CN110823474B - Fuel system leakage degree evaluation method and storage medium - Google Patents
Fuel system leakage degree evaluation method and storage medium Download PDFInfo
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- CN110823474B CN110823474B CN201910927969.6A CN201910927969A CN110823474B CN 110823474 B CN110823474 B CN 110823474B CN 201910927969 A CN201910927969 A CN 201910927969A CN 110823474 B CN110823474 B CN 110823474B
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- G01M3/00—Investigating fluid-tightness of structures
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Abstract
The invention discloses a fuel system leakage degree evaluation method and a storage medium, belonging to the technical field of vehicle fault detection, wherein the method comprises the following steps: s1, acquiring a plurality of groups of sample data, wherein the sample data comprises a pressure value, a rotating speed value and an oil injection quantity value; s2, determining a current working condition interval according to the rotating speed value and the oil injection quantity value; s3, acquiring multiple groups of normal data in the current working condition interval, wherein the normal data comprise normal pressure values, normal rotating speed values and normal oil injection quantity values; s4, processing the multiple groups of sample data and the multiple groups of normal data simultaneously, and determining a first central value corresponding to the multiple groups of sample data and a second central value corresponding to the multiple groups of normal data; and S5, estimating the leakage degree of the fuel system according to the first central value and the second central value. The leakage condition of the fuel system is not required to be detected under a specific working condition, and the efficiency of detecting the leakage condition of the fuel system is improved.
Description
Technical Field
The invention relates to the technical field of vehicle fault detection, in particular to a fuel system leakage degree evaluation method and a storage medium.
Background
Fuel systems typically include a source of pressurized fuel, fuel injectors, and a distribution system for directing the pressurized fuel from the source to the fuel injectors. The safety of the fuel system affects the safety of the vehicle, and therefore, fault detection of the fuel system is required to ensure the safety of the fuel system.
In the prior art, the principle adopted when judging whether a fuel system leaks is generally as follows: the rail pressure attenuation is detected under the working condition that an oil pump of the pressurized oil source stops oil delivery, and when the difference between the rail pressure attenuation value and the historical record value under the current working condition is detected to be larger, the probability of leakage of the fuel oil system is considered to be larger.
Therefore, in the prior art, the judgment of whether the fuel system leaks needs to be performed under a specific working condition (such as the working condition that the oil pump stops delivering oil), so that the normal use of the fuel system is influenced, and the leakage condition cannot be effectively evaluated.
Disclosure of Invention
The invention aims to provide a fuel system leakage degree evaluation method and a storage medium, which do not need to detect the leakage condition of the fuel system under a specific working condition and improve the detection efficiency of the leakage condition of the fuel system.
As the conception, the technical scheme adopted by the invention is as follows:
a fuel system leakage degree evaluation method comprises the following steps:
s1, acquiring a plurality of groups of sample data, wherein the sample data comprises a pressure value, a rotating speed value and an oil injection quantity value;
s2, determining a current working condition interval according to the rotating speed value and the oil injection quantity value;
s3, acquiring multiple groups of normal data in the current working condition interval, wherein the normal data comprise normal pressure values, normal rotating speed values and normal oil injection quantity values;
s4, processing the multiple groups of sample data and the multiple groups of normal data simultaneously, and determining a first central value corresponding to the multiple groups of sample data and a second central value corresponding to the multiple groups of normal data;
and S5, estimating the leakage degree of the fuel system according to the first central value and the second central value.
Preferably, step S4 includes:
s41, determining a first principal component and a second principal component of the multiple groups of sample data and the multiple groups of normal data simultaneously by adopting a principal component analysis method;
s42, determining a first sample principal component value and a second sample principal component value of each group of sample data according to the first principal component and the second principal component, and determining a first normal principal component value and a second normal principal component value of each group of normal data;
s43, determining the first central value according to the plurality of first sample principal component values and the plurality of second sample principal component values;
and S44, determining the second central value according to the plurality of first normal main component values and the plurality of second normal main component values.
Preferably, step S43 includes:
and S431, processing the plurality of first sample principal component values and the plurality of second sample principal component values by adopting a clustering algorithm, and determining the first central value.
Preferably, step S44 includes:
s441, processing the plurality of first normal principal component values and the plurality of second normal principal component values by adopting a clustering algorithm, and determining the second central value.
