuracy and reliability of the fault warning.

[0068] Specifically, when analyzing the historical operation records and determining whether it is necessary to optimize the fault monitoring threshold based on the analysis result, it includes:

[0069] Extract the historical operation records of the key monitoring areas respectively, denoted as key historical operation records; extract the historical operation records of the general monitoring areas, denoted as general historical operation records;

[0070] Analyze the key historical operation records to obtain key historical normal operation records and key historical abnormal operation records;

[0071] Obtain the abnormal occurrence frequency of the key monitoring area according to the key historical normal operation records and key historical abnormal operation records, denoted as key abnormal frequency;

[0072] Analyze the general historical operation records to obtain general historical normal operation records and general historical abnormal operation records;

[0073] Obtain the abnormal occurrence frequency of the general monitoring area according to the general historical normal operation records and general historical abnormal operation records, denoted as general abnormal frequency;

[0074] Judge whether it is necessary to optimize the fault monitoring threshold according to the key abnormal frequency and general abnormal frequency.

[0075] In this embodiment, in the key historical operation records, the number of key historical normal operation records is denoted as the first quantity, and the number of key historical abnormal operation records is denoted as the second quantity. Then the key abnormal frequency is equal to the ratio of the second quantity to the sum of the first quantity and the second quantity; in the general historical operation records, the number of general historical normal operation records is denoted as the third quantity, and the number of general historical abnormal operation records is denoted as the fourth quantity. Then the general abnormal frequency is equal to the ratio of the fourth quantity to the sum of the third quantity and the fourth quantity.

[0076] Specifically, when judging whether it is necessary to optimize the fault monitoring threshold according to the key abnormal frequency and general abnormal frequency, it includes:

[0077] Obtain the ratio of the key abnormal frequency and the general abnormal frequency, denoted as abnormal frequency ratio;

[0078] Compare the abnormal frequency ratio with the abnormal frequency ratio threshold, and judge whether it is necessary to optimize the fault monitoring threshold according to the comparison result;

[0079] When the abnormal frequency ratio is greater than the abnormal frequency ratio threshold, it is determined to optimize the fault monitoring threshold;

[0080] When the abnormal frequency ratio is less than or equal to the abnormal frequency ratio threshold, it is determined not to optimize the fault monitoring threshold.

[0081] It is understandable that the abnormal frequency ratio represents the relative relationship between the abnormal occurrence frequencies of the key monitoring area and the ordinary monitoring area, reflecting the stability and fault tendency of different parts of the pump unit during operation. When the abnormal occurrence frequency in the key monitoring area increases significantly relative to the ordinary monitoring area, it means that the key components may face higher fault risks. At this time, optimizing and adjusting the fault monitoring threshold can more effectively capture these potential fault signals, avoid missed alarms or false alarms, thereby improving the accuracy and timeliness of pump unit fault monitoring. In addition, through continuous optimization of the fault monitoring threshold, it is also possible to adapt to the dynamic changes in the operating state of the pump unit, ensuring the long-term effectiveness and stability of the monitoring system.

[0082] Specifically, when setting the optimization coefficient corresponding to the fault monitoring threshold and obtaining the optimized fault monitoring threshold, it includes:

[0083] Set an optimization coefficient interval, which includes a first optimization coefficient, a second optimization coefficient, and a third optimization coefficient;

[0084] Perform weighted summation on the key abnormal frequency and the ordinary abnormal frequency to obtain a comprehensive abnormal frequency;

[0085] Compare the comprehensive abnormal frequency with a first comprehensive abnormal frequency and a second comprehensive abnormal frequency, and determine the optimization coefficient corresponding to the fault monitoring threshold according to the comparison result; where the first comprehensive abnormal frequency is less than the second comprehensive abnormal frequency;

[0086] When the comprehensive abnormal frequency is less than the first comprehensive abnormal frequency, determine that the optimization coefficient corresponding to the fault monitoring threshold is the first optimization coefficient, and use the product value of the first optimization coefficient and the fault monitoring threshold as the optimized fault monitoring threshold;

[0087] When the comprehensive abnormal frequency is greater than or equal to the first comprehensive abnormal frequency and less than the second comprehensive abnormal frequency, determine that the optimization coefficient corresponding to the fault monitoring threshold is the second optimization coefficient, and use the product value of the second optimization coefficient and the fault monitoring threshold as the optimized fault monitoring threshold;

[0088] When the comprehensive abnormal frequency is greater than or equal to the second comprehensive abnormal frequency, determine that the optimization coefficient corresponding to the fault monitoring threshold is the third optimization coefficient, and use the product value of the third optimization coefficient and the fault monitoring threshold as the optimized fault monitoring threshold.

