CN117433978B - Monitoring and early warning method and system of dust remover for transmission - Google Patents
Monitoring and early warning method and system of dust remover for transmission Download PDFInfo
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Abstract
The invention provides a monitoring and early warning method and a system of a dust remover for transmission, which relate to the technical field of monitoring and early warning and comprise the following steps: establishing a dust removing space of the dust remover and arranging a particle detection sensor group; space environment data acquisition configuration of a dust removing space is carried out to configure a real-time working environment; configuring a stream direction time node map; a wind flow feedback sensor is arranged in the feeding air channel, a feedback particle detection sensor is arranged at the discharging port, and equipment association is carried out; the detection result after association and the real-time working environment are sent to an evaluation processing channel, and a processing evaluation result is generated; interactive operation data is used for carrying out equipment operation abnormality analysis to generate operation abnormality results; and carrying out abnormality verification, and carrying out monitoring and early warning of the dust remover according to an abnormality verification result. The invention solves the technical problems that the traditional dust remover monitoring method cannot capture the abnormal running condition of equipment in real time, lacks comprehensive analysis on environmental data, and causes the lack of timeliness and accuracy of monitoring and early warning.
Description
Technical Field
The invention relates to the technical field of monitoring and early warning, in particular to a monitoring and early warning method and system of a dust remover for transmission.
Background
Transmission dust collectors are commonly used in industrial processes for removing particulates and contaminants from air to maintain the cleanliness of the production environment and the health of personnel, and these devices are widely used in various industrial fields. However, there are a number of problems with monitoring and early warning of dust collectors. On the one hand, the traditional dust collector monitoring method generally depends on periodic inspection or periodic detection, and cannot provide real-time equipment state information, so that instantaneous or periodic abnormal operation conditions cannot be captured; on the other hand, the dust collector is operated by involving a plurality of parameters and environmental factors, and the traditional method has difficulty in comprehensively analyzing the data to identify abnormal states of equipment operation, so that the performance of the equipment under different working conditions is difficult to understand, and key information is missed.
Thus, there is a need for a new approach that provides more efficient equipment monitoring and anomaly early warning to ensure proper operation of the equipment and reduce potential risks.
Disclosure of Invention
The application provides a monitoring and early warning method and system of a dust remover for transmission, and aims to solve the technical problems that the traditional dust remover monitoring method is generally based on regular inspection or detection at fixed time intervals, cannot capture the abnormal operation condition of equipment in real time, and lacks comprehensive environmental data analysis such as information of particle concentration, wind flow feedback and the like so as to identify the abnormal operation state of the equipment, so that the performance of the equipment under different working conditions is difficult to understand.
In view of the above problems, the present application provides a method and a system for monitoring and early warning of a dust remover for transmission.
In a first aspect of the disclosure, a method for monitoring and early warning of a dust remover for transmission is provided, the method comprising: establishing a dust removing space of the dust remover, wherein the dust removing space is a working dust removing area of the dust remover, and arranging a particle detection sensor group in the dust removing space; the particle detection sensor group is used for collecting space environment data of a dust removing space, and a real-time working environment is configured; configuring a flow direction time node mapping of dust removal, wherein the flow direction time node mapping is a mapping relation between wind flow positions of the dust remover in different working modes and time nodes, and the flow direction time node mapping is built by fitting after reading size information, structure information and working mode power data of the dust remover; a wind flow feedback sensor is arranged in a feeding air duct of the dust remover, a feedback particle detection sensor is arranged at a discharge port of the dust remover, and a particle detection sensor group, a wind flow feedback sensor and equipment association of the feedback particle detection sensor are established through flow direction time node mapping; the detection results of the wind flow feedback sensor and the feedback particle detection sensor after being correlated and the real-time working environment are sent to an evaluation processing channel, and a processing evaluation result is generated; the operation data of the dust remover are interacted, equipment operation abnormality analysis is carried out according to the operation data, and an operation abnormality result is generated; and carrying out abnormality verification according to the operation abnormality result and the processing evaluation result, and carrying out monitoring and early warning of the dust remover according to the abnormality verification result.
