A typical fault diagnosis system and method for diesel vehicle particulate filter (DPF)

Abstract

The application discloses a typical fault diagnosis system and method for diesel particulate filter (DPF) of diesel vehicle, and belongs to the field of fault diagnosis of diesel particulate filter (DPF) of diesel vehicle.The system comprises the following steps: a DPF controller ECU acquires DPF inlet and outlet pressure and temperature, outlet exhaust volume flow and GPS data through CAN line communication mode; the data acquired by the ECU is transmitted to a monitoring system by using internet of vehicles technology, and the actual pressure drop of the DPF and vehicle speed data are dynamically calculated and stored; the theoretical pressure drop model of the DPF is corrected according to the product parameters of the DPF, and then the theoretical pressure drop data in actual operation are combined and compared with the actual pressure drop value of the DPF; the fault state of the DPF is preliminarily judged according to the typical fault characteristics of carrier blockage and damage; the fault confirmation method based on time accumulation and the fault confirmation method based on cumulative occurrence frequency are introduced to finally confirm whether the DPF exists blockage or damage fault.The application can ensure that the system can make accurate judgment in time when the DPF appears carrier blockage and damage fault, and guarantee the efficient and reliable operation of the DPF.

Description

Technical Field

[0001] This invention belongs to the field of diesel vehicle particulate matter emission control technology. Specifically, it relates to a typical fault diagnosis system and method for diesel particulate matter filter (DPF) devices, and in particular, a diagnosis system and method based on vehicle networking technology for typical faults such as DPF device blockage or carrier damage. Background Technology

[0002] With increasingly stringent emission regulations for diesel vehicles, diesel particulate filters (DPFs), as the most effective and primary control device and technology for reducing particulate emissions from diesel vehicles, have been widely applied in the emission control of both newly manufactured and in-use diesel vehicles. As a crucial component of diesel engine online fault diagnosis systems, DPF fault diagnosis technology has become key to the market adoption of this technology. The core and challenge of this technology lies in its timely diagnosis and early warning system when DPF failures occur.

[0003] In actual operation, typical DPF failures mainly involve carrier blockage and damage, which can be divided into two categories: one is blockage of the DPF channels caused by improper regeneration control or excessive carbon soot accumulation, leading to increased engine exhaust pressure drop and affecting vehicle power and fuel economy; the other is damage such as melting or breakage of the carrier due to thermal shock or mechanical vibration, resulting in carbon soot leakage from the channels and particulate matter emissions exceeding regulatory limits. DPF carrier blockage and damage failures cause safety, functionality, and reliability issues, hindering the widespread use of DPFs.

[0004] Traditional DPF fault diagnosis methods mainly include direct sensor detection and methods based on engine and DPF signal diagnosis, but these have shortcomings in practical applications, such as:

[0005] In the direct sensor detection method, the relevant particulate matter sensor technology is still immature and the experimental samples are expensive;

[0006] The diagnostic method based on engine and DPF signals mainly relies on the change in average exhaust pressure drop before and after the DPF for diagnosis, which results in large data fluctuations and poor accuracy.

[0007] Therefore, real-time monitoring of DPF status and fault diagnosis become even more important.

[0008] With the application of technologies such as "Internet+", OBD (On-Board Diagnostics), and cloud computing, online monitoring of diesel vehicles via the Internet of Vehicles (IoV) can accurately reflect the actual road conditions of vehicles, and it covers a wide range of road types with low testing costs, which is beneficial for conducting actual DPF operation and fault diagnosis monitoring. However, in practical applications, due to reasons such as abnormal GPRS signals, network outages, data storage errors, and network congestion during concurrent data uploads, abnormal data such as sudden changes, loss, and drift may occur in the online monitoring of IoV. Therefore, it is necessary to clean and process the online IoV data before data analysis and application. Summary of the Invention

[0009] The purpose of this invention is to provide a fault diagnosis system and method for diesel particulate filter (DPF) blockage or carrier damage in diesel vehicles, ensuring that the system can make accurate judgments when DPF failure occurs, and facilitating large-scale monitoring and supervision of the actual operating performance of diesel vehicle DPFs by management departments and enterprises.

