CN113448301A - Fault diagnosis method and system based on OBD system - Google Patents
Fault diagnosis method and system based on OBD system Download PDFInfo
- Publication number
- CN113448301A CN113448301A CN202010224969.2A CN202010224969A CN113448301A CN 113448301 A CN113448301 A CN 113448301A CN 202010224969 A CN202010224969 A CN 202010224969A CN 113448301 A CN113448301 A CN 113448301A
- Authority
- CN
- China
- Prior art keywords
- fault
- limit value
- lower limit
- preset
- upper limit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 100
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000001914 filtration Methods 0.000 claims description 26
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 8
- 230000001960 triggered effect Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 208000019901 Anxiety disease Diseases 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000036506 anxiety Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0267—Fault communication, e.g. human machine interface [HMI]
- G05B23/027—Alarm generation, e.g. communication protocol; Forms of alarm
Abstract
The invention provides a fault diagnosis method based on an OBD system, which comprises the following steps: acquiring characteristic variables of vehicle parts in real time, and comparing the acquired characteristic variables with a preset upper limit value and a preset lower limit value; if the characteristic variable is smaller than the lower limit value or larger than the upper limit value, a fault alarm is sent out; if the characteristic variable is greater than or equal to the lower limit value and less than or equal to the upper limit value, calculating the fault degree of the part according to the characteristic variable, the upper limit value and the lower limit value, and then judging whether the fault degree of the part meets the condition of sending out a fault early warning alarm according to a preset fault early warning strategy. Therefore, the system not only can timely alarm the fault when the fault occurs, but also can pre-diagnose the fault.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to a fault diagnosis method and system based on an OBD system.
Background
When automobile parts are in failure, particularly important parts such as a throttle valve and the like can cause damage to the drivability of the automobile, and the driving safety is seriously influenced. At present, an automotive EMS (Engine Management System) System is integrated with an On-Board Diagnostics (OBD) System, and can perform real-time fault diagnosis On vehicle power related parts. When a fault occurs, the OBD system can report a fault code in time to provide a reference basis for vehicle maintenance. However, current OBD systems can only detect a fault after it has occurred, and cannot make a pre-diagnosis of the fault. The pre-diagnosis means that the occurrence time or the occurrence possibility of a fault is predicted in advance by the characteristics of the relevant vehicle state signal before the fault occurs. The vehicle pre-diagnosis function is realized, so that vehicle owner anxiety can be reduced for preventing vehicle half-way breakdown, and application scenes such as service experience can be improved by the 4S store parts in advance.
Disclosure of Invention
The invention aims to provide a fault diagnosis method and system based on an OBD system, which aim to solve the problem that the conventional OBD system can only detect a fault after the fault occurs and cannot perform pre-diagnosis on the fault.
In order to solve the technical problem, the invention provides a fault diagnosis method based on an OBD system, which comprises the following steps:
collecting characteristic variables of vehicle parts in real time;
comparing the collected characteristic variables with a preset upper limit value and a preset lower limit value; if the characteristic variable is smaller than the lower limit value or larger than the upper limit value, a fault alarm is sent out;
if the characteristic variable is greater than or equal to the lower limit value and less than or equal to the upper limit value, calculating the fault degree of the part according to the characteristic variable, the upper limit value and the lower limit value, and then judging whether the fault degree of the part meets the condition of sending out a fault early warning alarm according to a preset fault early warning strategy.
Optionally, in the fault diagnosis method based on the OBD system, the preset fault early warning policy includes: if the fault degree of the part is continuously greater than a preset alarm lower limit for y times in x driving cycles, a fault early warning alarm is sent out, wherein x and y are natural numbers, x is greater than or equal to 1, and y is greater than 1; wherein x and y are changed depending on the number of times the failure diagnosis of the component part is diagnosed in one driving cycle.
Optionally, in the fault diagnosis method based on the OBD system, when the fault degree of the component is calculated according to the characteristic variable, the upper limit value, and the lower limit value, the following formula is adopted:
wherein, faultlvl represents the fault degree of the part, faultsig represents the characteristic variable, upperthres represents the upper limit value, and lowerthres represents the lower limit value.
