Jet flow and axial flow fan integrated safety monitoring control method and system

Abstract

The invention discloses an integrated safety monitoring control method and system for a jet flow axial flow fan, and relates to the field of fan monitoring. The method comprises the steps that fan vibration data of a current fan are collected in real time through a vibration sensor, loosening state data of fan hardware are detected in real time through a displacement sensor, and motor temperature data are collected in real time through a temperature sensor; waveform analysis is carried out on the vibration data to obtain abnormal vibration characteristics, spectral analysis is carried out on the loosening state data to obtain a specific loosening part, and analysis is carried out on the motor temperature data to obtain a temperature abnormal part; carrying out correlation analysis on the abnormal vibration characteristic, the specific loose part and the temperature abnormal part to obtain an overall fault state of the fan; feature matching is conducted on the overall fault state of the draught fan and a preset fault feature library, and when the feature matching degree exceeds a preset matching threshold value, the fault type and the fault danger level of the current draught fan are determined. By implementing the method, the accuracy of fault diagnosis can be improved.

Description

Technical Field

[0001] The present application relates to the field of fan monitoring, and in particular to an integrated safety monitoring and control method and system for a jet axial flow fan. Background Art

[0002] As an important ventilation equipment, jet axial flow fans are widely used in various ventilation systems. Their safe and stable operation is of great significance to the normal operation of the entire system. With the continuous improvement of industrial automation level, higher requirements are put forward for the operation reliability and safety of jet axial flow fans.

[0003] In the related technology, regular inspection and single parameter monitoring can be adopted. Maintenance personnel observe the operation status of the fan through fixed-period on-site inspection, or monitor specific operation parameters through a single type of sensor, and judge the equipment status based on experience.

[0004] However, due to the fixed inspection cycle, single parameter monitoring cannot fully reflect the operating status of the equipment, resulting in inaccurate fault diagnosis and increasing the risk of major equipment failure. Summary of the invention

[0005] The present application provides an integrated safety monitoring and control method and system for a jet axial flow fan, which are used to improve the accuracy of fault diagnosis.

[0006] In the first aspect, the present application provides an integrated safety monitoring and control method for a jet axial flow fan, which is applied to an integrated safety monitoring and control system for a jet axial flow fan, the method comprising: real-time acquisition of fan vibration data of the current fan through a vibration sensor, real-time detection of loose status data of the fan hardware through a displacement sensor, and real-time acquisition of motor temperature data through a temperature sensor; waveform analysis of the vibration data to obtain abnormal vibration characteristics, spectrum analysis of the loose status data to obtain specific loose locations, and analysis of the motor temperature data to obtain temperature abnormal locations; correlation analysis of the abnormal vibration characteristics, the specific loose locations, and the temperature abnormal locations to obtain an overall fault state of the fan; feature matching of the overall fault state of the fan with a preset fault feature library, and when the feature matching degree exceeds a preset matching threshold, determining the fault type and fault hazard level of the current fan.

[0007] By adopting the above technical solution, vibration sensors, displacement sensors and temperature sensors are used to collect fan vibration data, hardware looseness status data and motor temperature data in real time, and comprehensive fan operation status information can be obtained. Waveform analysis, spectrum analysis and temperature analysis are performed on these data respectively, and then correlation analysis is performed to obtain the overall fault status. Finally, the fault type and danger level are determined by matching with the preset fault feature library, realizing multi-dimensional real-time monitoring and accurate diagnosis of the fan operation status, which helps to timely discover potential faults and improve the accuracy of fault diagnosis.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of correlating and analyzing the abnormal vibration characteristics, the specific loose parts and the abnormal temperature parts to obtain the overall fault state of the fan specifically includes: grading the abnormal vibration characteristics according to the amplitude to obtain the vibration level, grading the specific loose parts according to the degree of looseness to obtain the looseness level, and grading the abnormal temperature parts according to the temperature rise rate to obtain the temperature level; judging the correlation between the vibration level and the loose level according to the preset vibration-looseness correlation rule to obtain a first correlation result; judging the correlation between the temperature level and the vibration level according to the preset temperature-vibration correlation rule to obtain a second correlation result; analyzing the first correlation result and the second correlation result to obtain the overall fault state of the fan.

[0009] By adopting the above technical solution, the abnormal vibration characteristics, specific loose parts and abnormal temperature parts are classified according to specific rules to obtain the vibration level, loose level and temperature level. The correlation between the levels is judged according to the preset vibration-looseness association rules and temperature-vibration association rules, and the first association result and the second association result are obtained, and then the overall fault state of the fan is obtained by comprehensive analysis, which can more carefully evaluate the relationship between the states of the various components of the fan, making the judgment of the overall fault state more accurate.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of feature matching the overall fault state of the fan with a preset fault feature library, and when the feature matching degree exceeds the preset matching threshold, determining the fault type and fault danger level of the current fan, the method also includes: obtaining the position information and associated process data of the current fan in the production line; determining the coverage area and influence range of the current fan based on the position information; identifying the upstream and downstream equipment associated with the current fan based on the associated process data; when the fault danger level reaches the preset level, calculating the production line shutdown duration and the chain impact range caused by the current fan failure; determining the maintenance priority based on the shutdown duration and the chain impact range.

