CN115822765A - Typical fault diagnosis system and method for diesel vehicle particulate matter trap DPF - Google Patents
Typical fault diagnosis system and method for diesel vehicle particulate matter trap DPF Download PDFInfo
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Abstract
The invention discloses a typical fault diagnosis system and method for a diesel vehicle particulate matter catcher DPF, which comprises the following steps: the DPF controller ECU acquires data such as pressure and temperature at an inlet end and an outlet end of the DPF, exhaust volume flow at the outlet end, GPS and the like in a CAN (controller area network) line communication mode; the data acquired by the ECU is transmitted to a monitoring system by adopting the Internet of vehicles technology, and the actual pressure drop and the vehicle speed data of the DPF are dynamically calculated and stored; and correcting the DPF theoretical pressure drop model according to DPF product parameters, combining the DPF theoretical pressure drop data in actual operation, and comparing the data with the DPF actual pressure drop value. Preliminarily judging the fault state of the DPF according to the typical fault characteristics of carrier blockage and damage; a failure confirmation method based on time accumulation and a failure confirmation method based on accumulated occurrence number are introduced to finally confirm whether the DPF has blockage or breakage failure. The method can ensure that a system can accurately make a judgment in time when the DPF has carrier blockage and damage faults, and ensure the efficient and reliable operation of the DPF.
Description
Technical Field
The invention belongs to the technical field of diesel vehicle particulate matter emission control, and particularly relates to a diesel vehicle particulate matter trap DPF typical fault diagnosis system and method, and particularly relates to a vehicle networking technology-based system and method for diagnosing typical faults such as DPF device blockage or carrier damage.
Background
With the stricter and stricter emission regulations of diesel vehicles, a particulate matter trap (DPF), which is the most effective and dominant control device and technology for reducing the particulate matter emission of diesel vehicles, has been widely used in the new production of diesel vehicles or in the emission control of diesel vehicles. As an important component of an online fault diagnosis system of a diesel engine, the DPF fault diagnosis technology becomes a key of the whole technical route to the market, and the core and difficulty of the technology is timely diagnosis and early warning when the DPF fails.
In actual operation, typical failure failures of DPFs are mainly carrier plugging and breakage, which are mainly classified into two categories: one is DPF pore channel blockage caused by improper regeneration control, excessive soot accumulation and the like, so that the exhaust pressure drop of an engine is increased, and the dynamic property and the fuel economy of an automobile are influenced; the other is that the carriers are burnt, broken and other damaged conditions occur due to thermal shock, mechanical vibration and the like, so that the leakage of soot in the pore channels occurs, and the emission of particulate matters exceeds the regulation limit value. The problems of DPF safety, functionality and reliability can be caused by the faults of DPF carrier blockage and breakage failure, and the popularization and the application of the DPF are restricted.
The conventional DPF failure diagnosis method is mainly a direct sensor detection method and a method based on engine and DPF signal diagnosis, but has disadvantages in practical applications, such as:
(1) the related particle sensor technology in the direct sensor detection method is not mature, and the test sample piece is expensive;
(2) the method for diagnosing based on the engine and DPF signals mainly relies on the change of average exhaust pressure drop at the front end and the rear end of the DPF to diagnose, and has large data volatility and poor accuracy.
Therefore, it is more important to monitor the DPF state in real time and determine a failure.
With the application of technologies such as internet plus, on-Board Diagnostic (OBD) and cloud computing, the On-line monitoring of the diesel vehicle networking can truly reflect the actual road working condition data of the vehicle, the covered road type is comprehensive, the testing cost is low, and the DPF real operation and fault diagnosis monitoring are favorably carried out. However, in practical applications, due to reasons such as GPRS signal abnormality, network disconnection, data storage error, data concurrent uploading network congestion, etc., abnormal data such as mutation, loss, drift, etc. occur in the online monitoring of the internet of vehicles, and the online data of the internet of vehicles needs to be cleaned before data analysis and application.
Disclosure of Invention
The invention aims to provide a diesel vehicle particulate matter trap DPF blockage or carrier damage fault diagnosis system and a method, which can ensure that the system can make accurate judgment when the DPF fails, and are convenient for management departments and enterprises to carry out large-scale monitoring and supervision on the actual running performance of the diesel vehicle DPF.