[0089] In this embodiment, the first optimization coefficient < the second optimization coefficient < the third optimization coefficient.

[0090] It is understandable that by setting different optimization coefficients, the fault monitoring threshold can be flexibly adjusted according to the abnormal frequency of the actual operation state of the pump unit, so as to achieve more sensitive and accurate capture of potential fault signals. When the comprehensive abnormal frequency is low, a smaller optimization coefficient is adopted to maintain the relative stability of the fault monitoring threshold and avoid false alarms; while when the comprehensive abnormal frequency is high, a larger optimization coefficient is adopted to appropriately reduce the fault monitoring threshold and improve the response speed to potential faults, ensuring the timeliness and effectiveness of monitoring. This dynamic optimization strategy can significantly improve the intelligent level and practicality of pump unit fault monitoring.

[0091] Specifically, when collecting the abnormal working condition data of the ordinary monitoring area and analyzing the abnormal working condition data, and judging whether it is necessary to compensate the optimized fault monitoring threshold based on the analysis result, it includes:

[0092] Extract the characteristics of the abnormal working condition data to obtain abnormal working condition characteristic data; wherein, the abnormal working condition characteristic data includes abnormal sound characteristic data, abnormal vibration characteristic data and abnormal temperature characteristic data;

[0093] Obtain the abnormal working condition characteristic value of each abnormal working condition characteristic data and the abnormal working condition standard value corresponding to each abnormal working condition characteristic value;

[0094] Compare all the abnormal working condition characteristic values with the corresponding abnormal working condition standard values, and judge whether it is necessary to compensate the optimized fault monitoring threshold according to the comparison result.

[0095] It is understandable that the abnormal working condition data often contains important information about the operation state of the pump unit. By deeply analyzing these data, potential fault hazards can be discovered and processed in time. In this embodiment, the abnormal sound characteristic data can reflect the operation state of the internal mechanical components of the pump unit, such as bearing wear, seal leakage, etc.; the abnormal vibration characteristic data can reveal the vibration situation of the pump unit during operation, which helps to judge whether there are problems such as imbalance and looseness; while the abnormal temperature characteristic data can reflect the temperature distribution of each component of the pump unit, which is of great significance for discovering faults such as overheating and poor cooling.

[0096] When comparing the abnormal working condition characteristic value with the abnormal working condition standard value, if an abnormal working condition characteristic value exceeds its corresponding abnormal working condition standard value, it is considered that the characteristic data is abnormal, which may indicate that there are corresponding fault hazards in the pump unit. At this time, in order to more accurately reflect the actual operation state of the pump unit, it is necessary to compensate and adjust the optimized fault monitoring threshold.

[0097] Specifically, when comparing all the abnormal condition characteristic values with the corresponding abnormal condition standard values and determining whether to compensate the optimized fault monitoring threshold according to the comparison results, it includes:

[0098] When all the abnormal condition characteristic values are less than or equal to the corresponding abnormal condition standard values, it is determined that there is no need to compensate the optimized fault monitoring threshold;

[0099] When there are abnormal condition characteristic values greater than the corresponding abnormal condition standard values, it is determined that the optimized fault monitoring threshold needs to be compensated.

[0100] Specifically, when calculating the abnormal condition influence index of the abnormal condition data, it includes:

[0101] Perform weighted processing on each abnormal condition characteristic value greater than the corresponding abnormal condition standard value to obtain a weighted abnormal condition characteristic value;

[0102] Sum up all the weighted abnormal condition characteristic values to obtain the abnormal condition influence index.