Preferably, step S5 includes:
s51, establishing a first coordinate system, wherein the horizontal axis of the first coordinate system is the first principal component value, and the vertical axis of the first coordinate system is the second principal component value;
s52, marking a first central point corresponding to the first central value and a second central point corresponding to the second central value on the first coordinate system;
and S53, estimating the leakage degree of the fuel system according to the first distance between the first central point and the second central point.
Preferably, the degree of leakage of the fuel system is positively correlated to a first distance between the first center point and the second center point.
Preferably, after step S44, the method further comprises:
s6, determining whether the fuel system has a leakage fault according to the first sample principal component value, the second sample principal component value and the second central value;
and S46, when the fuel system has a leakage fault, giving out a fault alarm.
Preferably, step S6 includes:
s61, establishing a second coordinate system, wherein the horizontal axis of the second coordinate system is the first principal component value, and the vertical axis of the second coordinate system is the second principal component value;
s62, labeling, according to the first sample principal component value and the second sample principal component value corresponding to each sample data, a sample point corresponding to each sample data on the second coordinate system;
s63, marking a second central point corresponding to the second central value on the second coordinate system;
and S64, determining whether the fuel system has a leakage fault according to the second distance between each sample point and the second central point.
Preferably, step S64 includes:
s641, determining a second distance between each sample point and the second central point;
s642, processing the second distance by adopting a sealing performance formula, and determining a sealing parameter;
s643, when the sealing parameter is smaller than a sealing parameter threshold value, determining that the fuel system has a leakage fault;
wherein the sealing performance formula is
H denotes a sealing parameter, N denotes the number of groups of sample data, i denotes the number of sample data, D denotes the number of sample data groupsiDenotes a second distance, DbDenotes the normal distance threshold, DtIndicating an anomaly threshold distance, δiRepresenting the confidence coefficient of the ith sample data.
A storage medium having stored thereon a computer program which, when executed by a fuel system leakage degree evaluation device, implements the fuel system leakage degree evaluation method described above.
The beneficial effects of the invention at least comprise:
the embodiment of the invention provides a method for evaluating the leakage degree of a fuel system, which comprises the steps of firstly obtaining a plurality of groups of sample data when the fuel system is in operation and a plurality of groups of normal data in a current working condition interval, then simultaneously processing the plurality of groups of sample data and the plurality of groups of normal data, determining a first central value and a second central value, and evaluating the leakage degree of the fuel system according to the first central value and the second central value. Compared with the prior art, the leakage condition of the fuel system does not need to be detected under a specific working condition, so that the normal use of the fuel system is not influenced, the efficiency of detecting the leakage condition of the fuel system is improved, and the leakage degree of the fuel system can be effectively evaluated.
Drawings
FIG. 1 is a flow chart of a method for estimating a leakage level of a fuel system according to an embodiment of the present invention;
FIG. 2 is a partial flow chart of a method for estimating a leakage level of a fuel system according to an embodiment of the present invention;
FIG. 3 is a partial flow chart of another method for estimating leakage level of a fuel system according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a first coordinate system according to an embodiment of the present invention;
FIG. 5 is a flow chart of another method for estimating a leakage level of a fuel system according to an embodiment of the present invention;
FIG. 6 is a flowchart of step S6 of a method for estimating a leakage level of a fuel system according to an embodiment of the present invention;
FIG. 7 is a flowchart illustrating step S64 of another method for estimating a leakage level of a fuel system according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a fuel system leakage degree evaluation device according to a second embodiment of the present invention;
FIG. 9 is a schematic structural diagram of a fuel system according to a third embodiment of the present invention.
In the figure:
1. a fuel tank; 2. a fuel fine filter; 3. a high pressure fuel pump; 4. a pressure relief valve; 5. a common rail pipe; 6. a fuel pressure sensor; 7. an oil injector; 8. an electronic control unit; 9. a sensor.
Detailed Description
In order to make the technical problems solved, the technical solutions adopted and the technical effects achieved by the present invention clearer, the technical solutions of the present invention are further described below by way of specific embodiments with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some but not all of the elements associated with the present invention are shown in the drawings.
Example one
Fig. 1 is a flowchart of a method for estimating a leakage degree of a fuel system according to an embodiment of the present invention, where the method is applicable to detecting and estimating a leakage of a fuel system, and the method may be executed by a fuel system leakage degree estimating apparatus, which may be implemented by software and/or hardware, and integrated in a vehicle or implemented by a computer. Specifically, as shown in fig. 1, the method for evaluating the leakage degree of the fuel system mainly comprises the following steps:
and S1, acquiring multiple groups of sample data, wherein the sample data comprises a pressure value, a rotating speed value and an oil injection quantity value.