[0010] This invention is achieved through the following technical solution:

[0011] According to a first aspect, the present invention provides a fault diagnosis system for diesel particulate filter (DPF) blockage or carrier damage in diesel vehicles, comprising 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 disposed at the DPF inlet end, and the rear pressure sensor, the rear temperature sensor, and the exhaust volume flow sensor are disposed at the DPF exhaust outlet end; the DPF inlet end is connected to the engine exhaust pipe end; the GPS module is fixedly placed in the vehicle cab or on the roof.

[0012] The power supply module is connected to the DPF controller ECU, front pressure sensor, rear pressure sensor, front temperature sensor, rear temperature sensor, exhaust volume flow sensor, and GPS module via power lines, and provides the necessary power after the engine starts. The front pressure sensor, rear pressure sensor, front temperature sensor, rear temperature sensor, exhaust volume flow sensor, and GPS module communicate with the DPF controller ECU via CAN lines. The DPF controller ECU aligns and cleans the data collected from each sensor. The DPF controller ECU transmits the aligned and cleaned data remotely to the data monitoring and analysis system in real time via its built-in GPRS module, and stores it locally on the server. The data monitoring and analysis system performs DPF fault diagnosis using typical fault diagnosis methods for DPF carriers.

[0013] According to a second aspect, the present invention provides a method for diagnosing clogging or carrier damage in a diesel particulate filter (DPF) of a diesel vehicle, comprising:

[0014] (1) The typical fault evaluation index for DPF blockage or carrier damage is the DPF pressure drop relative deviation factor δ, and the formula is as follows:

[0015] δ=|△P model -△ Preal | / △P model (1)

[0016] Where || represents the absolute value, △P real Under actual operating conditions, the voltage drop across the DPF (ΔP) is measured. model Under actual operating conditions, the actual volumetric flow rate is combined with the estimated values ​​of pressure drop before and after the DPF are obtained through the pressure drop model.

[0017] (2) DPF theoretical pressure drop model ΔP model The specific model is as follows:

[0018] (2)

[0019] DPF theoretical pressure drop model △P model In this context, μ represents the exhaust dynamic viscosity, which is temperature-dependent; V trap ω is the DPF carrier volume; α is the carrier pore density; ω s K represents the carrier wall thickness; K0 represents the permeability avoided by the carrier in its fresh state, usually obtained through testing or given by the manufacturer; K p ρ is the permeability of the microparticle layer; ω is the thickness of the microparticle layer; s Where is the exhaust density; F is the friction coefficient, taken as a constant of 28.454; L is the length of the carrier channel; D is the carrier diameter; ε is the sum of the local loss coefficients at the carrier inlet and outlet (generally taken as ε=0.82).

[0020] In the parameters of the above formula (2), except for the DPF outlet exhaust volume flow rate Qv which has a quadratic relationship with the DPF pressure drop, the other parameters are mainly determined by the characteristics of the DPF product itself.

[0021] Therefore, for a DPF with fixed carrier parameters, its theoretical pressure drop model ΔP model It can be simplified to:

[0022] (3)

[0023] in:

[0024] (4)

[0025] (5)

[0026] (6)

[0027] In the formula: T DPF The bed temperature of the DPF, T DPF = (T1+T2) / 2; P DPF The voltage drop between the front and rear ends of the DPF is P2-P1;

[0028] DPF theoretical pressure drop model △P model In the process, the exhaust volume flow rate Q is collected by an exhaust volume flow sensor. v The signals, namely the bed temperature of the DPF and the pressure drop signals at the front and rear ends of the DPF, are calculated by collecting signals from the front pressure sensor, the rear pressure sensor, the front temperature sensor, and the rear temperature sensor.

[0029] Actual pressure drop of DPF △ Preal The pressure drop of the DPF during use is monitored in real time by front and rear pressure sensors installed at both ends of the DPF. The pressure signal is transmitted to the DPF controller ECU via a CAN bus, and the Δ is calculated from the real-time data. Preal =P2-P1;

[0030] (3) The calculated pressure drop Δ between the front and rear ends of the DPF under actual operating conditions is obtained by measurement. Preal Combined with the actual volumetric flow rate under actual operating conditions and the estimated pressure drop value ΔP obtained through the pressure drop model before and after the DPF, the pressure drop model is used. model By comparison, the relative deviation factor of DPF pressure drop δ=|ΔP model -△P real | / △P model When δ is above a certain set threshold, it is preliminarily determined that the DPF carrier has a blockage or damage fault; furthermore, when δ≤25%, it is preliminarily determined that the DPF carrier has a blockage or damage fault, and a pre-list of DPF carrier blockage or damage faults is established.