Optionally, in the fault diagnosis method based on the OBD system, after the fault degree of the component is calculated, the method further includes:
and performing low-pass filtering processing on the calculated fault degree of the part, and then judging whether the fault degree of the part subjected to low-pass filtering processing meets the condition of sending out a fault early warning alarm according to a preset fault early warning strategy.
Optionally, in the fault diagnosis method based on the OBD system, when the calculated fault degree of the component is low-pass filtered, the following formula is adopted:
wherein, faultlvl represents the fault degree of the parts, faultlvlm is the fault degree after low-pass filtering, m is the coefficient of low-pass filtering, and n is the signal serial number of the discrete time sequence.
Optionally, in the fault diagnosis method based on an OBD system, the method further includes:
and if the characteristic variable is smaller than the lower limit value or larger than the upper limit value, outputting a first identifier to trigger sending out a fault alarm.
Optionally, in the fault diagnosis method based on an OBD system, the method further includes:
setting a fault degree valid bit, outputting a second identifier when the fault diagnosis of the part does not reach the diagnosis enabling condition, and outputting a third identifier when the fault diagnosis of the part reaches the diagnosis enabling condition, wherein the third identifier is used for triggering and comparing the characteristic variable of the fault diagnosis of the part with a preset upper limit value and a preset lower limit value.
Based on the same idea, the invention also provides a fault diagnosis system, which comprises: an on-board electronic controller and a cloud server, wherein,
the vehicle-mounted electronic controller is configured to acquire a characteristic variable of a vehicle part acquired in real time, compare the acquired characteristic variable with a preset upper limit value and a preset lower limit value, send a first identifier to the cloud server when the characteristic variable is smaller than the lower limit value or larger than the upper limit value, calculate a fault degree of the part according to the characteristic variable, the upper limit value and the lower limit value when the characteristic variable is larger than or equal to the lower limit value and smaller than or equal to the upper limit value, and send a calculation result to the cloud server;
the cloud server is configured to send out a fault alarm after receiving the first identification, and judge whether to send out a fault early warning alarm according to a preset fault early warning strategy after receiving the calculation result.
Optionally, in the fault diagnosis system, the preset fault early warning policy includes: if the fault degree of the part is continuously greater than a preset alarm lower limit for y times in x driving cycles, a fault early warning alarm is sent out, wherein x and y are natural numbers, x is greater than or equal to 1, and y is greater than 1; wherein x and y are changed depending on the number of times the failure diagnosis of the component part is diagnosed in one driving cycle.
Optionally, in the fault diagnosis system, the cloud server is further configured to perform low-pass filtering on the calculated fault degree of the component, and then determine whether the fault degree of the component after the low-pass filtering meets the requirement of issuing a fault early warning alarm according to a preset fault early warning policy
The fault diagnosis method and system based on the OBD system provided by the invention comprise the following steps: acquiring characteristic variables of vehicle parts in real time, and comparing the acquired characteristic variables with a preset upper limit value and a preset lower limit value; if the characteristic variable is smaller than the lower limit value or larger than the upper limit value, a fault alarm is sent out; if the characteristic variable is greater than or equal to the lower limit value and less than or equal to the upper limit value, calculating the fault degree of the part according to the characteristic variable, the upper limit value and the lower limit value, and then judging whether the fault degree of the part meets the condition of sending out a fault early warning alarm according to a preset fault early warning strategy. Namely, the upper limit value and the lower limit value of the characteristic variable of the fault diagnosis of the part are reasonably set, and the conditions for sending out the fault alarm and the fault early warning alarm are set and judged according to the magnitude relation of the characteristic variable of the fault diagnosis of the part, the upper limit value and the lower limit value, so that the fault alarm can be timely given out when the fault occurs, and the fault can be pre-diagnosed.
Drawings
Fig. 1 is a flowchart of a fault diagnosis method based on an OBD system according to an embodiment of the present invention;
fig. 2 is a composition diagram of a fault diagnosis method provided in the embodiment of the present invention;
fig. 3 is a specific judgment process diagram of the fault diagnosis method based on the OBD system according to the embodiment of the present invention.