In another aspect of the disclosure, there is provided a monitoring and early warning system of a dust collector for transmission, the system being used in the above method, the system comprising: the dust removing space establishing module is used for establishing a dust removing space of the dust remover, wherein the dust removing space is a working dust removing area of the dust remover, and a particle detection sensor group is arranged in the dust removing space; the environment data acquisition module is used for acquiring space environment data of a dust removing space by the particle detection sensor group and configuring a real-time working environment; the node mapping configuration module is used for configuring flow direction time node mapping of dust removal, wherein the flow direction time node mapping is a mapping relation between wind flow positions of the dust remover in different working modes and time nodes, and the flow direction time node mapping is built by fitting after reading size information, structure information and working mode power data of the dust remover; the device association establishing module is used for setting an air flow feedback sensor in a feeding air channel of the dust remover, setting a feedback particle detection sensor at a discharge port of the dust remover, and establishing a particle detection sensor group, an air flow feedback sensor and device association of the feedback particle detection sensor through flow direction time node mapping; the evaluation result generation module is used for sending the detection results of the wind flow feedback sensor and the feedback particle detection sensor after being correlated with the real-time working environment to an evaluation processing channel to generate a processing evaluation result; the equipment abnormality analysis module is used for interacting the operation data of the dust remover, carrying out equipment operation abnormality analysis on the operation data and generating an operation abnormality result; and the monitoring and early warning module is used for carrying out abnormal verification according to the operation abnormal result and the processing evaluation result and executing the monitoring and early warning of the dust remover according to the abnormal verification result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
by arranging the particle detection sensor group, the wind flow feedback sensor and the feedback particle detection sensor, the working environment and the particle concentration of the dust remover can be monitored in real time, so that measures can be rapidly taken when abnormal conditions occur, and the safety and the reliability of equipment are improved; the environmental data of the dust remover is collected and comprehensively analyzed, including information such as particle concentration, wind flow feedback and the like, so that a real-time working environment and a processing evaluation result are generated, the performance of equipment is more comprehensively understood, and more information about abnormal conditions is provided; by establishing the flow direction time node mapping, the relationship between the wind flow position of the equipment in different working modes and the time node is considered, so that the equipment can be monitored more accurately according to the actual working state, and abnormal conditions in different time periods can be captured. In a word, the monitoring and early warning method of the dust remover for transmission provides more efficient equipment monitoring and abnormal early warning capability through technical means such as real-time monitoring, comprehensive analysis of environmental data, flow direction time node mapping and the like so as to maintain normal operation of equipment and reduce potential risks.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of a monitoring and early warning method of a dust remover for transmission according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a monitoring and early warning system of a dust remover for transmission according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a dust removal space establishment module 10, an environment data acquisition module 20, a node mapping configuration module 30, an equipment association establishment module 40, an evaluation result generation module 50, an equipment abnormality analysis module 60 and a monitoring and early warning module 70.
Detailed Description
According to the monitoring and early warning method for the dust remover for transmission, the technical problems that the traditional dust remover monitoring method is generally based on regular inspection or detection at a fixed time interval, abnormal operation conditions of equipment cannot be captured in real time, comprehensive environmental data analysis such as information of particle concentration, wind flow feedback and the like is lacking, abnormal operation states of the equipment are identified, and the performance of the equipment under different working conditions is difficult to understand are solved.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides a monitoring and early warning method for a dust remover for transmission, where the method includes:
establishing a dust removing space of the dust remover, wherein the dust removing space is a working dust removing area of the dust remover, and arranging a particle detection sensor group in the dust removing space;
the working dust removal area of the dust remover is identified and defined, which means the main treatment area of dust or particulate matter to be monitored, which may be a specific area in an industrial environment, including areas where production lines, work stations or other dust is generated, from which dust removal area a dust removal space is established. A particle detection sensor group is arranged in the dust removing space and is used for monitoring data of dust or particles in air, the sensor group is composed of a plurality of sensors, the sensors are distributed in the dust removing space so as to improve the accuracy and coverage range of monitoring, and the sensors can adopt different technologies, such as laser scattering, photoelectric effect and the like, and are used for detecting the concentration, the size and other relevant parameters of the particles.
The particle detection sensor group is used for collecting space environment data of a dust removing space, and a real-time working environment is configured;
the data about the particulate matter in the dust removing space is collected by using a distributed particulate detection sensor group, and the sensor group comprises a plurality of sensors, so that the concentration, the size and other parameters of the particulate matter in the air can be measured periodically. The collected data is used for configuring a real-time working environment, which means that a dynamic environment state can be established according to the concentration, the size and other data of the particulate matters, so that the real-time state of the concentration and the change condition of the particulate matters in the working dust removing area of the dust remover can be reflected in real time, and the real-time working environment can be a real-time monitoring system for presenting visual information of the current air environment state.
Configuring a flow direction time node mapping of dust removal, wherein the flow direction time node mapping is a mapping relation between wind flow positions of the dust remover in different working modes and time nodes, and the flow direction time node mapping is built by fitting after reading size information, structure information and working mode power data of the dust remover;
the method comprises the steps of establishing a mapping relation between the wind flow position of the dust remover under different working modes and a time node, namely a flow direction time node mapping, wherein the mapping relation is established by reading size information, structural information and working mode power data of the dust remover and fitting, and the configuration of the flow direction time node mapping allows the system to know the change of the wind flow position along with time under different working conditions.
Specifically, first, size information, structure information and operation mode power data of the dust remover are read, wherein the data are key information required for establishing a flow direction time node map, the size information comprises the geometric size of the dust remover, the structure information comprises the layout and the component structure inside the dust remover, and the operation mode power data comprise power consumption information of the dust remover in different operation modes. Then, by using the read data, mathematical fitting is performed to establish a mapping relation between the wind flow position and the time node, and the mapping relation can accurately reflect the operation condition of the dust remover in different working modes, is a relation which dynamically correlates the time node with the wind flow at different positions in the dust remover, and is helpful for understanding the internal wind flow condition of the dust remover in different working modes. Since operating conditions may change over time, the flow-to-time node map may be updated periodically by periodically re-reading and analyzing real-time data of the duster to maintain accurate monitoring of the duster operation status.
