Fault monitoring system of air compressor unit

Abstract

The invention discloses a fault monitoring system of an air compressor set, and relates to the technical field of air compressor fault monitoring, and the system comprises a basic information obtaining module which is used for obtaining N pieces of basic information of N air compressors of a target air compressor set; the temperature influence analysis module is used for carrying out temperature influence analysis; the overheating monitoring point laying module is used for generating N overheating monitoring point fields; the real-time overheating analysis module is used for performing real-time monitoring temperature overheating analysis; an overheating influence diffusion analysis module; and the target monitoring result obtaining module is used for obtaining the target monitoring result of the target air compressor unit. The technical problems that in the prior art, a traditional fault monitoring means is difficult to accurately quantify the temperature delay influence and the overheating diffusion influence, and the real-time performance and the reliability of monitoring are low are solved, and the purposes that potential fault risks are recognized in advance through temperature influence delay analysis and overheating influence diffusion prediction are achieved. And the real-time performance and the reliability of fault monitoring are improved.

Description

Technical Field

[0001] The invention relates to the technical field of air compressor fault monitoring, and in particular to a fault monitoring system for an air compressor unit. Background Art

[0002] As an important industrial equipment, the operating status of the air compressor is directly related to the safety and stability of the production line. Therefore, timely and accurate monitoring of the failure of the air compressor unit is an important measure to ensure production safety.

[0003] In complex industrial environments, there is a time delay in the impact of temperature changes on different air compressors, and overheating failures will not only affect the failed compressor, but may also spread to surrounding compressors, increasing the overall failure risk. Traditional fault monitoring methods are difficult to accurately quantify the impact of temperature delays and overheating diffusion, and the real-time and reliability of monitoring are low. Summary of the invention

[0004] The present application provides a fault monitoring system for an air compressor unit, which is used to solve the technical problems in the prior art that traditional fault monitoring means are difficult to accurately quantify the impact of temperature delay and overheat diffusion, and the real-time nature and reliability of monitoring are low.

[0005] The present application provides a fault monitoring system for an air compressor group, the system comprising: a basic information acquisition module, the basic information acquisition module is used to acquire N basic information of N air compressors of a target air compressor group, wherein the N air compressors have N position identifiers; a temperature impact analysis module, the temperature impact analysis module is used to retrieve a set of overheating fault record data of the target air compressor group within a preset historical window, perform temperature impact analysis in combination with the N position identifiers, and generate N sets of temperature impact delay coefficients; an overheating monitoring point layout module, the overheating monitoring point layout module is used to layout overheating monitoring points for the N air compressors according to the N basic information , generating N overheating monitoring point fields; a real-time overheating analysis module, the real-time overheating analysis module is used to use the overheating monitoring network layer to perform real-time monitoring temperature overheating analysis on the N overheating monitoring point fields, and obtain real-time overheating fault monitoring results; an overheating impact diffusion analysis module, the overheating impact diffusion analysis module is used to perform overheating impact diffusion analysis according to the real-time overheating fault monitoring results and the N temperature impact delay coefficient sets, and obtain overheating fault monitoring results; a target monitoring result acquisition module, the target monitoring result acquisition module is used to use the real-time overheating fault monitoring results and the overheating fault monitoring results as the target monitoring results of the target air compressor group.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The air compressor unit fault monitoring system provided by the present application relates to the technical field of air compressor fault monitoring. By retrieving the overheating fault record data set of the target air compressor unit, temperature impact analysis is performed, a temperature impact time delay coefficient set is generated, an overheating monitoring point field is arranged, and real-time monitoring temperature overheating analysis is performed to obtain real-time overheating fault monitoring results. Overheating impact diffusion analysis is performed in combination with the temperature impact time delay coefficient set to obtain the affected overheating fault monitoring results. The real-time overheating fault monitoring results and the affected overheating fault monitoring results are used as target monitoring results. The technical problem that traditional fault monitoring means in the prior art are difficult to accurately quantify the temperature delay impact and the overheating diffusion impact, and the real-time and reliability of monitoring are low is solved. The technical effect of identifying potential fault risks in advance and improving the real-time and reliability of fault monitoring is achieved through temperature impact delay analysis and overheating impact diffusion prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of the structure of a fault monitoring system for an air compressor unit provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of a flow chart for generating N sets of temperature-affected delay coefficients in a fault monitoring system for an air compressor unit provided in an embodiment of the present application;

[0011] Figure 3 A schematic diagram of a flow chart for generating N overheat monitoring point fields in a fault monitoring system for an air compressor unit provided in an embodiment of the present application.