Detailed Description
The OBD system-based fault diagnosis method according to the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention. Further, the structures illustrated in the drawings are often part of actual structures. In particular, the drawings may have different emphasis points and may sometimes be scaled differently.
As shown in fig. 1, an embodiment of the present invention provides a fault diagnosis method based on an OBD system, where the method includes the following steps:
and S1, collecting the characteristic variables of the vehicle parts in real time. The characteristic variables are used as the calculation basis of fault diagnosis, and can be the temperature of vehicle parts, the air flow pressure inside the parts, the oxygen content of the parts and the like.
S2, comparing the collected characteristic variable with a preset upper limit value and a preset lower limit value, if the characteristic variable is smaller than the lower limit value or larger than the upper limit value, executing the step S3, and if the characteristic variable is larger than or equal to the lower limit value and smaller than or equal to the upper limit value, executing the step S4;
s3, sending out a fault alarm;
s4, calculating the fault degree of the parts according to the characteristic variable, the upper limit value and the lower limit value; and the number of the first and second groups,
and S5, judging whether the fault degree of the part meets the requirement of sending out a fault early warning alarm according to a preset fault early warning strategy.
As described above, the OBD system can be used to perform real-time fault diagnosis on the vehicle power-related components, but the current OBD system can only detect the fault after the fault occurs, and cannot perform pre-diagnosis on the fault. The fault diagnosis method based on the OBD system provided by the embodiment can not only alarm when a fault occurs, but also pre-diagnose the fault, so that the problem that the current OBD system can only detect the fault after the fault occurs and cannot pre-diagnose the fault is solved.
The fault diagnosis method based on the OBD system provided in this embodiment is described in further detail below.
Preferably, before step S1, the method further includes: it is determined whether the component failure diagnosis reaches the diagnosis enabling condition, and if the diagnosis enabling condition is reached, step S1 is executed. And if the current working condition has a theoretical model which meets the theoretical minimum value and the theoretical maximum value of the characteristic variables for calculating and diagnosing the fault and the measurement system has no serious fault, the fault diagnosis of the part is considered to reach the diagnosis enabling condition.
In addition, the fault diagnosis of the component can be made to correspond to the diagnosis enabling condition and the fault degree valid bit, specifically, the fault degree valid bit faultvld can be set, when the fault diagnosis of the component does not reach the diagnosis enabling condition, the fault degree valid bit faultvld outputs a second identifier, when the fault diagnosis of the component is made to reach the diagnosis enabling condition, the fault degree valid bit faultvld outputs a third identifier, and only when the third identifier is output, the comparison of the characteristic variable of the fault diagnosis of the component with the preset upper limit value and the preset lower limit value can be triggered. The second identifier may be false, and correspondingly, the third identifier may be true, or alternatively, the second identifier is 0, the third identifier is 1, and so on.
In step S3, the failure alarm may be triggered by a trigger signal, so the following steps may be performed: and if the characteristic variable is smaller than the lower limit value or larger than the upper limit value, outputting a first identifier to trigger sending out a fault alarm.
That is, the data used to perform the fault diagnosis includes the following two cases: (1) after the characteristic variable is greater than or equal to the lower limit value and less than or equal to the upper limit value, calculating the fault degree of the part according to the characteristic variable, the upper limit value and the lower limit value; (2) after the characteristic variable is calculated to be smaller than the lower limit value or larger than the upper limit value, the first identifier for sending out a fault alarm is triggered; for the case (2), after the characteristic variable is calculated to be smaller than the lower limit value or larger than the upper limit value, calculation is not required, and only the first identifier needs to be sent, so that the cloud server can be triggered to generate a fault alarm, and in view of the fact that the output result of the case (1) is embodied by the fault degree, the first identifier may also be set to be a fixed fault degree value, for example, the fault degree is 100%.
In step S4, when calculating the fault degree of the component according to the characteristic variable, the upper limit value, and the lower limit value, in order to facilitate subsequent fault information analysis, a related algorithm needs to ensure the normalization effect of the fault characteristic signal, for example, the following formula may be used:
wherein, faultlvl represents the fault degree of the part, faultsig represents the characteristic variable, upperthres represents the upper limit value, and lowerthres represents the lower limit value.