A wind flow feedback sensor is arranged in a feeding air duct of the dust remover, a feedback particle detection sensor is arranged at a discharge port of the dust remover, and a particle detection sensor group, a wind flow feedback sensor and equipment association of the feedback particle detection sensor are established through flow direction time node mapping;
In the feed air duct of the dust collector, typically the air inlet, an air flow feedback sensor is provided, the task of which is to monitor parameters of the air flow entering the dust collector, such as flow rate, temperature, humidity, etc., which can affect the performance and efficiency of the dust collector; at the outlet, typically the outlet or discharge, of the dust separator, a feedback particle detection sensor is provided, which is tasked with monitoring particulate matter, such as dust or particle concentration, size, etc., in the gas stream exiting the dust separator, which parameters are used to evaluate the filter effect and particulate matter removal rate of the dust separator.
By means of the flow-to-time node mapping, a device association between the particle detection sensor group, the wind flow feedback sensor and the feedback particle detection sensor is established, which means that the data of these sensors are correlated in order to better understand the operation of the precipitator. When a device association is established, data from the various sensors is integrated to obtain more comprehensive operational status information, such as by analyzing characteristics of the inlet air flow, and comparing with particulate matter data in the outlet air flow to see if the precipitator is effectively removing particulate matter. In this way, the data of various sensors can be comprehensively utilized to more accurately monitor and evaluate the performance of the dust collector.
The detection results of the wind flow feedback sensor and the feedback particle detection sensor after being correlated and the real-time working environment are sent to an evaluation processing channel, and a processing evaluation result is generated;
when the data of the wind flow feedback sensor and the feedback particle detection sensor are collected and correlated, the data is transmitted with the real-time working environment data to an evaluation processing channel, which is a special part for processing the data from the sensors and the working environment, where the data is further analyzed, processed and evaluated. The evaluation processing channel analyzes the sensor data in combination with the real-time operating environment data to generate process evaluation results including information regarding the performance of the precipitator, such as particulate matter removal efficiency, operating conditions, etc., based on which the system may determine whether anomalies are present or further action is required.
Further, the method further comprises:
the working mode of the dust remover is called by the evaluation processing channel, and initialization configuration is completed;
analyzing the detection result, transmitting wind flow data to the evaluation processing channel, and generating a wind flow response result;
Taking the real-time working environment as initial state data, taking a feedback particle detection result in the detection result as final state data, executing processing fitting through an evaluation processing channel, and executing fitting compensation through the wind flow response result to generate a particle processing evaluation result;
and integrating the particle treatment evaluation result and the wind flow response result into a treatment evaluation result to be output.
The dust collector is communicated with an evaluation processing channel, which is a channel for processing and configuring the dust collector, and is operated by software or a control system. In the evaluation process channel, parameters related to the operation mode of the dust remover are acquired, including operation mode types such as manual, automatic, cleaning, maintenance and the like, operation parameters such as wind speed, temperature, pressure and the like, and other configuration information, and the initialization configuration is completed by using the parameters.
Analyzing detection results obtained by the wind flow feedback sensor and the feedback particle detection sensor, transmitting analyzed wind flow data to an evaluation processing channel, wherein the wind flow data are related to wind flow state data such as wind speed, wind quantity, temperature, humidity and the like, and in the evaluation processing channel, the wind flow data are evaluated, including wind flow dynamics, abnormal conditions, performance evaluation and the like, so as to evaluate whether wind flow is in a normal range or not, the normal range can be customized according to historical data or actual requirements, and wind flow response results are generated according to the evaluation results.
The initial state data refers to data of a real-time working environment, and comprises current working conditions, temperature, humidity and other environmental parameters; the final state data refers to the result of the particulate matter detection provided by the feedback particulate matter detection sensor, and are used to evaluate the particulate removal performance of the dust collector.
Using the evaluation process channel, a process fit is performed, such as by establishing a fit equation, correlating the initial state data with the final state data to evaluate the performance of the duster, and the resulting fit may be used to evaluate the performance of the duster, such as particulate matter removal efficiency, performance trend, etc.
When the process fitting is completed, the fitting result is compensated for using the wind flow response result, which means that the fitting result is corrected by the wind flow response result to more accurately reflect the performance of the dust remover. Specifically, the wind flow response result may provide information about environmental conditions and wind flow characteristics, which may affect the accuracy of the particle processing evaluation, the relationship between the wind flow response result and the fitting result is analyzed to generate an influence coefficient, the larger the influence of the wind flow characteristics on the fitting result is, the larger the influence coefficient is, the weighting calculation is performed on the generated fitting result according to the influence coefficient of the wind flow response result to complete the fitting compensation, which may include adjusting parameter values in the fitting result to reflect the actual influence of the wind flow, and finally, the particle processing evaluation result is generated, which reflects the information of the dust remover on the removal efficiency, performance, and the like of the particle matters.
The particle treatment evaluation results and the airflow response results are combined together, for example, the particle treatment evaluation results and the airflow response results are integrated in a weighted combination and summary mode to form a comprehensive treatment evaluation result so as to better show the overall performance of the dust remover.