The invention is realized by the following technical scheme:
according to a first aspect, the invention provides a diesel vehicle particulate matter trap DPF blockage or carrier breakage fault diagnosis system, which comprises a front pressure sensor, a rear pressure sensor, a front temperature sensor, a rear temperature sensor, an exhaust volume flow sensor, a DPF controller ECU, a GPS module and a power supply module; the front pressure sensor and the front temperature sensor are arranged at the air inlet end of the DPF, and the rear pressure sensor, the rear temperature sensor and the exhaust volume flow sensor are arranged at the exhaust port end of the DPF; the air inlet end of the DPF is connected with the end of an engine exhaust pipe; the GPS module is fixedly arranged in a vehicle cab or on the roof of the vehicle.
The power supply module is respectively connected with the DPF controller ECU, the front pressure sensor, the rear pressure sensor, the front temperature sensor, the rear temperature sensor, the exhaust volume flow sensor and the GPS module through power lines, and provides required power after the engine is started; the front pressure sensor, the rear pressure sensor, the front temperature sensor, the rear temperature sensor, the exhaust volume flow sensor and the GPS module are respectively in communication connection with the DPF controller ECU through CAN lines; the DPF controller ECU aligns and cleans the received data collected by each sensor; the DPF controller ECU remotely transmits the aligned and cleaned data to a data monitoring and analyzing monitoring system in real time through a built-in GPRS module of the DPF controller ECU, the data is locally stored at a server end, and the data monitoring and analyzing monitoring system realizes DPF fault diagnosis through a DPF carrier typical fault diagnosis method.
According to a second aspect, the present invention provides a diesel vehicle particulate trap DPF clogging or carrier breakage failure diagnosis method, comprising:
(1) The typical failure evaluation index for evaluating DPF blockage or carrier breakage is calculated as a DPF pressure drop relative deviation factor delta, and the formula is as follows:
δ＝△ Preal /△P model (1)
wherein, Δ P real : pressure drop, delta P, at the front end and the rear end of the DPF is obtained through measurement under actual operation working conditions model : under the actual operation condition, the estimated values of the pressure drop models at the front end and the rear end of the DPF are obtained by combining the actual volume flow with the pressure drop model;
(2) DPF theoretical pressure drop model Δ P model The concrete model is as follows:
DPF theoretical pressure drop model Δ P model μ is exhaust dynamic viscosity, temperature dependent; v trap Is the DPF carrier volume; alpha is the carrier pore channel density; omega s The carrier wall thickness;K 0 the permeability avoided for the support in the fresh state is generally obtained by experimentation or given by the manufacturer; k p Permeability of the particulate layer; omega is the thickness of the microparticle layer; ρ is a unit of a gradient s Is the density of the exhaust gas; f is a friction factor, and a constant is 28.454; l is the length of the carrier pore channel; d is the diameter of the carrier; ε is the sum of the local loss coefficients at the entrance and exit of the vector (typically ε = 0.82).
In the parameters of the above formula (2), except that the exhaust volume flow Qv at the outlet end of the DPF and the pressure drop of the DPF are in a quadratic curve relationship, other parameters are mainly determined by the characteristics of the DPF product itself.
Therefore, for a DPF with certain carrier parameters, the theoretical pressure drop model delta P model Can be simplified as follows:
wherein:
in the formula: t is a unit of DPF Bed temperature of DPF, (T) 1 +T 2 )/2；P DPF Is the pressure drop P of front and rear ends of DPF 2 -P 1 ；
DPF theoretical pressure drop model Δ P model In the method, an exhaust volume flow Q is detected by an exhaust volume flow sensor v The signals, the bed temperature of DPF and pressure drop signals of front and back ends of DPF are obtained by collecting signals of front pressure sensor, back pressure sensor, front temperature sensor and back temperature sensor;
DPF actual pressure drop value Delta Preal By applyingThe front pressure sensor and the rear pressure sensor which are arranged at two ends of the DPF are used for monitoring the pressure drop of the DPF in real time in the using process, pressure signals are transmitted to a DPF controller ECU through a CAN line, and the delta is obtained through real-time data calculation Preal ＝P 2 -P 1 ；
(3) The pressure drop delta of the front end and the rear end of the DPF is obtained by measurement under the actual operation condition obtained by calculation Preal Obtaining the estimated value delta P of the pressure drop model at the front end and the rear end of the DPF by combining the actual volume flow with the pressure drop model under the actual operation working condition model Comparing, calculating to obtain DPF pressure drop relative deviation factor delta = × [ delta ] Preal /△P model (ii) a When delta is larger than or equal to 25%, the DPF carrier is preliminarily judged to have blockage or damage faults, and a DPF carrier blockage or damage fault pre-list is established.