The fuel system can be provided with a plurality of sensors, and the sensors can detect pressure values, rotating speed values, fuel injection quantity values and the like in the fuel system. Alternatively, the sensor may periodically detect the pressure value, the rotation speed value and the fuel injection quantity value of the fuel system, and at this time, the pressure value, the rotation speed value and the fuel injection quantity value detected at the same time may be used as a set of sample data. Multiple groups of sample data within a preset time length can be acquired for data processing.
Optionally, the rotation speed value in this embodiment is a rotation speed value of the engine, and the sample data may further include a pressure characteristic, a rotation speed characteristic, a cycle fuel injection amount of the engine, a pressure control input amount of the fuel system, and the like. That is, the sample data includes at least three data values of physical quantities. The pressure characteristic is defined as the variation of the pressure of the fuel system before and after the high-pressure oil pump supplies oil, and can be obtained by subtracting the pressure values before and after the oil supply is collected, or can be obtained by processing and post-processing the pressure signal which is collected instantly through a filter. The rotation speed characteristic is defined as the variation of the engine rotation speed before and after oil injection; the engine speed signal acquisition device can be obtained by subtracting the engine speed before and after oil injection, and can also be obtained by post-processing the instantaneously acquired speed signal through a filter.
And S2, determining the current working condition interval according to the rotating speed value and the fuel injection quantity value.
The working condition interval can be determined by the rotating speed value of a high-pressure fuel pump of the fuel system and the fuel injection quantity value of a fuel injector, and the fuel system can have a plurality of working condition intervals. Each operating mode interval may correspond to a rotation speed range and an oil injection amount range, for example, the operating mode in which the rotation speed range is 1000 rpm to 1500 rpm and the oil injection amount range is 10 mg per cylinder to 20 mg per cylinder may be used as one operating mode interval, and when the current rotation speed is 1200 rpm and the oil injection amount is 11 mg per cylinder, the current operating mode may be considered to belong to the operating mode interval.
And S3, acquiring multiple groups of normal data in the current working condition interval, wherein the normal data comprise normal pressure values, normal rotating speed values and normal fuel injection quantity values.
In this embodiment, the normal data refers to a pressure value, a rotation speed value, and an injection quantity value of the fuel system that are acquired when the fuel system normally operates within the working condition interval. Namely, the pressure value, the rotating speed value and the fuel injection quantity value when the fuel system does not leak under the working condition interval. In order to distinguish the pressure value, the rotation speed value and the fuel injection quantity value from those in the sample data, the pressure value, the rotation speed value and the fuel injection quantity value when no leakage occurs are referred to as a normal pressure value, a normal rotation speed value and a normal fuel injection quantity value. In addition, in order to improve the accuracy of the fault diagnosis method, the present embodiment obtains multiple sets of normal data and compares the normal data with sample data.
And S4, processing the multiple groups of sample data and the multiple groups of normal data simultaneously, and determining first central values corresponding to the multiple groups of sample data and second central values corresponding to the multiple groups of normal data.
The first central value can reflect the overall situation of a plurality of groups of sample data, namely, the overall data change trend in the pressure value, the rotating speed value and the fuel injection quantity value. In short, the first central value has a relatively large correlation with the parameter values (i.e., the pressure value, the rotation speed value, and the fuel injection quantity value) in each sample data. The second central value can reflect the overall situation of multiple groups of normal data, namely, the second central value can reflect the overall data change trend of the normal pressure value, the rotating speed value and the fuel injection quantity value. The second center value has a large correlation with the parameter value in each normal data.
And S5, estimating the leakage degree of the fuel system according to the first central value and the second central value.
The leakage degree of the fuel system may be evaluated according to a difference between the first central value and the second central value, or the first central value and the second central value may be represented by a plurality of data values, and at this time, the leakage degree of the fuel system may be evaluated according to a difference between one of the first central value and the second central value, which is not limited in this embodiment.
In summary, an embodiment of the present invention provides a method for evaluating a leakage degree of a fuel system, which includes obtaining multiple sets of sample data during operation of the fuel system and multiple sets of normal data in a current working condition interval, processing the multiple sets of sample data and the multiple sets of normal data at the same time, determining a first central value and a second central value, and evaluating the leakage degree of the fuel system according to the first central value and the second central value. Compared with the prior art, the leakage condition of the fuel system does not need to be detected under a specific working condition, so that the normal use of the fuel system is not influenced, the efficiency of detecting the leakage condition of the fuel system is improved, and the leakage degree of the fuel system can be effectively evaluated.
