CN116718534B - Particulate filter filtration efficiency detecting system based on data analysis - Google Patents
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Abstract
The application discloses a particle filter filtering efficiency detection system based on data analysis, relates to the technical field of filtering efficiency detection, and solves the technical problem that in the prior art, filtering efficiency detection cannot be performed under different operation scenes, so that filtering efficiency cannot be accurately detected; according to the application, the trapping efficiency detection analysis is carried out on the particle filter, the efficiency evaluation is carried out on the particle filter through the trapping efficiency detection analysis, so that the running efficiency of the particle filter meets the requirement, the high-efficiency dust filtration can be carried out in the area corresponding to the current dust particle to be filtered, the filtration analysis is carried out on the analysis object, and whether the filtration efficiency of the analysis object meets the requirement in different running scenes is judged, so that the running efficiency of the analysis object is ensured, and the qualification and the high efficiency of the particle filtration are ensured.
Description
Technical Field
The application relates to the technical field of filtering efficiency detection, in particular to a particle filter filtering efficiency detection system based on data analysis.
Background
The particle filter is also called an elastic filter and is applied to the aspects of circulating water filtration and high-pressure water cutting in large-scale places such as the automobile industry (processing machinery, cleaning machinery and the like), the machine tool industry (processing cooling liquid, burr treatment machine and the like), the food industry (drainage treatment, recovery of frying residues and the like);
however, in the prior art, when the particulate filter is put into use, the trapping efficiency cannot be detected, so that the running efficiency of the particulate filter cannot accurately meet the requirements, and meanwhile, the filtering efficiency cannot be detected under different running scenes, so that the filtering efficiency cannot be accurately detected, and the running efficiency of the particulate filter cannot be managed and controlled in real time;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The present application is directed to a particulate filter filtration efficiency detection system based on data analysis, in order to solve the above-mentioned problems.
The aim of the application can be achieved by the following technical scheme: the system comprises an efficiency detection platform, a particle trapping efficiency analysis unit, a filtering efficiency analysis unit and an efficiency detection deviation analysis unit;
the particle trapping efficiency analysis unit performs trapping efficiency detection analysis on the particle filter, marks the particle filter as an analysis object, acquires the dust particles and the unfiltered dust particles which are correspondingly filtered by the analysis object, marks the acquired filtered dust particles and unfiltered dust particles as a filtering main body and an unfiltered main body respectively, and analyzes and judges whether the trapping efficiency is qualified or not;
the filtering efficiency analysis unit carries out filtering analysis on the analysis objects, divides the operation scene of the analysis objects into an instantaneous filtering scene and a slow time filtering scene, and judges whether the operation scene analysis objects are qualified to be filtered according to the instantaneous filtering scene and the slow time filtering scene; the efficiency detection deviation analysis unit performs deviation analysis on the efficiency detection of the analysis object and judges whether deviation exists in the filtering efficiency detection of the current risk object.
As a preferred embodiment of the present application, the particle catch efficiency analysis unit operates as follows:
the reduction speed of the particle size corresponding to the unfiltered main body and the ratio of the particle number corresponding to the maximum particle size to the total number of unfiltered main bodies in the operation process of the analysis object are obtained, and the reduction speed of the particle size corresponding to the unfiltered main body and the ratio of the particle number corresponding to the maximum particle size to the total number of unfiltered main bodies in the operation process of the analysis object are respectively compared with a particle size reduction speed threshold and a particle number ratio threshold.
As a preferred embodiment of the present application, if the reduction speed of the particle size of the unfiltered main body exceeds the threshold value of the reduction speed of the particle size, or the ratio of the number of particles corresponding to the maximum particle size to the total number of unfiltered main bodies does not exceed the threshold value of the ratio of the number of particles during the operation of the analysis object, generating an unfiltered main body anomaly signal and transmitting the unfiltered main body anomaly signal to the efficiency detection platform; if the reduction speed of the particle size corresponding to the unfiltered main body in the operation process of the analysis object does not exceed the particle size reduction speed threshold value, and the ratio of the particle number corresponding to the maximum particle size to the total number of the unfiltered main body exceeds the particle number ratio threshold value, generating an unfiltered main body normal signal and sending the unfiltered main body normal signal to the efficiency detection platform.
As a preferred embodiment of the present application, the ratio of the particle diameter of the filter body to the shortage of the set particle diameter during operation of the analysis object and the number of filter bodies higher than the set particle diameter during operation are obtained, and the ratio of the particle diameter of the filter body to the shortage of the set particle diameter during operation of the analysis object and the number of filter bodies higher than the set particle diameter during operation are compared with the threshold of the shortage of the particle diameter and the threshold of the number of filter bodies respectively.