Further, the method further comprises:
performing power adaptation evaluation of a clean room and a fan on the dust remover to generate an adaptation evaluation space;
the flow direction time node of the discharge port is mapped to be an initial node, and a hysteresis delay window is established through the adaptation evaluation space;
in the process of executing the data acquisition of the feedback particle detection sensor, acquiring delay data in the delay window, and associating the delay data with node data, wherein the node data is data acquired by the feedback particle detection sensor for the flow direction time node mapping of the discharge port;
and sending the data association result to an evaluation processing channel, and updating the processing evaluation result.
Data acquisition is performed, including environmental parameters in the clean room, such as temperature, humidity, pressure, and the operating state of the blower, such as power output, wind speed, pressure difference, which are used to evaluate the power adaptation of the clean room to the blower, i.e. to evaluate the degree of match between the air handling capacity of the clean room and the power output of the blower, to determine if they match properly. Based on the adaptation evaluation result, an adaptation evaluation space is generated, and the evaluation space comprises various conditions and states of the power adaptation of the clean room and the fan, including indexes such as matching degree, power fluctuation, energy efficiency and the like.
The flow direction time nodes of the discharge port are taken as starting nodes, and the time nodes refer to the moment when the discharge port starts discharging particulate matters or the time points related to specific operation steps. Using the established adaptation evaluation space, a hysteresis delay window is established based on the information of the discharge port flow direction time node and the adaptation evaluation space, which window represents a state after a certain time delay from the discharge port time node, which helps to determine the adaptation situation or the specific operation state within a specific time period. By means of a hysteresis delay window, it is possible to evaluate the change or stability of the adaptation evaluation space over a certain period of time, which helps to understand whether the power adaptation between the clean room and the fan is continuously stable.
During the data acquisition process performed by the feedback particle detection sensor, the feedback particle detection sensor collects detection data of particulate matter, including particle concentration, particle size distribution, or other relevant information, which is real-time for monitoring the performance of the dust collector. In the data acquisition process, delay data are acquired, wherein the delay data refer to data acquired after a certain time delay from a flow direction time node of a discharge hole, and the delay time is used for observing the behavior or property change of the particulate matters in time.
The lag time delay data is correlated to data mapped to the flow direction time node of the discharge port so that the system can correlate the time delay data with specific time node data to learn particulate matter behavior at different points in time. The node data is data acquired by feeding back a particle detection sensor through flow direction time node mapping of the discharge port, and the data comprises information such as particle concentration, particle size distribution, particle removal efficiency and the like, and is recorded according to the time node. In this way, information about the behaviour and properties of the particulate matter at different points in time can be obtained, helping to monitor the performance of the precipitator and to improve its operation and maintenance.
Transmitting the data correlation results to an evaluation processing channel, the evaluation processing channel receiving the data, and updating the processing evaluation results using the data correlation results, including improving performance and environmental conditions of the device, to provide more accurate evaluation information.
Further, the method further comprises:
performing delay time window activation judgment by the real-time working environment, and if the delay time window is judged to be activated, determining an activation value according to the real-time working environment;
When the delay time window is activated, calling the delay data with a time sequence identifier, and sending the delay data and the activation value to an incremental analysis network to generate an incremental analysis result, wherein the incremental analysis network is an additional processing network of the evaluation processing channel;
and updating the processing evaluation result according to the increment analysis result.
And judging the activation of the hysteresis delay window according to the data of the real-time working environment, and monitoring the particulate matter level in the real-time working environment, wherein when the particulate matter level reaches a certain degree, the judgment condition is met, and the hysteresis delay window is activated. After the hysteresis delay window is activated, an activation value is determined, which can be determined according to the related factors such as the particulate matter level, the system state and the like of the real-time working environment, wherein the purpose of the activation value is to be used for subsequent data processing so as to adapt to the change of the real-time environment, and the activation value depends on the design and the application scene of the system, and can be used for adjusting the data acquisition frequency, controlling the sensitivity of the hysteresis delay window and the like.
When the hysteresis delay window is activated, the corresponding delay data is invoked, with timing identification, to ensure the correct order and temporal relationship of the data. The delay data and the previously determined activation values are sent to an incremental analysis network, which is an additional part of the evaluation processing channel, which is a network specifically designed for deeper processing and analysis of the transmitted data for data-dependent operations such as calculation, comparison, analysis, etc., to generate incremental analysis results. And through the processing of the incremental analysis network, incremental analysis results are obtained, and the results comprise relevant information such as deeper data analysis, pattern detection, trend identification and the like.
Updating the process evaluation results using the obtained incremental analysis results, including integrating new information into previous process evaluation results, or adjusting certain aspects of the process evaluation results to reflect new data, the updated process evaluation results may provide more accurate information.
The operation data of the dust remover are interacted, equipment operation abnormality analysis is carried out according to the operation data, and an operation abnormality result is generated;
the system interacts with the dust collector to acquire data related to the operation of the dust collector, including real-time performance parameters, working modes, power consumption, operation time and the like of the dust collector, and the data are acquired through monitoring equipment or sensors connected with the dust collector. After the operational data is obtained, the data is analyzed based on predefined rules or anomaly detection models to identify if there is an equipment operational anomaly, including checking if there is an abnormal performance parameter, power consumption exceeding a threshold, operating time anomalies, or other anomalies. Based on the equipment operation anomaly analysis, operation anomaly results are generated, which can indicate which aspects are abnormal, and the severity of the anomaly, such as reduced filtration efficiency or abnormally high power consumption of the duster.