[0031] Preferably, in DPF fault diagnosis, when abnormal signals in DPF fault diagnosis-related parameters cause δ to fluctuate within a large range, a DPF fault cannot be immediately determined and an alarm cannot be triggered. To filter inaccurate fault information, avoid interference from abnormal signal jitter caused by varying operating conditions, and reduce the false alarm rate of the fault diagnosis system, this invention employs a fault signal identification, confirmation, and judgment method based on the cumulative fault occurrence time and the cumulative number of fault occurrences within 24 hours. A logic judgment algorithm for identifying "true / false" DPF faults is established, as follows:

[0032] 1) The confirmation method based on the cumulative occurrence time of the fault mainly involves comparing the cumulative occurrence time of the fault within 24 hours with a pre-set threshold for the duration required for fault confirmation. This is determined through calibration; experimental studies have shown that a cumulative fault duration ≥30 minutes can confirm a blockage or damage to the DPF carrier.

[0033] 2) The confirmation method based on the cumulative number of fault occurrences mainly involves comparing the cumulative number of fault occurrences within 24 hours with a pre-set threshold for the number of consecutive occurrences required for fault confirmation. This is determined through calibration; experimental studies have shown that a cumulative occurrence of the same fault ≥ 10 times is sufficient to confirm a blockage or damage to the DPF carrier.

[0034] When the detected fault signal disappears, the fault management module also needs to judge the signal based on timing or counting, and make a comprehensive judgment using a confirmation method based on the cumulative fault occurrence time and the cumulative number of fault occurrences (whichever comes first) to ensure the real-time and sensitivity of the fault diagnosis system.

[0035] If δ≤25% and the comprehensive judgment method of confirming the duration of the fault signal and the number of fault occurrences is met, it can be determined that the DPF has a carrier blockage and damage fault, and the fault status is marked as "yes" in the fault pre-list; otherwise, it is marked as "no".

[0036] Preferably, the alignment process is as follows: using the vehicle speed data acquisition time in the GPS module as a reference, the data is aligned with the front pressure sensor (1), rear pressure sensor (2), front temperature sensor (3), rear temperature sensor (4), and exhaust volume flow sensor (5), respectively; after the parameters of each sensor are aligned, a new database Excel file is created, and the database parameter items are "time - vehicle speed - front pressure - rear pressure - front temperature - rear temperature - exhaust flow".

[0037] Preferably, the data cleaning process is as follows: Excluding the time parameter, the database Excel file contains invalid and missing data for different parameter items. Data cleaning is performed using both linear interpolation and smoothing methods.

[0038] 1) Repair missing or invalid data by linear interpolation. Perform linear interpolation on parameter data points with a time difference of 2 to 4 seconds. For some feature parameter data values ​​that obviously exceed the reasonable range, replace the point by averaging the values ​​before and after.

[0039] 2) For data where the parameter data is within the valid range but there are some outliers, a time series standard smoothing algorithm based on the T4253H filtering method is proposed for smoothing.

[0040] When a DPF experiences channel blockage or carrier damage, the DPF pressure drop becomes highly sensitive and can be used as a characteristic parameter for DPF fault diagnosis. In diagnosing DPF blockage or damage, pressure drop sensors placed at both ends of the DPF are used to dynamically monitor the pressure drop across the DPF in real time. The DPF model in the ECU module pre-estimates the theoretical pressure drop of the DPF based on engine operating conditions; the measured pressure drop value ΔP is then used to diagnose the problem. real and model estimated value △P model Comparisons are made, and the relative deviation δ is calculated for analysis; when δ exceeds a certain range, it is determined that the DPF has a typical fault of pore blockage or carrier damage.

