CN114658542A - Non-road engine fault detection method based on oil quantity consistency of oil injector - Google Patents
Non-road engine fault detection method based on oil quantity consistency of oil injector Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 22
- 239000000446 fuel Substances 0.000 claims abstract description 65
- 238000002347 injection Methods 0.000 claims abstract description 57
- 239000007924 injection Substances 0.000 claims abstract description 57
- 238000000034 method Methods 0.000 claims abstract description 56
- 239000003921 oil Substances 0.000 claims description 70
- 239000010705 motor oil Substances 0.000 claims description 10
- 239000002826 coolant Substances 0.000 claims description 5
- 230000008439 repair process Effects 0.000 claims description 5
- 230000004044 response Effects 0.000 claims description 5
- 230000008569 process Effects 0.000 abstract description 28
- 238000003745 diagnosis Methods 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 239000000110 cooling liquid Substances 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
- 238000010276 construction Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000005507 spraying 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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- 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 quantity consistency of an oil sprayer, 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 to-be-iterated working condition data to obtain a similarity comparison result; and responding 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 not similar to the fuel injection quantity to be iterated, and determining that the target engine has a fault. And in the fault detection process, judging the state of the oil sprayer according to the oil quantity comparison of the oil sprayer under the same state and different time periods. The oil quantity is used for judging the state of the oil sprayer more visually, pre-calibration before delivery is not needed, the 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 non-road vehicles and other industrial machinery, non-road engines play an important role as driving members thereof. Since the operating state of the engine has a great correlation with the operating efficiency of the industrial machinery, it is necessary to detect and process the engine failure in real time. The working state of an 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 usually carried out by combining the oil quantity of the injector.
In the related art, the fuel injector may perform data acquisition before leaving a factory or in a preset state, and generate a corresponding table correspondingly, for example, form a table of rail pressure frequency domain amplitude values under different working conditions of the engine, and then determine the fuel quantity consistency of the fuel injector based on the table.
However, the oil amount determination method in the related art is sometimes ductile 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 quantity consistency of an oil sprayer, which can eliminate time-lapse property and visually embody the running state of an engine. The non-road engine fault detection method based on oil quantity consistency 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 coolant temperature, engine oil pressure, engine oil temperature, engine intake temperature and boost pressure;
comparing the target working condition data with the to-be-iterated working condition data to obtain a similarity comparison result, wherein the similarity comparison result is used for indicating the oil quantity deviation range of the target working condition data and the to-be-iterated working condition data, the similarity comparison result comprises a sensor parameter comparison sub-result and an oil injection quantity comparison sub-result, and the to-be-iterated working condition data comprises to-be-iterated sensor parameters and to-be-iterated oil injection quantity;
and responding 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 not similar to the fuel injection quantity to be iterated, and determining that the target engine has a fault.
In an optional 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 parameters are not similar to the parameters of the sensor to be iterated, and recording target working condition data as the working condition data to be iterated.
In an alternative embodiment, obtaining target operating condition data for a target engine over a target time period comprises:
the method comprises the steps that a rotating speed domain, a rail pressure domain and a power domain of a target engine in a target time period are obtained, 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 matching through the self-learning requirement.
In an optional embodiment, the method further comprises:
determining the working condition of a target engine based on the rotating speed domain, the rail pressure domain and the power domain, wherein the working condition comprises at least one of idling no-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.
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 optional embodiment, after sending the alarm signal, the method further includes:
receiving a working condition updating signal, wherein the working condition updating signal is used for indicating that the target engine completes part repair;
and resetting the 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 repair.
The beneficial effect that technical scheme that this application provided brought includes at least:
when whether the non-road engine has faults or not is detected, on the basis of a self-learning technology, after target working condition data are obtained, corresponding to target sensor parameters and target fuel injection quantity in the working condition data, pre-stored working condition data to be iterated in computer equipment are respectively compared, so that engine fuel injection quantity comparison under the same state and different specific working conditions is carried out, and whether the engine has faults or not is determined. And in the fault detection process, judging the state of the oil sprayer according to the oil quantity comparison of the oil sprayer under the same state and different time periods. The oil quantity is used for judging the state of the oil sprayer more visually, pre-calibration before delivery is not needed, the ductility is eliminated, and the running state of the engine is visually embodied.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 illustrates a flow diagram of a method for off-road engine fault detection based on fuel injector fuel quantity consistency according to an exemplary embodiment of the present application;
FIG. 2 is a schematic flow chart diagram illustrating another method for off-road engine fault detection based on fuel injector consistency provided by an exemplary embodiment of the present application;
FIG. 3 is a process schematic illustrating a method for performing off-road engine fault detection based on fuel injector consistency provided by an exemplary embodiment of the present application;
FIG. 4 illustrates a block diagram of a method for performing an off-road engine fault detection based on fuel injector quantity consistency according to an exemplary embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, the following detailed description of the embodiments of the present application will be made with reference to the accompanying drawings.