[0011] By adopting the above technical solution, after obtaining the location information of the fan in the production line and the associated process data, its coverage area, impact range and associated upstream and downstream equipment can be determined. When the fault risk level reaches the preset level, the production line shutdown duration and the chain impact range are calculated, and the maintenance priority is determined accordingly. This allows the impact on the production line to be quickly assessed when a fan fails, and the maintenance sequence can be reasonably arranged to reduce production losses caused by fan failures and ensure the overall operating efficiency of the production line.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the maintenance priority based on the production suspension duration and the chain impact range, the method further includes: matching the maintenance personnel number corresponding to the maintenance priority, and sending the maintenance instruction containing the maintenance personnel number to the monitoring center.

[0013] By adopting the above technical solution, after determining the maintenance priority, the corresponding maintenance personnel number is matched. The maintenance personnel number contains information such as job category, technical level, professional direction, etc., which can accurately locate maintenance personnel with corresponding professional qualifications and technical levels, and send the maintenance instructions containing the maintenance personnel number to the monitoring center. The monitoring center can coordinate maintenance resources according to the instructions to ensure that the right personnel carry out maintenance work in a timely manner, realize the accurate allocation of maintenance tasks, and improve maintenance efficiency.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of feature matching the overall fault state of the fan with a preset fault feature library, and when the feature matching degree exceeds the preset matching threshold, determining the fault type and fault danger level of the current fan, the method also includes: collecting fan fault data, the fan fault data including the vibration data, the loose state data and the motor temperature data of the current fan in a fault state; establishing a correspondence between the fan fault data and the fault type and the fault danger level to obtain a fault sample; and adding the fault sample to the preset fault feature library.

[0015] By adopting the above technical solution, the vibration data, loose state data and motor temperature data of the fan under fault state are collected. These data comprehensively record the operation state of the fan at the moment of fault and in the subsequent process. The corresponding relationship between them and the fault type and fault danger level is established to form a fault sample. The fault sample fully presents the relationship between the fault characteristics and related information. The fault sample is added to the preset fault feature library, which enriches the data resources of the fault feature library and helps to optimize the fault diagnosis model.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of matching the overall fault state of the fan with a preset fault feature library and determining the fault type and fault danger level of the current fan when the feature matching degree exceeds the preset matching threshold, the method further includes: upon receiving a self-test instruction sent by a monitoring center, detecting whether the sampling frequency of the vibration data is within a preset frequency range, detecting whether the collection interval of the loose state data meets the preset time requirements, and detecting whether the sampling period of the motor temperature data meets the preset period requirements; when any data collection abnormality is detected, recording the abnormal data type, abnormal value and abnormal occurrence time; and sending abnormal information including the abnormal data type, the abnormal value and the abnormal occurrence time to the monitoring center.

[0017] By adopting the above technical solution, when the system receives the self-test command from the monitoring center, it detects the vibration data sampling frequency, the loose state data collection interval and the motor temperature data sampling period. Once a data collection abnormality is detected, the abnormal data type, value and occurrence time are recorded. These abnormal information can accurately reflect the problem of the data collection system, and send a message containing detailed abnormal information to the monitoring center. The monitoring center can quickly locate the cause of the fault and take timely measures to repair the data collection system.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of performing feature matching on the overall fault state of the fan with a preset fault feature library, and when the feature matching degree exceeds the preset matching threshold, determining the fault type and fault danger level of the current fan, the method further includes: generating a control instruction based on the fault type and the fault danger level, the control instruction containing fan operating parameters, the fan operating parameters including fan speed, fan power and operating time; and transmitting the control instruction to a monitoring center.

[0019] By adopting the above technical solution, control instructions are generated according to the determined fault type and fault danger level, in which the fan operating parameters such as fan speed, fan power and operating time are adjusted according to the fault situation. Setting corresponding parameter adjustment strategies for different fault types and danger levels can ensure that the fan maintains the best operating state under fault conditions. For example, reducing the speed or power under certain faults can prevent the fault from worsening, and reasonably setting the operating time provides a time window for maintenance. This control instruction is transmitted to the monitoring center, and the monitoring center accurately regulates the fan operation accordingly to ensure the safety and stability of the fan operation.

[0020] In the second aspect, an embodiment of the present application provides an integrated safety monitoring and control system for a jet axial flow fan, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the integrated safety monitoring and control system of the jet axial flow fan to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In the third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product runs on an integrated safety monitoring and control system for a jet axial flow fan, the above-mentioned integrated safety monitoring and control system for a jet axial flow fan executes the method described in the first aspect and any possible implementation method in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the above instructions are executed on an integrated safety monitoring and control system for a jet axial flow fan, the above integrated safety monitoring and control system for a jet axial flow fan executes the method described in the first aspect and any possible implementation method in the first aspect.

[0023] It can be understood that the jet axial fan integrated safety monitoring and control system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application can fully obtain the fan operation status information by using vibration sensors, displacement sensors and temperature sensors to collect fan vibration data, hardware looseness status data and motor temperature data in real time. Waveform analysis, spectrum analysis and temperature analysis are performed on these data respectively, and then correlation analysis is performed to obtain the overall fault status. Finally, the fault type and danger level are determined by matching with the preset fault feature library, realizing multi-dimensional real-time monitoring and accurate diagnosis of the fan operation status, which helps to timely discover potential faults and improve the accuracy of fault diagnosis.

[0025] 2. This application can determine the coverage area, impact range and related upstream and downstream equipment by obtaining the location information and related process data of the fan in the production line. When the fault risk level reaches the preset level, the production line shutdown duration and the chain impact range are calculated, and the maintenance priority is determined accordingly. This allows the impact on the production line to be quickly assessed when a fan fails, and the maintenance sequence to be reasonably arranged, reducing production losses caused by fan failures and ensuring the overall operating efficiency of the production line.