Preferably, in the DPF fault diagnosis, when abnormal signals appear on parameters related to DPF fault diagnosis, which cause that delta fluctuates in a large range, the DPF fault cannot be immediately judged and an alarm is given; in order to filter inaccurate fault information, avoid abnormal signal jitter interference diagnosis caused by variable working conditions and reduce the misjudgment rate of a fault diagnosis system, the invention adopts a fault signal screening, confirming and judging method based on fault accumulation occurrence time and fault accumulation occurrence frequency within 24 hours to establish a logic judgment algorithm for screening DPF 'true/false' fault diagnosis, which is concretely as follows:
1) The method for confirming the time of cumulative occurrence of the faults mainly carries out comprehensive judgment by comparing the numerical value of the time of cumulative occurrence of the faults in 24 hours with a preset threshold value of the duration time required by fault confirmation. The method is determined in a calibration mode, and the DPF carrier can be confirmed to have blockage or damage faults by experimental study, wherein the accumulated fault duration time is more than or equal to 30 minutes;
2) The method for confirming the number of accumulated occurrence times of the faults mainly carries out comprehensive judgment by comparing the numerical value of the number of the accumulated occurrence times of the faults with a preset threshold value of the number of the required continuous times of fault confirmation within 24 hours. The method is determined in a calibration mode, and the DPF carrier can be confirmed to have blockage or damage faults by experimental study when the cumulative occurrence frequency of the same fault is more than or equal to 10 times;
when the monitored fault signal disappears, the fault management module also needs to judge the signal based on a timing or counting mode, and adopts a confirmation method based on the fault accumulation occurrence time and the fault accumulation occurrence frequency to carry out comprehensive judgment (on the basis of a first-come person), so as to ensure the real-time performance and sensitivity of monitoring of the fault diagnosis system;
when delta is larger than or equal to 25% and a comprehensive judgment mode of confirming the fault signal duration and the fault occurrence frequency is met, the carrier blockage and damage faults of the DPF can be judged, and the fault state is marked as 'yes' in the fault pre-list, otherwise, the fault state is 'no'.
Preferably, the alignment processing method is as follows: the method comprises the following steps of taking the vehicle speed data acquisition time in a GPS module, namely day/hour/minute/second as a reference, and respectively aligning with a front pressure sensor (1), a rear pressure sensor (2), a front temperature sensor (3), a rear temperature sensor (4) and an exhaust volume flow sensor (5); after the parameters of each sensor are aligned, a new database Excel file is built in a gathering way, and the database parameter items are time, vehicle speed, front pressure, rear pressure, front temperature, rear temperature and exhaust flow in sequence.
Preferably, the data cleaning processing method is as follows: except for the time parameter item, invalid data and lost data exist in different parameter items in the database Excel file, and data cleaning processing is carried out by utilizing two modes of linear interpolation and smooth processing:
1) Carrying out missing or invalid data restoration by a linear interpolation method, carrying out linear interpolation on parameter data points with time difference of 2-4 seconds, and replacing the characteristic parameter data points with partial characteristic parameter data values obviously exceeding a reasonable range by a front-back average value method;
2) And for the data of which the parameter data is in the valid value range but has partial abnormal points, a time series standard smoothing algorithm based on a T4253H filtering method is proposed for smoothing.