[0103] It can be understood that the abnormal condition influence index comprehensively reflects the severity and potential impact of abnormal conditions in the ordinary monitoring area, and is an important basis for determining whether to compensate the optimized fault monitoring threshold and the degree of compensation. Weighted processing can give different weights according to the importance and sensitivity of different abnormal condition characteristic values, so as to more accurately evaluate their impact on the operating state of the pump unit. By summing up the weighted abnormal condition characteristic values, the obtained abnormal condition influence index can intuitively reflect the overall situation of abnormal conditions and provide strong support for subsequent optimization and adjustment.

[0104] Specifically, when setting the compensation coefficient of the optimized fault monitoring threshold based on the abnormal condition influence index and obtaining the compensated fault monitoring threshold, it includes:

[0105] Compare the abnormal condition influence index with historical data, set the compensation coefficient of the optimized fault monitoring threshold according to the comparison results, and obtain the compensated fault monitoring threshold;

[0106] When there is a historical abnormal condition influence index in the historical data that is the same as the abnormal condition influence index, use the historical optimization coefficient corresponding to the historical abnormal condition influence index as the compensation coefficient, and use the product value of the compensation coefficient and the optimized fault monitoring threshold as the compensated fault monitoring threshold;

[0107] When there is no historical abnormal condition impact index in the historical data that is the same as the abnormal condition impact index, calculate the difference between the abnormal condition impact index and the historical data one by one, record it as the abnormal difference, and construct an abnormal difference set. Set the compensation coefficient of the optimized fault monitoring threshold according to the abnormal difference set, and obtain the compensated fault monitoring threshold.

[0108] In this embodiment, the historical data includes a number of historical abnormal condition impact indexes.

[0109] Specifically, when setting the compensation coefficient of the optimized fault monitoring threshold according to the abnormal difference set and obtaining the compensated fault monitoring threshold, it includes:

[0110] Obtain the minimum value of the abnormal differences in the abnormal difference set, and record it as the minimum abnormal difference;

[0111] Compare the minimum abnormal difference with the first minimum abnormal difference and the second minimum abnormal difference, and determine the compensation coefficient of the optimized fault monitoring threshold according to the comparison result; where the first minimum abnormal difference is less than the second minimum abnormal difference;

[0112] When the minimum abnormal difference is less than the first minimum abnormal difference, determine that the compensation coefficient corresponding to the optimized fault monitoring threshold is the first compensation coefficient, and use the product value of the first compensation coefficient and the optimized fault monitoring threshold as the compensated fault monitoring threshold;

[0113] When the minimum abnormal difference is greater than or equal to the first minimum abnormal difference and less than the second minimum abnormal difference, determine that the compensation coefficient corresponding to the optimized fault monitoring threshold is the second compensation coefficient, and use the product value of the second compensation coefficient and the optimized fault monitoring threshold as the compensated fault monitoring threshold;

[0114] When the minimum abnormal difference is greater than or equal to the second minimum abnormal difference, determine that the compensation coefficient corresponding to the optimized fault monitoring threshold is the third compensation coefficient, and use the product value of the third compensation coefficient and the optimized fault monitoring threshold as the compensated fault monitoring threshold.

[0115] It is understandable that after determining that compensation for the optimized fault monitoring threshold is required, a compensation coefficient for the optimized fault monitoring threshold is set based on the abnormal working condition influence index. Specifically, a compensation coefficient range is set, which includes a first compensation coefficient, a second compensation coefficient, and a third compensation coefficient, and the first compensation coefficient < the second compensation coefficient < the third compensation coefficient. The setting of the compensated fault monitoring threshold further improves the accuracy and flexibility of the pump unit fault monitoring. Through in-depth analysis and compensation processing of the abnormal working condition data, it can more accurately reflect the actual operating state of the pump unit, timely detect and warn of potential fault hazards, and provide a strong guarantee for the stable operation of the pump unit. At the same time, this fault monitoring method based on multi-dimensional perception fusion recognition integrates a variety of monitoring means and information, realizes comprehensive, accurate, and timely monitoring of the operating state of the pump unit, and provides a scientific basis and technical support for the maintenance and management of the pump unit.