As a preferred embodiment of the present application, if the particle size of the filter body and the shortage of the set particle size exceeds a particle size shortage threshold during operation of the analysis object or the number of filter bodies higher than the set particle size exceeds a filter body number ratio threshold during operation, determining that the efficiency of capturing the filter body of the analysis object is abnormal, generating a filter body abnormality signal and transmitting the filter body abnormality signal to the efficiency detection platform; if the particle size of the filtering main body and the shortage amount of the set particle size do not exceed the particle size shortage amount threshold in the operation process of the analysis object, and the number proportion of the filtering main bodies higher than the set particle size in the operation process does not exceed the number proportion threshold of the filtering main bodies, judging that the trapping efficiency of the filtering main body of the analysis object is normal, generating a normal signal of the filtering main body and sending the normal signal of the filtering main body to the efficiency detection platform.
As a preferred embodiment of the present application, the filter efficiency analysis unit operates as follows:
the method comprises the steps of obtaining the increase of particles which are not filtered by a filtering main body with the analysis object exceeding a set particle size in an instantaneous filtering scene and the decrease of the filtering speed of the filtering main body of the analysis object of the instantaneous filtering scene, and comparing the increase of particles which are not filtered by the filtering main body with the analysis object exceeding the set particle size in the instantaneous filtering scene and the decrease of the filtering speed of the filtering main body of the analysis object of the instantaneous filtering scene with a threshold of the increase of particles and a threshold of the decrease of the filtering speed respectively.
As a preferred embodiment of the present application, if the particle increasing amount of the filtering body, which is not filtered by the analysis object corresponding to the filtering body exceeding the set particle diameter, in the instantaneous filtering scene exceeds the particle increasing amount threshold, or the filtering speed decreasing amount of the filtering body of the analysis object of the instantaneous filtering scene exceeds the filtering speed decreasing amount threshold, determining that the filtering efficiency analysis of the analysis object is abnormal in the instantaneous filtering scene, generating an instantaneous filtering efficiency abnormal signal and transmitting the instantaneous filtering efficiency abnormal signal to the efficiency detection platform; if the particle increment of the filtering main body which is corresponding to the analysis object and exceeds the set particle diameter in the instantaneous filtering scene is not filtered and the filtering speed reduction of the filtering main body of the analysis object of the instantaneous filtering scene is not more than the filtering speed reduction threshold, judging that the filtering efficiency analysis of the analysis object is normal in the instantaneous filtering scene, generating an instantaneous filtering efficiency normal signal and transmitting the instantaneous filtering efficiency normal signal to an efficiency detection platform.
As a preferred embodiment of the application, the floating amount of the non-filtering main body and the filtering main body operation anti-execution probability of the analysis object in the slow filtering scene and the excessive amount of the filtering layer dust thickness increasing period and the cleaning period of the analysis object in the slow filtering scene are obtained, and the floating amount of the non-filtering main body and the filtering main body operation anti-execution probability of the analysis object in the slow filtering scene and the excessive amount of the filtering layer dust thickness increasing period and the cleaning period of the analysis object in the slow filtering scene are respectively compared with the anti-execution probability floating amount threshold and the period excessive amount threshold.
As a preferred embodiment of the application, if the floating amount of the unfiltered main body of the analysis object and the operation reverse execution probability of the filtering main body in the slow filtering scene exceeds the floating amount threshold of the reverse execution probability, or the dust thickness increasing period of the filtering layer of the analysis object and the excessive amount of the cleaning period in the slow filtering scene do not exceed the period excessive amount threshold, judging that the analysis object has abnormal filtering efficiency analysis in the slow filtering scene, generating a slow filtering efficiency abnormal signal and sending the slow filtering efficiency abnormal signal to an efficiency detection platform;
if the floating quantity of the non-filtering main body of the analysis object and the operation reverse execution probability of the filtering main body in the slow filtering scene does not exceed the floating quantity threshold of the reverse execution probability, and the filter layer dust thickness increasing period and the excessive amount of the cleaning period of the analysis object in the slow filtering scene exceed the period excessive amount threshold, generating a slow filtering efficiency normal signal and sending the slow filtering efficiency normal signal to the efficiency detection platform.
As a preferred embodiment of the present application, the filter efficiency analysis unit operates as follows: marking the filtering efficiency detection time period as a filtering detection time period, acquiring a natural dust precipitation amount increase span of a filtering layer corresponding to an analysis object in the filtering detection time period and a moving speed floating span of dust particles driven by a filtering wind speed of the analysis object in the filtering detection time period, and marking the natural dust precipitation amount increase span of the filtering layer corresponding to the analysis object in the filtering detection time period and the moving speed floating span of the dust particles driven by the filtering wind speed of the analysis object in the filtering detection time period as ZZK and YSK respectively; collecting a time period average deviation value of the total dust content in the analysis object use area in the filtering and detecting time period, and marking the time period average deviation value of the total dust content in the analysis object use area in the filtering and detecting time period as PCZ; and obtaining an analysis object efficiency detection deviation analysis coefficient G in the filtering and detecting period through a formula, and comparing the analysis object efficiency detection deviation analysis coefficient G in the filtering and detecting period with an efficiency detection deviation analysis coefficient threshold.