[0026] 3. This application matches the corresponding maintenance personnel number after determining the maintenance priority. The maintenance personnel number contains information such as job category, technical level, professional direction, etc., which can accurately locate maintenance personnel with corresponding professional qualifications and technical levels, and send maintenance instructions containing maintenance personnel numbers to the monitoring center. The monitoring center can coordinate maintenance resources according to the instructions to ensure that appropriate personnel carry out maintenance work in a timely manner, realize the accurate allocation of maintenance tasks, and improve maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the integrated safety monitoring and control method of the jet axial flow fan in the embodiment of the present application; Figure 2 It is another flow chart of the integrated safety monitoring and control method for a jet axial flow fan in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the integrated safety monitoring and control system of the jet axial flow fan in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "a", "a", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the following is a description of the process of the method provided by this implementation. Figure 1, which is a flow chart of the integrated safety monitoring and control method for a jet axial flow fan in an embodiment of the present application.

[0031] S101, collecting fan vibration data of the current fan in real time through a vibration sensor, detecting the loose state data of the fan hardware in real time through a displacement sensor, and collecting motor temperature data in real time through a temperature sensor.

[0032] Among them, vibration data refers to the time domain signal that reflects the vibration state of each component when the fan is running, usually including characteristic parameters such as amplitude, frequency and phase. The displacement sensor is a sensor used to measure the relative position changes between fan components, and can detect the looseness of components such as bolts and bearings. The temperature sensor is used to measure the real-time temperature values ​​of various parts of the motor to reflect the thermal state of the motor. The loose state data indicates the stability of the fixed connection between the fan components, and the motor temperature data indicates the temperature changes of various parts of the motor.

[0033] This step is continuously executed during the normal operation of the fan. Specifically, the system first continuously collects vibration signals at a preset sampling frequency (such as 1kHz) through vibration sensors arranged at key parts of the fan (such as bearing seats, frames, etc.). At the same time, the relative displacement between components is monitored in real time through displacement sensors installed at various connection parts of the fan, and the sampling interval is usually 10ms. In addition, the temperature sensor collects temperature data of the motor stator, rotor and other parts at a cycle of 500ms. These data are transmitted to the data acquisition unit in real time via the field bus for storage and processing.

[0034] In some embodiments, real-time data collection can be achieved through distributed data collection: first, a sensor network is arranged at the key monitoring points of the wind turbine, and multiple sensors are connected to the distributed collection modules nearby; then each collection module independently collects data according to the preset sampling parameters; finally, the collected data is transmitted to the central processing unit through the industrial Ethernet. Optionally, a centralized data collection method can also be used: all sensors are directly connected to a centralized data collector, and the collector uniformly controls the sampling timing to achieve synchronous data collection; then the collected raw data is preprocessed and screened; finally, the processed valid data is transmitted to the monitoring system. It can be understood that other data collection methods can also be used to achieve real-time collection of sensor data, which is not limited here.

[0035] S102, performing waveform analysis on the vibration data to obtain abnormal vibration characteristics, performing spectrum analysis on the loose state data to obtain specific loose locations, and analyzing the motor temperature data to obtain abnormal temperature locations.

[0036] Among them, waveform analysis refers to a method of analyzing the time domain characteristics of vibration signals, which is used to extract abnormal vibration characteristics. Spectral analysis refers to a method of converting time domain signals to frequency domain for analysis, which is used to locate specific loose parts. Abnormal vibration characteristics indicate vibration characteristics that deviate from normal operating conditions. Specific loose parts refer to the specific hardware locations where looseness occurs, including various components such as fans, brackets, and bolts. Abnormal temperature parts indicate motor parts with abnormal temperature rise, such as the temperature of the front and rear shafts and windings of the motor.

[0037] This step is executed immediately after completing data collection. Specifically, the system first performs time domain analysis on the collected vibration data, including calculating characteristic parameters such as the root mean square value and crest factor, and identifying abnormal vibration characteristics by comparing with normal values. Then, the loose state data is subjected to fast Fourier transform, the spectrum characteristics are analyzed, and the loose parts are located according to the amplitude distribution of different frequency bands. At the same time, trend analysis is performed on the temperature data, and the abnormal heating location is determined by the temperature change rate and temperature distribution characteristics.

[0038] In some embodiments, an intelligent algorithm analysis method can be used: first, wavelet analysis is used to pre-process the raw data for noise reduction; then, data features are extracted through a deep learning model; and finally, the abnormal location and type are determined based on the trained recognition model. Optionally, an expert system analysis method can also be used: first, the collected data is pattern matched with a preset fault feature library; then, the specific fault location is inferred based on the matching results and expert rules; and finally, the degree of abnormality is determined through comprehensive evaluation. It is understandable that other analysis methods can also be used to achieve data feature extraction and abnormality diagnosis, which are not limited here.

[0039] S103, performing correlation analysis on the abnormal vibration characteristics, the specific loose location and the abnormal temperature location to obtain the overall fault status of the fan.

[0040] This step specifically includes: The abnormal vibration characteristics are classified according to the amplitude to obtain the vibration grade, the specific loose parts are classified according to the looseness degree to obtain the looseness grade, and the abnormal temperature parts are classified according to the temperature rise rate to obtain the temperature grade; Determine the correlation between the vibration level and the looseness level according to a preset vibration-looseness correlation rule to obtain a first correlation result; Determine the correlation between the temperature level and the vibration level according to a preset temperature-vibration correlation rule to obtain a second correlation result; The first correlation result and the second correlation result are analyzed to obtain the overall fault state of the wind turbine.

