CN114658542B - Non-road engine fault detection method based on oil mass consistency of oil injector - Google Patents
Non-road engine fault detection method based on oil mass consistency of oil injector Download PDFInfo
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- 238000001514 detection method Methods 0.000 title abstract description 21
- 239000000446 fuel Substances 0.000 claims abstract description 65
- 238000002347 injection Methods 0.000 claims abstract description 59
- 239000007924 injection Substances 0.000 claims abstract description 59
- 238000000034 method Methods 0.000 claims abstract description 53
- 239000003921 oil Substances 0.000 claims description 73
- 239000010705 motor oil Substances 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 6
- 239000000110 cooling liquid Substances 0.000 claims description 5
- 230000008439 repair process Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 abstract description 27
- 238000003745 diagnosis Methods 0.000 abstract description 4
- 230000000007 visual effect Effects 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 239000002826 coolant Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000005067 remediation Methods 0.000 description 1
- 238000005507 spraying Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02B—INTERNAL-COMBUSTION PISTON ENGINES; COMBUSTION ENGINES IN GENERAL
- F02B77/00—Component parts, details or accessories, not otherwise provided for
- F02B77/08—Safety, indicating, or supervising devices
- F02B77/083—Safety, indicating, or supervising devices relating to maintenance, e.g. diagnostic device
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- 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
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Abstract
The application relates to a non-road engine fault detection method based on oil mass consistency of an oil injector, and relates to the field of fault diagnosis in an electric control system of a diesel engine. The method comprises the steps of obtaining target working condition data of a target engine in a target time period; comparing the target working condition data with the working condition data to be iterated to obtain a similarity comparison result; and responding to the sensor parameter comparison sub-result to indicate that the target sensor parameter is similar to the sensor parameter to be iterated, and the oil injection quantity comparison sub-result to indicate that the target oil injection quantity is dissimilar to the oil injection quantity to be iterated, and determining that the target engine has faults. In the fault detection process, the state of the fuel injector is judged according to the fuel quantity comparison of the fuel injector under the same state and different time periods. The fuel injector state judgment by using the fuel quantity is more visual, pre-calibration before delivery is not needed, the time ductility is eliminated, and the running state of the engine is visually embodied.
Description
Technical Field
The application relates to the field of fault diagnosis in an electric control system of a diesel engine, in particular to a non-road engine fault detection method based on self-learning and engine spraying working conditions.
Background
In off-road vehicles and other industrial machinery, off-road engines play an important role as their driving components. Since the operating state of an engine has a great correlation with the operating efficiency of industrial machinery, real-time detection and processing of engine faults is required. The working state of the fuel injector of the common rail system of the engine is related to the working condition of the engine, so that the fault detection of the engine is generally carried out by combining the fuel quantity of the fuel injector.
In the related art, the fuel injector can collect data before leaving the factory or in a preset state, and correspondingly generate corresponding tables, for example, a table of rail pressure frequency domain amplitude values under different working conditions of the engine is formed, and then the consistency of the fuel amount of the fuel injector is judged according to the table.
However, the oil amount judging method in the related art has temporal ductility, and cannot intuitively display the operating state of the engine.
Disclosure of Invention
The application relates to a non-road engine fault detection method based on oil mass consistency of an oil injector, which can eliminate time ductility and intuitively embody the running state of an engine. The non-road engine fault detection method based on the consistency of the oil mass of the oil injector comprises the following steps:
acquiring target working condition data of a target engine in a target time period, wherein the target working condition data comprises target sensor parameters and target fuel injection quantity, and the sensor parameters comprise at least one of cooling liquid temperature, engine oil pressure, engine oil temperature, engine air inlet temperature and supercharging pressure;
comparing the target working condition data with the working condition data to be iterated to obtain a similarity comparison result, wherein the similarity comparison result is used for indicating the oil mass deviation range of the target working condition data and the working condition data to be iterated, the similarity comparison result comprises a sensor parameter comparison sub-result and an oil injection quantity comparison sub-result, and the working condition data to be iterated comprises a sensor parameter to be iterated and an oil injection quantity to be iterated;
and responding to the sensor parameter comparison sub-result to indicate that the target sensor parameter is similar to the sensor parameter to be iterated, and the oil injection quantity comparison sub-result to indicate that the target oil injection quantity is dissimilar to the oil injection quantity to be iterated, and determining that the target engine has faults.