In step S5, the preset fault early warning policy may specifically include: and if the fault degree of the parts in the x driving cycles is continuously greater than the preset lower alarm limit for y times, sending out a fault early warning alarm, wherein x and y are natural numbers, x is greater than or equal to 1, and y is greater than 1. Wherein x and y are changed depending on the number of times the failure diagnosis of the component part is diagnosed in one driving cycle.
In order to eliminate the contingency of the system calculation, in step S3, preferably, after the failure degree of the component is calculated, the calculated failure degree of the component is further subjected to a low-pass filtering process, and when the calculated failure degree of the component is subjected to a low-pass filtering process, the following formula may be adopted:
wherein, faultlvl represents the fault degree of the parts, faultlvlm is the fault degree after low-pass filtering, m is the coefficient of low-pass filtering, and n is the signal serial number of the discrete time sequence.
On the basis, the data output by the electronic controller in the (1) th condition comprises the calculated fault degree of the part and the fault degree of the part after low-pass filtering processing. In step S5, the data for analyzing whether to issue the fault warning alarm may be the fault degree of the component after low-pass filtering, and as long as the preset fault warning policy is formulated based on the data after low-pass filtering, the x and y values may remain unchanged compared to the case where low-pass filtering is not performed, and only the lower alarm limit needs to be adaptively adjusted. In addition, the lower alarm limit can be updated in time based on uploaded data of the vehicle, the updating strategy comprises classification statistics according to the service life of the vehicle, the geographic position and other information, prediction can be carried out according to the failure occurrence and development mechanism of the monitored object, and the timeliness and effectiveness of failure alarm are guaranteed.
When the vehicle power related parts are subjected to fault diagnosis, data collected by a sensor can be generally processed by a vehicle-mounted electronic controller, but due to the limitation of computing resources and storage space, the existing vehicle-mounted electronic controller is difficult to process and store a large amount of data, and the processing of a large amount of time sequence signal data is a precondition for realizing the pre-diagnosis of the vehicle parts. Therefore, in order to realize the vehicle part pre-diagnosis function, in this embodiment, the vehicle-mounted electronic controller is combined with the cloud server, so as to provide a fault diagnosis system.
Specifically, as shown in fig. 2, the fault diagnosis system 1 includes an on-board electronic controller 12 and a cloud server 13, where the on-board electronic controller 12 is configured to obtain a feature variable of a vehicle component collected in real time, compare the collected feature variable with a preset upper limit value and a preset lower limit value, and send a first identifier to the cloud server 13 when the feature variable is smaller than the lower limit value or larger than the upper limit value, and calculate a fault degree of the component according to the feature variable, the upper limit value and the lower limit value and send a calculation result to the cloud server 13 when the feature variable is larger than or equal to the lower limit value and smaller than or equal to the upper limit value; the cloud server 13 is configured to issue a fault alarm after receiving the first identifier, and determine whether to issue a fault early warning alarm according to a preset fault early warning policy after receiving the calculation result.
The on-board electronic controller 12 is mainly an engine management controller, and the on-board electronic controller 12 may upload related data to the cloud server 13 through an in-vehicle bus network and an on-board network device (generally, a T-BOX or a smart car).
Corresponding to the fault diagnosis method provided in this embodiment, preferably, the cloud server 13 is further configured to perform low-pass filtering processing on the calculated fault degree of the component, and further determine whether the fault degree of the component after the low-pass filtering processing meets the requirement of sending a fault early warning alarm according to a preset fault early warning policy.
The preset fault early warning strategy, the calculation method of the fault degree of the component, and the low-pass filtering processing method of the fault degree of the component are already introduced in the foregoing section, and are not described herein again. It is only necessary to understand that, similarly, when the data after the low-pass filtering process is used to determine whether to perform the fault pre-warning, the fault pre-warning strategy should be adjusted accordingly.
It should be noted that, with the increasing computing resources and storage space of the on-board electronic controller, the computing and analyzing process of the fault diagnosis method based on the OBD system provided in this embodiment may also be implemented by the on-board electronic controller independently, and the common use of the on-board electronic controller 12 and the cloud server 13 should not constitute a limitation to the fault diagnosis method based on the OBD system provided in this embodiment.