Further, the method further comprises:
invoking basic information of the dust remover, extracting and generating equipment characteristics, and matching a training data set with the equipment characteristics, wherein the training data set is an equipment operation data set which meets the preset similarity with the equipment characteristics, and the equipment operation data set is provided with an operation state identifier;
taking the running state identifier as authentication data, taking the equipment running data set as identification data, and executing an abnormality detection model construction;
and after the abnormal detection model is in a convergence state, sending the operation data to the abnormal detection model for discrimination, and generating an operation abnormal result based on the discrimination result.
Basic information of the dust remover is called, and the basic information comprises data related to the model, manufacturer, technical specification, operation parameters and the like of the dust remover. Based on the basic information of the call, the equipment characteristics are extracted, and the equipment characteristics are used for describing key attributes of the dust remover, such as physical characteristics, working capacity, technical specifications and the like.
The extracted device features are used to match a training data set, the training data set contains operation data corresponding to devices with similar features, the operation data includes information of operation states, performance parameters, abnormal events and the like of the devices, and the operation state identification is a label related to the operation data of the devices, and indicates whether the devices corresponding to the data are in normal operation states or abnormal states, and the identification can be binary labels, such as normal labels, abnormal labels or multi-category labels, such as slight abnormality, serious abnormality and the like.
An abnormality detection model is constructed using a machine learning method, for example, based on a neural network, is trained using a training data set, and is subsequently used to detect abnormalities by authenticating the data and identifying differences in the normal operating mode and abnormality of the data learning device, and is evaluated, for example, using a cross-validation method to verify its performance and accuracy.
The convergence condition, such as the number of iterations or accuracy, is preset to meet the requirements, and when either condition is met, the model reaches a convergence state, indicating that it has completed training and is ready for anomaly detection. And sending the operation data to a model for discrimination, wherein the operation data comprises real-time equipment operation state information. The abnormality detection model analyzes the received operation data according to the learned behavior pattern, generates a discrimination result, the discrimination result indicates whether the operation data has an abnormal condition, and generates an operation abnormality result based on the discrimination result, wherein the result comprises detailed abnormality information, abnormality type, abnormality degree and the like so as to help operators understand the operation condition of the equipment.
Further, the method further comprises:
carrying out data clustering on the operation data set by using a similarity identifier to generate N data clustering clusters, wherein N is an integer greater than 1, and the similarity identifier is a characteristic similarity identifier of the equipment operation data set and equipment characteristics;
and carrying out trust identification on the N data clusters, configuring training constraints according to trust identification results, and constructing an anomaly detection model according to the training constraints.
The use of the device operational dataset and the device features to calculate feature similarity between them, including comparing the values or other attributes of each feature to determine similarity between them, may employ different similarity metrics such as euclidean distance, cosine similarity, etc. Based on the similarity identification, the running data sets are subjected to data clustering, which means that the data are divided into N data clustering clusters, each cluster contains the running data with similar characteristics, so that the data are grouped into different categories for subsequent model training and anomaly detection. N is an integer greater than 1, and the selection of N is determined according to the requirements of the system and the application scene, so that the number of data clustering clusters is enough to capture the diversity of data, and meanwhile, too many clusters cannot be caused, so that the model training complexity is too high.
And carrying out trust identification on the generated N data clustering clusters, and distributing a trust degree for each data clustering cluster according to the result of the trust identification, wherein the score reflects the trust degree of the system on the clusters. Using the results of the trust identification, training constraints are configured, including adjusting parameters of model training, setting a particular learning rate, or focusing more on certain clusters of data during training. According to the configured training constraint, the construction of the anomaly detection model is carried out, so that the model is ensured to pay more attention to the data clustering cluster with high reliability in the training process, and the understanding of the model on the normal running state is improved.
And carrying out abnormality verification according to the operation abnormality result and the processing evaluation result, and carrying out monitoring and early warning of the dust remover according to the abnormality verification result.
Comparing and verifying the abnormal operation result of the equipment with the processing evaluation result, namely checking whether the actual operation condition of the equipment accords with the previous evaluation and expectation, and if the verification result shows that a problem or abnormal condition exists, further executing corresponding monitoring and early warning measures, including sending alarm notification to related personnel, recording the abnormal condition, suspending or adjusting the operation mode of the dust remover, and the like. Therefore, the monitoring and early warning can be triggered when the equipment is abnormal, so that the normal operation and the safety of the dust remover are ensured.
Further, the method further comprises:
judging whether the operation abnormal result and the processing evaluation result are independent abnormal results or not;
if the result is an independent abnormal result, establishing a continuous sensitive time interval, and executing continuous sensitive monitoring of the dust remover through the continuous sensitive time interval;
if the result is not the independent abnormal result, carrying out abnormal association analysis according to the operation abnormal result and the processing evaluation result, and executing abnormal trust update of the operation abnormal result and the processing evaluation result according to the association value;
and executing monitoring and early warning of the dust remover according to the abnormal trust updating result.
And performing independence detection on the operation abnormal result and the processing evaluation result, specifically performing data association analysis, checking the association degree between the operation abnormal result and the processing evaluation result, setting an independence judgment standard, for example, setting based on a correlation threshold value, and judging that the operation abnormal result and the processing evaluation result are independent abnormal results if the association degree is lower than the set independence standard.