[0041] Among them, the vibration level refers to the fault severity level divided according to the vibration amplitude, which is usually divided into four levels: slight, general, severe and dangerous. The looseness level indicates the degree of looseness of the mechanical parts, including initial looseness, obvious looseness, severe looseness and other levels. The temperature level refers to the temperature abnormality level based on the temperature rise rate, which is generally divided into four levels: normal, warning, alarm and dangerous. The vibration-looseness association rule is used to represent the correspondence between vibration characteristics and looseness status. The temperature-vibration association rule refers to the mapping relationship between temperature change and vibration characteristics. The first association result represents the comprehensive evaluation result of vibration and looseness status. The second association result refers to the correlation analysis result of temperature and vibration status. The overall fault state refers to the comprehensive fault evaluation result of each monitoring parameter of the fan.

[0042] This step performs classification and correlation analysis after obtaining the abnormal features. Specifically, the system first divides the abnormal vibration features into four levels according to the vibration amplitude: when the amplitude is less than 5mm / s, it is slight, 5-10mm / s is general, 10-15mm / s is severe, and greater than 15mm / s is dangerous. At the same time, the loose parts are classified: relative displacement less than 0.5mm is initial looseness, 0.5-1mm is obvious looseness, and greater than 1mm is severe looseness. Temperature anomalies are classified according to the temperature rise rate: temperature rise rate less than 2℃ / min is warning, 2-5℃ / min is alarm, and greater than 5℃ / min is dangerous. Then, according to the pre-established association rule base, the correlation between vibration-looseness and temperature-vibration is analyzed to generate two correlation results. Finally, the two correlation results are weighted fused to obtain a comprehensive evaluation result reflecting the overall fault status of the fan.

[0043] In some embodiments, the hierarchical evaluation and correlation analysis of fault states can be achieved in a variety of ways: Optionally, a fuzzy logic evaluation method is used: first, a membership function of each parameter is established, and the specific numerical value is mapped to a fuzzy set; then, reasoning operations are performed according to fuzzy rules to obtain fuzzy evaluation values ​​of various indicators; finally, the centroid method is used for clarification processing to obtain the final fault level judgment result. Optionally, a data-driven machine learning method is used: first, a multi-layer neural network model is trained using historical data; then, the current monitoring data is input for feature extraction and pattern recognition; finally, the fault state evaluation result is obtained through model prediction, and the level is divided according to the confidence level. It can be understood that other analysis methods can also be used to achieve the grading and correlation analysis of fault states, which are not limited here.

[0044] S104: feature matching is performed on the overall fault state of the wind turbine with a preset fault feature library, and when the feature matching degree exceeds a preset matching threshold, the fault type and fault risk level of the current wind turbine are determined.

[0045] Among them, the fault feature library refers to a database that stores various types of fan fault feature patterns for fault diagnosis and identification. Feature matching refers to the process of calculating the similarity between the current fault state and the standard pattern in the feature library. The matching degree indicates the degree of similarity between the current fault state and the standard pattern in the feature library, usually expressed as a numerical value between 0-1. The preset matching threshold refers to the minimum similarity requirement for determining a successful match, which is generally set between 0.75-0.85. Fault type refers to various possible fault forms of the fan, such as bearing failure, blade failure, etc. The fault danger level is used to indicate the severity of the fault, which is usually divided into four levels: minor, general, severe and dangerous.

[0046] This step performs feature matching and fault diagnosis after obtaining the overall fault status of the wind turbine. Specifically, the system first matches the fault status feature vector with the standard pattern in the preset fault feature library, and uses the improved Euclidean distance algorithm to calculate the similarity. When the matching degree of a standard pattern exceeds the preset threshold of 0.8, the system determines the specific fault type based on the fault information corresponding to the pattern. At the same time, based on the size of the matching degree, the fault development trend and the scope of influence, the fault hazard level is divided into four levels: a matching degree of 0.8-0.85 is a minor fault, 0.85-0.9 is a general fault, 0.9-0.95 is a serious fault, and greater than 0.95 is a dangerous fault. The system records the identified fault type and hazard level in the fault log and triggers the corresponding alarm mechanism.

[0047] In some embodiments, fault feature matching and diagnosis can be achieved in a variety of ways: Optionally, an intelligent diagnosis method based on support vector machines is used: first, the fault state features are standardized and preprocessed to eliminate dimensional effects; then the kernel function is used to map the features to a high-dimensional space, and the similarity with the preset mode is calculated; finally, the fault type is determined based on the classification results, and the hazard level is evaluated through probability output. Optionally, a method combining expert rules and fuzzy reasoning is used: first, a complete expert knowledge base is established, including fault feature rules and diagnostic experience; then, a multi-dimensional evaluation of the current state is performed through a fuzzy reasoning mechanism; finally, the fault type and level are comprehensively judged based on confidence and severity. It is understandable that other methods can also be used to achieve fault feature matching and diagnosis, which are not limited here.

[0048] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the integrated safety monitoring and control method for a jet axial flow fan in an embodiment of the present application.

[0049] S201, obtaining the position information and associated process data of the current fan in the production line.

[0050] Position information refers to the specific installation position coordinates of the jet axial flow fan in the spatial layout of the entire production line, including the X, Y, and Z three-dimensional coordinate values ​​and identification information such as the workshop area number and production line number. The associated process data includes the process parameters, equipment operating status, material flow information and other data of the upstream and downstream production processes directly related to the fan, including process number, process type, processing time, material code, equipment number, etc. The acquisition process reads these data in real time through the communication interface with the production line control system.