As a preferred embodiment of the application, the detection deviation analysis coefficient acquisition formula isWherein, fd1, fd2 and fd3 are all preset proportionality coefficients, fd1 > fd2 > fd3 > 1, beta is an error correction factor, and the value is 0.976.
As a preferred embodiment of the present application, if the efficiency detection deviation analysis coefficient G of the analysis object exceeds the efficiency detection deviation analysis coefficient threshold in the filtering period, determining that the efficiency detection deviation analysis is abnormal in the filtering period, generating a high deviation signal and transmitting the high deviation signal to the efficiency detection platform, and after the efficiency detection platform receives the high deviation signal, re-acquiring the data acquired in the corresponding filtering period, and taking the dust filtering duty ratio of the current area of the analysis object as the efficiency detection parameter of the analysis object; the efficiency detection parameter is used as a verification parameter for detecting the efficiency re-detection of the analysis object; if the efficiency detection deviation analysis coefficient G of the analysis object in the filtering period does not exceed the efficiency detection deviation analysis coefficient threshold value, judging that the efficiency detection deviation analysis is normal in the filtering period, generating a low deviation signal and sending the low deviation signal to the efficiency detection platform.
Compared with the prior art, the application has the beneficial effects that:
1. in the application, the trapping efficiency detection analysis is carried out on the particle filter, the efficiency evaluation is carried out on the particle filter through the trapping efficiency detection analysis, so that the running efficiency of the particle filter is ensured to meet the requirement, and the high-efficiency dust filtration can be carried out in the area corresponding to the current dust particle filtration requirement; and filtering and analyzing the analysis object to judge whether the filtering efficiency of the analysis object meets the requirements under different operation scenes, thereby ensuring the operation efficiency of the analysis object and ensuring the qualification and the high efficiency of the particle filtering.
2. According to the application, deviation analysis is carried out on the efficiency detection of the analysis object, whether the deviation exists in the filtering efficiency detection of the current risk object is judged, and the influence of the filtering efficiency detection deviation on the efficiency judgment result of the analysis object is avoided, so that the running efficiency of the analysis object is reduced, and the filtering efficiency of dust particles in an area is influenced.
3. According to the application, the real-time filtration pressure loss analysis is carried out on the analysis object, so that whether the real-time pressure loss of the analysis object affects the filtration efficiency in the filtration process is judged, and meanwhile, the increase of the operation cost of the analysis object caused by the overlarge pressure loss of the analysis object in the filtration process is avoided.
Drawings
The present application is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a schematic block diagram of embodiment 1 of the present application;
fig. 2 is a schematic block diagram of embodiment 2 of the present application.
Detailed Description
In order to make the present application better understood by those skilled in the art, the following description of the technical solutions of the present application will be made in detail, but it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments of the present application, with reference to the accompanying drawings in the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, a system for detecting filtering efficiency of a particulate filter based on data analysis includes an efficiency detection platform, a particulate capturing efficiency analysis unit, a filtering efficiency analysis unit, and an efficiency detection deviation analysis unit;
example 1
The method comprises the steps that in the using process of a particle filter, filtering efficiency is required to be detected in real time, an efficiency detection platform detects the filtering efficiency when the particle filter is put into use, a particle trapping efficiency analysis signal is generated and sent to a particle trapping efficiency analysis unit, after the particle trapping efficiency analysis unit receives the particle trapping efficiency analysis signal, the particle filter is subjected to trapping efficiency detection analysis, and efficiency evaluation is performed on the particle filter through the trapping efficiency detection analysis, so that the running efficiency of the particle filter is ensured to meet the requirement, and therefore high-efficiency dust filtration can be performed in a region corresponding to the current region where dust particles need to be filtered;
marking the particle filter as an analysis object, acquiring the analysis object corresponding to the filtered dust particles and the unfiltered dust particles, and marking the acquired filtered dust particles and unfiltered dust particles as a filter body and an unfiltered body respectively;
the method comprises the steps of obtaining the reduction speed of the corresponding particle size of an unfiltered main body and the ratio of the number of particles corresponding to the maximum particle size to the total number of unfiltered main bodies in the operation process of an analysis object, and comparing the reduction speed of the corresponding particle size of the unfiltered main body and the ratio of the number of particles corresponding to the maximum particle size to the total number of unfiltered main bodies with a threshold value of the reduction speed of the particle size and a threshold value of the ratio of the number of particles respectively in the operation process of the analysis object:
if the reduction speed of the particle size corresponding to the unfiltered main body exceeds the particle size reduction speed threshold value or the ratio of the particle number corresponding to the maximum particle size to the total number of unfiltered main bodies does not exceed the particle number ratio threshold value in the operation process of the analysis object, judging that the unfiltered main body of the analysis object captures the abnormal efficiency, generating an unfiltered main body abnormal signal and sending the unfiltered main body abnormal signal to an efficiency detection platform, and after the efficiency detection platform receives the unfiltered main body abnormal signal, analyzing the set parameters corresponding to the unfiltered main body and resetting according to the model of the analysis object;
if the reduction speed of the particle size corresponding to the unfiltered main body does not exceed the particle size reduction speed threshold value in the operation process of the analysis object, and the ratio of the particle number corresponding to the maximum particle size to the total number of the unfiltered main body exceeds the particle number ratio threshold value, judging that the trapping efficiency of the unfiltered main body of the analysis object is normal, generating an unfiltered main body normal signal and transmitting the unfiltered main body normal signal to an efficiency detection platform;
the method comprises the steps of obtaining the ratio of the particle size of a filtering main body to the shortage of a set particle size in the operation process of an analysis object to the quantity of the filtering main bodies higher than the set particle size in the operation process of the analysis object, and comparing the ratio of the particle size of the filtering main body to the shortage of the set particle size in the operation process of the analysis object to the quantity of the filtering main bodies higher than the set particle size in the operation process of the analysis object to a threshold value of the shortage of the particle size and a threshold value of the quantity of the filtering main bodies higher than the set particle size respectively:
if the particle size of the filtering main body and the shortage amount of the set particle size exceed a particle size shortage amount threshold in the running process of the analysis object, or the quantity proportion of the filtering main bodies higher than the set particle size exceeds a filtering main body quantity proportion threshold in the running process, judging that the filtering main body of the analysis object captures the abnormal efficiency, generating a filtering main body abnormal signal and sending the filtering main body abnormal signal to an efficiency detection platform, and after the efficiency detection platform receives the filtering main body abnormal signal, controlling running equipment of the analysis object to avoid deformation or parameter setting abnormality at the filtering part of the running equipment;
if the particle size of the filtering main body and the shortage amount of the set particle size do not exceed the particle size shortage amount threshold in the operation process of the analysis object, and the quantity proportion of the filtering main bodies higher than the set particle size in the operation process does not exceed the quantity proportion threshold of the filtering main bodies, judging that the trapping efficiency of the filtering main body of the analysis object is normal, generating a normal signal of the filtering main body and sending the normal signal of the filtering main body to an efficiency detection platform; the particle trapping efficiency analysis is carried out on the analysis object through the filtering main body and the non-filtering main body, so that the setting qualified efficiency and the real-time using qualified efficiency of the analysis object are improved, the particle trapping efficiency of the analysis object is analyzed, and the running efficiency of the analysis object is ensured;
after the particle trapping efficiency analysis unit simultaneously generates a normal filter body signal and a normal unfiltered body signal, judging that the trapping efficiency analysis of the analysis object is normal, generating a filtering efficiency analysis signal and transmitting the filtering efficiency analysis signal to the filtering efficiency analysis unit; after receiving the filtering efficiency analysis signals, the filtering efficiency analysis unit carries out filtering analysis on the analysis object and judges whether the filtering efficiency of the analysis object meets the requirements under different operation scenes, so that the operation efficiency of the analysis object is ensured, and the qualification and the high efficiency of particle filtration are ensured;
dividing the operation scene of the analysis object into an instantaneous filtering scene and a slow-time filtering scene, wherein the instantaneous filtering scene is represented as a working scene with particles to be filtered increasing in a short time, and the slow-time filtering scene is represented as a working scene with particles to be filtered floating in a small amount in a time period;
the method comprises the steps of obtaining an increase of particles which are not filtered by a filtering main body with the analysis object exceeding a set particle size in an instantaneous filtering scene and a decrease of the filtering speed of the filtering main body of the instantaneous filtering scene, and comparing the increase of particles which are not filtered by the filtering main body with the analysis object exceeding the set particle size in the instantaneous filtering scene and the decrease of the filtering speed of the filtering main body of the instantaneous filtering scene with a threshold of the increase of particles and a threshold of the decrease of the filtering speed respectively:
if the particle increment of the filtering main body which is corresponding to the analysis object and exceeds the set particle diameter in the instantaneous filtering scene is not filtered, or the filtering speed of the filtering main body of the analysis object of the instantaneous filtering scene exceeds the filtering speed reduction threshold, judging that the analysis object is abnormal in filtering efficiency analysis in the instantaneous filtering scene, generating an instantaneous filtering efficiency abnormal signal and sending the instantaneous filtering efficiency abnormal signal to an efficiency detection platform, and after the instantaneous filtering efficiency abnormal signal is received by the efficiency detection platform, adjusting the set particle diameter according to the specification of the current analysis object and the occurrence frequency of the instantaneous filtering scene, and controlling the filtering wind speed in the instantaneous filtering scene;
if the particle increment of the filtering main body which is corresponding to the analysis object and exceeds the set particle diameter in the instantaneous filtering scene is not filtered and the filtration speed reduction of the filtering main body of the analysis object of the instantaneous filtering scene is not more than the filtration speed reduction threshold, judging that the analysis object is normal in the filtering efficiency analysis in the instantaneous filtering scene, generating an instantaneous filtering efficiency normal signal and transmitting the instantaneous filtering efficiency normal signal to an efficiency detection platform;
the floating quantity of the operation reverse execution probability of the unfiltered main body and the filtering main body of the analysis object in the slow filtering scene and the excessive quantity of the dust thickness increasing period and the cleaning period of the filtering layer of the analysis object in the slow filtering scene are obtained, and the floating quantity of the operation reverse execution probability of the unfiltered main body and the filtering main body of the analysis object in the slow filtering scene and the excessive quantity of the dust thickness increasing period and the cleaning period of the filtering layer of the analysis object in the slow filtering scene are respectively compared with a threshold value of the floating quantity of the reverse execution probability and a threshold value of the excessive quantity of the period: the reverse execution of the operation means that the filtering body performs filtering and the filtering body performs no filtering;
if the floating quantity of the unfiltered main body and the operation reverse execution probability of the filtering main body of the analysis object in the slow filtering scene exceeds the floating quantity threshold of the reverse execution probability, or the multi-output quantity of the dust thickness increasing period and the cleaning period of the filtering layer of the analysis object in the slow filtering scene does not exceed the multi-output quantity threshold of the period, judging that the analysis efficiency of the analysis object in the slow filtering scene is abnormal, generating a slow filtering efficiency abnormal signal and sending the slow filtering efficiency abnormal signal to an efficiency detection platform; the efficiency detection platform receives the slow filtering efficiency abnormal signal and then reselects the model of the corresponding analysis object;
if the floating amount of the unfiltered main body of the analysis object and the operation reverse execution probability of the filtering main body in the slow filtering scene does not exceed the floating amount threshold of the reverse execution probability, and the filter layer dust thickness increasing period and the excessive amount of the cleaning period of the analysis object in the slow filtering scene exceed the period excessive amount threshold, judging that the analysis efficiency analysis of the analysis object is normal in the slow filtering scene, generating a slow filtering efficiency normal signal and transmitting the slow filtering efficiency normal signal to an efficiency detection platform;
the filtering efficiency analysis unit generates a slow filtering efficiency normal signal and an instantaneous filtering efficiency normal signal at the same time, judges that the filtering efficiency analysis of the analysis object is qualified, generates an efficiency detection deviation analysis signal and sends the efficiency detection deviation analysis signal to the efficiency detection deviation analysis unit, and after receiving the efficiency detection deviation analysis signal, the efficiency detection deviation analysis unit carries out deviation analysis on the efficiency detection of the analysis object, judges whether the current risk object filtering efficiency detection has deviation or not, and avoids that the filtering efficiency detection deviation influences the efficiency judgment result of the analysis object, so that the running efficiency of the analysis object is reduced, and the filtering efficiency of dust particles in an area is influenced;
marking the filtering efficiency detection time period as a filtering detection time period, acquiring a natural dust precipitation amount increase span of a filtering layer corresponding to an analysis object in the filtering detection time period and a moving speed floating span of dust particles driven by a filtering wind speed of the analysis object in the filtering detection time period, and marking the natural dust precipitation amount increase span of the filtering layer corresponding to the analysis object in the filtering detection time period and the moving speed floating span of the dust particles driven by the filtering wind speed of the analysis object in the filtering detection time period as ZZK and YSK respectively; collecting a time period average deviation value of the total dust content in the analysis object use area in the filtering and detecting time period, and marking the time period average deviation value of the total dust content in the analysis object use area in the filtering and detecting time period as PCZ;
by the formulaObtaining an analysis object efficiency detection deviation analysis coefficient G in a filtering and detecting period, wherein fd1, fd2 and fd3 are preset proportionality coefficients, fd1 is more than fd2 is more than fd3 is more than 1, beta is an error correction factor, and the value is 0.976;
comparing the analysis object efficiency detection deviation analysis coefficient G with an efficiency detection deviation analysis coefficient threshold value in the filtering and detecting period:
if the efficiency detection deviation analysis coefficient G of the analysis object exceeds the efficiency detection deviation analysis coefficient threshold value in the filtering and detecting period, judging that the efficiency detection deviation analysis is abnormal in the filtering and detecting period, generating a high deviation signal, sending the high deviation signal to an efficiency detection platform, and re-acquiring data acquired in the corresponding filtering and detecting period after the efficiency detection platform receives the high deviation signal, and taking the dust filtering duty ratio of the current area of the analysis object as an efficiency detection parameter of the analysis object; the efficiency detection parameter is used as a verification parameter for detecting the efficiency re-detection of the analysis object;
if the efficiency detection deviation analysis coefficient G of the analysis object in the filtering and detecting period does not exceed the efficiency detection deviation analysis coefficient threshold, judging that the efficiency detection deviation analysis is normal in the filtering and detecting period, generating a low deviation signal and transmitting the low deviation signal to the efficiency detection platform;
example 2
Referring to fig. 2, the efficiency detection platform is further communicatively connected with a filtering pressure analysis unit, and when the analysis object performs efficiency detection, the filtering pressure analysis unit analyzes the filtering pressure of the analysis object, and the filtering pressure analysis unit performs real-time filtering pressure loss analysis on the analysis object to determine whether the real-time pressure loss of the analysis object affects the filtering efficiency in the filtering process, and meanwhile, avoids the increase of the operation cost of the analysis object caused by excessive pressure loss of the analysis object in the filtering process;
the method comprises the steps of obtaining the void ratio of the corresponding unfiltered particles of the analysis objects in the slow filtering scene and the average particle diameter of the filter materials used by the corresponding filtered particles of the analysis objects in the instantaneous filtering scene, and comparing the void ratio of the corresponding unfiltered particles of the analysis objects in the slow filtering scene and the average particle diameter of the filter materials used by the corresponding filtered particles of the analysis objects in the instantaneous filtering scene with a void ratio threshold and a filter material average particle diameter threshold respectively: wherein the filter material is represented as a filter material used when filtering dust;
if the void ratio of the corresponding unfiltered particles of the analysis object in the slow-time filtering scene exceeds a void ratio threshold value, or the average particle diameter of the filter material used by the corresponding filtered particles of the analysis object in the instantaneous-time filtering scene does not exceed a filter material average particle diameter threshold value, judging that the real-time pressure loss of the analysis object is abnormal, marking the current moment as the pressure loss moment, acquiring the dust filtering duty ratio reaching the pressure loss moment, if the dust filtering duty ratio is within the duty ratio threshold value range, generating a pressure loss uninfluenced signal and sending the pressure loss uninfluenced signal to an efficiency detection platform, and after the efficiency detection platform receives the pressure loss uninfluenced signal, carrying out early warning on the analysis object and controlling the void ratio and the filter material average particle diameter of the analysis object, wherein the void ratio and the filter material average particle diameter are only one of parameters influencing the pressure, and the size of an internal space can be influenced in the filtering process of the analysis object, and the system selects two parameters to carry out system operation disclosure and are not exhaustive; if the dust filtering duty ratio is within the duty ratio threshold range, generating a pressure loss and influence signal and sending the pressure loss and influence signal to an efficiency detection platform, and after the efficiency detection platform receives the pressure loss and influence signal, alarming an analysis object, filtering and interrupting the current analysis object and carrying out filtering adjustment;
if the void ratio of the corresponding unfiltered particles of the analysis object in the slow-time filtering scene does not exceed the void ratio threshold value, and the average particle diameter of the filter materials used by the corresponding filtered particles of the analysis object in the instantaneous filtering scene exceeds the filter material average particle diameter threshold value, judging that the real-time pressure loss of the analysis object is normal, and marking the current moment as the pressure normal moment; comparing the dust filtering duty ratio at the time of normal pressure with the dust filtering duty ratio at the time of pressure loss, if the more than the preset more than threshold value of the dust filtering duty ratio at the time of normal pressure and the dust filtering duty ratio at the time of pressure loss, judging that the pressure loss of the current analysis object is not influenced, generating a low influence signal and transmitting the low influence signal to an efficiency detection platform; if the excessive amount of the dust filtering duty ratio at the time of normal pressure and the excessive amount of the dust filtering duty ratio at the time of pressure loss do not exceed the set excessive amount threshold, judging that the pressure loss of the current analysis object has influence risk, generating an influence monitoring signal and sending the influence monitoring signal to an efficiency detection platform, and after the efficiency detection platform receives the influence monitoring signal, performing pressure monitoring on the current analysis object.
