Fault monitoring system for air compressor units

Abstract

The present invention discloses a fault monitoring system for an air compressor unit, which relates to the technical field of air compressor fault monitoring. The system includes: a basic information acquisition module for acquiring N basic information of N air compressors of a target air compressor unit; a temperature impact analysis module for performing temperature impact analysis; an overheat monitoring point layout module for generating N overheat monitoring point fields; a real-time overheat analysis module for performing real-time monitoring temperature overheat analysis; an overheat impact diffusion analysis module; and a target monitoring result acquisition module for acquiring target monitoring results of a target air compressor unit. The present invention solves the technical problem in the prior art that traditional fault monitoring methods are difficult to accurately quantify the impact of temperature delay and overheat diffusion, and the real-time and reliability of monitoring are low. The present invention achieves the technical effect of identifying potential fault risks in advance and improving the real-time and reliability of fault monitoring through temperature impact delay analysis and overheat impact diffusion prediction.

Description

Technical Field

[0001] The present 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 air compressor faults is an important measure to ensure production safety.

[0003] In complex industrial environments, temperature changes affect different air compressors with a time delay, and overheating failures not only affect the faulty compressor but may also spread to surrounding compressors, increasing the overall failure risk. Traditional fault monitoring methods cannot 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 overheating diffusion, and the real-time 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 being 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 being 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 a set of N temperature impact time delay coefficients; an overheating monitoring point layout module, the overheating monitoring point layout module being 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 to 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 based on the real-time overheating fault monitoring results and the N temperature impact delay coefficient sets to obtain an impact overheating fault monitoring result; 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 impact 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 in 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 delay coefficient set is generated, an overheating monitoring point field is arranged, and real-time 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 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. This solves the technical problem in the existing technology that traditional fault monitoring means are difficult to accurately quantify temperature delay effects and overheating diffusion effects, and the real-time and reliability of monitoring are low. This achieves the technical effect of identifying potential fault risks in advance and improving the real-time and reliability of fault monitoring 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 overheating 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 , overheating monitoring point layout module 13 , real-time overheating analysis module 14 , overheating 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 overheating diffusion, and the real-time and reliability of monitoring are low.

[0014] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts 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 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 this application is the foundation of the entire fault monitoring system and is responsible for data collection. By integrating multiple data acquisition technologies, such as sensor networks, Internet of Things (IoT) communication technologies, and database management systems, this module acquires N basic information about the N air compressors in the target air compressor unit. This basic information includes, but is not limited to, the compressor model, operating status, environmental conditions, and, most critically, the location identifier of each compressor.

[0020] The location identifier refers to the specific physical location of each air compressor within the compressor unit, or a unique identifier within the system. These identifiers not only distinguish between compressors but also lay the foundation for subsequent temperature impact analysis and the precise placement of overheat monitoring points. By obtaining the location information of each compressor, temperature data and fault records can be subsequently linked to specific equipment, enabling targeted monitoring and analysis.

[0021] In practice, this module might collect this basic information through a series of sensors, control system interfaces, or a central management platform. The collected data includes not only static equipment parameters but also real-time compressor operating data, such as current operating pressure and temperature, as well as historical operating records. This data is stored in a structured format in the system's database, ensuring efficient access to the temperature impact analysis module and the real-time monitoring module.

[0022] Furthermore, the module must possess intelligent processing capabilities to verify the integrity and accuracy of information during data collection to eliminate potential erroneous data input. This provides a reliable data foundation for the entire system, enabling subsequent fault monitoring and diffusion analysis to be conducted based on precise device information, ensuring the accuracy and effectiveness of 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 temperature impact delay coefficient sets.

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

[0025] P21: Using N location 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 time sequence 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 this application is responsible for analyzing and evaluating the impact of temperature changes on overheating failures in air compressor units. This module retrieves a data set of overheating failure records for the target air compressor unit within a preset historical window, combines it with the location identifier of each air compressor, and performs a detailed temperature impact analysis, ultimately generating a set of N temperature impact delay coefficients. These delay coefficients quantify the degree of temperature's impact on equipment overheating failures and how it changes over time.