[0012] Explanation of the reference numerals: basic information acquisition module 11 , temperature impact analysis module 12 , overheat monitoring point layout module 13 , real-time overheat analysis module 14 , overheat impact diffusion analysis module 15 , target monitoring result acquisition module 16 . DETAILED DESCRIPTION

[0013] The present application provides a fault monitoring system for an air compressor unit, which is used to solve the technical problems in the prior art that traditional fault monitoring means are difficult to accurately quantify the impact of temperature delay and overheat diffusion, and the real-time nature and reliability of monitoring are low.

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0015] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0016] Example

[0017] like Figure 1 As shown, the present application provides a fault monitoring system for an air compressor unit, the system comprising:

[0018] The basic information acquisition module 11 is used to acquire N basic information of N air compressors of the target air compressor group, wherein the N air compressors have N location identifiers.

[0019] Specifically, the basic information acquisition module 11 of the present application is the basic part of the entire fault monitoring system and is responsible for data collection. The module integrates multiple data acquisition technologies, such as sensor networks, Internet of Things (IoT) communication technologies, and database management systems, to obtain N basic information of N air compressors in the target air compressor unit, including but not limited to the model, operating status, environmental conditions, and most importantly, the location identification of each compressor.

[0020] The location identifier refers to the specific physical location of each air compressor in the air compressor unit or a unique identifier in the system. These identifiers are not only used to distinguish different compressors, but also lay the foundation for subsequent temperature impact analysis and accurate layout of overheating monitoring points. By obtaining the location information of each compressor, the temperature data and fault records can be associated with specific equipment in the subsequent steps, thereby realizing fixed-point monitoring and analysis.

[0021] In actual operation, the module may collect this basic information through a series of sensors, control system interfaces or central management platforms. The collected data includes not only static equipment parameters, but also real-time operating data of the compressor, such as current working pressure, temperature, and historical operation records. These data will be stored in the system database in a structured form to ensure that they can be efficiently called in the temperature impact analysis module and the real-time monitoring module.

[0022] In addition, the module must also have a certain level of intelligent processing capabilities to verify the integrity and accuracy of information during data collection to eliminate possible erroneous data input. In this way, a reliable data foundation can be provided for the entire system, so that subsequent fault monitoring and diffusion analysis can be carried out based on accurate equipment information, ensuring the accuracy and effectiveness of the monitoring results.

[0023] The temperature impact analysis module 12 is used to retrieve the overheating fault record data set of the target air compressor unit within a preset historical window, perform temperature impact analysis in combination with the N position identifiers, and generate N sets of temperature impact delay coefficients.

[0024] Further, such as Figure 2 As shown, the temperature impact analysis module 12 is also used to perform the following steps:

[0025] P21: Using N position identifiers as indexes, perform fault source location retrieval on the overheating fault record data set to obtain N sub-overheating fault record data sets; P22: Perform fault timing extraction based on the N sub-overheating fault record data sets to obtain N fault node sets; P23: Using the overheating impact monitoring time node as an index, perform retrieval on the N sub-overheating fault record data to obtain N overheating impact node sets; P24: Perform delay coefficient analysis based on the N fault node sets and the N overheating impact node sets to obtain the N temperature impact delay coefficient sets.

[0026] Optionally, the temperature impact analysis module 12 of the present application is responsible for analyzing and evaluating the impact of temperature changes on overheating failures of air compressor units. The module retrieves the overheating failure record data set of the target air compressor unit within a preset historical window, combines the location identification of each air compressor, and performs a detailed temperature impact analysis, and finally generates N sets of temperature impact delay coefficients. These delay coefficients can quantify the degree of impact of temperature on equipment overheating failures and its changes over time.

[0027] Specifically, first, the overheat fault record data set is indexed by N position identifiers to perform fault source location retrieval. The purpose of fault source location retrieval is to associate the fault data with a specific air compressor through the position identifier, thereby obtaining N sub-overheat fault record data sets. These sub-sets contain specific overheat fault records that occurred in each compressor within the historical window, providing basic data for subsequent analysis.

[0028] Furthermore, after obtaining the sub-overheat fault record data sets, fault time series extraction is performed based on these sets. By analyzing the time axis of the fault records, the time nodes at which each overheat fault occurs are identified and extracted, thereby generating N fault node sets. These fault node sets identify the specific time points at which each compressor has experienced overheat faults in history, providing a time series basis for analyzing fault development and diffusion.

[0029] Next, the N sub-overheating fault record data sets are retrieved with the overheating impact monitoring time node as the index to obtain N overheating impact node sets. Overheating impact nodes refer to the key time points that may cause subsequent faults identified during the monitoring process. The purpose of this step is to link the time nodes of overheating faults with possible fault diffusion effects, laying the foundation for further delay analysis.