When the pore channel of the DPF is blocked or the carrier is damaged, the pressure drop performance of the DPF is sensitive and can be used as a characteristic parameter for judging DPF fault diagnosis. During the diagnostic process of clogging or breakage of a DPF, transmission is mainly through the pressure drop arranged across the DPFThe sensor is used for dynamically monitoring the pressure drop before and after the DPF in real time; a DPF model in an ECU module estimates the theoretical pressure drop of the DPF in advance according to the working condition of the engine; measuring the pressure drop by delta P real Sum model estimate Δ P model Comparing, calculating relative deviation delta and analyzing; when delta exceeds a certain range, the DPF is judged to have pore channel blockage or carrier breakage typical failure.
In order to reduce the fault diagnosis error rate of the DPF, a DPF fault diagnosis confirmation method based on the fault signal duration and on the fault occurrence frequency is provided. The whole fault diagnosis process comprises the following steps: firstly, dynamically acquiring temperature and pressure data of the front end and the rear end of a DPF and flow data of the rear end of the DPF in real time by utilizing various sensors through a DPF controller ECU; secondly, aligning and cleaning data respectively acquired by the sensor data by utilizing a DPF controller ECU; thirdly, the DPF controller ECU remotely transmits the aligned and cleaned data to a data monitoring and analyzing monitoring system in real time by using an embedded GPRS module, and locally stores the data at a server end; fourthly, carrying out statistical analysis on the data of the Internet of vehicles by using a DPF fault diagnosis algorithm in the data monitoring and analyzing monitoring system, preliminarily judging whether a DPF carrier has a blockage or damage fault, and establishing a fault pre-list; fifthly, finally determining whether a fault exists through a DPF fault confirmation method, and marking the fault state 'yes' or 'no' in a list; and sixthly, dynamically feeding back the confirmed faults to a management department, an enterprise end system and a cab by using the data monitoring and analyzing monitoring system, and prompting all relevant parties to maintain the faults of the DPF in time by lighting an MIL indicator lamp so as to ensure the stable and efficient operation of the DPF.
The invention has the beneficial effects that:
through formulating a DPF device carrier jam and damaged typical fault diagnosis system based on car networking technology, guarantee that DPF system can make accurate judgement when the inefficacy takes place, guarantee DPF can high-efficient, the reliable operation.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
Fig. 2 is a schematic diagram of the fault diagnosis process of the present invention.
FIG. 3 is a schematic diagram of the fault diagnosis method and logic of the present invention.
Fig. 4 is a schematic diagram of abnormal data points in the internet of vehicles online monitoring.
FIG. 5 is a schematic view of online monitoring data processing of the Internet of vehicles.
Fig. 6 is a schematic diagram of fault confirmation based on time accumulation (left diagram) and on cumulative number of occurrences (right diagram).
Detailed Description
Embodiments of the present invention will now be described with reference to the accompanying drawings, and it will be understood by those skilled in the art that the following embodiments are illustrative of the present invention only and should not be taken as limiting the scope of the invention. In the examples, specific techniques, connections, conditions, and processes are not specified, but are performed according to the techniques, connections, conditions, processes, and product specifications described in the literature in the art. The materials, instruments or equipment are not indicated by manufacturers, and all the materials, instruments or equipment are conventional products which can be obtained by purchasing.
As shown in fig. 1, a typical failure diagnosis system and method for DPF device carrier blockage and breakage based on internet of vehicles technology comprises a front pressure sensor 1, a rear pressure sensor 2, a front temperature sensor 3, a rear temperature sensor 4, an exhaust volume flow sensor 5, a DPF controller ECU6, a power supply module 7; the front pressure sensor 1 and the front temperature sensor 3 are arranged at an air inlet end 8 of the DPF, and the rear pressure sensor 2, the rear temperature sensor 4 and the exhaust volume flow sensor 5 are arranged at an exhaust end 9 of the DPF; the DPF air inlet end 8 is connected with the end of an engine exhaust pipe, and the DPF exhaust end 9 discharges engine exhaust gas filtered by the DPF to ambient air; the GPS module 11 is fixedly arranged in a vehicle cab or on the roof of a vehicle and is used for collecting the vehicle speed and longitude and latitude geographic position data information.