[0027] Specifically, the overheat fault record data set is first indexed by N location identifiers to perform a fault source location search. The purpose of this fault source location search is to associate the fault data with a specific air compressor using the location identifiers, thereby obtaining N subsets of overheat fault record data sets. These subsets contain the specific overheat fault records that occurred for each compressor within the historical window, providing foundational data for subsequent analysis.

[0028] Furthermore, after obtaining the sub-overheat fault record data sets, we perform fault time series extraction based on these sets. By analyzing the timeline of the fault records, we identify and extract the time nodes at which each overheat fault occurred, thereby generating N fault node sets. These fault node sets identify the specific time points at which each compressor experienced an overheat fault in history, providing a time series basis for analyzing the development and spread of faults.

[0029] Next, we search the N sub-sets of overheating fault records using the overheating impact monitoring time node as an index to obtain a set of N overheating impact nodes. Overheating impact nodes are critical time points identified during monitoring that may trigger subsequent faults. This step aims to link the time nodes of overheating faults with the potential impact of fault propagation, laying the foundation for further delay analysis.

[0030] Finally, a delay coefficient analysis is performed based on the N sets of faulty nodes and the N sets of overheating-affected nodes, resulting in N sets of temperature-affected delay coefficients. This delay coefficient analysis compares the time difference between the faulty node and the overheating-affected node to calculate the time delay caused by temperature changes on device failures. This analysis helps the system understand the dynamic impact of temperature changes on device operating status, providing critical reference data for preventing and responding to overheating failures.

[0031] Furthermore, step P24 of the embodiment of the present application further 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 calculation on the N-1 overheating affected node clusters and the first fault node set corresponding to the first air compressor to obtain N-1 first overheating affected delay time sets. P24-4: traverse the N-1 first overheating impact delay time sets and perform 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 one possible embodiment of this application, the specific delay coefficient analysis process is expanded to include cluster analysis of overheating-affected nodes and precise calculation of delay time. Through these expanded steps, the system can more carefully analyze the impact of temperature on different air compressors and generate a more accurate set of temperature-affected delay coefficients.

[0034] First, a subset is randomly selected from the N sets of overheating-affected nodes as the first set of overheating-affected nodes, which corresponds to the first air compressor. The random selection method ensures that various possible influencing factors can be covered during the analysis process, rather than relying solely on fixed monitoring points. Next, the first set of overheating-affected nodes is subjected to a clustering analysis of the same air compressor using the N-1 air compressors other than the first air compressor among the N air compressors as indexes. The randomly selected set of overheating-affected nodes is clustered with the overheating nodes of other air compressors to identify the closest overheating-affected nodes among these compressors, and ultimately N-1 clusters of overheating-affected nodes are obtained. This clustering analysis helps the system identify the correlation and conduction path of temperature impacts 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 the 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 and avoids 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. These coefficients are then used as the first temperature impact delay coefficient set for the first air compressor. Finally, the delay coefficient analysis is performed on the N sets of overheating impact nodes, and N sets of temperature impact delay coefficients are integrated to obtain. The results of this step provide the final output for the temperature impact analysis of the entire air compressor group, 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 further 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: Taking the N-1 clustering centers as the starting point, 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, use the N-1 iteration centers as the N-1 update starting points, and use the N-1 iteration directions as the search directions. 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. Use 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 affected delay times.

[0039] Optionally, the specific process for determining the N-1 first sets of overheating-affected delay times can be as follows: first, traverse the N-1 first sets of overheating-affected delay times and extract cluster centers. During this process, the mean of each set is calculated to obtain N-1 cluster centers. The cluster center refers to the central value of each delay time set and reflects the average level of the delay time distribution within that set. The goal of this step is to provide an initial reference point for subsequent iterative analysis, thereby more accurately locating the time delays affected by overheating.