[0116] Specifically, when determining the fault warning level of the pump unit to be monitored according to the compensated fault monitoring threshold, it includes:

[0117] Comparing the compensated fault monitoring threshold with a first compensated fault monitoring threshold and a second compensated fault monitoring threshold, and determining the fault warning level of the pump unit to be monitored according to the comparison result; wherein, the first compensated fault monitoring threshold is less than the second compensated fault monitoring threshold;

[0118] When the compensated fault monitoring threshold is less than the first compensated fault monitoring threshold, it is determined that the fault warning level of the pump unit to be monitored is a first-level warning level;

[0119] When the compensated fault monitoring threshold is greater than or equal to the first compensated fault monitoring threshold and less than the second compensated fault monitoring threshold, it is determined that the fault warning level of the pump unit to be monitored is a second-level warning level;

[0120] When the compensated fault monitoring threshold is greater than or equal to the second compensated fault monitoring threshold, it is determined that the fault warning level of the pump unit to be monitored is a third-level warning level;

[0121] Wherein, the first-level warning level is less than the second-level warning level, and the second-level warning level is less than the third-level warning level.

[0122] It can be understood that the setting of the fault warning level aims to issue warning signals of different levels in a timely manner according to the actual operating status of the pump unit, so that the operation and maintenance personnel can respond quickly and take corresponding treatment measures. The first-level warning level indicates that the pump unit is relatively loose, indicating that the operation status of the pump unit is basically normal, but regular inspections and maintenance are still required to ensure its long-term stable operation; the second-level warning level means that there are certain potential fault hazards in the pump unit, and the operation and maintenance personnel need to strengthen monitoring, closely pay attention to the operation status of the pump unit, and be prepared to take corresponding repair or replacement measures; the third-level warning level indicates that there may be serious fault risks in the pump unit. At this time, measures should be taken immediately to conduct a comprehensive inspection of the pump unit, eliminate faults in a timely manner, and avoid greater losses or accidents. By reasonably setting and timely adjusting the fault warning level, it is possible to achieve refined management of the operation status of the pump unit, improve the operation and maintenance efficiency and fault handling ability, and provide a strong guarantee for the safe and stable operation of the pump unit.

[0123] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0125] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the function specified in one process or a plurality of processes and / or boxes Figure 1 one process or a plurality of processes and / or boxes Figure 1 in one box or a plurality of boxes.

[0127] Finally, 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 them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific embodiments of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A pump unit fault monitoring method based on multi-dimensional perception fusion recognition, characterized in that: include: Determine the pump unit to be monitored, divide the monitoring area of ​​the pump unit to be monitored, and obtain key monitoring areas and common monitoring areas; Acquiring the operation stage information of the key monitoring area, and determining the fault monitoring threshold of the pump unit to be monitored based on the operation stage information; Extracting corresponding historical operation records from a historical operation record library based on the key monitoring area, the common monitoring area and the fault monitoring threshold, analyzing the historical operation records, and judging whether it is necessary to optimize the fault monitoring threshold based on the analysis result, and if so, setting the optimization coefficient corresponding to the fault monitoring threshold, and obtaining the optimized fault monitoring threshold; Collecting abnormal operating condition data of the common monitoring area, analyzing the abnormal operating condition data, judging whether it is necessary to compensate the optimized fault monitoring threshold value based on the analysis result, and if so, calculating the abnormal operating condition impact index of the abnormal operating condition data, and setting the compensation coefficient of the optimized fault monitoring threshold value based on the abnormal operating condition impact index, and obtaining the compensated fault monitoring threshold value; Determining the fault warning level of the pump unit to be monitored according to the compensation fault monitoring threshold; When the compensation coefficient of the optimized fault monitoring threshold is set based on the abnormal operating condition impact index and the compensated fault monitoring threshold is obtained, it includes: Comparing the abnormal operating condition impact index with historical data, setting a compensation coefficient of the optimized fault monitoring threshold according to the comparison result, and obtaining a compensated fault monitoring threshold; When there is a historical abnormal operating condition impact index that is the same as the abnormal operating condition impact index in the historical data, the historical optimization coefficient corresponding to the historical abnormal operating condition impact index is used as the compensation coefficient, and the product value of the compensation coefficient and the optimized fault monitoring threshold is used as the compensated fault monitoring threshold; When there is no historical abnormal operating condition impact index that is the same as the abnormal operating condition impact index in the historical data, the difference between the abnormal operating condition impact index and the historical data is calculated one by one, recorded as the abnormal difference, and an abnormal difference set is constructed. The compensation coefficient of the optimized fault monitoring threshold is set according to the abnormal difference set, and the compensated fault monitoring threshold is obtained.