[0030] Finally, based on the N fault node sets and the N overheating affected node sets, a delay coefficient analysis is performed to obtain N temperature-affected delay coefficient sets. The delay coefficient analysis refers to calculating the time delay of the temperature change affecting the equipment failure by comparing the time difference between the fault node and the overheating affected node. This analysis result can help the system understand the dynamic impact of temperature changes on the equipment operation status, and provide key reference data for the prevention and response to overheating failures.

[0031] Furthermore, step P24 of the embodiment of the present application also includes:

[0032] P24-1: randomly extract a first overheating affected node set from the N overheating affected node sets, wherein the first overheating affected node set corresponds to the first air compressor; P24-2: using N-1 air compressors other than the first air compressor among the N air compressors as indexes, perform same air compressor clustering analysis on the first overheating affected node set to obtain N-1 overheating affected node clusters; P24-3: perform difference calculations on the N-1 overheating affected node clusters and the first fault node set corresponding to the first air compressor, respectively, to obtain N-1 first overheating affected delay time sets. P24-4: traverse the N-1 first overheating impact delay time sets for cluster analysis to determine the N-1 first concentrated overheating impact delay times; P24-5: respectively divide the N-1 first concentrated overheating impact delay times by the sum of the N-1 first concentrated overheating impact delay times to obtain N-1 first temperature impact delay coefficients, and use the N-1 first temperature impact delay coefficients as the first temperature impact delay coefficient set of the first air compressor; P24-6: perform delay coefficient analysis on the N overheating impact node sets to obtain the N temperature impact delay coefficient sets.

[0033] In a possible embodiment of the present application, the specific process of delay coefficient analysis is expanded, and cluster analysis of overheating-affected nodes and accurate calculation of delay time are added. Through these extended steps, the system can analyze the impact of temperature on different air compressors in more detail and generate a more accurate set of temperature-affected delay coefficients.

[0034] First, a subset is randomly selected from the N overheating affected node sets as the first overheating affected node set, which corresponds to the first air compressor. The random selection method ensures that various possible influencing factors can be covered in the analysis process, rather than relying solely on fixed monitoring points. Next, the first overheating affected node set is clustered with the N-1 air compressors other than the first air compressor as indexes. The randomly selected overheating affected node set is clustered with the overheating nodes of other air compressors to identify the closest overheating affected nodes among these compressors, and finally N-1 overheating affected node clusters are obtained. This cluster analysis helps the system identify the correlation and conduction path of temperature effects between different devices.

[0035] Furthermore, after obtaining N-1 overheating-affected node clusters, the difference between each node cluster and the first fault node set corresponding to the first air compressor is calculated. By calculating the time difference between these nodes, N-1 first overheating-affected delay time sets are obtained. These time sets represent the time delay of the impact of temperature changes on overheating faults of different compressors, laying the foundation for the next step of analysis. Subsequently, the N-1 first overheating-affected delay time sets are traversed and cluster analysis is performed to determine the N-1 first concentrated overheating-affected delay times. Cluster analysis refers to the comprehensive processing of multiple delay time data to determine the concentrated overheating-affected time that represents the overall impact. This step improves the robustness of the analysis by clustering data to avoid analysis deviations caused by individual outliers.

[0036] Furthermore, the N-1 first concentrated overheating impact delay times are divided by the sum of these delay times to calculate N-1 first temperature impact delay coefficients. These coefficients represent the relative delay effect of each air compressor in the overall temperature impact. Then, these coefficients are used as the first temperature impact delay coefficient set of the first air compressor. Finally, the delay coefficient analysis is performed on the N overheating impact node sets, and the N temperature impact delay coefficient sets are integrated and obtained. The results of this step provide the final output for the temperature impact analysis of the entire air compressor unit, ensuring that the temperature response characteristics of each device are fully considered and evaluated.

[0037] Furthermore, step P24-4 of the embodiment of the present application also includes:

[0038] P24-41: Traverse the N-1 first overheating affected delay time sets to extract clustering centers and obtain N-1 clustering centers, wherein the N-1 clustering centers are respectively the means of the N-1 first overheating affected delay time sets; P24-42: Take the N-1 clustering centers as the starting point and search in the N-1 first overheating affected delay time sets according to the preset clustering granularity to obtain N-1 iteration centers, wherein the N-1 iteration centers have N-1 iteration directions; P24-43: Determine whether the iteration density of the N-1 iteration centers is greater than or equal to the clustering density of the N-1 clustering centers. If so, take the N-1 iteration centers as the N-1 update starting points, and take the N-1 iteration directions as the search directions, and continue to search and iterate in the N-1 first overheating affected delay time sets according to the preset clustering granularity until the preset iteration stop condition is met, and take the N-1 update starting points corresponding to the maximum values ​​of the N-1 iteration densities in the iteration process as the N-1 first set of overheating affected delay times.