The power supply module 7 is connected with the DPF controller ECU6 through a power line, provides a 24V power supply after the engine is started, and automatically stops supplying power within 5 seconds after the engine is shut down; the front pressure sensor 1, the rear pressure sensor 2, the front temperature sensor 3, the rear temperature sensor 4, the exhaust volume flow sensor 5 and the GPS module 11 are respectively in communication connection with the DPF controller ECU6 through CAN lines, the data acquisition and transmission frequency is 1Hz, and the data formats are 'data acquisition time + data parameter value'; the DPF controller ECU6 aligns and cleans the received data collected by each sensor; the DPF controller ECU6 remotely transmits the aligned and cleaned data to the data monitoring and analyzing monitoring system 10 in real time through the embedded GPRS module and stores the data locally at the server side.
As shown in fig. 2 to 6, the whole fault diagnosis process is as follows:
firstly, dynamically acquiring temperature and pressure data of the front end and the rear end of a DPF and flow data of the rear end of the DPF in real time by utilizing various sensors through a DPF controller ECU;
secondly, aligning and cleaning data respectively acquired by the sensor data by utilizing a DPF controller ECU;
thirdly, the DPF controller ECU remotely transmits the aligned and cleaned data to a data monitoring and analyzing monitoring system in real time by using an embedded GPRS module, and locally stores the data at a server end;
fourthly, performing statistical analysis on the data of the Internet of vehicles by using a DPF fault diagnosis algorithm in the data monitoring and analyzing monitoring system, preliminarily judging whether a DPF carrier has a blockage or damage fault, and establishing a fault pre-list;
fifthly, finally determining whether a fault exists through a DPF fault confirmation method, and marking the fault state 'yes' or 'no' in a list;
and sixthly, dynamically feeding back the confirmed faults to a management department, an enterprise end system and a cab by using the data monitoring and analyzing monitoring system, and prompting all relevant parties to maintain the faults of the DPF in time by lighting an MIL indicator lamp so as to ensure the stable and efficient operation of the DPF.
The typical failure evaluation index for evaluating DPF blockage or carrier breakage is a DPF pressure drop relative deviation factor delta, and the formula is as follows:
DPF pressure drop relative deviation factor delta =delta Preal /△P model (1)
Wherein, Δ P real : obtaining DPF front and rear end pressure drop through measurement under actual operation working condition，△P model : under the actual operation condition, the estimated values of the pressure drop models at the front end and the rear end of the DPF are obtained by combining the actual volume flow with the pressure drop model;
embedding DPF theoretical pressure drop model delta P in data monitoring and analyzing monitoring system 10 model The concrete model is as follows:
DPF theoretical pressure drop model Δ P model μ is exhaust dynamic viscosity, temperature dependent; v trap Is the DPF carrier volume; alpha is the density of the carrier pore channel; omega s The carrier wall thickness; k 0 The permeability to be avoided for the support in the fresh state is generally obtained by experimentation or given by the manufacturer; k p Permeability of the particulate layer; omega is the thickness of the microparticle layer; rho s Is the density of the exhaust gas; f is a friction factor, and a constant is 28.454; l is the length of the carrier pore channel; d is the diameter of the carrier; and epsilon is the sum of local loss coefficients at the entrance and exit of the carrier (generally, epsilon = 0.82).
In the above formula parameters, except that the exhaust volume flow Qv at the outlet end of the DPF and the pressure drop of the DPF are in a quadratic curve relationship, other parameters are mainly determined by the characteristics of the DPF product itself. In the data monitoring and analyzing monitoring system 10, the above-mentioned related parameter settings can be performed according to DPF product manufacturing enterprises, so as to establish theoretical pressure drop parameters of different types of DPF carriers.