[0041] To reduce the DPF fault diagnosis error rate, a DPF fault diagnosis confirmation method based on fault signal duration and fault occurrence frequency is proposed. The entire fault diagnosis process is as follows: First, the DPF controller ECU uses various sensors to dynamically acquire real-time temperature and pressure data at the front and rear ends of the DPF, as well as flow data at the rear end. Second, the DPF controller ECU aligns and cleans the data collected by each sensor. Third, the DPF controller ECU uses its embedded GPRS module to remotely transmit the aligned and cleaned data to the data monitoring and analysis system in real time, and stores it locally on the server. Fourth, the DPF fault diagnosis algorithm in the data monitoring and analysis system performs statistical analysis on the vehicle network data to preliminarily determine whether there is blockage or damage to the DPF carrier and establish a fault pre-list. Fifth, the DPF fault confirmation method finally confirms whether a fault exists and marks the fault status as "yes" or "no" in the list. Sixth, the data monitoring and analysis system dynamically feeds back the confirmed faults to the management department, the enterprise system, and the driver's cab, prompting all relevant parties to repair the DPF fault in a timely manner by illuminating the MIL indicator light, so as to ensure the stable and efficient operation of the DPF.

[0042] The beneficial effects of this invention are:

[0043] By developing a typical fault diagnosis system for DPF device carrier blockage and damage based on vehicle networking technology, we can ensure that the system can make accurate judgments when DPF fails, thus ensuring that DPF can operate efficiently and reliably. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the system of the present invention.

[0045] Figure 2 This is a schematic diagram of the fault diagnosis process of the present invention.

[0046] Figure 3 This is a schematic diagram of the fault diagnosis method and logic of the present invention.

[0047] Figure 4 This is a schematic diagram of abnormal data points in the online monitoring of vehicle networks.

[0048] Figure 5 This is a schematic diagram of online monitoring data processing for vehicle-to-everything (V2X) networks.

[0049] Figure 6 These are schematic diagrams illustrating fault confirmation based on time accumulation (left figure) and cumulative occurrence count (right figure). Detailed Implementation

[0050] Embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art will understand that the following embodiments are for illustrative purposes only and should not be construed as limiting the scope of the invention. Where specific techniques, connections, conditions, or processes are not specified in the embodiments, they are performed in accordance with techniques, connections, conditions, processes described in the literature in the art or according to product instructions. Materials, instruments, or equipment used, unless otherwise specified, are all conventional products that can be purchased.

[0051] like Figure 1 As shown, a typical fault diagnosis system and method for DPF device carrier blockage and damage based on vehicle networking technology includes 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 installed at the DPF intake port 8, and the rear pressure sensor 2, the rear temperature sensor 4, and the exhaust volume flow sensor 5 are installed at the DPF exhaust port 9. The DPF intake port 8 is connected to the engine exhaust pipe, and the DPF exhaust port 9 discharges engine exhaust gas filtered by the DPF into the ambient air. A GPS module 11 is fixedly placed in the vehicle's cab or on the roof to collect vehicle speed and latitude / longitude geographical location data.

[0052] The power supply module 7 is connected to the DPF controller ECU6 via a power cable and provides 24V power after the engine starts. When the engine is turned off, the power supply module 7 automatically stops supplying power within 5 seconds. The front pressure sensor 1, rear pressure sensor 2, front temperature sensor 3, rear temperature sensor 4, exhaust volume flow sensor 5, and GPS module 11 are connected to the DPF controller ECU6 via CAN lines. The data acquisition and transmission frequency is 1Hz, and the data format is "data acquisition time + data parameter value". The DPF controller ECU6 aligns and cleans the data collected by each sensor. The DPF controller ECU6 transmits the aligned and cleaned data remotely to the data monitoring and analysis system 10 in real time through its embedded GPRS module, and stores it locally on the server.

[0053] like Figures 2-6 As shown, the entire fault diagnosis process is as follows:

[0054] The first step is to use the DPF controller ECU to dynamically acquire real-time temperature and pressure data at the front and back ends of the DPF, as well as the flow data at the back end of the DPF, through various sensors.

[0055] The second step is to use the DPF controller ECU to align and clean the data collected by each sensor.

[0056] The third step involves the DPF controller ECU using its embedded GPRS module to remotely transmit the aligned and cleaned data to the data monitoring and analysis system in real time, and then storing it locally on the server.

[0057] The fourth step involves using the DPF fault diagnosis algorithm in the data monitoring and analysis system to perform statistical analysis on the vehicle network data, initially determining whether there is blockage or damage to the DPF carrier, and establishing a fault pre-list.