[0039] Optionally, the specific process of determining the overheating-affected delay time in the N-1 first sets may be, first, traversing the N-1 first overheating-affected delay time sets and extracting the clustering centers. In this process, by calculating the mean of each set, N-1 clustering centers are obtained. The clustering center refers to the central value of each delay time set, reflecting the average level of the delay time distribution in the set. The goal of this step is to provide an initial reference point for subsequent iterative analysis, so as to more accurately locate the time delay of the overheating effect.

[0040] After obtaining N-1 clustering centers, these clustering centers are used as starting points to perform iterative center searches in the N-1 first overheating impact delay time sets according to the preset clustering granularity. The clustering granularity defines the time interval or accuracy range considered in each search. Through this granularity setting, a more accurate delay center position can be found in the delay time set, and finally N-1 iterative centers are obtained. Each iterative center also has a corresponding iterative direction, indicating the direction of further search, that is, the search path in the delay time set.

[0041] Next, it is determined whether the iteration density of the N-1 iteration centers is greater than or equal to the cluster density of the corresponding cluster centers. The iteration density indicates the distribution density of the delay time values ​​near the current iteration center, while the cluster density indicates the density of the cluster center. If the iteration density is greater than or equal to the cluster density, the system uses these iteration centers as N-1 update starting points and continues to search and iterate in the delay time set in their corresponding iteration directions. This process will continue until the preset iteration stop condition is met.

[0042] When the iteration stop condition is met, the update starting point corresponding to the maximum iteration density in each delay time set during the iteration process is used as the N-1 first set of overheating delay times. This method of dynamic iterative optimization can more accurately locate the key points in the delay time set, ensuring that the system has a more accurate grasp of the time delay affected by overheating.

[0043] Furthermore, step P24-4 of the embodiment of the present application also includes:

[0044] P24-43a: Determine whether the iteration density of the N-1 iteration centers is greater than or equal to the aggregation density of the N-1 aggregation centers. If not, accept the N-1 iteration centers as the N-1 update starting points according to probability, and take the opposite direction of the N-1 iteration direction as the retrieval direction, and continue to perform retrieval iterations in the N-1 first overheating impact delay time sets according to the preset aggregation granularity until the preset iteration stop condition is met, and take the N-1 update starting points corresponding to the maximum value of the N-1 iteration density in the iteration process as the N-1 first concentrated overheating impact delay times, wherein the preset iteration stop condition is when the iteration density difference between the two update starting points of two adjacent iterations is less than or equal to the preset iteration density difference.

[0045] Specifically, if the system determines that the iteration density of N-1 iteration centers is not greater than or equal to the aggregation density of the corresponding aggregation centers, that is, it fails to meet the expectation of density improvement, the system will not directly abandon these iteration centers. Instead, it will accept these iteration centers as new update starting points based on a certain probability. This probabilistic acceptance mechanism allows the system to explore possible optimal solutions when facing complex or irregular data distribution, without being restricted by local low-density areas.

[0046] Once these iteration centers are accepted as update starting points, the system will reverse the iteration direction, that is, continue to perform retrieval iterations in the opposite direction of the initial iteration direction according to the preset aggregation granularity. This reverse retrieval strategy aims to discover high-density areas that may be missed by the initial direction, thereby improving the accuracy of the delay time. Among them, the setting of the iteration stop condition is to control the duration and termination conditions of the iteration process. When the iteration density difference between the two update starting points of two adjacent iterations is less than or equal to the preset iteration density difference, the system considers that it is close to the optimal solution and stops iterating. During this process, the system records the update starting point with the highest density in each iteration, and finally uses the update starting points corresponding to these maximum densities as the N-1 first concentrated overheating impact delay times.

[0047] Through this mechanism, the system can not only obtain accurate delay time when the density meets expectations, but also find potential high-density areas through probability acceptance and reverse retrieval when the density does not meet expectations, thereby avoiding falling into the local optimal solution. The whole process combines density analysis and reverse retrieval to improve the robustness and accuracy of overheating delay time calculation.

[0048] The overheat monitoring point layout module 13 is used to layout overheat monitoring points for the N air compressors according to the N basic information to generate N overheat monitoring point fields.