For a DPF with certain carrier parameters, the theoretical pressure drop model delta P model Can be simplified as follows:
wherein:
in the formula: t is a unit of DPF Bed temperature of DPF, (T) 1 +T 2 )/2；P DPF Pressure drop P for DPF front and rear ends 2 -P 1 ；
DPF theoretical pressure drop model Δ P model In the method, the exhaust volume flow Q is acquired by an exhaust volume flow sensor 5 v The signals, the bed temperature of DPF and pressure drop signals of front and back ends of DPF are obtained by acquiring signals of a front pressure sensor 1, a back pressure sensor 2, a front temperature sensor 3 and a back temperature sensor 4;
DPF actual pressure drop value Delta Preal The pressure drop of the DPF in the using process is monitored in real time through a front pressure sensor 1 and a rear pressure sensor 2 which are arranged at two ends of the DPF, a pressure signal is transmitted to a DPF controller ECU6 through a CAN line, and delta is obtained through real-time data calculation Preal ＝P 2 -P 1 ；
Through the data monitoring and analyzing monitoring system 10, the pressure drop delta of the front end and the rear end of the DPF is obtained through measurement under the actual operation working condition Preal Obtaining the estimated value delta P of the pressure drop model at the front end and the rear end of the DPF by combining the actual volume flow with the pressure drop model under the actual operation working condition model Comparing, calculating to obtain DPF pressure drop relative deviation factor delta = × [ delta ] Preal /△P model (ii) a Through experimental research, when delta is larger than or equal to 25%, the DPF carrier is preliminarily judged to have blockage or damage faults, and a DPF carrier blockage or damage fault pre-list is established.
According to the further optimization scheme, in DPF fault diagnosis, when abnormal signals occur in relevant parameters of DPF fault diagnosis, delta fluctuates in a large range, DPF faults cannot be immediately judged, and an alarm is given. In order to filter inaccurate fault information, avoid abnormal signal jitter interference diagnosis caused by variable working conditions and reduce the misjudgment rate of a fault diagnosis system, a fault signal screening, confirming and judging method based on fault accumulation occurrence time and fault accumulation occurrence frequency in 24 hours is provided, and a DPF 'true/false' fault diagnosis screening logic judgment algorithm is established, which is concretely as follows:
1) The method for confirming the time of cumulative occurrence of the faults mainly carries out comprehensive judgment by comparing the numerical value of the time of cumulative occurrence of the faults in 24 hours with a preset threshold value of the duration time required by fault confirmation. The method is determined in a calibration mode, and the DPF carrier can be confirmed to have blockage or damage faults by experimental study, wherein the accumulated fault duration time is more than or equal to 30 minutes;
2) The method for confirming the number of accumulated occurrence times of the faults mainly carries out comprehensive judgment by comparing the numerical value of the number of the accumulated occurrence times of the faults with a preset threshold value of the number of the required continuous times of fault confirmation within 24 hours. The method is determined in a calibration mode, and the DPF carrier can be confirmed to have blockage or damage faults by experimental research on the condition that the cumulative occurrence frequency of the same fault is more than or equal to 10 times;
when the monitored fault signal disappears, the fault management module also needs to judge the signal based on a timing or counting mode, and adopts a confirmation method based on the fault accumulation occurrence time and the fault accumulation occurrence frequency to carry out comprehensive judgment (on the basis of a first-come person), so as to ensure the real-time performance and sensitivity of monitoring of the fault diagnosis system;
when delta is larger than or equal to 25 percent and a comprehensive judgment mode for confirming the duration time of the fault signal and the occurrence frequency of the fault is met, the carrier blockage and damage fault of the DPF can be judged, the fault state is marked as 'yes' in a fault pre-list, and otherwise, the fault state is 'no'; and for the confirmed faults, the confirmed faults are dynamically fed back to a management department, an enterprise end system and a cab by using a data monitoring and analyzing and monitoring system, and all related parties are reminded to maintain the DPF faults in time in a fault prompt mode or a fault flicker mode and the like.
Specifically, the alignment processing mode is as follows: the vehicle speed data acquisition time in the GPS module, namely day/hour/minute/second, is taken as a reference and is respectively communicated with the front pressure sensor 1, the rear pressure sensor 2, the front temperature sensor 3, the rear temperature sensor 4 and the exhaust volume flowThe sensor 5 is aligned; after the parameters of each sensor are aligned, a new database Excel file is built in a summary mode, and the database parameter items are time, vehicle speed (km/h), front pressure (kPa), rear pressure (kPa), front temperature (DEG C), rear temperature (DEG C), exhaust flow (m/h) in sequence 3 H) ", the data frequency is still 1Hz.
