LU502777B1 - Intelligent diagnosis and treatment method and system for tail gas of motor vehicle - Google Patents
Intelligent diagnosis and treatment method and system for tail gas of motor vehicle Download PDFInfo
- Publication number
- LU502777B1 LU502777B1 LU502777A LU502777A LU502777B1 LU 502777 B1 LU502777 B1 LU 502777B1 LU 502777 A LU502777 A LU 502777A LU 502777 A LU502777 A LU 502777A LU 502777 B1 LU502777 B1 LU 502777B1
- Authority
- LU
- Luxembourg
- Prior art keywords
- diagnosis
- tail gas
- fault
- maintenance
- motor vehicle
- Prior art date
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 148
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000012423 maintenance Methods 0.000 claims abstract description 65
- 238000001514 detection method Methods 0.000 claims abstract description 32
- 238000005457 optimization Methods 0.000 claims abstract description 18
- 238000000605 extraction Methods 0.000 claims description 12
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 4
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000007789 gas Substances 0.000 description 51
- 238000010586 diagram Methods 0.000 description 4
- 230000007935 neutral effect Effects 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- XSQUKJJJFZCRTK-UHFFFAOYSA-N Urea Chemical compound NC(N)=O XSQUKJJJFZCRTK-UHFFFAOYSA-N 0.000 description 2
- 239000004202 carbamide Substances 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 239000003054 catalyst Substances 0.000 description 2
- 230000008021 deposition Effects 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 230000003647 oxidation Effects 0.000 description 2
- 238000007254 oxidation reaction Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 210000002364 input neuron Anatomy 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M15/00—Testing of engines
- G01M15/04—Testing internal-combustion engines
- G01M15/10—Testing internal-combustion engines by monitoring exhaust gases or combustion flame
- G01M15/102—Testing internal-combustion engines by monitoring exhaust gases or combustion flame by monitoring exhaust gases
- G01M15/104—Testing internal-combustion engines by monitoring exhaust gases or combustion flame by monitoring exhaust gases using oxygen or lambda-sensors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
- G01N33/0034—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array comprising neural networks or related mathematical techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
- G01N33/0037—NOx
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
- G01N33/004—CO or CO2
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
- G01N33/0047—Organic compounds
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Combustion & Propulsion (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Emergency Medicine (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Exhaust Gas After Treatment (AREA)
- Combined Controls Of Internal Combustion Engines (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The present invention discloses an intelligent diagnosis and treatment method and system for tail gas of a motor vehicle and relates to the technical field of treatment of tail gas of motor vehicles. The method comprises the following steps: S101, a data acquisition step: acquiring original tail gas detection data of a vehicle; S102, a fault diagnosis step: performing fault diagnosis on the acquired original tail gas detection data of the vehicle to obtain a diagnosis result and maintenance advice; and S103, a fault diagnosis result optimization step: acquiring the diagnosis result and the maintenance advice, and optimizing the diagnosis result and the maintenance advice in combination with historical diagnosis and maintenance records. The present invention solves the problem that tail gas of motor vehicles exceeding the standard cannot be rapidly and accurately diagnosed and treated in the prior art, thereby improving the diagnosis efficiency and the accuracy.
Description
le LU502777
INTELLIGENT DIAGNOSIS AND TREATMENT METHOD AND SYSTEM FOR
TAIL GAS OF MOTOR VEHICLE
The present invention relates to the technical field of treatment for tail gas of motor vehicles, particularly to an intelligent diagnosis and treatment method and system for tail gas of a motor vehicle.
There are many methods for detecting emission faults of motor vehicles, but a majority of experts are intended to faults of a certain specific type, and corresponding relationships between features of analyzed objects and faults are not clear. At present, there are mainly two methods for diagnosing emission faults of motor vehicles: first, engine faults are diagnosed by analyzing vibration signals and multisensor signals in an operating process of an engine; and second, engine faults are diagnosed by analyzing the component contents of tail gas and combining various sensor information. At present, researches of an artificial intelligence technology applied to diagnosing engine failures by most experts at home and abroad stay at a theoretical perspective, and the experts only verify constructed models by means of a small amount of test data or simulation.