When the particle trapping efficiency analysis unit is used, the particle trapping efficiency analysis unit detects and analyzes the particle filter, marks the particle filter as an analysis object, acquires the dust particles and the unfiltered dust particles which are correspondingly filtered by the analysis object, marks the acquired filtered dust particles and unfiltered dust particles as a filtering main body and an unfiltered main body respectively, and analyzes and judges whether the trapping efficiency is qualified or not; the filtering efficiency analysis unit carries out filtering analysis on the analysis objects, divides the operation scene of the analysis objects into an instantaneous filtering scene and a slow time filtering scene, and judges whether the operation scene analysis objects are qualified to be filtered according to the instantaneous filtering scene and the slow time filtering scene; the efficiency detection deviation analysis unit performs deviation analysis on the efficiency detection of the analysis object and judges whether deviation exists in the filtering efficiency detection of the current risk object.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. The system is characterized by comprising an efficiency detection platform, a particle trapping efficiency analysis unit, a filtering efficiency analysis unit and an efficiency detection deviation analysis unit;
the particle trapping efficiency analysis unit performs trapping efficiency detection analysis on the particle filter, marks the particle filter as an analysis object, acquires the dust particles and the unfiltered dust particles which are correspondingly filtered by the analysis object, marks the acquired filtered dust particles and unfiltered dust particles as a filtering main body and an unfiltered main body respectively, and analyzes and judges whether the trapping efficiency is qualified or not; the operation process of the particle trapping efficiency analysis unit is as follows:
the method comprises the steps of obtaining the reduction speed of the corresponding particle size of an unfiltered main body and the ratio of the number of particles corresponding to the maximum particle size to the total number of unfiltered main bodies in the operation process of an analysis object, and comparing the reduction speed of the corresponding particle size of the unfiltered main body and the ratio of the number of particles corresponding to the maximum particle size to the total number of unfiltered main bodies with a particle size reduction speed threshold and a particle number ratio threshold respectively in the operation process of the analysis object; obtaining the ratio of the particle size of the filtering main body to the shortage amount of the set particle size in the operation process of the analysis object to the quantity of the filtering main bodies higher than the set particle size in the operation process of the analysis object, and comparing the ratio of the particle size of the filtering main body to the shortage amount of the set particle size in the operation process of the analysis object to the quantity of the filtering main bodies higher than the set particle size in the operation process to the threshold of the shortage amount of the particle size and the threshold of the quantity of the filtering main bodies;
the filtering efficiency analysis unit carries out filtering analysis on the analysis objects, divides the operation scene of the analysis objects into an instantaneous filtering scene and a slow time filtering scene, and judges whether the operation scene analysis objects are qualified to be filtered according to the instantaneous filtering scene and the slow time filtering scene; the operation process of the filtration efficiency analysis unit is as follows: the method comprises the steps of obtaining an increase of particles which are not filtered by a filtering main body with the analysis object exceeding a set particle size in an instantaneous filtering scene and a decrease of the filtering speed of the filtering main body of the analysis object of the instantaneous filtering scene, and comparing the increase of particles which are not filtered by the filtering main body with the analysis object exceeding the set particle size in the instantaneous filtering scene and the decrease of the filtering speed of the filtering main body of the analysis object of the instantaneous filtering scene with a threshold of the increase of particles and a threshold of the decrease of the filtering speed respectively; the floating quantity of the operation reverse execution probability of the unfiltered main body and the filtering main body of the analysis object in the slow filtering scene and the excessive quantity of the dust thickness increasing period and the cleaning period of the filtering layer of the analysis object in the slow filtering scene are obtained, and the floating quantity of the operation reverse execution probability of the unfiltered main body and the filtering main body of the analysis object in the slow filtering scene and the excessive quantity of the dust thickness increasing period and the cleaning period of the filtering layer of the analysis object in the slow filtering scene are respectively compared with a reverse execution probability floating quantity threshold and a period excessive quantity threshold;
the efficiency detection deviation analysis unit performs deviation analysis on the efficiency detection of the analysis object and judges whether deviation exists in the filtering efficiency detection of the current risk object; the operation process of the efficiency detection deviation analysis unit is as follows: marking the filtering efficiency detection time period as a filtering detection time period, acquiring a natural dust precipitation amount increase span of a filtering layer corresponding to an analysis object in the filtering detection time period and a floating span of a moving speed of dust particles driven by a filtering wind speed of the analysis object in the filtering detection time period, and naturally precipitating dust of the filtering layer corresponding to the analysis object in the filtering detection time periodThe floating span of the moving speed of dust particles driven by the filtering wind speed of the analysis object in the filtering period is marked as ZZK and YSK respectively; collecting a time period average deviation value of the total dust content in the analysis object use area in the filtering and detecting time period, and marking the time period average deviation value of the total dust content in the analysis object use area in the filtering and detecting time period as PCZ; by the formulaObtaining an analysis object efficiency detection deviation analysis coefficient G in a filtering and detecting period, wherein fd1, fd2 and fd3 are preset proportionality coefficients, fd1 is more than fd2 is more than fd3 is more than 1, beta is an error correction factor, and the value is 0.976; comparing the analysis object efficiency detection deviation analysis coefficient G with an efficiency detection deviation analysis coefficient threshold value in the filtering and detecting period:
if the efficiency detection deviation analysis coefficient G of the analysis object exceeds the efficiency detection deviation analysis coefficient threshold value in the filtering and detecting period, judging that the efficiency detection deviation analysis is abnormal in the filtering and detecting period, generating a high deviation signal, sending the high deviation signal to an efficiency detection platform, and re-acquiring data acquired in the corresponding filtering and detecting period after the efficiency detection platform receives the high deviation signal, and taking the dust filtering duty ratio of the current area of the analysis object as an efficiency detection parameter of the analysis object; the efficiency detection parameter is used as a verification parameter for detecting the efficiency re-detection of the analysis object; if the efficiency detection deviation analysis coefficient G of the analysis object in the filtering period does not exceed the efficiency detection deviation analysis coefficient threshold value, judging that the efficiency detection deviation analysis is normal in the filtering period, generating a low deviation signal and sending the low deviation signal to the efficiency detection platform.