In an alternative embodiment, the method further comprises:
responding to the sensor parameter comparison sub-result to indicate that the target sensor parameter is similar to the sensor parameter to be iterated, and the fuel injection quantity comparison sub-result to indicate that the target fuel injection quantity is similar to the fuel injection quantity to be iterated, and replacing the working condition data to be iterated with the target working condition data;
and responding to the sensor parameter comparison sub-result to indicate that the target sensor parameter is dissimilar to the sensor parameter to be iterated, and recording the target working condition data as the working condition data to be iterated.
In an alternative embodiment, acquiring target operating condition data of a target engine over a target period of time includes:
acquiring a rotating speed domain, a rail pressure domain and a power domain of a target engine in a target time period, wherein the rotating speed domain indicates a rotating speed numerical range of the target engine in the target time period, the rail pressure domain indicates a rail pressure numerical range of the target engine in the target time period, and the power domain indicates a power range of the target engine in the target time period;
performing self-learning requirement matching based on the power domain, the rail pressure domain and the rotating speed domain, wherein the self-learning requirement matching is used for comparing the power domain with a preset power domain, comparing the rail pressure domain with the preset rail pressure domain and comparing the rotating speed domain with the preset rotating speed domain;
and acquiring target working condition data of the target engine in a target time period in response to the self-learning requirement matching.
In an alternative embodiment, the method further comprises:
determining working conditions of the target engine based on the rotating speed domain, the rail pressure domain and the power domain, wherein the working conditions comprise at least one of idle load, 10% rated power working conditions, 25% rated power working conditions, 50% rated power working conditions, 75% rated power working conditions and rated power working conditions;
and determining working condition data to be iterated based on the working condition of the target engine.
In an alternative embodiment, after determining that the target engine has a fault, the method further comprises:
and sending an alarm signal, wherein the alarm signal is used for indicating that the target engine has a fault, and the fault is characterized by inconsistent oil quantity.
In an alternative embodiment, after sending the alarm signal, the method further comprises:
receiving a working condition updating signal, wherein the working condition updating signal is used for indicating that the target engine finishes repairing parts;
and resetting working condition data to be iterated.
In an alternative embodiment, the component replacement includes at least one of a fuel injector replacement, a valve lash adjustment, and a supercharger fault remediation.
The beneficial effects that this application provided technical scheme brought include at least:
when detecting whether the non-road engine has faults, based on a self-learning technology, after acquiring target working condition data, respectively comparing the target sensor parameters and target fuel injection quantity in the working condition data with the working condition data to be iterated, which are prestored in corresponding computer equipment, so as to compare the fuel injection quantity of the engine under the same state and different specific working conditions, and further determining whether the engine has faults. In the fault detection process, the state of the fuel injector is judged according to the fuel quantity comparison of the fuel injector under the same state and different time periods. The fuel injector state judgment by using the fuel quantity is more visual, pre-calibration before delivery is not needed, the time ductility is eliminated, and the running state of the engine is visually embodied.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present utility model, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present utility model, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart illustrating a method for off-road engine fault detection based on fuel injector fuel consistency according to an exemplary embodiment of the present application;
FIG. 2 is a flow chart illustrating another method for off-road engine fault detection based on injector oil mass consistency provided in accordance with an exemplary embodiment of the present application;
FIG. 3 illustrates a process diagram of a method for performing off-road engine fault detection based on injector oil mass consistency provided by an exemplary embodiment of the present application;
FIG. 4 illustrates a schematic diagram of modules that execute a method of off-road engine fault detection based on injector oil mass consistency, as provided by an exemplary embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
First, the terms involved in the embodiments of the present application will be briefly described:
off-road engines refer to a generic term that is used for other purposes, not specifically for engines in automobiles. The term is commonly used by regulatory authorities to categorize engines to control their emissions. . In the related art, at least one sensor is installed in an off-road engine to acquire parameters such as an engine speed, a coolant temperature, an oil pressure, an oil temperature, and the like. Alternatively, no such sensor is present in a road engine, so the fault detection method of the present application is used in an off-road engine. Optionally, the engine related to the application comprises at least one of an engine in a generator set, an engine for engineering machinery with constant working conditions and an engine for marine machinery.