Alternatively, as shown in fig. 2, the step S4 may include:
and S41, determining the first principal component and the second principal component of the multiple groups of sample data and the multiple groups of normal data simultaneously by adopting a principal component analysis method.
The principal component analysis method is a multivariate statistical method for examining the correlation among a plurality of variables, and researches how to disclose the internal structure among the plurality of variables through a few principal components, namely, deriving the few principal components from the original variables so that the few principal components keep the information of the original variables as much as possible and are not correlated with each other. In general, multiple variables can be linearly combined to form a new composite index. The first principal component may be a linear combination of the variables, and the second principal component may be another linear combination of the variables. Alternatively, the first principal component may be in the nature of a relational expression, in which the independent variables are the pressure value, the rotation speed value, the fuel injection quantity value, and the like in the sample data, and the dependent variable is the first principal component value, that is, the pressure value, the rotation speed value, and the fuel injection quantity value in a set of sample data are brought into the relational expression to obtain a first principal component value. The nature of the second principal component may be the same as that of the first principal component, except that the coefficients before the argument are different, which is not described herein.
Preferably, the variance of the first principal component is greater than the variance of the second principal component, the variance of the first principal component is the largest among the variances of the linear combinations that can be composed of a plurality of variables, and the variance of the second principal component is only smaller than the variance of the first principal component, so that the first principal component and the second principal component can effectively reflect information in the plurality of variables.
And S42, determining a first sample principal component value and a second sample principal component value of each group of sample data according to the first principal component and the second principal component, and determining a first normal principal component value and a second normal principal component value of each group of normal data.
In this embodiment, after the first principal component and the second principal component of the sample data are determined, the pressure value, the rotation speed value, and the fuel injection quantity value in each group of sample data may be brought into the first principal component and the second principal component to obtain a first sample principal component value (i.e., the first principal component value) and a second sample principal component value corresponding to the sample data, and further obtain multiple groups of first sample principal component values and multiple groups of second sample principal component values corresponding to multiple groups of sample data. That is, each set of sample data corresponds to a first sample principal component value and a second sample principal component value.
And the normal pressure value, the normal rotating speed value and the normal oil injection quantity value in each group of normal data are substituted into the first principal component and the second principal component to obtain a first normal principal component value and a second normal principal component value corresponding to the normal data. And then obtaining a plurality of groups of first normal principal component values and a plurality of groups of second normal principal component values corresponding to the plurality of groups of normal data. That is, each set of normal data corresponds to a first normal principal component value and a second normal principal component value.
And S43, determining a first central value according to the plurality of first sample principal component values and the plurality of second sample principal component values.
As shown in fig. 3, step S43 may include:
and S431, processing the plurality of first sample principal component values and the plurality of second sample principal component values by adopting a clustering algorithm, and determining a first central value.
Optionally, the first center value may include two data, one of the two data may be used to represent the center of the plurality of first sample principal component values, and the other may represent the center of the plurality of second sample principal component values. For example, the first central value may include first data and second data, and the first data may be a mean value of a plurality of first sample principal component values or a value determined according to a preset algorithm; the second data may be a mean value of a plurality of second sample component values or a value determined according to a preset algorithm.
And S44, determining a second central value according to the plurality of first normal main component values and the plurality of second normal main component values.
Alternatively, as shown in fig. 3, step S44 may include:
s441, processing the plurality of first normal principal component values and the plurality of second normal principal component values by adopting a clustering algorithm, and determining a second central value.
Alternatively, two data may be included in the second central value, and one of the two data may be used to represent the center of the plurality of first normal principal component values and the other may represent the center of the plurality of second normal principal component values. For example, the second central value may include third data and fourth data, and the third data may be a mean value of a plurality of first normal principal component values or a value determined according to a preset algorithm; the fourth data may be a mean value of a plurality of second normal component values or a value determined according to a preset algorithm.
Still further, as shown in fig. 2, the step S5 includes:
and S51, establishing a first coordinate system, wherein the horizontal axis of the first coordinate system is the first principal component value, and the vertical axis of the first coordinate system is the second principal component value.
And S52, marking a first center point corresponding to the first center value and a second center point corresponding to the second center value on the first coordinate system.
As shown in fig. 4, the first data in the first center value may be used as an abscissa of the first center point, and the second data in the first center value may be used as an ordinate of the first center point, so as to label the first center point on the first coordinate system. Similarly, a third datum of the second center value may be used as an abscissa of the second center point, a fourth datum of the second center value may be used as an ordinate of the second center point, and the second center point may be labeled on the first coordinate system. This may be referred to as projecting the first center value into the first coordinate system and projecting the second center value into the first coordinate system.