First, the terms referred to in the embodiments of the present application will be briefly described:
by off-road engine is meant a generic term for engines used for other purposes than exclusively in automobiles. This term is commonly used by regulatory agencies to classify engines to control their emissions. . In the related art, at least one sensor is installed in the off-road engine to acquire parameters such as engine speed, coolant temperature, oil pressure, oil temperature, and the like. Alternatively, such sensors are not present in road engines, so the fault detection method to which the present application relates is used in off-road engines. Optionally, the engine of the present application includes at least one of an engine within a genset, an engine for a construction machine having a constant operating condition, and an engine for a marine machine.
Fig. 1 is a schematic flow chart of a method for detecting an off-road engine fault based on fuel injector fuel quantity consistency according to an exemplary embodiment of the present application, which is described by way of example as being applied to a computer device, and the method includes:
Optionally, the computer is connected to a sensor in the target engine to obtain 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 injected fuel quantity, the sensor parameter including at least one of a coolant temperature, an oil pressure, an oil temperature, an engine intake air temperature, and a boost pressure.
It should be noted that the target operating condition data described in the embodiments of the present application are 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 to directly acquire the target condition data.
And 102, comparing the target working condition data with the to-be-iterated working condition data to obtain a similarity comparison result.
In the embodiment of the application, the similarity comparison result is used for indicating the oil quantity deviation range of the target working condition data and the to-be-iterated working condition data, the similarity comparison result comprises a sensor parameter comparison sub-result and an oil injection quantity comparison sub-result, and the to-be-iterated working condition data comprises to-be-iterated sensor parameters and to-be-iterated oil injection quantity.
Optionally, in this embodiment of the application, based on a self-learning logic of the computer device, the condition data to be iterated is the condition data stored in the computer device, and the condition data indicates the sensor parameters and the fuel injection amount corresponding to the engine during operation in the previous normal operation time period. Under the condition, the target sensor parameters in the target working condition data are compared with the parameters of the sensor to be iterated, and the target fuel injection quantity is compared with the respective similarity of the fuel injection quantity to be iterated, so that the similarity between the target working condition data and the working condition data to be iterated is obtained.
And 103, responding to the sensor parameter comparison sub-result indicating that the target sensor parameter is similar to the parameter of the sensor to be iterated and the fuel injection quantity comparison sub-result indicating that the target fuel injection quantity is not similar to the fuel injection quantity to be iterated, and determining that the target engine has a fault.
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 parameter of the sensor to be iterated, the target fuel injection quantity is not similar to the fuel injection quantity to be iterated, and the data of the working condition to be iterated is determined when the engine operates normally, the engine operates normally under the working condition, but the fuel injection quantity is abnormal, so that the engine can be determined to have a fault.
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, according to the method provided by the embodiment of the application, when detecting whether the non-road engine has a fault, after obtaining target working condition data based on a self-learning technique, corresponding to target sensor parameters and target fuel injection quantity in the working condition data, the pre-stored working condition data to be iterated in the computer device are respectively compared, so as to compare the fuel injection quantity of the engine under the same state and different specific working conditions, and further determine whether the engine has the fault. And in the fault detection process, judging the state of the oil sprayer according to the oil quantity comparison of the oil sprayer under the same state and different time periods. The oil quantity is used for judging the state of the oil sprayer more visually, pre-calibration before delivery is not needed, the ductility is eliminated, and the running state of the engine is visually embodied.
Fig. 2 is a schematic flow chart of another method for detecting an off-road engine fault based on fuel injector quantity consistency according to an exemplary embodiment of the present application, which is described by way of example as being applied to a computer device, and includes:
In the embodiment of the application, the rotation speed domain indicates a rotation 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. Namely, the rotating speed domain can reflect the highest rotating speed and the lowest rotating speed of the engine in the target time period; the rail pressure area can show the highest rail pressure and the lowest rail pressure of the engine in a target time period; the power domain can represent the highest power and the lowest power of the engine in the target time period.
And 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 comparison process, the self-learning requirement matching is used for comparing the power domain with the 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. After the comparison, the computer device determines whether to record the current operating condition.
And step 203, responding to matching through self-learning requirements, and acquiring target working condition data of the target engine in a target time period.
Optionally, after matching through the self-learning requirement, it can be determined that the computer device will perform the oil quantity consistency detection. In this case, the computer device obtains the target condition data for the target time period.
And step 204, determining the working condition of the target engine based on the rotating speed domain, the rail pressure domain and the power domain.
The process from step 204 to step 205 is the determination process of the working condition data to be iterated.