Fig. 1 is a schematic flow chart of a method for detecting a fault of an off-road engine based on consistency of fuel amounts of fuel injectors according to an exemplary embodiment of the present application, and the method is applied to a computer device for explanation, and includes:
Optionally, the computer is connected with a sensor in the target engine to acquire data transmitted by the target sensor in real time. In an embodiment of the present application, the target operating condition data includes a target sensor parameter and a target injection amount, and the sensor parameter includes at least one of a coolant temperature, an oil pressure, an oil temperature, an engine intake temperature, and a boost pressure.
It should be noted that, the target working condition data in the embodiments of the present application are all data obtained from sensors in the off-road engine. In this case, the computer device may be connected to an upper computer of the engine, and directly acquire the target operating condition data.
And 102, comparing the target working condition data with the working condition data to be iterated to obtain a similarity comparison result.
In the embodiment of the application, the similarity comparison result is used for indicating the oil mass deviation range of the target working condition data and the working condition data to be iterated, the similarity comparison result comprises a sensor parameter comparison sub-result and an oil injection quantity comparison sub-result, and the working condition data to be iterated comprises a sensor parameter to be iterated and an oil injection quantity to be iterated.
Optionally, in the embodiment of the present application, based on self-learning logic of the computer device, the working condition data to be iterated is working condition data stored in the computer device, where the working condition data indicates a sensor parameter and an oil injection amount corresponding to the engine during the previous period of normal operation. Under the condition, comparing the target sensor parameters in the target working condition data with the sensor parameters to be iterated, and comparing the similarity between the target fuel injection quantity and the fuel injection quantity to be iterated respectively to obtain the similarity between the target working condition data and the working condition data to be iterated.
And step 103, determining that the target engine has a fault in response to the sensor parameter comparison sub-result indicating that the target sensor parameter is similar to the sensor parameter to be iterated and the fuel injection quantity comparison sub-result indicating that the target fuel injection quantity is dissimilar to the fuel injection quantity to be iterated.
In the embodiment of the application, a possible situation exists in the comparison result, under the possible situation, the target sensor parameter is similar to the sensor parameter to be iterated, the target oil injection quantity is dissimilar to the oil injection quantity to be iterated, and as the working condition data to be iterated are the data determined when the engine normally operates, the working condition indicates that the engine normally operates, but the oil injection quantity has an abnormal result, and then the engine can be determined to have faults.
Optionally, after the engine fails, the engine can be detected and troubleshooted according to the abnormal condition of the data until the fault is repaired.
In summary, in the method provided by the embodiment of the present application, when detecting whether a non-road engine has a fault, based on a self-learning technology, after acquiring target working condition data, the target sensor parameter and the target fuel injection amount in the working condition data are compared respectively with the working condition data to be iterated pre-stored in the corresponding computer device, so as to compare the fuel injection amounts of the engine under the same state and different specific working conditions, and further determine whether the engine has a fault. In the fault detection process, the state of the fuel injector is judged according to the fuel quantity comparison of the fuel injector under the same state and different time periods. The fuel injector state judgment by using the fuel quantity is more visual, pre-calibration before delivery is not needed, the time ductility is eliminated, and the running state of the engine is visually embodied.
FIG. 2 is a flow chart of another method for detecting a fault of an off-road engine based on consistency of fuel amounts of fuel injectors according to an exemplary embodiment of the present application, and the method is applied to a computer device for illustration, and includes:
In the embodiment of the application, the rotation speed domain indicates the rotation speed numerical range of the target engine in the target time period, the rail pressure domain indicates the rail pressure numerical range of the target engine in the target time period, and the power domain indicates the power range of the target engine in the target time period. Namely, the rotation speed domain can reflect the highest rotation speed and the lowest rotation speed of the engine in the target time period; the rail pressure domain can reflect the highest rail pressure and the lowest rail pressure of the engine in the target time period; the power domain can represent the highest power and the lowest power of the engine in the target time period.