[0049] Further, such as Figure 3As shown, the overheat monitoring point layout module 13 is also used to perform the following steps:

[0050] P31: Based on the N basic information, overheating monitoring points are arranged with the air compressor body as the first monitoring object, and N first internal overheating monitoring point sets are obtained, wherein each basic information includes the air compressor model, exhaust volume, lubrication method, air output and working principle; P32: According to the N basic information, overheating monitoring points are arranged with the air intake temperature as the second monitoring object, and N second internal overheating monitoring point sets are obtained; P33: According to the N basic information, overheating monitoring points are arranged with the air compressor auxiliary system as the third monitoring object, and N third internal overheating monitoring point sets are obtained; P34: According to the N first internal overheating monitoring point sets, the N second internal overheating monitoring point sets and the N third internal overheating monitoring point sets and the positions of each overheating monitoring point, the N overheating monitoring point fields are generated.

[0051] It should be understood that the overheat monitoring point layout module 13 of the present application is responsible for scientifically and reasonably arranging overheat monitoring points based on the basic information of N air compressors to build a comprehensive overheat monitoring network. This process not only involves in-depth consideration of the compressor body, intake air temperature and auxiliary systems, but also makes full use of key information such as the air compressor model, exhaust volume, lubrication method, air output and working principle.

[0052] First, based on N basic information, the air compressor body is taken as the first monitoring object, and the overheating monitoring points are arranged. Monitoring points are arranged at key parts of the compressor to capture the temperature changes inside the equipment. The first set of internal overheating monitoring points is thus obtained, and the arrangement of each monitoring point is carefully selected based on basic information such as the model, exhaust volume, lubrication method, air output and working principle of the air compressor. Through comprehensive analysis of this information, the system can determine which parts are most prone to overheating, and thus set monitoring points at these locations.

[0053] Next, the system uses the air intake temperature as the second monitoring object and arranges the overheat monitoring points. The intake temperature is one of the key factors affecting the operating efficiency and safety of the compressor. Therefore, setting monitoring points near the air intake can monitor the changes in air temperature in real time and detect abnormal conditions in time. Through this step, the system obtains a set of N second internal overheat monitoring points, which focus on detecting fluctuations in intake temperature, thereby providing important temperature data for the system.

[0054] Then, based on the N basic information, the system takes the auxiliary system of the air compressor as the third monitoring object and arranges overheating monitoring points. The auxiliary system includes the cooling system and lubrication system of the compressor. The normal operation of these systems is directly related to the overall performance and overheating risk of the compressor. By arranging monitoring points in key parts of these auxiliary systems, the system can monitor parameters such as the temperature of the coolant, the temperature and pressure of the lubricating oil, and collect detailed data in the N third internal overheating monitoring point sets to ensure the stable operation of the auxiliary system.

[0055] Finally, after obtaining the above N internal overheat monitoring point sets, the system integrates these sets. According to the location of each overheat monitoring point, N overheat monitoring point fields are generated. The overheat monitoring point field refers to a comprehensive monitoring network composed of multiple monitoring points, which can cover the entire operating environment of the air compressor. Through real-time monitoring of these fields, the system can fully grasp the temperature status of the compressor and warn and deal with potential overheating risks.

[0056] The real-time overheating analysis module 14 is used to utilize the overheating monitoring network layer to perform real-time monitoring temperature overheating analysis on the N overheating monitoring point fields to obtain real-time overheating fault monitoring results.

[0057] Furthermore, the real-time overheat analysis module 14 is also used to perform the following steps:

[0058] P41: Construct an overheating monitoring network layer; P42: Perform real-time monitoring temperature extraction on the N overheating monitoring point fields to obtain N real-time monitoring temperature fields; P43: Use the overheating monitoring network layer to perform temperature overheating analysis on the N real-time monitoring temperature fields to obtain real-time overheating fault monitoring results, wherein the real-time overheating fault monitoring results include a first real-time overheating air compressor identifier.

[0059] Optionally, the real-time overheat analysis module 14 of the present application is responsible for real-time overheat fault monitoring and diagnosis of the air compressor unit. Specifically, firstly, the data from N overheat monitoring point fields are integrated and managed by constructing an overheat monitoring network layer. The overheat monitoring network layer is a highly integrated monitoring network that connects temperature monitoring points distributed in different locations to form a unified data acquisition and processing platform. Through this network layer, the system can summarize the temperature information of each monitoring point in real time and provide a comprehensive data basis for subsequent temperature analysis.