In a continuous using process, an automobile is prone to mechanical wear and aging such as carbon deposition in ignition plugs and an engine combustor, and severe wear of an air cylinder and a piston ring. These faults will not affect normal operation of the vehicle but will increase fuel consumption and emission of the vehicle. It is difficult to rapidly and accurately diagnose the faults by a conventional motor vehicle diagnosis instrument during treatment of tail gas exceeding the standard. Maintenance personnel only can diagnose the reason that the tail gas exceeds the standard by their own knowledge and experience. It is unable to rapidly and accurately find out the reason that the tail gas exceeds the standard by utilizing the existing fault diagnosis instrument, and use of the existing fault diagnosis instrument not only wastes time, but also increases the maintenance cost.
7 LU502777
Therefore, to provide an intelligent diagnosis and treatment method and system for tail gas of a motor vehicle to overcome the difficulty in the prior art is the problem urgently needed to be solved by those skilled in the art.
In view of this, the present invention provides an intelligent diagnosis and treatment method and system for tail gas of a motor vehicle to solve the problem that tail gas of motor vehicles exceeding the standard cannot be rapidly and accurately diagnosed and treated in the prior art.
In order to achieve the purpose, the present invention adopts a technical solution as follows: an intelligent diagnosis and treatment method for tail gas of a motor vehicle, including the following steps:
S101, a data acquisition step: acquiring original tail gas detection data of a vehicle;
S102, a fault diagnosis step: performing fault diagnosis on the acquired original tail gas detection data of the vehicle to obtain a diagnosis result and maintenance advice; and
S103, a fault diagnosis result optimization step: acquiring the diagnosis result and the maintenance advice, and optimizing the diagnosis result and the maintenance advice in combination with historical diagnosis and maintenance records.
Optionally, the original tail gas detection data of the vehicle with tail gas exceeding the standard is acquired by an in-use vehicle detection condition method.
Optionally, the original tail gas detection data in S101 includes information of gas contents of HC, CO», PM, CO and O».
Optionally, the fault diagnosis in S102 includes fault feature extraction, classification and output processes.
Optionally, the fault feature extraction specifically includes: acquiring concentrations of tail gas components through the original tail gas detection data, and pre-processing the concentrations of the tail gas components to obtain fault features.
Optionally, the classification process specifically includes: performing classified
3 LU502777 diagnosis on the obtained fault features by utilizing a trained classification neural network, so as to obtain the diagnosis result and the maintenance advice.
Optionally, the output process specifically includes: outputting the obtained diagnosis result and maintenance advice.
Optionally, the method further includes S104: a fault diagnosis process optimization step: automatically optimizing a fault diagnosis process in combination with accurate diagnosis and maintenance records according to true and accurate diagnosis result and maintenance advice inputted by maintenance personnel.
An intelligent diagnosis and treatment system for tail gas of a motor vehicle includes a data acquisition module, a fault diagnosis module and a fault diagnosis process optimization module connected successively by utilizing the above-mentioned intelligent diagnosis and treatment method for tail gas of a motor vehicle, where the data acquisition module is configured to acquire original tail gas detection data of a vehicle; the fault diagnosis module is configured to perform fault diagnosis on the acquired original tail gas detection data of the vehicle to obtain a diagnosis result and maintenance advice; and the fault diagnosis result optimization module is configured to acquire the diagnosis result and the maintenance advice, and to optimize the diagnosis result and the maintenance advice in combination with historical diagnosis and maintenance records.
Optionally, the fault diagnosis module includes a fault feature extraction unit, a classification unit and an output unit connected successively; the fault feature extraction unit acquires concentrations of tail gas components through the original tail gas detection data, and pre-processes the concentrations of the tail gas components; the classification unit performs classified diagnosis on the obtained fault features by utilizing a trained classification neural network, so as to obtain the diagnosis result and the maintenance advice; and
TAT LU502777 the output unit outputs the obtained diagnosis result and maintenance advice.