2. The system according to claim 1, wherein if the reduction speed of the particle size of the unfiltered main body exceeds the threshold value of the reduction speed of the particle size, or the ratio of the number of particles corresponding to the maximum particle size to the total number of unfiltered main bodies does not exceed the threshold value of the ratio of the number of particles, an unfiltered main body abnormality signal is generated and transmitted to the efficiency detection platform; if the reduction speed of the particle size corresponding to the unfiltered main body in the operation process of the analysis object does not exceed the particle size reduction speed threshold value, and the ratio of the particle number corresponding to the maximum particle size to the total number of the unfiltered main body exceeds the particle number ratio threshold value, generating an unfiltered main body normal signal and sending the unfiltered main body normal signal to the efficiency detection platform.
3. The system according to claim 1, wherein if the particle size of the filter body and the shortage of the set particle size exceeds a threshold value of the shortage of the particle size during operation of the analysis object or the number of filter bodies higher than the set particle size during operation exceeds a threshold value of the number of filter bodies, it is determined that the collection efficiency of the filter body of the analysis object is abnormal, a filter body abnormality signal is generated and the filter body abnormality signal is sent to the efficiency detection platform; if the particle size of the filtering main body and the shortage amount of the set particle size do not exceed the particle size shortage amount threshold in the operation process of the analysis object, and the number proportion of the filtering main bodies higher than the set particle size in the operation process does not exceed the number proportion threshold of the filtering main bodies, judging that the trapping efficiency of the filtering main body of the analysis object is normal, generating a normal signal of the filtering main body and sending the normal signal of the filtering main body to the efficiency detection platform.
4. The system according to claim 1, wherein if the increase of particles in the instantaneous filtration scene corresponding to the filtering subject not being filtered exceeding a set particle size exceeds a threshold of the increase of particles, or the decrease of the filtering speed of the filtering subject in the instantaneous filtration scene exceeds a threshold of the decrease of the filtering speed, it is determined that the analysis subject is abnormal in the instantaneous filtration scene in the filtration efficiency analysis, an instantaneous filtration efficiency abnormality signal is generated and the instantaneous filtration efficiency abnormality signal is sent to the efficiency detection platform; if the particle increment of the filtering main body which is corresponding to the analysis object and exceeds the set particle diameter in the instantaneous filtering scene is not filtered and the filtering speed reduction of the filtering main body of the analysis object of the instantaneous filtering scene is not more than the filtering speed reduction threshold, judging that the filtering efficiency analysis of the analysis object is normal in the instantaneous filtering scene, generating an instantaneous filtering efficiency normal signal and transmitting the instantaneous filtering efficiency normal signal to an efficiency detection platform.
5. The system for detecting the filtration efficiency of the particle filter based on the data analysis according to claim 1, wherein if the floating amount of the unfiltered main body of the analysis object and the operation reverse execution probability of the filtering main body in the slow filtering scene exceeds the floating amount threshold of the reverse execution probability or the excessive output of the dust thickness increasing period and the cleaning period of the filtering layer of the analysis object in the slow filtering scene does not exceed the excessive output threshold of the period, it is determined that the analysis object has abnormal filtration efficiency analysis in the slow filtering scene, a slow filtration efficiency abnormal signal is generated, and the slow filtration efficiency abnormal signal is sent to the efficiency detection platform;
if the floating quantity of the non-filtering main body of the analysis object and the operation reverse execution probability of the filtering main body in the slow filtering scene does not exceed the floating quantity threshold of the reverse execution probability, and the filter layer dust thickness increasing period and the excessive amount of the cleaning period of the analysis object in the slow filtering scene exceed the period excessive amount threshold, generating a slow filtering efficiency normal signal and sending the slow filtering efficiency normal signal to the efficiency detection platform.
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| GB201509638D0 (en) * | 2015-06-03 | 2015-07-15 | Gm Global Tech Operations Inc | Method and system of testing the proper functioning of a catalyzed particulate filter of an internal combustion engine |
| WO2015158375A1 (en) * | 2014-04-16 | 2015-10-22 | Continental Automotive Gmbh | Method for monitoring the filtering capability of a particle filter |
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| WO2015158375A1 (en) * | 2014-04-16 | 2015-10-22 | Continental Automotive Gmbh | Method for monitoring the filtering capability of a particle filter |
| GB201509638D0 (en) * | 2015-06-03 | 2015-07-15 | Gm Global Tech Operations Inc | Method and system of testing the proper functioning of a catalyzed particulate filter of an internal combustion engine |
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