Independent exception results refer to no direct or significant correlation between the run exception results and the process evaluation results in a particular situation, meaning that they represent two different exception conditions, possibly caused by different causes or trigger conditions. For example, an operational anomaly of a device may be caused by a fault of the device itself, while a process evaluation result may be caused by a process decision by an operator, and if there is no obvious causal relationship or common root cause between the two, they are treated as independent anomaly results.
If the abnormal operation result and the processing evaluation result are judged to be independent abnormal results, a continuous sensitive time interval is established, wherein the time interval is used for monitoring the operation condition of the equipment, can be predefined and can be dynamically adjusted according to the system requirement, is a continuous time period and is usually short in duration, and is used for monitoring the operation state of the equipment in real time, and the aim of the method is to ensure that measures can be quickly taken when the equipment is in an abnormal state so as to reduce potential damage or production interruption.
Continuous sensitive monitoring is performed during the established time interval, which includes real-time monitoring of the equipment operational data to capture any new operational anomalies, which can be accomplished using various sensors and monitoring equipment. If a new abnormal running condition is found in the continuous sensitive time interval, appropriate measures are taken to process, including operations such as shutdown, alarm notification, maintenance and the like, so as to ensure the normal running of the equipment.
If the operation abnormality result and the processing evaluation result are judged to be non-independent abnormality results, performing abnormality association analysis on the operation abnormality result and the processing evaluation result to determine the association degree and the association characteristic between them. An association value of the abnormality association analysis is calculated, which represents the degree of association between the operation abnormality result and the process evaluation result, which may be a numerical value reflecting the degree of interaction therebetween. And according to the association value, performing abnormal trust update of the operation abnormal result and the processing evaluation result, wherein the update involves adjusting the weight of the abnormal detection model, updating the trust degree score and the like to reflect the association between the abnormal detection model and the processing evaluation result. The abnormal trust update is a process of updating an abnormal detection model or a trust score according to the result of abnormal association analysis, and aims to more accurately reflect the association relationship between the operation abnormal result and the processing evaluation result so as to improve the accuracy of abnormal detection.
An abnormal confidence threshold is defined that is used to determine when to trigger the monitoring alarm, which may be set according to the system requirements and performance metrics. And calculating the current abnormal trust degree by using an abnormal trust updating result, triggering monitoring and early warning when the abnormal trust degree exceeds a predefined threshold value, immediately taking action if the abnormal trust degree exceeds the threshold value, and executing corresponding operations according to the triggered monitoring and early warning, including sending alarm notification to an operator, recording the abnormal condition, starting automatic control operation or taking other appropriate measures to cope with the abnormal condition, so as to ensure that the system can take appropriate measures according to the severity and the relativity of the abnormal condition to reduce damage or production interruption.
In summary, the monitoring and early warning method and system for the dust remover for transmission provided by the embodiment of the application have the following technical effects:
1. by arranging the particle detection sensor group, the wind flow feedback sensor and the feedback particle detection sensor, the working environment and the particle concentration of the dust remover can be monitored in real time, so that measures can be rapidly taken when abnormal conditions occur, and the safety and the reliability of equipment are improved;
2. The environmental data of the dust remover is collected and comprehensively analyzed, including information such as particle concentration, wind flow feedback and the like, so that a real-time working environment and a processing evaluation result are generated, the performance of equipment is more comprehensively understood, and more information about abnormal conditions is provided;
3. by establishing the flow direction time node mapping, the relationship between the wind flow position of the equipment in different working modes and the time node is considered, so that the equipment can be monitored more accurately according to the actual working state, and abnormal conditions in different time periods can be captured.
In a word, the monitoring and early warning method of the dust remover for transmission provides more efficient equipment monitoring and abnormal early warning capability through technical means such as real-time monitoring, comprehensive analysis of environmental data, flow direction time node mapping and the like so as to maintain normal operation of equipment and reduce potential risks.
Example 2
Based on the same inventive concept as the monitoring and early warning method of a dust collector for transmission in the foregoing embodiments, as shown in fig. 2, the present application provides a monitoring and early warning system of a dust collector for transmission, the system includes:
the dust removing space establishing module 10 is used for establishing a dust removing space of the dust remover, wherein the dust removing space is a working dust removing area of the dust remover, and a particle detection sensor group is arranged in the dust removing space;
The environment data acquisition module 20 is used for acquiring space environment data of a dust removing space by the particle detection sensor group, and configuring a real-time working environment;
the node mapping configuration module 30 is configured to configure a flow direction time node mapping of dust removal, wherein the flow direction time node mapping is a mapping relationship between a wind flow position of the dust remover in different working modes and a time node, and the flow direction time node mapping is established by fitting after reading size information, structure information and working mode power data of the dust remover;
the device association establishing module 40 is configured to set an airflow feedback sensor in a feeding air duct of the dust remover, set a feedback particle detection sensor in a discharge port of the dust remover, and establish a device association of a particle detection sensor group, an airflow feedback sensor and a feedback particle detection sensor through flow direction time node mapping;
the evaluation result generation module 50 is configured to send the detection results of the wind flow feedback sensor and the feedback particle detection sensor after correlation and the real-time working environment to an evaluation processing channel, so as to generate a processing evaluation result;
The equipment abnormality analysis module 60 is used for interacting the operation data of the dust remover, carrying out equipment operation abnormality analysis on the operation data and generating an operation abnormality result;
and the monitoring and early warning module 70 is used for carrying out abnormal verification according to the operation abnormal result and the processing evaluation result, and carrying out monitoring and early warning of the dust remover according to the abnormal verification result.