[0040] After obtaining N-1 cluster centers, the algorithm uses these cluster centers as starting points and performs an iterative search for centers within the N-1 first overheating impact delay time sets according to the preset cluster granularity. The cluster granularity defines the time interval or precision range considered in each search. This granularity allows for more precise delay center locations within the delay time sets, ultimately obtaining N-1 iteration centers. Each iteration center also has an associated iteration direction, indicating the direction of further search, i.e., the search path within the delay time set.

[0041] Next, the system determines 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 represents the distribution density of delay time values ​​around the current iteration center, while the cluster density represents the density of cluster centers. 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 searching and iterating within the delay time set in their corresponding iteration directions. This process continues until the preset iteration stop condition is met.

[0042] When the iteration stopping 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 first N-1 overheating-affected delay times. This dynamic iterative optimization method can more accurately locate the key points in the delay time set, ensuring that the system has a more accurate understanding of the time delay affected by overheating.

[0043] Furthermore, step P24-4 of the embodiment of the present application further 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 use the opposite direction of the N-1 iteration direction as the retrieval direction. 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. The N-1 update starting points corresponding to the maximum value of the N-1 iteration density in the iteration process are used 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 cluster density of the corresponding cluster centers, meaning that the expected density increase is not met, the system does not directly abandon these iteration centers. Instead, it accepts 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 even when faced with complex or irregular data distributions, 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 the search iteration in the opposite direction of the initial iteration direction according to the preset aggregation granularity. This reverse search strategy aims to discover high-density areas that may have been 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] This mechanism not only allows the system to accurately calculate delay times when density meets expectations, but also helps avoid falling into local optimal solutions by identifying potential high-density areas through probabilistic acceptance and reverse search when density deviates. This entire process, combining density analysis and reverse search, improves the robustness and accuracy of delay calculations affected by overheating.

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

[0051] It should be understood that the overheat monitoring point placement module 13 of the present application is responsible for scientifically and rationally placing 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 itself, intake air temperature, and auxiliary systems, but also fully utilizes key information such as the air compressor model, exhaust volume, lubrication method, output volume, and operating principle.

[0052] First, based on N basic information, the system determines overheating monitoring points, with the air compressor itself as the primary monitoring target. These points are placed in key areas of the compressor to capture temperature fluctuations within the equipment. This results in a first set of internal overheating monitoring points, each carefully selected based on basic information such as the compressor model, exhaust volume, lubrication method, air output, and operating principle. Through comprehensive analysis of this information, the system identifies areas most susceptible to overheating and sets monitoring points accordingly.

[0053] Next, the system uses air intake temperature as the secondary monitoring target and deploys overheat monitoring points. Intake air temperature is a key factor affecting compressor operating efficiency and safety. Therefore, placing monitoring points near the air intake allows for real-time monitoring of air temperature changes and prompt detection of abnormalities. Through this step, the system obtains a set of N secondary internal overheat monitoring points, which focus on detecting fluctuations in intake air temperature, providing the system with important temperature data.

[0054] Then, based on this N basic information, the system deploys overheat monitoring points, using the air compressor's auxiliary systems as the third monitoring target. Auxiliary systems include the compressor's cooling and lubrication systems, and their proper operation directly impacts the compressor's overall performance and overheating risk. By placing monitoring points in key locations within these auxiliary systems, the system monitors parameters such as coolant temperature, lubricant oil temperature, and pressure. This allows the system to collect detailed data from the N third internal overheat monitoring points, ensuring stable operation of the auxiliary systems.

[0055] Finally, after obtaining the N sets of internal overheat monitoring points, the system integrates these sets. Based on the locations of each overheat monitoring point, N overheat monitoring point fields are generated. An overheat monitoring point field is a comprehensive monitoring network consisting of multiple monitoring points that covers the entire air compressor operating environment. By monitoring these fields in real time, the system can fully understand the compressor's temperature status and provide early warning and treatment for potential overheating risks.