2. The pump unit fault monitoring method based on multi-dimensional perception fusion recognition according to claim 1 is characterized in that: Analyzing the historical operation records and judging whether the fault monitoring threshold needs to be optimized based on the analysis results includes: Extracting the historical operation records of the key monitoring areas respectively and recording them as key historical operation records; extracting the historical operation records of the common monitoring areas and recording them as common historical operation records; Parsing the key historical operation records to obtain key historical normal operation records and key historical abnormal operation records; According to the key historical normal operation records and the key historical abnormal operation records, the abnormal occurrence frequency of the key monitoring area is obtained, and recorded as the key abnormal frequency; Parsing the common historical operation records to obtain common historical normal operation records and common historical abnormal operation records; According to the common historical normal operation records and the common historical abnormal operation records, the abnormal occurrence frequency of the common monitoring area is obtained, and recorded as the common abnormal frequency; Whether the fault monitoring threshold needs to be optimized is determined according to the key abnormal frequency and the common abnormal frequency.

3. The pump unit fault monitoring method based on multi-dimensional perception fusion recognition according to claim 2 is characterized in that: When judging whether the fault monitoring threshold needs to be optimized according to the key abnormal frequency and the common abnormal frequency, it includes: Obtaining a ratio of the key abnormal frequency to the common abnormal frequency, recorded as an abnormal frequency ratio; Comparing the abnormal frequency ratio with the abnormal frequency ratio threshold, and judging whether it is necessary to optimize the fault monitoring threshold according to the comparison result; When the abnormal frequency ratio is greater than the abnormal frequency ratio threshold, determining to optimize the fault monitoring threshold; When the abnormal frequency ratio is less than or equal to the abnormal frequency ratio threshold, it is determined not to optimize the fault monitoring threshold.

4. The pump unit fault monitoring method based on multi-dimensional perception fusion recognition according to claim 3 is characterized in that: Setting the optimization coefficient corresponding to the fault monitoring threshold and obtaining the optimized fault monitoring threshold includes: Setting an optimization coefficient interval, wherein the optimization coefficient interval includes a first optimization coefficient, a second optimization coefficient, and a third optimization coefficient; Performing weighted summation on the key abnormal frequency and the common abnormal frequency to obtain a comprehensive abnormal frequency; Comparing the comprehensive abnormal frequency with the first comprehensive abnormal frequency and the second comprehensive abnormal frequency, and determining the optimization coefficient corresponding to the fault monitoring threshold according to the comparison result; wherein the first comprehensive abnormal frequency is less than the second comprehensive abnormal frequency; When the comprehensive abnormal frequency is less than the first comprehensive abnormal frequency, determining that the optimization coefficient corresponding to the fault monitoring threshold is the first optimization coefficient, and taking the product value of the first optimization coefficient and the fault monitoring threshold as the optimized fault monitoring threshold; When the comprehensive abnormal frequency is greater than or equal to the first comprehensive abnormal frequency and less than the second comprehensive abnormal frequency, determining that the optimization coefficient corresponding to the fault monitoring threshold is the second optimization coefficient, and taking the product value of the second optimization coefficient and the fault monitoring threshold as the optimized fault monitoring threshold; When the comprehensive abnormal frequency is greater than or equal to the second comprehensive abnormal frequency, the optimization coefficient corresponding to the fault monitoring threshold is determined to be the third optimization coefficient, and the product value of the third optimization coefficient and the fault monitoring threshold is used as the optimized fault monitoring threshold.

5. The pump unit fault monitoring method based on multi-dimensional perception fusion recognition according to claim 1 is characterized in that: Collecting abnormal operating condition data of the common monitoring area, analyzing the abnormal operating condition data, and judging whether it is necessary to compensate the optimized fault monitoring threshold based on the analysis result, including: Extracting features from the abnormal operating condition data to obtain abnormal operating condition feature data; wherein the abnormal operating condition feature data includes abnormal sound feature data, abnormal vibration feature data and abnormal temperature feature data; Acquire the abnormal operating condition characteristic value of each abnormal operating condition characteristic data, and the abnormal operating condition standard value corresponding to each abnormal operating condition characteristic value; All the abnormal operating condition characteristic values ​​are compared with the corresponding abnormal operating condition standard values, and it is determined whether the optimized fault monitoring threshold needs to be compensated based on the comparison result.