The data cleaning processing mode is as follows: except for the time parameter item, invalid data and lost data exist in different parameter items in the database Excel file, and data cleaning processing is carried out by utilizing two modes of linear interpolation and smooth processing:
1) Carrying out missing or invalid data restoration by a linear interpolation method, carrying out linear interpolation on parameter data points with time difference of 2-4 seconds, and replacing the characteristic parameter data points with partial characteristic parameter data values obviously exceeding a reasonable range by a front-back average value method;
2) For the data of which the parameter data is in the effective value range and part of abnormal points exist, the time series standard smoothing algorithm based on the T4253H filtering method is adopted for smoothing, the nonlinear data processing effect is good, and the abnormal points of the data are effectively reduced.
While the present invention has been described in detail with reference to the embodiments, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (10)
1. A typical fault diagnosis system of a diesel vehicle particulate matter trap DPF is characterized by comprising a front pressure sensor (1), a rear pressure sensor (2), a front temperature sensor (3), a rear temperature sensor (4), an exhaust volume flow sensor (5), a DPF controller ECU (6) and a power supply module (7); the front pressure sensor (1) and the front temperature sensor (3) are arranged at an air inlet end (8) of the DPF, and the rear pressure sensor (2), the rear temperature sensor (4) and the exhaust volume flow sensor (5) are arranged at an exhaust end (9) of the DPF; the DPF air inlet end (8) is connected with the exhaust pipe end of the engine;
the power supply module (7) is respectively connected with the front pressure sensor (1), the rear pressure sensor (2), the front temperature sensor (3), the rear temperature sensor (4), the exhaust volume flow sensor (5) and the DPF controller ECU (6) through power lines and supplies power; the front pressure sensor (1), the rear pressure sensor (2), the front temperature sensor (3), the rear temperature sensor (4) and the exhaust volume flow sensor (5) are respectively in communication connection with a DPF controller ECU (6); the DPF controller ECU (6) aligns and cleans the received data acquired by each sensor and transmits the data to a data monitoring and analyzing monitoring system (10) through a GPRS module embedded in the data monitoring and analyzing monitoring system, and the data monitoring and analyzing monitoring system (10) realizes the typical fault diagnosis of the DPF by evaluating a DPF pressure drop relative deviation factor delta;
the DPF pressure drop relative deviation factor δ is expressed as:
δ＝△P real /△P model (1)
wherein: delta P real Is the pressure P measured by the front pressure sensor (1) under the actual operating condition 1 And the pressure P measured by the rear pressure sensor (2) 2 Measuring to obtain actual pressure drop value, delta P, of the DPF real ＝P 2 -P 1 ；△P model The theoretical pressure drop value of the DPF is obtained by combining the actual volume flow with the pressure drop model under the actual operation condition, and is expressed as follows:
wherein: t is a unit of DPF Is the bed temperature of DPF and is expressed as the temperature T measured by the front temperature sensor (3) 1 The temperature T measured by the rear temperature sensor (4) 2 ，(T 1 +T 2 )/2；P DPF Pressure drop P for DPF front and rear ends 2 -P 1 ；
μ is exhaust dynamic viscosity; qv is the volume flow of exhaust gas at the outlet end of the DPF; v trap Is the DPF carrier volume; alpha is the carrier pore channel density; omega s The carrier wall thickness; k 0 Permeability to carrier avoidance for fresh state; k p Permeability of the particulate layer; omega is the thickness of the microparticle layer; rho s Is the density of the exhaust gas; f is a friction factor, and a constant is 28.454; l is the length of the carrier pore channel; d is the diameter of the carrier; xi is the sum of local loss coefficients at the entrance and exit of the carrier;
when delta is above a certain set threshold value, the existence of the blockage or damage faults of the DPF carrier is preliminarily judged, and a DPF carrier blockage or damage fault pre-list is established.
2. The diesel particulate trap DPF typical failure diagnostic system of claim 1, wherein:
the system also comprises a GPS module (11) which is respectively connected with the power supply module (7) in a power supply way and is in communication connection with the DPF controller ECU (6).
3. The diesel particulate trap DPF typical failure diagnostic system as set forth in claim 2, wherein the DPF controller ECU (6) aligning the received sensor acquisition data comprises:
the method comprises the following steps of taking the vehicle speed data acquisition time-day/hour/minute/second in a GPS module (11) as a reference, and respectively aligning with a front pressure sensor (1), a rear pressure sensor (2), a front temperature sensor (3), a rear temperature sensor (4) and an exhaust volume flow sensor (5); after the parameters of each sensor are aligned, a new database Excel file is built in a gathering way, and the database parameter items are time, vehicle speed, front pressure, rear pressure, front temperature, rear temperature and exhaust flow in sequence.