[0056] The real-time overheating analysis module 14 is used to use 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 further configured 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, an overheat monitoring network layer is first constructed to integrate and manage data from N overheat monitoring points. 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 collection and processing platform. Through this network layer, the system can summarize temperature information from each monitoring point in real time, providing a comprehensive data foundation for subsequent temperature analysis.

[0060] After building the overheat monitoring network layer, the system extracts real-time temperature data from N overheat monitoring points. This process involves collecting current temperature data from each monitoring point and generating N corresponding real-time temperature fields. A real-time temperature field refers to the temperature distribution recorded at each monitoring point at a specific point in time. Through this real-time temperature extraction, the system can understand the current temperature status of the air compressor and provide detailed data support for further analysis.

[0061] Next, the overheating monitoring network layer is used to perform temperature overheating 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. By analyzing the temperature field, areas with abnormal temperatures are detected, and the potential impact of these abnormalities on the operation of the air compressor is evaluated. Finally, a real-time overheating fault monitoring result is generated, which includes the first real-time overheating air compressor identification. The first real-time overheating air compressor identification refers to the specific identification information of the air compressor that is currently in an overheating state identified by the system. This identification helps operation and maintenance personnel quickly locate the problem equipment and take appropriate countermeasures.

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

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

[0064] P51: Based on the first real-time overheating air compressor identifier, the N temperature-affecting delay coefficient sets are matched 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 affecting air compressors to obtain N-1 continuous overheating fault monitoring data sets; P53: The N-1 continuous overheating fault monitoring data sets are identified using the overheating monitoring network layer to obtain an affecting overheating fault monitoring result, wherein the affecting overheating fault monitoring result includes multiple affecting overheating air compressor identifiers.

[0065] Specifically, the overheating impact diffusion analysis module 15 of this application is a key module in the system responsible for evaluating the scope and speed of overheating faults spreading within the air compressor unit. By integrating real-time overheating fault monitoring results with a set of temperature impact delay coefficients, this module enables accurate analysis of the overheating impact diffusion path and impact.

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

[0067] Next, based on the first real-time temperature impact delay coefficient set, asynchronous continuous temperature monitoring is performed on N-1 real-time affected 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 identifies N-1 sets of persistent overheating fault monitoring data to obtain the overheating fault monitoring results. During this process, the system conducts in-depth analysis of the continuously monitored data to identify other compressors that may be affected by overheating and generates multiple identifiers of air compressors that may be affected by overheating. These identifiers indicate other air compressors that may be affected by the current overheating fault, helping operations and maintenance personnel to proactively identify and address these potential fault risks.

[0069] The target monitoring result acquisition module 16 is configured to use the real-time overheating fault monitoring result and the impact 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 in summarizing and supporting decision-making within the entire fault monitoring system. Its primary task is to integrate real-time overheating fault monitoring results with those affecting overheating faults to form a comprehensive target monitoring result for the target air compressor unit. This result not only provides a complete perspective for real-time system monitoring but also provides direct data support for decision-making by operations and maintenance personnel.

[0071] Specifically, first, 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 an overheating fault (i.e., the first real-time overheating air compressor identifier), providing immediate target positioning for subsequent fault response.

[0072] The module then incorporates the overheating fault monitoring results from the Overheating Impact Diffusion Analysis Module 15. This result, based on an in-depth analysis of the temperature-impact delay coefficient, reveals the overheating fault's diffusion path and impact range within the compressor unit, specifically including the identifiers of multiple compressors directly or indirectly affected by the overheating fault. This inclusion of information extends the target monitoring results beyond a single fault point to encompass potentially affected areas throughout the entire unit, significantly improving the comprehensiveness and foresight of monitoring.

[0073] After integrating these two key monitoring results, data fusion and processing techniques 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 considers the immediacy of the fault, but also the dynamic and diffuse nature of the fault development, ensuring the accuracy and practicality of the monitoring results.