6. The pump unit fault monitoring method based on multi-dimensional perception fusion recognition according to claim 5 is characterized in that: Comparing all the abnormal operating condition characteristic values ​​with the corresponding abnormal operating condition standard values, and judging whether it is necessary to compensate the optimized fault monitoring threshold value according to the comparison result, includes: When all of the abnormal operating condition characteristic values ​​are less than or equal to the corresponding abnormal operating condition standard values, it is determined that there is no need to compensate the optimized fault monitoring threshold; When the abnormal operating condition characteristic value is greater than the corresponding abnormal operating condition standard value, it is determined that the optimized fault monitoring threshold needs to be compensated.

7. The pump unit fault monitoring method based on multi-dimensional perception fusion recognition according to claim 6 is characterized in that: When calculating the abnormal operating condition impact index of the abnormal operating condition data, it includes: Performing weighted processing on each abnormal operating condition characteristic value that is greater than the corresponding abnormal operating condition standard value to obtain a weighted abnormal operating condition characteristic value; All weighted abnormal operating condition characteristic values ​​are summed up to obtain the abnormal operating condition impact index.

8. The pump unit fault monitoring method based on multi-dimensional perception fusion recognition according to claim 1 is characterized in that: When the compensation coefficient of the optimized fault monitoring threshold is set according to the abnormal difference set and the compensated fault monitoring threshold is obtained, it includes: Obtaining the minimum value of the abnormal difference values ​​in the abnormal difference value set, and recording it as the minimum abnormal difference value; Comparing the minimum abnormal difference with the first minimum abnormal difference and the second minimum abnormal difference, and determining the compensation coefficient of the optimized fault monitoring threshold according to the comparison result; wherein the first minimum abnormal difference is smaller than the second minimum abnormal difference; When the minimum abnormal difference is less than the first minimum abnormal difference, determining that the compensation coefficient corresponding to the optimized fault monitoring threshold is the first compensation coefficient, and taking the product value of the first compensation coefficient and the optimized fault monitoring threshold as the compensated fault monitoring threshold; When the minimum abnormal difference is greater than or equal to the first minimum abnormal difference and less than the second minimum abnormal difference, determining that the compensation coefficient corresponding to the optimized fault monitoring threshold is the second compensation coefficient, and taking the product value of the second compensation coefficient and the optimized fault monitoring threshold as the compensated fault monitoring threshold; When the minimum abnormal difference is greater than or equal to the second minimum abnormal difference, the compensation coefficient corresponding to the optimized fault monitoring threshold is determined to be the third compensation coefficient, and the product value of the third compensation coefficient and the optimized fault monitoring threshold is used as the compensated fault monitoring threshold.

9. The pump unit fault monitoring method based on multi-dimensional perception fusion recognition according to claim 1 is characterized in that: When determining the fault warning level of the pump unit to be monitored according to the compensation fault monitoring threshold, it includes: Comparing the compensation fault monitoring threshold with the first compensation fault monitoring threshold and the second compensation fault monitoring threshold, and determining the fault warning level of the pump unit to be monitored according to the comparison result; wherein the first compensation fault monitoring threshold is less than the second compensation fault monitoring threshold; When the compensation fault monitoring threshold is less than the first compensation fault monitoring threshold, the fault warning level of the pump unit to be monitored is determined to be the first warning level. When the compensation fault monitoring threshold is greater than or equal to the first compensation fault monitoring threshold and less than the second compensation fault monitoring threshold, determining that the fault warning level of the pump unit to be monitored is a secondary warning level; When the compensation fault monitoring threshold is greater than or equal to the second compensation fault monitoring threshold, determining that the fault warning level of the pump unit to be monitored is a third warning level; Among them, the first-level warning level is lower than the second-level warning level, and the second-level warning level is lower than the third-level warning level.

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