[0060] After the overheat monitoring network layer is built, the system performs real-time monitoring temperature extraction on N overheat monitoring point fields. This process involves collecting current temperature data from each monitoring point and generating corresponding N real-time monitoring temperature fields. Real-time monitoring temperature field refers to the temperature distribution recorded at each monitoring point at a specific time point. Through this real-time temperature extraction, the system can grasp the current temperature status of the air compressor and provide detailed data support for the next step of analysis.

[0061] Next, the overheat monitoring network layer is used to perform temperature overheat analysis on N real-time monitoring temperature fields. During this process, the system analyzes the temperature data of each monitoring point and identifies possible overheating phenomena. Through the analysis of the temperature field, areas of abnormal temperature are detected, and the potential impact of these anomalies on the operation of the air compressor is evaluated. Finally, real-time overheating fault monitoring results are generated, including the first real-time overheated air compressor identification. The first real-time overheated air compressor identification refers to the specific identification information of the air compressor that is currently in an overheated state identified by the system. This identification helps operation and maintenance personnel quickly locate the problem equipment and take appropriate countermeasures.

[0062] The overheating influence diffusion analysis module 15 is used to perform overheating influence diffusion analysis according to the real-time overheating fault monitoring result and the N sets of temperature influence delay coefficients to obtain the overheating fault monitoring result.

[0063] Furthermore, the overheating impact diffusion analysis module 15 is also used to perform the following steps:

[0064] P51: Based on the first real-time overheating air compressor identifier, match the N temperature-affecting delay coefficient sets to generate a first real-time temperature-affecting delay coefficient set; P52: Based on the first real-time temperature-affecting delay coefficient set, asynchronous continuous temperature monitoring is performed on N-1 real-time air compressors to obtain N-1 continuous overheating fault monitoring data sets; P53: Using the overheating monitoring network layer, the N-1 continuous overheating fault monitoring data sets are identified to obtain overheating fault monitoring results, wherein the overheating fault monitoring results include multiple overheating air compressor identifiers.

[0065] Specifically, the overheating impact diffusion analysis module 15 of the present application is a key module in the system responsible for evaluating the diffusion range and speed of overheating faults in the air compressor unit. The module integrates the real-time overheating fault monitoring results and the temperature impact delay coefficient set to achieve accurate analysis of the overheating impact diffusion path and impact.

[0066] First, the first real-time temperature-affected delay coefficient set is generated by matching the first real-time overheated air compressor identifier with the N temperature-affected delay coefficient sets. In this process, the system compares the real-time detected overheated compressor identifier with the pre-calculated temperature-affected delay coefficient to determine the response delay of the compressor under the current temperature conditions. The first real-time temperature-affected delay coefficient set contains delay information related to the compressor, reflecting the immediate impact of temperature changes on the device.

[0067] Next, based on the first real-time temperature impact delay coefficient set, asynchronous continuous temperature monitoring is performed on the N-1 real-time impact air compressors. In this step, the system is not limited to the overheated compressors that have been detected, but expands the monitoring range to cover other compressors related to the overheated compressor. These related compressors may be affected due to their location, working environment or system configuration, so continuous temperature monitoring is required. Through this asynchronous monitoring method, the system can collect N-1 continuous overheating fault monitoring data sets, which record the temperature changes of these compressors at different time points.

[0068] Finally, the overheat monitoring network layer is used to identify N-1 continuous overheat fault monitoring data sets to obtain the overheat fault monitoring results. In this process, the system will conduct in-depth analysis of the continuously monitored data, identify other compressors that may be affected by overheating, and generate multiple identifiers of air compressors that affect overheating. These identifiers indicate other air compressors that may be affected when the current overheating fault occurs, helping operation and maintenance personnel to pre-identify and deal with these potential fault risks.

[0069] The target monitoring result acquisition module 16 is used to use the real-time overheating fault monitoring result and the influencing overheating fault monitoring result as the target monitoring result of the target air compressor group.

[0070] It should be understood that the target monitoring result acquisition module 16 of the present application plays a role of aggregation and decision support in the entire fault monitoring system. The main task of this module is to integrate the real-time overheating fault monitoring results and the influencing overheating fault monitoring results to form a comprehensive target monitoring result for the target air compressor unit. This result not only provides a complete perspective for the real-time monitoring of the system, but also provides direct data support for the decision-making of the operation and maintenance personnel.

[0071] Specifically, firstly, the real-time overheating fault monitoring result is received from the real-time overheating analysis module 14. This result directly points out the compressor number that is currently experiencing the overheating fault (i.e., the first real-time overheating air compressor identifier), providing immediate target positioning for subsequent fault response.