Optionally, the operating condition data to be iterated needs to be selected based on the operating condition gear of the target engine. The working condition gear is determined by a rotating speed domain, a rail pressure domain and a power domain and comprises at least one of idle idling, a 10% rated power working condition, a 25% rated power working condition, a 50% rated power working condition, a 75% rated power working condition and a rated power working condition. That is, in the embodiment of the present application, different operating mode gears correspond to different pre-stored operating mode data to be iterated.
And step 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 herein.
It should be noted that in the embodiments of the present application, similar requirements are that the readings of the sensors are in the same range; the reading of the oil injection quantity is in the oil quantity fluctuation range, and the condition that 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 is met. The corresponding threshold is a threshold pre-stored 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 form of the threshold is not limited in the present application.
And step 207, responding 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 not similar to the fuel injection quantity to be iterated, and determining that the target engine has a fault.
The process is similar to the process shown in step 103, and is not described herein.
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.
And step 210, resetting the working condition data to be iterated.
In the post-process after determining that the engine has failed, steps 208-210, in a preferred embodiment, the computer device sends an alarm signal indicating that the engine has failed. The warning signal is used to indicate the presence of a fault and a form indicative of the fault in the target engine. After the alarm is sent out, a maintenance worker can maintain the engine, and after the maintenance is finished and the engine can normally run, the computer equipment can receive a working condition updating signal indicating the completion of the maintenance and reset the pre-stored working condition data to be iterated.
And step 211, 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, replacing the working condition data to be iterated with the target working condition data.
The comparison sub-result indicates the condition that the target sensor parameter is similar to the sensor parameter to be iterated and the target fuel injection quantity is similar to the iterative fuel injection quantity after the target working condition data is compared with the working condition data to be iterated, under the condition, the current running condition of the engine is similar to the working condition data stored in the computer system, and at the moment, data coverage is carried out.
And 212, responding to the sensor parameter comparison sub-result to indicate that the target sensor parameters are not similar to the parameters of the sensor to be iterated, and recording target working condition data as the working condition data to be iterated.
The comparison sub-result indicates the condition that the target sensor parameter is not similar to the parameter of the sensor to be iterated and the target fuel injection quantity is not similar to the iterative fuel injection quantity after the target working condition data is compared with the working condition data to be iterated. In this case, that is, under the same operating condition, the engine has an operating state dissimilar to the currently pre-stored data, and at this time, the target operating condition data is recorded.
In summary, according to the method provided by the embodiment of the present application, when detecting whether the non-road engine has a fault, based on the self-learning technique, after obtaining the target working condition data, the target sensor parameter and the target fuel injection quantity in the working condition data are respectively compared with the pre-stored working condition data to be iterated in the computer device, so as to compare the fuel injection quantities of the engine under the same state and different specific working conditions, and further determine whether the engine has a fault. And in the fault detection process, judging the state of the oil sprayer according to the oil quantity comparison of the oil sprayer under the same state and different time periods. The oil quantity is used for judging the state of the oil sprayer more visually, pre-calibration before delivery is not needed, the ductility is eliminated, and the running state of the engine is visually embodied.
FIG. 3 is a process diagram illustrating a method for performing off-road engine fault detection based on fuel injector quantity consistency according to an exemplary embodiment of the present application, the process comprising:
and step 301, acquiring sensor parameters read by the ECU and power displayed by the controller.
In the process, the sensor parameters and the power displayed by the controller are information including a rotating speed domain, a rail pressure domain, a power domain and working condition data. In the embodiment of the present application, data collection is performed by an Electronic Control Unit (ECU).
And step 302, judging matching of a rotating speed domain, a rail pressure domain and a power domain.
The process is a determination process for the current operating condition of the engine.
And when the result is matched, executing step 303, and when the result is not matched, not performing self-learning of the oil injector and judgment on the oil quantity consistency.
In one example, the rotating speed range is 700r/min +/-5 r/min, the rail pressure range is 450 +/-15 bar, the power range is 30kW +/-5 kW, the self-learning requirement of the oil injector is met within the self-learning requirement range, and the temperature of cooling liquid of the engine, the pressure of engine oil, the temperature of engine oil and the oil injection quantity in the self-learning time under the current operating condition of the engine are recorded.
And 303, recording the working condition of the engine, the sensing parameters of the engine and the fuel injection quantity.
The process is the process of acquiring the current working condition of the engine by the computer equipment.
And step 304, judging the identification requirements of the temperature of the cooling liquid, the pressure of the engine oil and the temperature of the engine oil.
When the judgment of the acquaintance requirement is passed, step 305 is executed, and when the judgment of the acquaintance requirement is not passed, step 306 is executed.
In step 305, a judgment is made for the oil quantity acquaintance requirement.
And step 306, storing the engine working condition, the engine sensor parameters and the fuel injection quantity.