4. The DPF typical malfunction diagnosis system of the diesel vehicle particulate trap according to claim 1, wherein the DPF controller ECU (6) performing data cleaning processing on the received data collected by each sensor includes:
except for the time parameter item, invalid data and lost data exist in different parameter items in the Excel file of the database, and data cleaning processing is carried out through two modes of linear interpolation and smooth processing.
5. The diesel vehicle particulate trap DPF typical failure diagnosis system as set forth in any one of claims 1 to 4, wherein when δ ≧ 25%, it is preliminarily determined that the DPF carrier is clogged or broken, and a DPF carrier clogging or breakage failure preliminary list is established.
6. A typical failure diagnosis method of a typical failure diagnosis system of a DPF of a diesel particulate trap as set forth in claim 3, comprising:
1) Based on the confirmation method of the fault accumulation occurrence time, comprehensive judgment is carried out by comparing the numerical value of the fault accumulation occurrence time in 24 hours with the preset threshold value of the duration time required by fault confirmation;
2) On the basis of a method for confirming the cumulative occurrence frequency of the faults, comprehensive judgment is carried out by comparing the numerical value of the cumulative occurrence frequency of the faults with a preset threshold value of the required continuous frequency of fault confirmation within 24 hours;
3) When delta is larger than or equal to 25 percent and a comprehensive judgment mode for confirming the duration time of the fault signal and the occurrence frequency of the fault is met, the carrier blockage and damage fault of the DPF is judged, the fault state is marked as 'yes' in a fault pre-list, and otherwise, the fault state is 'no'.
7. The representative fault diagnosis method according to claim 6, wherein:
when the accumulated failure duration time is more than or equal to 30 minutes, the existence of blockage or breakage failure of the DPF carrier can be confirmed.
8. The representative fault diagnosis method according to claim 6, wherein:
if the cumulative occurrence frequency of the same fault is more than or equal to 10 times, the existence of blockage or damage fault of the DPF carrier can be confirmed.
9. The representative fault diagnosis method according to claim 6, wherein:
when the monitored fault signal disappears, the signal needs to be judged based on a timing or counting mode, and comprehensive judgment is carried out by adopting a confirmation method based on the fault accumulation occurrence time and the fault accumulation occurrence frequency.
10. The representative fault diagnosis method according to claim 9, wherein:
the adoption of the confirmation method based on the accumulated occurrence time of the faults and the accumulated occurrence times of the faults is based on the first arrival.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116990008A (en) * | 2023-09-28 | 2023-11-03 | 中汽研汽车检验中心(昆明)有限公司 | Particle catcher fault simulation device and fault simulation method thereof |
| CN117171921A (en) * | 2023-11-02 | 2023-12-05 | 上海市环境科学研究院 | Method and device for evaluating and processing health state of diesel particulate tail gas purification device |
| CN117514431A (en) * | 2023-12-08 | 2024-02-06 | 中汽研汽车检验中心(昆明)有限公司 | DPF fault diagnosis method, device, terminal equipment and storage medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116990008A (en) * | 2023-09-28 | 2023-11-03 | 中汽研汽车检验中心(昆明)有限公司 | Particle catcher fault simulation device and fault simulation method thereof |
| CN116990008B (en) * | 2023-09-28 | 2024-01-02 | 中汽研汽车检验中心(昆明)有限公司 | Particle catcher fault simulation device and fault simulation method thereof |
| CN117171921A (en) * | 2023-11-02 | 2023-12-05 | 上海市环境科学研究院 | Method and device for evaluating and processing health state of diesel particulate tail gas purification device |
| CN117171921B (en) * | 2023-11-02 | 2024-01-30 | 上海市环境科学研究院 | Method and device for evaluating and processing health state of diesel particulate tail gas purification device |
| CN117514431A (en) * | 2023-12-08 | 2024-02-06 | 中汽研汽车检验中心(昆明)有限公司 | DPF fault diagnosis method, device, terminal equipment and storage medium |
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