In addition, as shown in fig. 4, the first sample principal component value and the second sample principal component value corresponding to the sample data, and the first normal principal component value and the second normal principal component value corresponding to all normal data may also be marked on the first coordinate system, that is, multiple sets of normal data are projected onto the first coordinate system, and multiple sets of sample data are projected into the coordinate system. In fig. 4, a plus sign indicates a projection of normal data, and a square black dot indicates a projection of sample data.
And S53, estimating the leakage degree of the fuel system according to the first distance between the first central point and the second central point.
Optionally, the degree of leakage of the fuel system is positively correlated to a first distance between the first and second center points. That is, the greater the distance between the first center point and the second center point, the more serious the leakage degree of the fuel system, the smaller the distance between the first center point and the second center point, the smaller the leakage degree of the fuel system, and when the first center point and the second center point coincide, it indicates that the fuel system is not leaked.
When the first distance between the first central point and the second central point is larger, the difference between the information of the data in the sample data and the information of the data in the normal data is larger, namely, the difference between the working state of the current fuel system and the normal working state is larger, so that the leakage condition of the fuel system can be evaluated to be more serious.
The method for evaluating the leakage degree of the fuel system provided by the embodiment can evaluate the leakage degree of the fuel system and can determine whether the fuel system has leakage faults or not. Specifically, as shown in fig. 5, after step S44, the method for estimating the degree of leakage of the fuel system may further include:
and S6, determining whether the fuel system has a leakage fault according to the first sample principal component value, the second sample principal component value and the second central value. If there is a leak failure, go to step S7; if there is no leak failure, step S8 is executed.
And S7, giving out a fault alarm.
S8, ending the flow
Further, as shown in fig. 6, step S6 may include:
and S61, establishing a second coordinate system, wherein the horizontal axis of the second coordinate system is the first principal component value, and the vertical axis of the second coordinate system is the second principal component value.
Optionally, the second coordinate system and the first coordinate system may be the same coordinate system, which is not limited in this embodiment.
And S62, marking the sample point corresponding to each sample data on the second coordinate system according to the first sample principal component value and the second sample principal component value corresponding to each sample data.
Wherein the sample point may be a projection of the sample data on the second coordinate system.
And S63, marking a second central point corresponding to the second central value on the second coordinate system.
And S64, determining whether the fuel system has a leakage fault according to the second distance between each sample point and the second central point.
As shown in fig. 7, step S64 may include:
s641, determining a second distance between each sample point and the second center point.
And S642, processing the second distance by adopting a sealing performance formula, and determining a sealing parameter.
S643, judging whether the sealing parameter is smaller than the sealing parameter threshold value, if so, executing a step S7; if not, step S8 is executed.
Wherein the sealing performance formula is
H denotes a sealing parameter, N denotes the number of groups of sample data, i denotes the number of sample data, D denotes the number of sample data groupsiDenotes a second distance, DbDenotes the normal distance threshold, DtIndicating an anomaly threshold distance, δiRepresenting the confidence coefficient of the ith sample data.
Optionally, when aiming at different engines, the sealing performance formula can further consider the sealing performance of the engine, and in this case, the sealing performance formula can be used for the engine
Wherein β represents a sealing performance correction coefficient of the engine, which can be selected according to experiments or specifications of the engine.
In addition, the above-mentioned normal distance threshold value DbAnd an anomaly threshold distance DtThe value of (a) can be selected according to the current working condition interval.
Illustratively, as shown in table 1, the present embodiment provides 10 sample data. The operating condition interval of the 10 sample data and the confidence coefficient of the ith sample data are shown in table 1. The specific calculation results are shown in table 1. The value of H is then calculated to be 0.450848, where β is 1, according to the above formula. It is seen that the sealing performance is remarkably lowered.