When the oil amount requirement is passed, step 307 is executed, and when the oil amount identification requirement is not passed, step 308 is executed,
and 307, replacing the working condition of the engine, the parameters of the engine sensor and the fuel injection quantity.
The steps 304 to 307 are the working condition determination and oil quantity consistency determination processes shown in the embodiment of the present application.
Corresponding to the example shown in step 302, the coolant temperature, the oil pressure, the oil temperature, and the fuel injection quantity recorded data are compared under the same previous operating condition, and the sensor parameters are compared with the current sensor parameters: the temperature of the cooling liquid is within the range of 20-40 ℃, the pressure of the engine oil is within the range of 400-550 kPa, and the temperature of the engine oil is within the range of 10-30 ℃; and (3) comparing the fuel injection quantity: the average value of the readings of the last oil injection amount is 15mg/cyc, the average value of the readings of the current oil injection amount is 29mg/cyc, the oil amount obtained by looking up a table by checking the rotating speed/power-oil amount by the rotating speed and power is 20mg/cyc, the positive and negative values of a coefficient obtained by looking up the table by the rotating speed/power-coefficient are +/-50%, and the oil amount obtained by multiplying and looking up the table is 10-30 mg/cyc; the range of the oil quantity deviation is 15% obtained by checking an oil quantity-coefficient table according to the average oil quantity value, and the actual deviation 63% is larger than 15%, so that the oil quantity consistency alarm is carried out at the moment.
And step 308, alarming the consistency of the oil quantity.
The process is an alarm process carried out by a computer program after the target engine is determined to have a fault.
This process is the process of self-learning function reset after the problem is addressed.
FIG. 4 illustrates a block diagram of a method for performing an off-road engine fault detection based on fuel injector quantity consistency according to an exemplary embodiment of the present application. The module consists of an engine operation monitoring module and an oil quantity consistency diagnosis module, and the non-road engine fault detection method based on the oil quantity consistency of the oil sprayer in the embodiments of the application is executed together. In the embodiment of the present application, the engine operation monitoring module 401 is configured to acquire parameters by acquiring data through a sensor during a non-road engine operation process. The oil quantity consistency diagnosis module 402 is configured to process the data and generate a result based on the data acquired by the engine operation monitoring module. In the embodiment of the application, the engine operation monitoring module and the oil quantity consistency diagnosis module are implemented as different program segments in a computer program.
The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (7)
1. A non-road engine fault detection method based on fuel injector fuel quantity consistency is characterized in that 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 coolant temperature, engine oil pressure, engine oil temperature, engine intake temperature and boost pressure;
comparing the target working condition data with the to-be-iterated working condition data to obtain a similarity comparison result, wherein the similarity comparison result is used for indicating an oil quantity deviation range of the target working condition data and the to-be-iterated working condition data, the similarity comparison result comprises a sensor parameter comparison sub-result and an oil injection quantity comparison sub-result, and the to-be-iterated working condition data comprises to-be-iterated sensor parameters and to-be-iterated oil injection quantity;
and 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 not similar to the fuel injection quantity to be iterated.
2. The method of claim 1, further comprising:
in response to the sensor parameter comparator result indicating that the target sensor parameter is similar to the sensor parameter to be iterated and the fuel injection quantity comparator result indicating that the target fuel injection quantity is similar to the fuel injection quantity to be iterated, 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 not similar to the sensor parameter to be iterated, and recording the target working condition data as working condition data to be iterated.
3. The method of claim 1 or 2, 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 acquiring target working condition data of the target engine in a target time period in response to matching through the self-learning requirement.
4. The method of claim 3, further comprising:
determining the working condition of the target engine based on the rotating speed domain, the rail pressure domain and the power domain, wherein the working condition comprises at least one of idle idling, 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.
5. The method of claim 1 or 2, wherein after determining that the target engine has a fault, further comprising:
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.
6. The method according to claim 1 or 2, wherein after sending the alarm signal, further comprising:
receiving a working condition updating signal, wherein the working condition updating signal is used for indicating the target engine to finish part repair;
and resetting the working condition data to be iterated.
7. The method of claim 6, 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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| CN110985224A (en) * | 2019-12-16 | 2020-04-10 | 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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2022
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| JP2001329894A (en) * | 2000-05-19 | 2001-11-30 | Denso Corp | Fuel system abnormality diagnostic device for internal combustion engine |
| DE102011005981A1 (en) * | 2011-03-23 | 2012-09-27 | Robert Bosch Gmbh | Method for determining change in control quantity of injector of internal combustion engine of vehicle, involves detecting pressure drop in high-pressure accumulator during different time periods to determine change in control quantity |
| 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 |
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| CN110985224A (en) * | 2019-12-16 | 2020-04-10 | 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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