Optionally, the system further includes a fault diagnosis process optimization module connected to the fault diagnosis module and configured to automatically optimize a fault diagnosis process in combination with accurate diagnosis and maintenance records according to true and accurate diagnosis result and maintenance advice inputted by maintenance personnel.
It can be known from the above-mentioned technical solution that compared with the prior art, the present invention provides an intelligent diagnosis and treatment method and system for tail gas of a motor vehicle, which can provide an accurate diagnosis standard to vehicle owners and maintenance personnel by acquiring the original tail gas detection data of the vehicle with tail gas exceeding the standard by means of the in-use vehicle detection condition method, performing intelligent diagnosis by utilizing the fault diagnosis module according to detection data and giving the reasons for excessive emission and treatment and maintenance advice accurately and efficiently, and further can conduct self-learning and automatic optimization by utilizing the accurate diagnosis and maintenance records. As the number of times of diagnosis is increased, the accuracy will be higher, so that the present invention is suitable for wide vehicle models.
In order to describe the embodiments of the present invention or the technical solution in the prior art more clearly, brief introduction on drawings needed to be used in the embodiment will be made below. It is obvious that the drawings described below are embodiments of the present invention, and those skilled in the technical field further can obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flowchart of an intelligent diagnosis and treatment method for tail gas of a motor vehicle provided by the present invention.
FIG. 2 is a schematic structural diagram of a classification neutral network provided by the present invention.
FIG. 3 is a structural block diagram of an intelligent diagnosis and treatment method for oT LU502777 tail gas of a motor vehicle provided by the present invention.
Fig. 4 is a structural block diagram of a fault diagnosis module provided by the present invention.
The technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the accompanying drawings in the embodiments of the present invention. The described embodiments are merely a part of, rather than all of, the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention. On a basis of the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall into the scope of protection of the present invention.
Referring to FIG. 1, an intelligent diagnosis and treatment method for tail gas of a motor vehicle includes the following steps:
S101, a data acquisition step: original tail gas detection data of a vehicle is acquired,
S102, a fault diagnosis step: fault diagnosis is performed on the acquired original tail gas detection data of the vehicle to obtain a diagnosis result and maintenance advice; and
S103, a fault diagnosis result optimization step: the diagnosis result and the maintenance advice are acquired, and the diagnosis result and the maintenance advice are optimized in combination with historical diagnosis and maintenance records.
Further, tail gas emission detection is performed on the vehicle by utilizing the in-use vehicle emission detection condition method specified by laws and regulations. At present, the in-use vehicle emission detection condition method is as follows: gasoline vehicles are detected according to a simple instantaneous condition method, and diesel vehicles are detected according to a lug down method; and the original tail gas detection data is the concentrations of the components of the tail gas detected by the condition methods respectively.
Further, the original tail gas detection data in S101 includes information of gas contents
“oT LU502777 of HC, CO», PM, CO and Oa.
Further, the fault diagnosis in S102 includes fault feature extraction, classification and output processes.
Fault diagnosis adopts a fault model which is a model capable of representing a non-linear mapping relation between the fault features and a failure mode of parts and components of an engine. The fault diagnosis model further has a self-learning ability, and can conduct self-learning by combining the accurate diagnosis and maintenance records and automatically optimize parameters of the model.
The non-linear mapping relation includes: a sensitivity of the fault features to failure of parts and components of the engine and a coupling relation among failure of parts and components of the engine, for example, a sensitivity of a NOx concentration to failure of an oxidation catalyst of a diesel engine in a post-treatment apparatus and an influence of the failure of the oxidation catalyst of the diesel engine on an effect of a diesel particulate filter.
Further, the fault feature extraction specifically includes: acquiring concentrations of tail gas components through the original tail gas detection data, and pre-processing the concentrations of the tail gas components to obtain fault features. A pre-processing method can be a maximum normalization method, mean normalization or data standard normalization.