TABLE 1
| Number i | Di | Interval of operating mode | Dt | Db | δi |
| 1 | 2.4 | |
2.5 | 0.5 | 0.1 |
| 2 | 3.0 | |
2.5 | 0.5 | 0.1 |
| 3 | 2.7 | |
2.5 | 0.5 | 0.1 |
| 4 | 1.9 | |
2.1 | 1.2 | 0.15 |
| 5 | 1.8 | |
2.1 | 1.2 | 0.15 |
| 6 | 1.7 | |
2.1 | 1.2 | 0.15 |
| 7 | 2.0 | |
1.8 | 0.8 | 0.08 |
| 8 | 2.2 | |
1.8 | 0.8 | 0.08 |
| 9 | 1.7 | |
1.8 | 0.8 | 0.08 |
| 10 | 1.0 | |
1.6 | 0.3 | 0.04 |
The determination of the leak detection threshold value may be obtained by experiment or may be set by itself empirically. It is experimentally determined that the actual operation of a faulty engine can be performed using a high pressure leak in the fuel system, the fuel is calculated for its fuel system tightness by the aforementioned steps and then set as the leak detection threshold. A threshold value, here 0.6 for its leak detection threshold value, may also be set by itself empirically. Due to HjThe result is 0.450848, less than 0.6, so the jth engine fuel system high pressure seal failure can be identified as a leak failure.
The method for evaluating the leakage degree of the fuel system can effectively detect and diagnose the leakage condition of the high-pressure component of the fuel system of the engine under various working conditions and quantitatively evaluate the leakage degree. The high-voltage component can be timely informed to relevant maintenance personnel to timely overhaul the high-voltage component when the high-voltage component slightly leaks, and the further deterioration of leakage faults is avoided, so that unnecessary loss is caused. The method is suitable for most fuel systems, including common high-pressure common rail systems, monomer pump systems, pump nozzle systems and the like.
Example two
The present embodiment provides a fuel system leakage degree evaluation device, as shown in fig. 8, including:
the first obtaining module 81 is configured to obtain multiple sets of sample data, where the sample data includes a pressure value, a rotation speed value, and an oil injection quantity value.
The determining module 82 is used for determining a current working condition interval according to the rotating speed value and the oil injection quantity value;
and the second obtaining module 83 is configured to obtain multiple sets of normal data in the current working condition interval, where the normal data includes a normal pressure value, a normal rotation speed value, and a normal fuel injection quantity value.
The processing module 84 is configured to process the multiple sets of sample data and the multiple sets of normal data at the same time, and determine a first central value corresponding to the multiple sets of sample data and a second central value corresponding to the multiple sets of normal data.
And the evaluation module 85 is used for evaluating the leakage degree of the fuel system according to the first central value and the second central value.
The fuel system leakage degree evaluation device provided by the second embodiment of the invention can be used for executing the fuel system leakage degree evaluation method provided by the first embodiment, and has corresponding functions and beneficial effects.
EXAMPLE III
For further understanding of the content of the present embodiment, the present embodiment provides a schematic structural diagram of a fuel system, and as shown in fig. 9, the fuel system may include a fuel tank 1, a fine fuel filter 2, a high-pressure fuel pump 3, a pressure relief valve 4, a common rail pipe 5, a fuel pressure sensor 6, a fuel injector 7, an electronic control unit 8 and a sensor 9.
The working principle and the flow of the fuel oil system are generally as follows: after the fuel in the oil tank 1 with rough filtration is sucked into the fuel fine filter 2, a part of the fuel is pressurized in a plunger cavity of the high-pressure fuel pump 3 to form high-pressure fuel and flows into the common rail pipe 5 from a pump outlet valve through a high-pressure fuel pipe; and the other part of the fuel flows back to the fuel tank together with the return oil of the fuel injector 7 from the overflow valve on the high-pressure fuel pump 3. The injectors 7 inject high-pressure fuel into the combustion chambers of the cylinders of the engine under the drive of the electronic control unit 8. And a fuel pressure sensor 6 is arranged at one end of the common rail pipe 5, and the fuel pressure condition in the common rail pipe 5 is measured in real time and the result is transmitted to an electronic control unit 8. When the fuel pressure exceeds the maximum allowable pressure, the pressure relief valve 4 is opened, and the fuel pressure in the common rail pipe 5 is rapidly reduced to be within a safety range, so that the safety of the whole system is ensured. The electronic control unit 8 collects information in each sensor 9, sends out an accurate current pulse signal through a built-in control strategy and data, and enables the corresponding common rail pump electromagnetic valve, the corresponding oil injector electromagnetic valve and the like to generate electromagnetic force so as to drive the corresponding actuator to act, and enables the oil supply quantity, the rail pressure, the oil injection angle and the oil injection quantity to be fed back and adjusted according to requirements. The sensor includes: a rotation speed sensor, a common rail pressure sensor, a crank angle sensor (or a cam shaft angle sensor), an accelerator pedal sensor, and the like; the actuator drive signal of the electronic control unit 8 includes: the electromagnetic valve of the oil injector and the electromagnetic valve of the high-pressure fuel pump drive signals. The high-pressure components of the fuel system comprise a high-pressure fuel pump 3, a common rail pipe 5, a pressure release valve 4, a fuel pressure sensor 6, a fuel injector 7 and pipelines for connecting the components.