[0058] The fifth step is to use the DPF fault confirmation method to finally determine whether a fault exists and mark the fault status as "yes" or "no" in the list;

[0059] The sixth step involves using a data monitoring and analysis system to dynamically feed back confirmed faults to the management department, the enterprise system, and the driver's cab. By illuminating the MIL indicator light, all relevant parties are prompted to promptly repair DPF faults to ensure the stable and efficient operation of the DPF.

[0060] The typical fault evaluation index for DPF blockage or carrier damage is the DPF pressure drop relative deviation factor δ, as shown in the following formula:

[0061] DPF voltage drop relative deviation factor δ=|△P model -△P real | / △P model (1)

[0062] Among them, △P real Under actual operating conditions, the voltage drop across the DPF (ΔP) is measured. model Under actual operating conditions, the actual volumetric flow rate is combined with the estimated values ​​of pressure drop before and after the DPF are obtained through the pressure drop model.

[0063] The DPF theoretical pressure drop model ΔP is embedded in the data monitoring and analysis system 10. model The specific model is as follows:

[0064] (2)

[0065] DPF theoretical pressure drop model △Pmodel In this context, μ represents the exhaust dynamic viscosity, which is temperature-dependent; V trap ω is the DPF carrier volume; α is the carrier pore density; ω s K represents the carrier wall thickness; K0 represents the permeability avoided by the carrier in its fresh state, usually obtained through testing or given by the manufacturer; K p ρ is the permeability of the microparticle layer; ω is the thickness of the microparticle layer; s Where: exhaust density; F is the friction coefficient, taken as a constant 28.454; L is the length of the carrier channel; D is the carrier diameter; ε is the sum of the local loss coefficients at the carrier inlet and outlet (generally taken as ε=0.82), etc.

[0066] Of the parameters in the above formulas, except for the DPF outlet exhaust volume flow rate Qv which has a quadratic relationship with the DPF pressure drop, the other parameters are mainly determined by the characteristics of the DPF product itself. In the data monitoring and analysis system 10, the above-mentioned relevant parameters can be set according to the DPF product manufacturer, thereby establishing the theoretical pressure drop parameters for different types of DPF carriers.

[0067] For a DPF with fixed carrier parameters, its theoretical pressure drop model ΔP model It can be simplified to:

[0068] (3)

[0069] in:

[0070] (4)

[0071] (5)

[0072] (6)

[0073] In the formula: T DPF The bed temperature of the DPF, T DPF = (T1+T2) / 2; P DPF The voltage drop between the front and rear ends of the DPF is P2-P1;

[0074] DPF theoretical pressure drop model △P model In the process, the exhaust volume flow rate Q is collected by the exhaust volume flow sensor 5. v The signals, namely the bed temperature of the DPF and the pressure drop signals at the front and rear ends of the DPF, are calculated by collecting signals from the front pressure sensor 1, the rear pressure sensor 2, the front temperature sensor 3, and the rear temperature sensor 4.

[0075] Actual pressure drop of DPF △ PrealThe pressure drop of the DPF during use is monitored in real time by front pressure sensor 1 and rear pressure sensor 2 installed at both ends of the DPF, and the pressure signal is transmitted to the DPF controller ECU6 via CAN line. The pressure drop is calculated from the real-time data. Preal =P2-P1;

[0076] The data monitoring and analysis system 10 calculates the actual operating pressure drop Δ between the DPF front and rear ends under measured conditions. Preal Combined with the actual volumetric flow rate under actual operating conditions and the estimated pressure drop value ΔP obtained through the pressure drop model before and after the DPF, the pressure drop model is used. model By comparison, the relative deviation factor of DPF pressure drop δ=|ΔP model -△ Preal | / △P model Through experimental research, when δ≤25%, it is preliminarily determined that the DPF carrier has blockage or damage faults, and a preliminary list of DPF carrier blockage or damage faults is established.

[0077] Further optimization of the scheme is needed. In DPF fault diagnosis, when abnormal signals in relevant parameters cause large fluctuations in δ, a DPF fault cannot be immediately identified and an alarm triggered. To filter inaccurate fault information, avoid interference from abnormal signal jitter caused by varying operating conditions, and reduce the false judgment rate of the fault diagnosis system, a fault signal identification, confirmation, and judgment method based on the cumulative fault occurrence time and the cumulative number of fault occurrences within 24 hours is proposed. An algorithm for identifying "true / false" DPF fault diagnosis logic is established, as follows:

[0078] 1) The confirmation method based on the cumulative occurrence time of the fault mainly involves comparing the cumulative occurrence time of the fault within 24 hours with a pre-set threshold for the duration required for fault confirmation. This is determined through calibration; experimental studies have shown that a cumulative fault duration ≥30 minutes can confirm a blockage or damage to the DPF carrier.