Step 202, performing self-learning requirement matching based on the power domain, the rail pressure domain and the rotating speed domain.
In the embodiment of the application, in the process of comparison, the self-learning requirement matching is used for comparing the power domain with a preset power domain, comparing the rail pressure domain with a preset rail pressure domain, and comparing the rotating speed domain with a preset rotating speed domain. After the comparison, the computer device determines whether to record the current operating condition.
And step 203, acquiring target working condition data of the target engine in a target time period in response to the self-learning requirement matching.
Optionally, after matching through the self-learning requirement, it may be determined that the computer device is to perform the oil consistency detection. In this case, the computer device obtains the target operating condition data for the target period.
In step 205, operating condition data to be iterated is determined based on the operating conditions of the target engine.
The process described in steps 204 to 205 is a process for determining working condition data to be iterated.
Optionally, the operating condition data to be iterated needs to be selected based on an operating condition gear of the target engine. The operating mode gear is determined by a rotational speed domain, a rail pressure domain and a power domain and comprises at least one of idle speed no-load, 10% rated power operating mode, 25% rated power operating mode, 50% rated power operating mode, 75% rated power operating mode and rated power operating mode. That is, in the embodiment of the present application, different working condition gears correspond to different pre-stored working condition data to be iterated.
And 206, comparing the target working condition data with the working condition data to be iterated to obtain a similarity comparison result.
The process is the same as the process shown in step 102 and will not be described in detail here.
It should be noted that, in the embodiment of the present application, a similar requirement is that the readings of the sensors are within the same range; the oil quantity is read in the oil quantity fluctuation range, and the ratio of the absolute value of the oil quantity difference to the average value of the oil quantity is smaller than the oil quantity deviation range. The corresponding threshold is a pre-stored threshold in the computer device, or the corresponding threshold is a threshold indicated by a signal received by the computer device in the detection process. The actual implementation of the threshold is not limited in this application.
In step 207, a fault is determined to exist in the target engine in response to the sensor parameter versus sub-result indicating that the target sensor parameter is similar to the sensor parameter to be iterated and the fuel injection quantity versus sub-result indicating that the target fuel injection quantity is dissimilar to the fuel injection quantity to be iterated.
This process is similar to the process shown in step 103 and will not be described in detail here.
And step 208, sending an alarm signal, wherein the alarm signal is used for indicating that the target engine has a fault, and the fault is characterized by inconsistent oil quantity.
Step 211, replacing the working condition data to be iterated with the target working condition data in response to the sensor parameter comparison sub-result indicating that the target sensor parameter is similar to the sensor parameter to be iterated and the fuel injection quantity comparison sub-result indicating that the target fuel injection quantity is similar to the fuel injection quantity to be iterated.
The comparison sub-result indicates that the target working condition data and the working condition data to be iterated are similar in terms of the target sensor parameters and the parameters of the sensor to be iterated after being compared, and the target oil injection quantity and the iterated oil injection quantity are similar.
In step 212, responsive to the sensor parameter comparison sub-result indicating that the target sensor parameter is dissimilar to the sensor parameter to be iterated, the target operating condition data is recorded as operating condition data to be iterated.
The comparison sub-result indicates that the target sensor parameter is dissimilar to the sensor parameter to be iterated after the target working condition data and the working condition data to be iterated are compared, and the target oil injection quantity is dissimilar to the iterated oil injection quantity. In this case, that is, it is stated that under the same working condition, the engine has a working state dissimilar to the currently pre-stored data, and at this time, the target working condition data is recorded.
In summary, in the method provided by the embodiment of the present application, when detecting whether a non-road engine has a fault, based on a self-learning technology, after acquiring target working condition data, the target sensor parameter and the target fuel injection amount in the working condition data are compared respectively with the working condition data to be iterated pre-stored in the corresponding computer device, so as to compare the fuel injection amounts of the engine under the same state and different specific working conditions, and further determine whether the engine has a fault. In the fault detection process, the state of the fuel injector is judged according to the fuel quantity comparison of the fuel injector under the same state and different time periods. The fuel injector state judgment by using the fuel quantity is more visual, pre-calibration before delivery is not needed, the time ductility is eliminated, and the running state of the engine is visually embodied.