Further, the classification process specifically includes: classified diagnosis is performed on the obtained fault features by utilizing a trained classification neural network, so as to obtain the diagnosis result and the maintenance advice.
Further, the output process specifically includes: the obtained diagnosis result and maintenance advice are outputted. Its objective is to acquire a final specific fault type, and a
Softmax function can be used as an excitation function.
Further, the diagnosis results according to optimization in S103 are listed successively from high to low according to a probability, and primary reasons, secondary reasons and primary maintenance advice as well are given.
Further, the method further includes S104: a fault diagnosis process optimization step: a
TS LU502777 fault diagnosis process is automatically optimized in combination with accurate diagnosis and maintenance records according to true and accurate diagnosis result and maintenance advice inputted by maintenance personnel, so that the diagnosis process is more perfect, and the method is suitable for wide vehicle models and is more accurate to diagnose.
Referring to FIG. 2, the present invention discloses the schematic structural diagram of the classification neutral network, including an input layer, a hidden layer and an output layer;
Input data of the neural network model is information of gas contents such as HC, CO»,
PM, CO and Oz, and therefore, the number of corresponding network input neurons is designed to be 5; an output of the network is a result of fault diagnosis. It is assumed that there are six engine faults, the number of neutrons in the output layer is set to be 6 or 3, and an output result is represented by a binary number. The six faults are respectively DPF blockage, urea pump fault, severe carbon deposition of the engine, failure of an oxygen sensor, poor gas distribution adjustment and fuel filter blockage, which are represented by numbers 1-6 respectively, namely, the failure form 1 corresponds to the DPF blockage, the failure form 2 corresponds to the urea pump fault, and so on. Two encoding methods are described below.
The first method: the number of the neutrons in the output layer is set to be 6, as shown in Table 1:
Table 1
Là | | 5 | 6 | 0 | 0 | 0°
La | 0 | 4 | 6 | 0 | 0 | 0°
Cs | 0 | 5 | 4 | 0 | 0 | 0° a | 0 | 0 | 8 | 1 | 0 | 0
Cs | 0 | 5 | 6 | 0 | 5 | 0° 6 | 0 | 5 | 6 | 0 | 0 | 3
The second method: the number of the neutrons in the output layer is set to be 3, as
“8 LU502777 shown in Table 2:
Table 2
Là | 6 | 6 | 4
La | 6 | 4 | 6 15 | à | 6 | 6
EE Er ET oe | à | 4 | 6
The number of the neutrons in the output layer includes various forms, and the form specifically selected needs to be determined according to actual problems. The small number of neutrons contributes to rapid encoding and clear result, and the number of the neutrons in the output layer is set to be 6.
The number of the neutrons in the hidden layer of the neutral network is calculated by eye .. .. . n, = Jorn +a utilizing an empirical formula, and the empirical formula is a formula , where nı is the number of the neutrons in the hidden layer, n is the number of the neutrons in the input layer, m is the number of the neutrons in the output layer, and a is a constant, 1<a<10.
Referring to FIG. 3, the present invention discloses an intelligent diagnosis and treatment system for tail gas of a motor vehicle, including a data acquisition module, a fault diagnosis module and a fault diagnosis result optimization module connected successively by utilizing the above-mentioned intelligent diagnosis and treatment method for tail gas of a motor vehicle, where the data acquisition module 1s configured to acquire original tail gas detection data of a vehicle; the fault diagnosis module is configured to perform fault diagnosis on the acquired original tail gas detection data of the vehicle to obtain a diagnosis result and maintenance
Le LU502777 advice; and the fault diagnosis result optimization module is configured to acquire the diagnosis result and the maintenance advice, and to optimize the diagnosis result and the maintenance advice in combination with historical diagnosis and maintenance records.