[0079] 2) The confirmation method based on the cumulative number of fault occurrences mainly involves comparing the cumulative number of fault occurrences within 24 hours with a pre-set threshold for the number of consecutive occurrences required for fault confirmation. This is determined through calibration; experimental studies have shown that a cumulative occurrence of the same fault ≥ 10 times is sufficient to confirm a blockage or damage to the DPF carrier.

[0080] When the detected fault signal disappears, the fault management module also needs to judge the signal based on timing or counting, and make a comprehensive judgment using a confirmation method based on the cumulative fault occurrence time and the cumulative number of fault occurrences (whichever comes first) to ensure the real-time and sensitivity of the fault diagnosis system.

[0081] When δ≤25%, and the comprehensive judgment method of confirming the duration of the fault signal and the number of fault occurrences is met, it can be determined that the DPF has a carrier blockage and damage fault, and the fault status is marked as "yes" in the fault pre-list; otherwise, it is marked as "no". For confirmed faults, the data monitoring and analysis monitoring system is used to dynamically feed back the confirmed faults to the management department, the enterprise system, and the driver's cab, and remind all relevant parties to repair the DPF fault in a timely manner through "fault prompts" or "fault flashing".

[0082] Specifically, the alignment process is as follows: using the vehicle speed data acquisition time (day / hour / minute / second) in the GPS module as a reference, alignment is performed with the front pressure sensor 1, rear pressure sensor 2, front temperature sensor 3, rear temperature sensor 4, and exhaust volume flow sensor 5 respectively; after aligning the parameters of each sensor, a new database Excel file is created, with the database parameters listed as follows: "Time—Vehicle Speed ​​(km / h)—Front Pressure (kPa)—Rear Pressure (kPa)—Front Temperature (°C)—Rear Temperature (°C)—Exhaust Flow (m³ / h)." 3 The data frequency remains 1Hz ( / h).

[0083] The data cleaning process involves the following steps: Excluding the time parameter, the database Excel file contains invalid and missing data for various parameters. Data cleaning is performed using both linear interpolation and smoothing techniques.

[0084] 1) Repair missing or invalid data by linear interpolation. Perform linear interpolation on parameter data points with a time difference of 2 to 4 seconds. For some feature parameter data values ​​that obviously exceed the reasonable range, replace the point by averaging the values ​​before and after.

[0085] 2) For data where the parameter data is within the valid range but there are some outliers, the time series standard smoothing algorithm based on the T4253H filtering method is used for smoothing. This method has a good effect on processing nonlinear data and effectively reduces data outliers.

[0086] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A diesel vehicle particulate filter (DPF) typical failure diagnosis system characterized by comprising: It includes front pressure sensor (1), rear pressure sensor (2), front temperature sensor (3), rear temperature sensor (4), exhaust volume flow sensor (5), DPF controller ECU (6) and power supply module (7); the front pressure sensor (1) and the front temperature sensor (3) are arranged at the DPF air inlet end (8), and the rear pressure sensor (2), the rear temperature sensor (4) and the exhaust volume flow sensor (5) are arranged at the DPF exhaust end (9); the DPF air inlet end (8) is connected with the engine exhaust pipe end; The power supply module (7) is 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 connected with the DPF controller ECU (6) in communication; the DPF controller ECU (6) aligns and cleans the collected data of each sensor and transmits the data to the data monitoring and analysis monitoring system (10) through the embedded GPRS module, and the data monitoring and analysis monitoring system (10) realizes the typical fault diagnosis of the DPF by evaluating the relative deviation factor δ of the DPF pressure drop; The relative deviation factor δ of the DPF pressure drop is expressed as: δ = | ΔP model - Δ Preal | / ΔP model (1); Wherein: || represents the absolute value, △P real DPF actual pressure drop value measured by the front pressure sensor (1) and the pressure P2 measured by the rear pressure sensor (2) under actual operating conditions, △P real = P2-P1;△P model The actual operating conditions are combined with the actual volume flow and the DPF theoretical pressure drop value obtained by the pressure drop model, which is represented as: （3）； （4）； （5）； （6）； wherein: T DPF is the bed temperature of the DPF, expressed as T1, the temperature measured by the front temperature sensor (3), and T2, the temperature measured by the rear temperature sensor (4), T DPF = (T1+T2) / 2; P DPF is the pressure drop between the front and rear ends of the DPF, P2-P1; μ is the dynamic viscosity of the exhaust gas; Qv is the exhaust gas volume flow at the outlet end of the DPF; V trap is the volume of the DPF carrier; α is the carrier pore density; ω s is the carrier wall thickness; K0 is the permeability of the fresh carrier to be avoided; K p is the permeability of the particulate layer; ω is the thickness of the particulate layer; ρ s is the exhaust gas density; F is the friction factor, which is a constant of 28.454; L is the carrier pore length; D is the carrier diameter; ε is the sum of the local loss coefficients at the carrier inlet and outlet; When δ is above a certain threshold, it is preliminarily determined that the DPF carrier has a blockage or damage fault, and a DPF carrier blockage or damage fault pre-list is established.