[0051] In the specific implementation, the system first reads the spatial position coordinate value of the fan from the production line PLC system through the pre-configured data acquisition interface with a sampling period of 10ms. At the same time, the system establishes a data connection with the manufacturing execution system (MES) through industrial Ethernet to obtain the process information currently being executed, including the start time of the process, the estimated completion time, the process parameter setting value, etc. These data are stored in the system database for subsequent analysis. For example, the coordinates of a fan are (156.3, 423.8, 52.1), installed on the No. 3 production line in workshop No. 2, and are associated with the spraying process (process number P256) and the drying process (process number P257).

[0052] S202: Determine the coverage area and influence range of the current wind turbine according to the location information.

[0053] The coverage area refers to the space that can be directly affected by the fan's air supply or exhaust. It is usually distributed in a fan or circle with the fan as the center. Its size depends on the fan's rated air volume, installation height, air supply angle and other parameters. The impact range further includes the area that may be indirectly affected by air flow disturbances, and environmental factors such as obstacles in the workshop and the distribution of other equipment need to be considered.

[0054] Based on the acquired fan location information and the fan's technical parameters (such as rated air volume of 20,000 m³ / h, air supply radius of 30 m, etc.), the system determines the specific coverage area and impact range through a calculation model. First, the air supply range of the fan during normal operation is calculated to form a fan-shaped coverage area with the fan as the center and the air supply direction as the axis. Then analyze the workshop layout diagram, identify key objects such as equipment and workstations in the coverage area, and evaluate the impact of airflow on these objects. For example, the coverage area of ​​a fan is a 90° fan with a radius of 25 meters, and the impact range extends to an area of ​​40 meters around it, including 2 workstations and 3 associated equipment.

[0055] S203: Identify upstream and downstream equipment associated with the current wind turbine according to the associated process data.

[0056] Upstream and downstream equipment refers to the preceding and following equipment that is directly related to the current fan function in the production process. The operating status and process parameters of the upstream equipment will affect the air intake conditions of the fan, while the downstream equipment relies on the fan to provide appropriate environmental parameters to ensure the stability of the process. This relationship is reflected in many aspects such as process flow, material flow, and environmental control.

[0057] The system builds a network of equipment association relationships by analyzing the process flow information in the associated process data. First, determine the processes that directly interact with the fan, and then trace the specific equipment used in these processes. For each associated equipment, record its equipment type, operating parameters, distance from the fan and other information. For example, on a spraying production line, the upstream equipment of the fan includes a dust removal system (equipment number E2045) and a feeding system (equipment number E2046), and the downstream equipment includes a drying device (equipment number E2047) and a conveyor belt system (equipment number E2048). This equipment information is recorded in the system database for subsequent linkage control and fault analysis.

[0058] S204: When the fault danger level reaches a preset level, the production line shutdown duration and the scope of the chain reaction caused by the current fan fault are calculated.

[0059] The fault danger level refers to the severity of the fan fault, which is usually divided into four levels: level 1 (minor), level 2 (moderate), level 3 (serious) and level 4 (critical). Each level corresponds to different fault characteristics and impact levels. The downtime duration refers to the time required from the occurrence of a fan failure to the completion of maintenance and restoration of normal operation, including fault diagnosis time, maintenance preparation time, actual maintenance time and commissioning operation time. The scope of chain impact refers to the scope of the production system affected by chain reactions such as shutdown of related equipment and process interruption caused by fan failure.

[0060] During the specific implementation process, the system first compares the detected fault danger level with the preset level 3 (serious) level threshold. When the fault level reaches or exceeds level 3, the system starts the shutdown impact assessment module. This module calculates the estimated maintenance time based on the statistical information of similar fault cases in the historical maintenance database. At the same time, combined with the production line process flow chart and the equipment association matrix, the impact of the chain shutdown caused by the fan shutdown is analyzed. For example, a fan bearing failure is judged as a level 3 failure. According to historical data, the estimated maintenance time is 4 hours. Its shutdown will cause the related spraying process and drying process to stop production for 6 hours, and the impact range involves 2 production lines and a total of 8 equipment.

[0061] S205: Determine maintenance priority according to the production suspension duration and the scope of the chain reaction.

[0062] Maintenance priority refers to the order in which faulty equipment is repaired, and is usually divided into five levels: urgent (level 1), emergency (level 2), priority (level 3), normal (level 4), and deferred (level 5). The determination of maintenance priority requires comprehensive consideration of the impact of the production stoppage duration on the production plan, as well as the degree of loss of overall production efficiency due to the chain effect range. Longer production stoppage duration and larger chain effect range correspond to higher maintenance priority.

[0063] The system uses a quantitative scoring method to determine the maintenance priority. First, the downtime is divided into multiple intervals according to the time span (such as 0-2 hours, 2-4 hours, etc.), and each interval corresponds to a basic score. Then the impact factor is calculated based on the number and importance of the affected equipment within the chain impact range. The basic score and the impact factor are multiplied to get the final score and mapped to the corresponding priority level. For example, a fan failure causes a 6-hour downtime (basic score of 80 points), affects 8 devices (impact factor 1.5), and the final score is 120 points, corresponding to maintenance priority level 2 (urgent).

[0064] S206. Match the maintenance personnel number corresponding to the maintenance priority, and send the maintenance instruction including the maintenance personnel number to the monitoring center.

[0065] The maintenance personnel number refers to the unique identification code assigned to maintenance technicians within the enterprise, which contains information such as job category, technical level, and professional direction. The maintenance order is a work task notification issued to maintenance personnel, which contains faulty equipment information, fault type description, maintenance priority level, and estimated working time. The monitoring center is a dispatching management platform that uniformly receives and distributes maintenance orders and is responsible for coordinating various maintenance resources.