FIG. 3 illustrates a process diagram of a method for performing off-road engine fault detection based on injector oil mass consistency provided by an exemplary embodiment of the present application, the process comprising:
step 301, acquiring sensor parameters read by an ECU and power displayed by a controller.
In the process, the sensor parameters and the power displayed by the controller are information comprising a rotating speed domain, a rail pressure domain, a power domain and working condition data. In the present embodiment, data collection is performed by an electronic control unit (Electronic Control Unit, ECU).
And 302, performing a rotation speed domain, a rail pressure domain and a power domain matching judgment.
The process is the determination process of the current working condition of the engine.
When the result is a match, step 303 is executed, and when the result is a mismatch, the injector self-learning and the oil amount consistency judgment are not performed.
In one example, the rotation speed domain is 700 r/min+/-5 r/min, the rail pressure domain is 450+/-15 bar, the power domain is 30 kW+/-5 kW, the self-learning requirement of the oil injector is met in the range of the self-learning requirement, and then the temperature of the engine cooling liquid, the pressure of engine oil, the temperature of engine oil and the oil injection amount in the self-learning time under the current running working condition of the engine are recorded.
In step 303, the engine operating conditions, engine sensing parameters and fuel injection quantity are recorded.
The process is the process of acquiring the current working condition of the engine by the computer equipment.
And 304, judging the cooling liquid temperature, the engine oil pressure and the engine oil temperature.
When the acquaintance request is determined, step 305 is executed, and when the acquaintance request is not determined, step 306 is executed.
In step 305, oil mass acquaintance request judgment is performed.
And 306, saving the working condition of the engine, the parameters of the engine sensor and the fuel injection quantity.
After passing the oil quantity request judgment, step 307 is executed, and after not passing the oil quantity acquaintance request judgment, step 308 is executed,
Corresponding to the example shown in step 302, the comparison is made with the previous recorded data of coolant temperature, oil pressure, oil temperature and oil injection quantity under the same working condition, and the sensor parameters are compared with the current sensor parameters: the temperature of the cooling liquid is 20-40 ℃, the pressure of engine oil is 400-550 kPa, and the temperature of engine oil is 10-30 ℃; oil injection quantity contrast: the average value of the last oil injection quantity reading is 15mg/cyc, the average value of the current oil injection quantity reading is 29mg/cyc, the oil quantity obtained by checking the speed/power-oil quantity table by the speed and the power is 20mg/cyc, the coefficient obtained by checking the speed/power-coefficient table takes positive and negative values to be +/-50%, and the oil quantity obtained by multiplying and adding the table is 10-30 mg/cyc; the oil mass deviation range is 15% obtained by checking an oil mass-coefficient table of the average oil mass value, and if the actual deviation is 63% or more than 15%, the oil mass consistency alarm is carried out.
And 308, alarming the oil mass consistency.
The process is an alarm process performed by the computer program after determining that the target engine has faults.
The process is the process of resetting the self-learning function after the problem is processed.
FIG. 4 illustrates a schematic diagram of modules that execute a method of off-road engine fault detection based on injector oil mass consistency, as provided by an exemplary embodiment of the present application. The module consists of an engine operation monitoring module and an oil quantity consistency diagnosis module, and executes the non-road engine fault detection method based on the oil quantity consistency of the oil injector in the embodiment of the application together. In the embodiment of the present application, the engine operation monitoring module 401 is configured to acquire parameters by acquiring data through the sensor during the working process of the off-road engine. The oil consistency diagnostic module 402 is configured to process data and generate results based on the data obtained by the engine operation monitoring module. In the embodiment of the application, the engine operation monitoring module and the oil consistency diagnosis module are all implemented as different program segments in a computer program.
The foregoing description of the preferred embodiments is merely exemplary in nature and is not intended to limit the utility model, but is intended to cover various modifications, substitutions, improvements, and alternatives falling within the spirit and principles of the utility model.