Further, the system also comprises a processing evaluation result output module for executing the following operation steps:
the working mode of the dust remover is called by the evaluation processing channel, and initialization configuration is completed;
analyzing the detection result, transmitting wind flow data to the evaluation processing channel, and generating a wind flow response result;
taking the real-time working environment as initial state data, taking a feedback particle detection result in the detection result as final state data, executing processing fitting through an evaluation processing channel, and executing fitting compensation through the wind flow response result to generate a particle processing evaluation result;
and integrating the particle treatment evaluation result and the wind flow response result into a treatment evaluation result to be output.
Further, the system also comprises a processing evaluation result updating module for executing the following operation steps:
performing power adaptation evaluation of a clean room and a fan on the dust remover to generate an adaptation evaluation space;
the flow direction time node of the discharge port is mapped to be an initial node, and a hysteresis delay window is established through the adaptation evaluation space;
in the process of executing the data acquisition of the feedback particle detection sensor, acquiring delay data in the delay window, and associating the delay data with node data, wherein the node data is data acquired by the feedback particle detection sensor for the flow direction time node mapping of the discharge port;
and sending the data association result to an evaluation processing channel, and updating the processing evaluation result.
Further, the system further comprises an evaluation result updating module for executing the following operation steps:
performing delay time window activation judgment by the real-time working environment, and if the delay time window is judged to be activated, determining an activation value according to the real-time working environment;
when the delay time window is activated, calling the delay data with a time sequence identifier, and sending the delay data and the activation value to an incremental analysis network to generate an incremental analysis result, wherein the incremental analysis network is an additional processing network of the evaluation processing channel;
And updating the processing evaluation result according to the increment analysis result.
Further, the system also comprises an abnormal operation result generating module for executing the following operation steps:
invoking basic information of the dust remover, extracting and generating equipment characteristics, and matching a training data set with the equipment characteristics, wherein the training data set is an equipment operation data set which meets the preset similarity with the equipment characteristics, and the equipment operation data set is provided with an operation state identifier;
taking the running state identifier as authentication data, taking the equipment running data set as identification data, and executing an abnormality detection model construction;
and after the abnormal detection model is in a convergence state, sending the operation data to the abnormal detection model for discrimination, and generating an operation abnormal result based on the discrimination result.
Further, the system also comprises an abnormality detection model construction module for executing the following operation steps:
carrying out data clustering on the operation data set by using a similarity identifier to generate N data clustering clusters, wherein N is an integer greater than 1, and the similarity identifier is a characteristic similarity identifier of the equipment operation data set and equipment characteristics;
And carrying out trust identification on the N data clusters, configuring training constraints according to trust identification results, and constructing an anomaly detection model according to the training constraints.
Further, the system also comprises a monitoring and early warning module for executing the following operation steps:
judging whether the operation abnormal result and the processing evaluation result are independent abnormal results or not;
if the result is an independent abnormal result, establishing a continuous sensitive time interval, and executing continuous sensitive monitoring of the dust remover through the continuous sensitive time interval;
if the result is not the independent abnormal result, carrying out abnormal association analysis according to the operation abnormal result and the processing evaluation result, and executing abnormal trust update of the operation abnormal result and the processing evaluation result according to the association value;
and executing monitoring and early warning of the dust remover according to the abnormal trust updating result.
In the present disclosure, through the foregoing detailed description of a method for monitoring and early warning of a dust collector for transmission, it is clear to those skilled in the art that a system for monitoring and early warning of a dust collector for transmission in this embodiment is relatively simple to describe, and relevant places refer to the description of the method section for the device disclosed in the embodiment.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. The monitoring and early warning method of the dust remover for transmission is characterized by comprising the following steps of:
establishing a dust removing space of the dust remover, wherein the dust removing space is a working dust removing area of the dust remover, and arranging a particle detection sensor group in the dust removing space;
the particle detection sensor group is used for collecting space environment data of a dust removing space, and a real-time working environment is configured;
configuring a flow direction time node mapping of dust removal, wherein the flow direction time node mapping is a mapping relation between wind flow positions of the dust remover in different working modes and time nodes, and the flow direction time node mapping is built by fitting after reading size information, structure information and working mode power data of the dust remover;
A wind flow feedback sensor is arranged in a feeding air duct of the dust remover, a feedback particle detection sensor is arranged at a discharge port of the dust remover, and a particle detection sensor group, a wind flow feedback sensor and equipment association of the feedback particle detection sensor are established through flow direction time node mapping;
the detection results of the wind flow feedback sensor and the feedback particle detection sensor after being correlated and the real-time working environment are sent to an evaluation processing channel, and a processing evaluation result is generated;
the operation data of the dust remover are interacted, equipment operation abnormality analysis is carried out according to the operation data, and an operation abnormality result is generated;
performing abnormality verification according to the operation abnormality result and the processing evaluation result, and performing monitoring and early warning of the dust remover according to the abnormality verification result;
the method further comprises the steps of:
the working mode of the dust remover is called by the evaluation processing channel, and initialization configuration is completed;
analyzing the detection result, transmitting wind flow data to the evaluation processing channel, and generating a wind flow response result;
taking the real-time working environment as initial state data, taking a feedback particle detection result in the detection result as final state data, executing processing fitting through an evaluation processing channel, and executing fitting compensation through the wind flow response result to generate a particle processing evaluation result;
Integrating the particle treatment evaluation result and the wind flow response result into a treatment evaluation result output;
invoking basic information of the dust remover, extracting and generating equipment characteristics, and matching a training data set with the equipment characteristics, wherein the training data set is an equipment operation data set which meets the preset similarity with the equipment characteristics, and the equipment operation data set is provided with an operation state identifier;
taking the running state identifier as authentication data, taking the equipment running data set as identification data, and executing an abnormality detection model construction;
and after the abnormal detection model is in a convergence state, sending the operation data to the abnormal detection model for discrimination, and generating an operation abnormal result based on the discrimination result.