[0072] Next, the module imports the monitoring results of overheating faults from the overheating impact diffusion analysis module 15. This result, based on an in-depth analysis of the temperature impact delay coefficient, reveals the diffusion path and impact range of overheating faults in the compressor unit, including multiple compressor identifiers that are directly or indirectly affected by overheating faults. The addition of this information makes the target monitoring results not limited to a single fault point, but extends to the areas that may be affected in the entire unit, greatly improving the comprehensiveness and foresight of monitoring.

[0073] After integrating these two key monitoring results, data fusion and processing technology are used to organically combine them to form a comprehensive description of the current status of the target air compressor unit. This process not only takes into account the immediacy of the fault, but also takes into account the dynamics and diffusion of the fault development, ensuring the accuracy and practicality of the monitoring results.

[0074] Finally, the target monitoring results output by the module not only provide operators with clear information on the fault location and impact range, but also provide strong data support for them to formulate countermeasures and optimize the unit operation strategy. This result is not only the direct output of the monitoring system, but also an important decision-making basis for ensuring the safe and stable operation of the air compressor unit. Through the work of this module, the effectiveness of the entire monitoring system has been fully utilized, providing a solid guarantee for the continuity and efficiency of industrial production.

[0075] In summary, the embodiments of the present application have at least the following technical effects:

[0076] The present application retrieves the overheating fault record data set of the target air compressor unit, performs temperature impact analysis, generates a temperature impact time delay coefficient set, deploys an overheating monitoring point field, performs real-time monitoring of temperature overheating analysis, obtains real-time overheating fault monitoring results, combines the temperature impact time delay coefficient set to perform overheating impact diffusion analysis, obtains the impact overheating fault monitoring results, and uses the real-time overheating fault monitoring results and the impact overheating fault monitoring results as the target monitoring results.

[0077] The technical effect of identifying potential failure risks in advance and improving the real-time and reliability of failure monitoring is achieved through temperature impact delay analysis and overheating impact diffusion prediction.

[0078] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0080] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The fault monitoring system of the air compressor unit is characterized by: The system comprises: A basic information acquisition module 11, wherein the basic information acquisition module 11 is used to acquire N basic information of N air compressors of a target air compressor group, wherein the N air compressors have N location identifiers; A temperature impact analysis module 12, the temperature impact analysis module 12 is used to retrieve the overheating fault record data set of the target air compressor unit within a preset historical window, perform temperature impact analysis in combination with the N position identifiers, and generate N sets of temperature impact delay coefficients; An overheat monitoring point layout module 13, the overheat monitoring point layout module 13 is used to layout overheat monitoring points for the N air compressors according to the N basic information, and generate N overheat monitoring point fields; A real-time overheating analysis module 14, which is used to use an overheating monitoring network layer to perform real-time monitoring temperature overheating analysis on the N overheating monitoring point fields to obtain real-time overheating fault monitoring results; An overheating influence diffusion analysis module 15, the overheating influence diffusion analysis module 15 is used to perform overheating influence diffusion analysis according to the real-time overheating fault monitoring result and the N sets of temperature influence delay coefficients to obtain an overheating fault monitoring result; The target monitoring result acquisition module 16 is used to use the real-time overheating fault monitoring result and the influencing overheating fault monitoring result as the target monitoring result of the target air compressor group.

2. The air compressor unit fault monitoring system according to claim 1, characterized in that: Retrieve the overheating fault record data set of the target air compressor unit within the preset historical window, perform temperature impact analysis in combination with the N position identifiers, and generate N sets of temperature impact delay coefficients, including: Using the N position identifiers as indexes, performing fault source location retrieval on the overheating fault record data set to obtain N sub-overheating fault record data sets; Perform fault time sequence extraction based on the N sub-overheat fault record data sets to obtain N fault node sets; Using the overheating impact monitoring time node as an index, searching the N sub-overheating fault record data to obtain N overheating impact node sets; Delay coefficient analysis is performed based on the N fault node sets and the N overheating affected node sets to obtain the N temperature affected delay coefficient sets.

3. The fault monitoring system for an air compressor unit according to claim 2, characterized in that: include: Randomly extracting a first overheating affected node set from the N overheating affected node sets, wherein the first overheating affected node set corresponds to a first air compressor; Taking N-1 air compressors other than the first air compressor among the N air compressors as indexes, performing same-air compressor clustering analysis on the first overheating affected node set to obtain N-1 overheating affected node clusters; performing difference calculations on the N-1 overheating-affected node clusters and the first fault node set corresponding to the first air compressor respectively, to obtain N-1 first overheating-affected delay time sets; Traversing the N-1 first overheating impact delay time sets to perform cluster analysis and determine the N-1 first concentrated overheating impact delay times; respectively dividing the N-1 first concentrated overheating influence delay times by the sum of the N-1 first concentrated overheating influence delay times to obtain N-1 first temperature influence time delay coefficients, and using the N-1 first temperature influence time delay coefficients as a first temperature influence time delay coefficient set of the first air compressor; A delay coefficient analysis is performed on the N overheating affected node sets to obtain the N temperature affected delay coefficient sets.