[0066] The system matches personnel through the maintenance personnel database. First, maintenance personnel with corresponding professional qualifications are screened according to the fault type, and then personnel with appropriate technical levels are selected according to the maintenance priority. The system generates a maintenance instruction in a standard format, including fields such as equipment number, fault code, maintenance priority, and maintenance personnel number, and sends the instruction to the task management system of the monitoring center through the industrial network. For example, the system matches Mr. Zhang (personnel number M0256) who holds a senior maintenance certificate for a level 2 priority fan fault, generates a maintenance instruction (instruction number R20231219-086) and sends it to the monitoring center.

[0067] It should be noted that steps S201 to S206 are executed after step S104.

[0068] S207, collecting fan fault data, where the fan fault data includes the vibration data, the loose state data and the motor temperature data of the current fan in a fault state.

[0069] Fault data refers to the information collected by various sensors when the fan is in a fault state, including three main parts: vibration data, loose state data and motor temperature data. Vibration data is collected by vibration sensors installed at key parts of the fan, recording vibration waveform, frequency and amplitude. Loose state data is collected by displacement sensors, reflecting the relative position changes between fan components and the degree of looseness of fixings. Motor temperature data is collected by temperature sensors to monitor the temperature change trend of various parts of the motor.

[0070] The system starts the high-frequency data acquisition mode immediately after detecting the fan failure. The vibration sensor continuously records 20 seconds of vibration waveform data at a sampling frequency of 2kHz to obtain a complete fault characteristic waveform. The displacement sensor collects 5 minutes of displacement data at a frequency of 100Hz and records the displacement change process of each monitoring point. The temperature sensor collects 30 minutes of temperature data at a frequency of 1Hz to track the temperature change curve. For example, in a bearing failure case, the recorded vibration peak reached 15mm / s, the bearing seat displacement reached 0.8mm, and the motor temperature rose to 85℃. These data are transmitted to the data acquisition server in real time via the industrial bus and stored synchronously according to the timestamp.

[0071] S208: Establish a corresponding relationship between the fan fault data, the fault type and the fault risk level to obtain a fault sample.

[0072] The fault sample is a complete feature description of the fault state, including various sensor data at the time of the fault, as well as the fault type determination and hazard level assessment results. The fault type refers to the specific fault form determined based on the fault characteristics, such as bearing fault, blade fault, etc. The fault hazard level indicates the severity of the fault and is usually divided into four levels. The correspondence is a data structure that organizes this information in a standard format.

[0073] The process of establishing the corresponding relationship adopts a structured data storage method. The system first creates a new fault record and generates a unique fault number. The collected vibration data, loose state data, and motor temperature data are respectively subjected to feature extraction and data compression to obtain key feature parameters. These feature parameters are then associated with the determined fault type and hazard level and stored. For example, for a bearing fault, the system generates a fault record (number F20231219-025), which includes vibration characteristics (15mm / s@50Hz), displacement characteristics (0.8mm eccentricity), and temperature characteristics (85°C continuous rise). It is determined to be damage to the inner ring of the bearing, and the hazard level is level three.

[0074] S209: Add the fault sample to the preset fault feature library.

[0075] The preset fault feature library is a database that stores the features of various fault cases for fault diagnosis and prediction. Each record in the feature library contains complete fault sample information, and is indexed according to multiple dimensions such as fault type, feature parameters, and hazard level. The addition process needs to ensure the consistency of the newly added samples with the existing data and update the relevant statistical information.

[0076] The system uses a transaction processing mechanism when performing feature library update operations. First, check the data integrity of the new fault sample and verify whether the various feature parameters are within a reasonable range. Then insert the fault sample data into the corresponding data table of the feature library, and update the fault type index and feature parameter index at the same time. Finally, recalculate the statistical characteristics of this type of fault, such as the mean value, standard deviation, etc. For example, after adding the above bearing fault sample to the feature library, the system updates the number of cases under the bearing fault category (increased to 286 cases), corrects the typical vibration feature range of this type of fault (12-18mm / s@45-55Hz), and updates the hazard level distribution.

[0077] In some embodiments, steps S207 to S209 may be performed after step S104 or after step S206, which is not limited here.

[0078] S210. Upon receiving the self-test instruction sent by the monitoring center, detect whether the sampling frequency of the vibration data is within the preset frequency range, detect whether the collection interval of the loose state data meets the preset time requirement, and detect whether the sampling period of the motor temperature data meets the preset period requirement.

[0079] The self-test instruction is a self-test command issued by the monitoring center for the data acquisition system, which contains information such as test items, test parameters, and execution time. The preset frequency range refers to the effective sampling frequency range for vibration data acquisition, which is usually 1kHz-5kHz. The preset time requirement refers to the time interval standard between two loose state data acquisitions, which is generally 5ms-20ms. The preset period requirement refers to the periodic time standard for temperature data acquisition, which is usually set to 500ms-2000ms.

[0080] After receiving the self-test command, the system starts three parallel tests. The vibration data detection module reads the timestamps of the last 100 sets of vibration data, calculates the actual sampling frequency and compares it with the preset range of 1kHz-5kHz. The loose state detection module analyzes the acquisition time intervals of the last 500 sets of displacement data to verify whether they are all within the range of 5ms-20ms. The temperature data detection module counts the sampling periods of the last 1000 sets of temperature data to verify whether they meet the requirements of 500ms-2000ms. For example, a self-test found that the actual sampling frequency of vibration data was 850Hz, which was lower than the minimum requirement of 1kHz; the average acquisition interval of loose state data was 25ms, which exceeded the maximum allowable value of 20ms; the temperature data sampling period was stable at 1000ms, which was within the normal range.