2. The method of claim 1, wherein the method further comprises:
performing power adaptation evaluation of a clean room and a fan on the dust remover to generate an adaptation evaluation space;
the flow direction time node of the discharge port is mapped to be an initial node, and a hysteresis delay window is established through the adaptation evaluation space;
in the process of executing the data acquisition of the feedback particle detection sensor, acquiring delay data in the delay window, and associating the delay data with node data, wherein the node data is data acquired by the feedback particle detection sensor for the flow direction time node mapping of the discharge port;
And sending the data association result to an evaluation processing channel, and updating the processing evaluation result.
3. The method of claim 2, wherein the method further comprises:
performing delay time window activation judgment by the real-time working environment, and if the delay time window is judged to be activated, determining an activation value according to the real-time working environment;
when the delay time window is activated, calling the delay data with a time sequence identifier, and sending the delay data and the activation value to an incremental analysis network to generate an incremental analysis result, wherein the incremental analysis network is an additional processing network of the evaluation processing channel;
and updating the processing evaluation result according to the increment analysis result.
4. The method of claim 1, wherein the method further comprises:
carrying out data clustering on the operation data set by using a similarity identifier to generate N data clustering clusters, wherein N is an integer greater than 1, and the similarity identifier is a characteristic similarity identifier of the equipment operation data set and equipment characteristics;
and carrying out trust identification on the N data clusters, configuring training constraints according to trust identification results, and constructing an anomaly detection model according to the training constraints.
5. The method of claim 1, wherein the method further comprises:
judging whether the operation abnormal result and the processing evaluation result are independent abnormal results or not;
if the result is an independent abnormal result, establishing a continuous sensitive time interval, and executing continuous sensitive monitoring of the dust remover through the continuous sensitive time interval;
if the result is not the independent abnormal result, carrying out abnormal association analysis according to the operation abnormal result and the processing evaluation result, and executing abnormal trust update of the operation abnormal result and the processing evaluation result according to the association value;
and executing monitoring and early warning of the dust remover according to the abnormal trust updating result.
6. A monitoring and warning system for a dust collector for transmission, which is used for implementing the monitoring and warning method for the dust collector for transmission according to any one of claims 1 to 5, comprising:
the dust removing space establishing module is used for establishing a dust removing space of the dust remover, wherein the dust removing space is a working dust removing area of the dust remover, and a particle detection sensor group is arranged in the dust removing space;
the environment data acquisition module is used for acquiring space environment data of a dust removing space by the particle detection sensor group and configuring a real-time working environment;
The node mapping configuration module is used for configuring flow direction time node mapping of dust removal, wherein the flow direction time node mapping is a mapping relation between wind flow positions of the dust remover in different working modes and time nodes, and the flow direction time node mapping is built by fitting after reading size information, structure information and working mode power data of the dust remover;
the device association establishing module is used for setting an air flow feedback sensor in a feeding air channel of the dust remover, setting a feedback particle detection sensor at a discharge port of the dust remover, and establishing a particle detection sensor group, an air flow feedback sensor and device association of the feedback particle detection sensor through flow direction time node mapping;
the evaluation result generation module is used for sending the detection results of the wind flow feedback sensor and the feedback particle detection sensor after being correlated with the real-time working environment to an evaluation processing channel to generate a processing evaluation result;
the equipment abnormality analysis module is used for interacting the operation data of the dust remover, carrying out equipment operation abnormality analysis on the operation data and generating an operation abnormality result;
And the monitoring and early warning module is used for carrying out abnormal verification according to the operation abnormal result and the processing evaluation result and executing the monitoring and early warning of the dust remover according to the abnormal verification result.
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Denomination of invention: A monitoring and warning method and system for dust collectors used in transmission Granted publication date: 20240308 Pledgee: Jiangsu Zhangjiagang Rural Commercial Bank Co.,Ltd. Pledgor: Zhangjiagang Huashen Industrial Rubber Products Co.,Ltd. Registration number: Y2024980013199 |
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