Claims (6)
1. A method for detecting a fault of an off-road engine based on consistency of oil injection amounts of oil injection machines, wherein the method is applied to computer equipment and comprises the following steps:
acquiring target working condition data of a target engine in a target time period, wherein the target working condition data comprises target sensor parameters and target fuel injection quantity, and the sensor parameters comprise at least one of cooling liquid temperature, engine oil pressure, engine oil temperature, engine air inlet temperature and boost pressure;
comparing the target working condition data with working condition data to be iterated to obtain a similarity comparison result, wherein the similarity comparison result is used for indicating the oil mass deviation range of the target working condition data and the working condition data to be iterated, the similarity comparison result comprises a sensor parameter comparison sub-result and an oil injection quantity comparison sub-result, and the working condition data to be iterated comprises a sensor parameter to be iterated and an oil injection quantity to be iterated;
determining that the target engine has a fault in response to the sensor parameter comparison sub-result indicating that the target sensor parameter is similar to the sensor parameter to be iterated and the fuel injection quantity comparison sub-result indicating that the target fuel injection quantity is dissimilar to the fuel injection quantity to be iterated;
responding to the sensor parameter comparison sub-result to indicate that the target sensor parameter is similar to the sensor parameter to be iterated, and the fuel injection quantity comparison sub-result to indicate that the target fuel injection quantity is similar to the fuel injection quantity to be iterated, and replacing the working condition data to be iterated with the target working condition data;
and responding to the sensor parameter comparison sub-result to indicate that the target sensor parameter is dissimilar to the sensor parameter to be iterated, and recording the target working condition data as working condition data to be iterated.
2. The method of claim 1, wherein the obtaining target operating condition data for the target engine over the target period of time comprises:
acquiring a rotating speed domain, a rail pressure domain and a power domain of the target engine in the target time period, wherein the rotating speed domain indicates a rotating speed numerical range of the target engine in the target time period, the rail pressure domain indicates a rail pressure numerical range of the target engine in the target time period, and the power domain indicates a power range of the target engine in the target time period;
performing self-learning requirement matching based on the power domain, the rail pressure domain and the rotating speed domain, wherein the self-learning requirement matching is used for comparing the power domain with a preset power domain, comparing the rail pressure domain with a preset rail pressure domain and comparing the rotating speed domain with a preset rotating speed domain;
and responding to the self-learning requirement matching, and acquiring target working condition data of the target engine in a target time period.
3. The method according to claim 2, wherein the method further comprises:
determining a working condition of the target engine based on the rotational speed domain, the rail pressure domain and the power domain, wherein the working condition comprises at least one of idle load, 10% rated power working condition, 25% rated power working condition, 50% rated power working condition, 75% rated power working condition and rated power working condition;
and determining the working condition data to be iterated based on the working condition of the target engine.
4. The method of claim 1, wherein after the determining that the target engine is faulty, further comprising:
and sending an alarm signal, wherein the alarm signal is used for indicating that the target engine has faults, and the faults are characterized by inconsistent oil quantity.
5. The method of claim 4, further comprising, after the sending the alarm signal:
receiving a working condition updating signal, wherein the working condition updating signal is used for indicating that the target engine finishes part repair;
and resetting working condition data to be iterated.
6. The method of claim 5, wherein the component replacement comprises at least one of a fuel injector replacement, a valve lash adjustment, and a supercharger fault repair.
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| JP2001329894A (en) * | 2000-05-19 | 2001-11-30 | Denso Corp | Fuel system abnormality diagnostic device for internal combustion engine |
| DE102011005981B4 (en) * | 2011-03-23 | 2022-06-02 | Robert Bosch Gmbh | Method for determining a change in a control amount of an injector of an internal combustion engine |
| CN103061942A (en) * | 2013-02-05 | 2013-04-24 | 中国第一汽车股份有限公司无锡油泵油嘴研究所 | Device and method for detecting consistency of flow rate of control valve component of oil injector |
| CN104481769B (en) * | 2014-12-03 | 2017-03-01 | 中国第一汽车股份有限公司无锡油泵油嘴研究所 | A kind of conforming inline diagnosis method of common-rail injector |
| CN108361139B (en) * | 2018-01-29 | 2020-08-25 | 中国第一汽车股份有限公司 | Fuel injector small oil quantity control method |
| CN110985224B (en) * | 2019-12-16 | 2022-08-23 | 潍柴动力股份有限公司 | Method and system for judging working state of oil sprayer at initial starting stage of diesel engine |
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