2. The typical fault diagnosis system of the diesel particulate filter (DPF) of the diesel vehicle according to claim 1, characterized in that: It further comprises a GPS module (11) connected with the power supply module (7) for power supply and connected with the DPF controller ECU (6) for communication.

3. The diesel vehicle particulate filter (DPF) typical malfunction diagnosis system according to claim 2, characterized by, The alignment of the DPF controller ECU (6) to the collected data of each sensor includes: The vehicle speed "data collection time-day / hour / minute / second" in the GPS module (11) is used as a reference to align 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); after the alignment of the parameters of each sensor, a new database Excel file is established, and the database parameter items are "time-vehicle speed-front pressure-rear pressure-front temperature-rear temperature-exhaust flow" in turn.

4. The diesel vehicle particulate filter (DPF) typical failure diagnostic system according to claim 1, characterized by, The data cleaning processing of the DPF controller ECU (6) to the collected data of each sensor includes: Except for the time parameter item, there are invalid data and packet loss data in different parameter items in the database Excel file, and the data cleaning processing is carried out by linear interpolation and smoothing processing.

5. The system for diagnosing typical faults of a diesel particulate filter (DPF) of a diesel vehicle according to any one of claims 1 to 4, characterized in that, When δ≤25%, it is preliminarily determined that the DPF carrier has a blockage or damage fault, and a DPF carrier blockage or damage fault pre-list is established.

6. A typical failure diagnosis method of a typical failure diagnosis system for a diesel particulate filter (DPF) of a diesel vehicle according to claim 3, characterized by, It includes: 1) The confirmation method based on the cumulative fault occurrence time, which is based on the comparison between the cumulative fault occurrence time value within 24 hours and the threshold value of the pre-set required duration for fault confirmation, and then the comprehensive judgment is made; 2) The confirmation method based on the cumulative fault occurrence frequency, which is based on the comparison between the cumulative fault occurrence frequency value within 24 hours and the threshold value of the pre-set required frequency for fault confirmation, and then the comprehensive judgment is made; 3) When δ≤25%, and the comprehensive judgment of the fault signal duration and the fault occurrence frequency confirmation is met, it is determined that the DPF has a carrier blockage or damage fault, and the fault status is marked as "Yes" in the fault pre-list, otherwise it is "No".

7. The typical fault diagnosis method according to claim 6, characterized in that: When the cumulative fault duration is ≥30 minutes, it can be confirmed that the DPF carrier has a blockage or damage fault.

8. The typical fault diagnosis method according to claim 6, characterized in that: When the cumulative fault occurrence frequency is ≥10 times, it can be confirmed that the DPF carrier has a blockage or damage fault.

9. The typical fault diagnosis method according to claim 6, characterized in that: When the monitored fault signal disappears, the signal needs to be judged based on timing or counting, and the confirmation method based on the cumulative fault occurrence time and the cumulative fault occurrence frequency is used for comprehensive judgment.

10. The typical fault diagnosis method according to claim 9, characterized in that: The confirmation method based on the cumulative fault occurrence time and the cumulative fault occurrence frequency is based on the first one.

Images