Further, referring to FIG. 4, the fault diagnosis module includes a fault feature extraction unit, a classification unit and an output unit connected successively, where the fault feature extraction unit acquires concentrations of tail gas components through the original tail gas detection data, and pre-processes the concentrations of the tail gas components to obtain fault features; the classification unit performs classified diagnosis on the obtained fault features by utilizing a trained classification neural network, so as to obtain the diagnosis result and the maintenance advice; and the output unit outputs the obtained diagnosis result and maintenance advice.
Further, the system further includes a fault diagnosis process optimization module connected to the fault diagnosis module and configured to automatically optimize a fault diagnosis process in combination with accurate diagnosis and maintenance records according to true and accurate diagnosis result and maintenance advice inputted by maintenance personnel.
By explaining the disclosed embodiments in a progressive way, those skilled in the art can implement or use the present invention. Various modifications of these embodiments will be obvious for professionals in the art. À general principle defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to these embodiments disclosed herein but is in accordance with the widest scope consistent with principle and novel features of the disclosure.
Claims (10)
1. An intelligent diagnosis and treatment method for tail gas of a motor vehicle, comprising the following steps: S101, a data acquisition step: acquiring original tail gas detection data of a vehicle; S102, a fault diagnosis step: performing fault diagnosis on the acquired original tail gas detection data of the vehicle to obtain a diagnosis result and maintenance advice; and S103, a fault diagnosis result optimization step: acquiring the diagnosis result and the maintenance advice, and optimizing the diagnosis result and the maintenance advice in combination with historical diagnosis and maintenance records.
2. The intelligent diagnosis and treatment method for tail gas of a motor vehicle according to claim 1, wherein the original tail gas detection data in S101 comprises information of gas contents of HC, CO», PM, CO and O>.
3. The intelligent diagnosis and treatment method for tail gas of a motor vehicle according to claim 1, wherein the fault diagnosis in S102 comprises fault feature extraction, classification and output processes.
4. The intelligent diagnosis and treatment method for tail gas of a motor vehicle according to claim 3, wherein the fault feature extraction specifically comprises: acquiring concentrations of tail gas components through the original tail gas detection data, and pre-processing the concentrations of the tail gas components to obtain fault features.
5. The intelligent diagnosis and treatment method for tail gas of a motor vehicle according to claim 4, wherein the classification process specifically comprises: performing classified diagnosis on the obtained fault features by utilizing a trained classification neural network, so as to obtain the diagnosis result and the maintenance advice.
6. The intelligent diagnosis and treatment method for tail gas of a motor vehicle according to claim 5, wherein the output process specifically comprises: outputting the obtained diagnosis result and maintenance advice.
7. The intelligent diagnosis and treatment method for tail gas of a motor vehicle according to claim 1, further comprising S104: a fault diagnosis process optimization step: automatically optimizing a fault diagnosis process in combination with accurate diagnosis
I LU502777 and maintenance records according to true and accurate diagnosis result and maintenance advice inputted by maintenance personnel.
8. An intelligent diagnosis and treatment system for tail gas of a motor vehicle, comprising a data acquisition module, a fault diagnosis module and a fault diagnosis result optimization module connected successively by utilizing the intelligent diagnosis and treatment method for tail gas of a motor vehicle according to any one of claims 1-7, wherein the data acquisition module is configured to acquire original tail gas detection data of a vehicle; the fault diagnosis module is configured to perform fault diagnosis on the acquired original tail gas detection data of the vehicle to obtain a diagnosis result and maintenance advice; and the fault diagnosis result optimization module is configured to acquire the diagnosis result and the maintenance advice, and to optimize the diagnosis result and the maintenance advice in combination with historical diagnosis and maintenance records.
9. The intelligent diagnosis and treatment system for tail gas of a motor vehicle according to claim 8, wherein the fault diagnosis module comprises a fault feature extraction unit, a classification unit and an output unit connected successively; the fault feature extraction unit acquires concentrations of tail gas components through the original tail gas detection data, and pre-processes the concentrations of the tail gas components to obtain fault features; the classification unit performs classified diagnosis on the obtained fault features by utilizing a trained classification neural network, so as to obtain the diagnosis result and the maintenance advice; and the output unit outputs the obtained diagnosis result and maintenance advice.