[0081] S211. When any data collection abnormality is detected, the abnormal data type, abnormal value and abnormal occurrence time are recorded.

[0082] Abnormal data types refer to specific abnormal forms that occur during data collection, including abnormal sampling frequency, abnormal collection interval, and abnormal sampling period. Abnormal values ​​refer to the actual values ​​detected that do not meet the requirements. Abnormal occurrence time refers to the system timestamp when the abnormality is first detected. This information is used to trace and analyze the causes of failures in the data collection system.

[0083] The system uses a real-time anomaly detection mechanism to record abnormal situations. When any parameter is detected to be out of the preset range, the anomaly recording program is triggered immediately. The program creates a new anomaly record item, which contains three main fields: anomaly type identifier (such as frequency anomaly is recorded as E001, interval anomaly is recorded as E002, and period anomaly is recorded as E003), the actual measured anomaly value, and the time of the anomaly occurrence accurate to milliseconds. For example, a vibration data anomaly is recorded: type E001 (sampling frequency anomaly), value 850Hz, time 2023-12-1914:30:25.632.

[0084] S212: Sending abnormal information including the abnormal data type, the abnormal value and the abnormal occurrence time to the monitoring center.

[0085] Abnormal information is a standardized description of the detected abnormal situation, including the complete characteristics of the abnormal data. The monitoring center is the central management platform for receiving and processing abnormal information, responsible for the analysis, processing and notification of abnormal situations. The sending process needs to follow specific communication protocols and data format specifications.

[0086] The system encapsulates the exception information message in a standard format. First, a message header is generated, which contains basic information such as device identification, message type (abnormal notification), and timestamp. Then the abnormal data type, abnormal value, and abnormal occurrence time are encoded into the message body, and the data structure is organized in XML format. Finally, the message is sent to the exception processing server of the monitoring center via industrial Ethernet. For example, an abnormal message is sent: device number D2045, message type ERROR_NOTIFY, abnormal type E001, abnormal value 850Hz, and occurrence time 2023-12-1914:30:25.632. After receiving the message, the monitoring center parses the content, enters the abnormal information into the system, and triggers the corresponding processing flow.

[0087] S213: Generate a control instruction according to the fault type and the fault risk level, wherein the control instruction includes fan operation parameters, and the fan operation parameters include fan speed, fan power and operation time.

[0088] The control instruction is an operation management command for the faulty fan, which contains a series of specific operation parameter settings. The fan operation parameters are composed of three core indicators: the fan speed refers to the actual number of rotations of the fan impeller per minute (unit: r / min), the fan power refers to the actual working power of the fan motor (unit: kW), and the running time refers to the continuous working time of the fan under the current parameter settings (unit: min). The settings of these parameters need to be based on the fault type and hazard level to ensure that the fan can still maintain basic functions or shut down safely in the fault state.

[0089] The specific process of system parameter calculation and instruction generation is as follows: First, select the corresponding parameter adjustment strategy according to the fault type, such as the speed reduction strategy for bearing faults and the power reduction strategy for blade faults. Then determine the amplitude of parameter adjustment according to the fault danger level, such as the speed reduction of 20% for level 1 faults, 40% for level 2 faults, 60% for level 3 faults, and shutdown for level 4 faults. Finally, generate control instructions in a standard format, including equipment number, parameter setting value and execution time requirements. For example, for a level 3 bearing fault, the generated control instructions include: the speed is set to 480r / min (40% of the standard speed of 1200r / min), the power limit is 5.5kW (37% of the rated power of 15kW), and the operating time limit is 60min (used to arrange maintenance time).

[0090] S214: Transmit the control instruction to the monitoring center.

[0091] The monitoring center is a central control platform responsible for receiving and executing control instructions. It has functions such as instruction parsing, parameter setting, and operation supervision. Instruction transmission is the process of sending control instructions to the monitoring center through the industrial communication network. The transmission process needs to ensure the real-time, integrity, and reliability of the data.

[0092] The system adopts a step-by-step instruction transmission mechanism. First, the control instruction is packaged into a standard communication message. The message header contains information such as the source address, target address, message type, priority, etc., and the message body contains specific parameter setting values. Then the message is sent to the communication server of the monitoring center through industrial Ethernet. After receiving the message, the monitoring center immediately returns a confirmation message, and the system checks the correctness of the confirmation message. If a transmission error is found, the retransmission mechanism is automatically executed. For example, in a certain transmission process: the system generates a control instruction message (instruction number C20231219-102) and sends it to the monitoring center (target address 192.168.1.100) through the TCP / IP protocol. The message content includes the fan number (F1024), the current timestamp (2023-12-1915:45:30), the speed setting value (480r / min), the power limit value (5.5kW), and the running time limit (60min). After receiving the message, the monitoring center returns a confirmation code (ACK-102) to complete the instruction transmission.

[0093] In some embodiments, steps S210 to S214 may be performed after step S104 or after step S209, which is not limited here.

[0094] The following describes the integrated safety monitoring and control system for the jet axial flow fan in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the structure of a physical device of an integrated safety monitoring and control system for a jet axial flow fan in an embodiment of the present application.