[0074] Ultimately, the module's target monitoring results not only provide operators with clear information on the fault location and impact range, but also provide strong data support for developing countermeasures and optimizing unit operation strategies. This result is not only a direct output of the monitoring system but also a crucial basis for decision-making to ensure the safe and stable operation of the air compressor unit. Through the operation 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] This 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 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 fault monitoring is achieved through temperature impact delay analysis and overheating impact diffusion prediction.

[0078] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain 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 replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0080] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. The fault monitoring system of the air compressor unit is characterized by: The system comprises: A basic information acquisition module (11), 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 a set of overheating fault record data 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 overheat analysis module (14), the real-time overheat analysis module (14) is used to use the overheat monitoring network layer to perform real-time monitoring temperature overheat analysis on the N overheat monitoring point fields to obtain real-time overheat fault monitoring results; An overheating influence diffusion analysis module (15), the overheating influence diffusion analysis module (15) being used to perform overheating influence diffusion analysis based on the real-time overheating fault monitoring result and the N sets of temperature influence time delay coefficients to obtain an overheating fault monitoring result; a target monitoring result acquisition module (16), the target monitoring result acquisition module (16) being used to use the real-time overheating fault monitoring result and the impact overheating fault monitoring result as the target monitoring result of the target air compressor group; The overheating fault record data set of the target air compressor unit within a preset historical window is retrieved, and temperature impact analysis is performed in combination with the N location identifiers to 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; Performing fault time series extraction based on the N sub-overheating 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 a set of N overheating impact nodes; Performing a delay coefficient analysis based on the N fault node sets and the N overheating-affected node sets to obtain the N temperature-affected delay coefficient sets; Wherein, the system comprises: 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; 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; performing difference calculations on the N-1 overheating-affected node clusters and the first fault node set corresponding to the first air compressor 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; Dividing the N-1 first concentrated overheating impact delay times by the sum of the N-1 first concentrated overheating impact delay times respectively to obtain N-1 first temperature impact delay coefficients, and using the N-1 first temperature impact delay coefficients as a first temperature impact delay coefficient set for the first air compressor; Performing a delay coefficient analysis on the N overheating-affected node sets to obtain the N temperature-affected delay coefficient sets; The overheat monitoring network layer is used to perform real-time 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 overheating monitoring point fields to obtain N real-time monitoring temperature fields; Performing temperature overheat analysis on the N real-time monitored temperature fields using the overheat monitoring network layer to obtain a real-time overheat fault monitoring result, wherein the real-time overheat fault monitoring result includes a first real-time overheated air compressor identifier; The overheating impact diffusion analysis is performed based on the real-time overheating fault monitoring result and the N sets of temperature-affected delay coefficients to obtain the 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; Perform asynchronous continuous temperature monitoring on N-1 real-time impact air compressors based on the first real-time temperature impact delay coefficient set to obtain N-1 continuous overheating fault monitoring data sets; The N-1 continuous overheating fault monitoring data sets are identified using the overheating monitoring network layer to obtain an influencing overheating fault monitoring result, wherein the influencing overheating fault monitoring result includes multiple influencing overheating air compressor identifiers.

2. The air compressor fault monitoring system according to claim 1, characterized in that: include: Traversing the N-1 first overheating impact delay time sets to extract cluster centers and obtain N-1 cluster centers, wherein the N-1 cluster centers are respectively the means of the N-1 first overheating impact delay time sets; Taking the N-1 clustering centers as starting points, searching the N-1 first overheating impact 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, use the N-1 iteration centers as N-1 update starting points, and use 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. Use 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.

3. The air compressor fault monitoring system according to claim 2, 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 use the opposite direction of the N-1 iteration direction as the retrieval direction. 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. Then, use 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.

4. The air compressor fault monitoring system according to claim 1, wherein: Deploying overheat monitoring points for the N air compressors according to the N basic information to generate N overheat monitoring point fields includes: 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; Based on the N basic information, overheat monitoring points are arranged with the air intake temperature as the second monitoring object to obtain N second internal overheat monitoring point sets; Based on 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, the N third internal overheat monitoring point sets, and the positions of the respective overheat monitoring points.

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