4. The fault monitoring system for an air compressor unit according to claim 3, characterized in that: include: Traversing the N-1 first overheating influence delay time sets to extract clustering centers, and obtaining N-1 clustering centers, wherein the N-1 clustering centers are respectively the means of the N-1 first overheating influence delay time sets; Taking the N-1 clustering centers as starting points, searching in the N-1 first overheating influence delay time sets according to a preset clustering granularity to obtain N-1 iteration centers, wherein the N-1 iteration centers have N-1 iteration directions; Determine whether the iteration density of the N-1 iteration centers is greater than or equal to the aggregation density of the N-1 aggregation centers. If so, take the N-1 iteration centers as N-1 update starting points, and take the N-1 iteration directions as retrieval directions. Continue to perform retrieval iterations in the N-1 first overheating impact delay time sets according to the preset aggregation granularity until the preset iteration stop condition is met. Take the N-1 update starting points corresponding to the maximum values ​​of the N-1 iteration densities in the iteration process as the N-1 first concentrated overheating impact delay times.

5. The fault monitoring system for an air compressor unit according to claim 4, characterized in that: include: Determine whether the iteration density of the N-1 iteration centers is greater than or equal to the aggregation density of the N-1 aggregation centers. If not, accept the N-1 iteration centers as N-1 update starting points according to probability, and take the opposite direction of the N-1 iteration direction as the retrieval direction, and continue to perform retrieval iterations in the N-1 first overheating impact delay time sets according to the preset aggregation granularity until the preset iteration stop condition is met, and take the N-1 update starting points corresponding to the maximum value of the N-1 iteration density in the iteration process as the N-1 first concentrated overheating impact delay time, wherein the preset iteration stop condition is when the iteration density difference between the two update starting points of two adjacent iterations is less than or equal to the preset iteration density difference.

6. The air compressor unit fault monitoring system according to claim 1, characterized in that: The overheat monitoring network layer is used to perform real-time monitoring temperature overheat analysis on the N overheat monitoring point fields to obtain real-time overheat fault monitoring results, including: Build an overheat monitoring network layer; Performing real-time monitoring temperature extraction on the N overheat monitoring point fields to obtain N real-time monitoring temperature fields; The overheat monitoring network layer is used to perform temperature overheat analysis on the N real-time monitoring temperature fields to obtain a real-time overheat fault monitoring result, wherein the real-time overheat fault monitoring result includes a first real-time overheat air compressor identifier.

7. The air compressor unit fault monitoring system according to claim 6, characterized in that: An overheating influence diffusion analysis is performed according to the real-time overheating fault monitoring result and the N temperature-influenced delay coefficient sets to obtain an overheating fault monitoring result, including: Generate a first real-time temperature impact delay coefficient set based on matching the first real-time overheated air compressor identifier with the N temperature impact delay coefficient sets; Based on the first real-time temperature impact delay coefficient set, asynchronous continuous temperature monitoring is performed on N-1 real-time impact air compressors to obtain N-1 continuous overheating fault monitoring data sets; The N-1 continuous overheating fault monitoring data sets are identified by using the overheating monitoring network layer to obtain overheating fault monitoring results, wherein the overheating fault monitoring results include multiple overheating air compressor identifiers.

8. The fault monitoring system for an air compressor unit according to claim 1, characterized in that: Arrange overheat monitoring points for the N air compressors according to the N basic information to generate N overheat monitoring point fields, including: Based on the N basic information, overheat monitoring points are arranged with the air compressor body as the first monitoring object to obtain N first internal overheat monitoring point sets, wherein each basic information includes the air compressor model, exhaust volume, lubrication method, air output and working principle; According to the N basic information, overheat monitoring points are arranged with air intake temperature as the second monitoring object to obtain N second internal overheat monitoring point sets; According to the N basic information, overheat monitoring points are arranged with the air compressor auxiliary system as the third monitoring object to obtain N third internal overheat monitoring point sets; The N overheat monitoring point fields are generated according to the N first internal overheat monitoring point sets, the N second internal overheat monitoring point sets and the N third internal overheat monitoring point sets and the positions of the respective overheat monitoring points.

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