Example four
The present embodiment provides a storage medium on which a computer program is stored, which, when executed by a fuel system leakage degree evaluation apparatus, implements a fuel system leakage degree evaluation method as in the first embodiment of the present invention described above.
Of course, the storage medium provided by the embodiment of the present invention includes computer-executable instructions, and the computer-executable instructions are not limited to the operations in the fuel system leakage degree evaluation method described above, and may also perform related operations in the fuel system leakage degree evaluation method provided by the embodiment of the present invention, and have corresponding functions and advantages.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, and the computer software product may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes instructions for enabling a computer device (which may be a robot, a personal computer, a server, or a network device) to execute the method for estimating the leakage degree of the fuel system according to the embodiments of the present invention.
The foregoing embodiments are merely illustrative of the principles and features of this invention, which is not limited to the above-described embodiments, but rather is susceptible to various changes and modifications without departing from the spirit and scope of the invention, which changes and modifications are within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A fuel system leakage degree evaluation method is characterized by comprising the following steps:
s1, acquiring a plurality of groups of sample data, wherein the sample data comprises a pressure value, a rotating speed value and an oil injection quantity value;
s2, determining a current working condition interval according to the rotating speed value and the oil injection quantity value;
s3, acquiring multiple groups of normal data in the current working condition interval, wherein the normal data comprise normal pressure values, normal rotating speed values and normal oil injection quantity values;
s4, processing the multiple groups of sample data and the multiple groups of normal data simultaneously, and determining a first central value corresponding to the multiple groups of sample data and a second central value corresponding to the multiple groups of normal data;
s5, estimating the leakage degree of the fuel system according to the first central value and the second central value;
wherein, step S4 includes:
s41, determining a first principal component and a second principal component of the multiple groups of sample data by adopting a principal component analysis method, and determining a first principal component and a second principal component of the multiple groups of normal data by adopting a principal component analysis method;
s42, determining a first sample principal component value and a second sample principal component value of each group of sample data according to the first principal component and the second principal component of the multiple groups of sample data, and determining a first normal principal component value and a second normal principal component value of each group of normal data according to the first principal component and the second principal component of the multiple groups of normal data;
s43, processing the plurality of first sample principal component values and the plurality of second sample principal component values by adopting a clustering algorithm, and determining a first central value;
and S44, processing the plurality of first normal principal component values and the plurality of second normal principal component values by adopting a clustering algorithm, and determining a second central value.
2. The method according to claim 1, wherein step S5 includes:
s51, establishing a first coordinate system, wherein the horizontal axis of the first coordinate system is a first principal component value, and the vertical axis of the first coordinate system is a second principal component value;
s52, marking a first central point corresponding to the first central value and a second central point corresponding to the second central value on the first coordinate system;
and S53, estimating the leakage degree of the fuel system according to the first distance between the first central point and the second central point.
3. The method of claim 2, wherein a degree of leakage of the fuel system is positively correlated to a first distance between the first center point and the second center point.
4. The method according to claim 1, wherein after step S44, the method further comprises:
s45, determining whether the fuel system has a leakage fault according to the first sample principal component value, the second sample principal component value and the second central value;
and S46, when the fuel system has a leakage fault, giving out a fault alarm.
5. The method according to claim 4, wherein step S45 includes:
s451, establishing a second coordinate system, wherein a horizontal axis of the second coordinate system is a first main component value, and a vertical axis of the second coordinate system is a second main component value;
s452, according to the first sample principal component value and the second sample principal component value corresponding to each group of sample data, marking the sample point corresponding to each group of sample data on the second coordinate system;
s453, marking a second central point corresponding to the second central value on the second coordinate system;
s454, determining whether the fuel system has a leakage fault according to the second distance between each sample point and the second central point.
6. The method of claim 5, wherein step S454 comprises:
s4541, determining a second distance between each sample point and the second central point;
s4542, processing the second distance by adopting a sealing performance formula, and determining sealing parameters;
s4543, when the sealing parameter is smaller than the sealing parameter threshold value, determining that the fuel system has a leakage fault;
wherein the sealing performance formula is
H denotes a sealing parameter, N denotes the number of groups of sample data, i denotes the number of sample data, D denotes the number of sample data groupsiDenotes a second distance, DbDenotes the normal distance threshold, DtIndicating an anomaly threshold distance, δiRepresenting the confidence coefficient of the ith sample data.