[0095] It should be noted that Figure 3 The structure of the jet axial fan integrated safety monitoring and control system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0096] like Figure 3 As shown, the integrated safety monitoring and control system of the jet axial flow fan includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0097] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0098] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0099] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0101] Specifically, the integrated safety monitoring and control system of the jet axial flow fan of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the integrated safety monitoring and control method of the jet axial flow fan provided in the above embodiment is implemented.

[0102] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the integrated safety monitoring and control system for the jet axial flow fan described in the above embodiment; or may exist independently without being assembled into the integrated safety monitoring and control system for the jet axial flow fan. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the integrated safety monitoring and control system for the jet axial flow fan, the integrated safety monitoring and control system for the jet axial flow fan implements the integrated safety monitoring and control method for the jet axial flow fan provided in the above embodiment.

[0103] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0104] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0105] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. An integrated safety monitoring and control method for a jet axial flow fan, characterized in that: Applied to the integrated safety monitoring and control system of a jet axial flow fan, the method comprises: The vibration sensor is used to collect the fan vibration data of the current fan in real time, the displacement sensor is used to detect the loose state data of the fan hardware in real time, and the temperature sensor is used to collect the motor temperature data in real time; Performing waveform analysis on the vibration data to obtain abnormal vibration characteristics, performing spectrum analysis on the loose state data to obtain specific loose locations, and analyzing the motor temperature data to obtain abnormal temperature locations; Correlation analysis is performed on the abnormal vibration characteristics, the specific loose parts and the abnormal temperature parts to obtain the overall fault state of the fan; The overall fault state of the wind turbine is feature matched with a preset fault feature library, and when the feature matching degree exceeds a preset matching threshold, the fault type and fault risk level of the current wind turbine are determined.

2. The method according to claim 1, characterized in that: The step of correlating and analyzing the abnormal vibration characteristics, the specific loose parts and the abnormal temperature parts to obtain the overall fault state of the fan specifically includes: The abnormal vibration characteristics are classified according to the amplitude to obtain the vibration level, the specific loose parts are classified according to the looseness degree to obtain the looseness level, and the abnormal temperature parts are classified according to the temperature rise rate to obtain the temperature level; Determining the correlation between the vibration level and the looseness level according to a preset vibration-looseness correlation rule to obtain a first correlation result; Determine the correlation between the temperature level and the vibration level according to a preset temperature-vibration correlation rule to obtain a second correlation result; The first correlation result and the second correlation result are analyzed to obtain the overall fault state of the wind turbine.

3. The method according to claim 1, characterized in that After the step of matching the overall fault state of the wind turbine with a preset fault feature library and determining the fault type and fault risk level of the current wind turbine when the feature matching degree exceeds a preset matching threshold, the method further includes: Obtaining the position information and associated process data of the current fan in the production line; Determine the coverage area and influence range of the current wind turbine according to the location information; Identify upstream and downstream equipment associated with the current wind turbine according to the associated process data; When the fault danger level reaches a preset level, the production line shutdown duration and the chain reaction impact range caused by the current fan fault are calculated; The maintenance priority is determined according to the production suspension duration and the scope of the chain reaction impact.

4. The method according to claim 3, characterized in that After the step of determining the maintenance priority according to the production suspension duration and the chain reaction impact range, the method further includes: Match the maintenance personnel number corresponding to the maintenance priority, and send the maintenance instruction containing the maintenance personnel number to the monitoring center.

5. The method according to claim 1, characterized in that After the step of matching the overall fault state of the wind turbine with a preset fault feature library and determining the fault type and fault risk level of the current wind turbine when the feature matching degree exceeds a preset matching threshold, the method further includes: Collecting fan fault data, the fan fault data including the vibration data, the loose state data and the motor temperature data of the current fan in a fault state; Establishing a corresponding relationship between the fan fault data, the fault type and the fault risk level to obtain a fault sample; The fault sample is added to the preset fault feature library.

6. The method according to claim 1, characterized in that After the step of matching the overall fault state of the wind turbine with a preset fault feature library and determining the fault type and fault risk level of the current wind turbine when the feature matching degree exceeds a preset matching threshold, the method further includes: Upon receiving the self-test instruction sent by the monitoring center, detecting whether the sampling frequency of the vibration data is within the preset frequency range, detecting whether the collection interval of the loose state data meets the preset time requirement, and detecting whether the sampling period of the motor temperature data meets the preset period requirement; When any data collection anomaly is detected, the abnormal data type, abnormal value and abnormal occurrence time are recorded; Sending the abnormal information including the abnormal data type, the abnormal value and the abnormal occurrence time to the monitoring center.

7. The method according to claim 1 or 2, characterized in that: After the step of matching the overall fault state of the wind turbine with a preset fault feature library and determining the fault type and fault risk level of the current wind turbine when the feature matching degree exceeds a preset matching threshold, the method further includes: Generate a control instruction according to the fault type and the fault danger level, wherein the control instruction includes fan operation parameters, and the fan operation parameters include fan speed, fan power and operation time; The control instruction is transmitted to a monitoring center.

8. An integrated safety monitoring and control system for a jet axial flow fan, characterized in that: The integrated safety monitoring and control system of the jet axial flow fan includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the integrated safety monitoring and control system of the jet axial flow fan to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the integrated safety monitoring and control system of the jet axial flow fan, the integrated safety monitoring and control system of the jet axial flow fan executes the method as described in any one of claims 1-7.

10. A computer program product, characterized in that When the computer program product runs on an integrated safety monitoring and control system for a jet axial flow fan, the integrated safety monitoring and control system for a jet axial flow fan executes the method as described in any one of claims 1-7.

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