10. The intelligent diagnosis and treatment system for tail gas of a motor vehicle according to claim 8, further comprising a fault diagnosis process optimization module connected to the fault diagnosis module and configured to automatically optimize a fault diagnosis process in combination with accurate diagnosis and maintenance records according to true and accurate diagnosis result and maintenance advice inputted by maintenance personnel.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210409737.3A CN114720140A (en) | 2022-04-19 | 2022-04-19 | Intelligent diagnosis and treatment method and system for motor vehicle tail gas |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| LU502777B1 true LU502777B1 (en) | 2023-03-09 |
Family
ID=82244567
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| LU502777A LU502777B1 (en) | 2022-04-19 | 2022-09-09 | Intelligent diagnosis and treatment method and system for tail gas of motor vehicle |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN114720140A (en) |
| LU (1) | LU502777B1 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118225983B (en) * | 2024-05-23 | 2024-09-13 | 西安四腾环境科技有限公司 | Exhaust gas detection alarm system and detection method thereof |
-
2022
- 2022-04-19 CN CN202210409737.3A patent/CN114720140A/en active Pending
- 2022-09-09 LU LU502777A patent/LU502777B1/en active IP Right Grant
Also Published As
| Publication number | Publication date |
|---|---|
| CN114720140A (en) | 2022-07-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106227200B (en) | A kind of automobile on-line fault diagnosis method | |
| US6836708B2 (en) | Monitoring of vehicle health based on historical information | |
| CN103135515B (en) | Diagnostic method for vehicle condition | |
| CN108343497B (en) | System and method for rapidly diagnosing aging failure of SCR system of diesel vehicle | |
| US20200240346A1 (en) | Calculation of exhaust emissions of a motor vehicle | |
| CN113790094B (en) | Method, device, vehicle and medium for determining sulfur poisoning of aftertreatment system | |
| LU502777B1 (en) | Intelligent diagnosis and treatment method and system for tail gas of motor vehicle | |
| WO2018215430A1 (en) | Method for the diagnosis of an internal combustion engine of a motor vehicle, and of the devices connected to the internal combustion engine, and system for carrying out the method. | |
| CN112964476A (en) | On-line prediction method for actual road particulate matter emission of heavy-duty diesel vehicle | |
| CN112177811B (en) | Remote online monitoring method for flow of EGR (exhaust gas Recirculation) system of electric control diesel engine | |
| CN108035789B (en) | Gasoline vehicle multi-stage three-way catalyst performance online monitoring system and method | |
| CN111042901B (en) | Method and device for adjusting diagnosis times of nitrogen oxide conversion efficiency | |
| US20240013594A1 (en) | Fault isolation using on-board diagnostic (obd) capability data | |
| CN115392116A (en) | Heavy diesel truck excess emission judgment method based on OBD data restoration | |
| CN108825344B (en) | NO (nitric oxide)xMethod and device for judging reasonability of sensor measurement value | |
| Guardiola et al. | From OBD to connected diagnostics: a game changer at fleet, vehicle and component level | |
| CN115238651A (en) | Method for analyzing NOX emission value by ECU data | |
| CN117514431A (en) | DPF fault diagnosis method, device, terminal equipment and storage medium | |
| DE102012203196B4 (en) | Hydrocarbon conversion diagnostic system | |
| CN114467090A (en) | System and method for identifying field replaceable units using digital twins | |
| CN114060132B (en) | NO based on emission remote monitoring x Sensor cheating discrimination method | |
| WO2020072523A1 (en) | Engine emission status-based maintenance | |
| CN113339115B (en) | Diesel engine, and diesel engine post-processing fault detection system and method | |
| CN110410189B (en) | Fault diagnosis method and device for diesel particulate filter, vehicle and storage medium | |
| CN118622447A (en) | DPF blocking early warning method, system, storage medium and equipment based on cloud service |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FG | Patent granted |
Effective date: 20230309 |