7. A storage medium having stored thereon a computer program, characterized in that the program, when executed by a fuel system leakage degree evaluation device, implements a fuel system leakage degree evaluation method as claimed in any one of claims 1 to 6.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0674123A (en) * | 1992-08-26 | 1994-03-15 | Nippondenso Co Ltd | Common rail pressure detecting device |
| CN102245437A (en) * | 2008-12-10 | 2011-11-16 | 本田技研工业株式会社 | Vehicle failure diagnostic device |
| CN102748181A (en) * | 2012-08-01 | 2012-10-24 | 潍柴动力股份有限公司 | Method and device for diagnosing faults of fuel injector |
| CN103092191A (en) * | 2013-01-09 | 2013-05-08 | 中国第一汽车股份有限公司无锡油泵油嘴研究所 | Rail pressure simulation value generation method and combustion motor fuel system fault diagnosis method applying rail pressure simulate value |
| CN106126385A (en) * | 2016-06-14 | 2016-11-16 | 电子科技大学 | A kind of unit exception real-time detection method based on synchronous data flow compression |
| CN106156485A (en) * | 2016-06-16 | 2016-11-23 | 广州供电局有限公司 | Method for diagnosing fault of power transformer and device |
| CN110060374A (en) * | 2019-04-19 | 2019-07-26 | 中国航空无线电电子研究所 | A kind of aircraft fuel system method for detecting abnormality and device |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100343502C (en) * | 2005-07-25 | 2007-10-17 | 无锡油泵油嘴研究所 | Common-rail fuel oil injection system injector failure diagnosing method |
| KR101178235B1 (en) * | 2010-10-15 | 2012-08-30 | 한국전력공사 | Prediction and fault detection method and system for performance monitoring of plant instruments using principal component analysis, response surface method, Fuzzy Support Vector Regression and Generalized Likelihood Ratio Test |
| CN103590915B (en) * | 2013-11-22 | 2016-01-06 | 镇江恒驰科技有限公司 | A kind of method utilizing fuel pressure to calculate diesel engine cycle fuel injection quantity in real time |
| CN105003372B (en) * | 2015-06-15 | 2017-05-31 | 天津大学 | A kind of distributive value method for diagnosing faults based on the observation of rail pressure waveform feature parameter |
| CN106844922A (en) * | 2017-01-11 | 2017-06-13 | 重庆邮电大学 | The engine fire fault diagnosis method with manifold learning is estimated based on cylinder pressure |
| CN107390086A (en) * | 2017-06-30 | 2017-11-24 | 昆明理工大学 | A kind of list based on principal component analysis SVMs fault recognition method forever |
-
2019
- 2019-09-27 CN CN201910927969.6A patent/CN110823474B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0674123A (en) * | 1992-08-26 | 1994-03-15 | Nippondenso Co Ltd | Common rail pressure detecting device |
| CN102245437A (en) * | 2008-12-10 | 2011-11-16 | 本田技研工业株式会社 | Vehicle failure diagnostic device |
| CN102748181A (en) * | 2012-08-01 | 2012-10-24 | 潍柴动力股份有限公司 | Method and device for diagnosing faults of fuel injector |
| CN103092191A (en) * | 2013-01-09 | 2013-05-08 | 中国第一汽车股份有限公司无锡油泵油嘴研究所 | Rail pressure simulation value generation method and combustion motor fuel system fault diagnosis method applying rail pressure simulate value |
| CN106126385A (en) * | 2016-06-14 | 2016-11-16 | 电子科技大学 | A kind of unit exception real-time detection method based on synchronous data flow compression |
| CN106156485A (en) * | 2016-06-16 | 2016-11-23 | 广州供电局有限公司 | Method for diagnosing fault of power transformer and device |
| CN110060374A (en) * | 2019-04-19 | 2019-07-26 | 中国航空无线电电子研究所 | A kind of aircraft fuel system method for detecting abnormality and device |
Non-Patent Citations (2)
| Title |
|---|
| 一种共轨喷油器故障诊断策略开发;马超等;《现代车用动力》;20160831(第3期);全文 * |
| 主成分分析法在机车柴油机故障诊断中的应用;王俊国等;《华中科技大学学报》;20010731;第29卷(第7期);全文 * |
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