With reference to the fault diagnosis system 1 provided in this embodiment, as shown in fig. 3, the fault diagnosis method of the OBD system provided in this embodiment generally includes the following processes:
(1) in the on-board electronic controller 12, the degree of failure of the corresponding component in the OBD system is calculated based on the diagnostic strategy for that component.
The initial fault degree faultlvl is 0, the fault degree after low-pass filtering is 0, and the fault degree valid bit is faultvld is false. The characteristic variable of fault diagnosis of a certain part in the OBD system is set to be faultsig, the upper limit value of the characteristic variable is upperthres, and the lower limit value of the characteristic variable is lowerthres. In addition, the coefficient of the low-pass filter is set to m.
When the fault diagnosis of the part does not reach the diagnosis enabling condition, the faultlvl does not carry out calculation, the initial value is kept to be 0, and the faultvld is false.
When the fault diagnosis of the part reaches the diagnosis enabling condition, if the fault diagnosis is performed on the part, the faultlvl is 100%, and the faultlld is true, which indicates that the fault diagnosis is performed at the moment, and a fault alarm can be directly sent out.
When the fault diagnosis of the part reaches the diagnosis enabling condition, if the faultsig > is lowerthres and the faultsig is < upperthres, then the faultlvl is calculated according to the following formula, and the faultlld is true:
in order to eliminate the contingency of system calculation, a low-pass filtering method is adopted for all the faultlvls with the faultlld being true and the value being not 100% to obtain the faultlvlm, the faultlvlm and the faultlvls are used as data bases which are finally uploaded to a cloud for further analysis, and the calculation is carried out according to the following formula (n is a signal serial number of a discrete time sequence):
(2) in the cloud server 13, the fault tlvlm is further analyzed to determine whether a fault warning alarm needs to be sent to the user.
And if the faultvld is true and the faultlvl is 100%, directly sending out a fault alarm.
If the faultvld is true and the faultlvl is not 100%, different fault early warning strategies can be provided according to different parts and fault diagnosis strategies thereof. For example, if the fault diagnosis is performed only once in a driving cycle, if three consecutive effective faultlvlm values are greater than the lower alarm threshold1 and occur within x1 consecutive driving cycles of the driving cycle observation interval, a fault pre-warning alarm is issued. If the fault diagnosis can be carried out for a plurality of times in one driving cycle, if three continuous effective fault tlvlm are larger than the lower alarm limit threshold2 and the three values appear in the number of times of x2 continuous driving cycles in the driving cycle observation interval, a fault early warning alarm is sent out.
Generally, the accuracy and reliability of the solution depend on the reliability of the diagnostic strategy of the corresponding component in the OBD system. The real-time performance of the alarm depends on the diagnosis frequency of corresponding parts in the OBD diagnosis system and the difficulty of entering a diagnosis interval. In the scheme, the low-pass filter coefficient m needs to be tested for multiple times, and whether the filter coefficient can enable the faultlvlm of the corresponding part to gradually converge to a stable value is checked to determine. The alarm lower limits of threshold1, threshold2 and the times x1 and x2 of the driving cycle observation interval need to be installed on the vehicle by using parts with different aging degrees from brand-new to near-damage, and the parts can be finally determined after a real-time test is carried out. In addition, the lower limit value calibrated under the line of a large number of vehicles and the number of times of the driving cycle observation interval are also used for calculating the objective limit value of the same batch of vehicles by combining big data and an artificial intelligence technology, and finally used for correcting the alarm lower limit value and the number of times of the driving cycle observation interval of each vehicle.
In summary, the fault diagnosis method and system based on the OBD system provided by the invention solve the problem that the current OBD system can only detect the fault after the fault occurs, but cannot perform pre-diagnosis on the fault.
The above description is only for the purpose of describing the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention, and any variations and modifications made by those skilled in the art based on the above disclosure are within the scope of the appended claims.
Claims (10)
1. An OBD system-based fault diagnosis method for fault diagnosis of vehicle parts, comprising:
collecting characteristic variables of vehicle parts in real time;
comparing the collected characteristic variables with a preset upper limit value and a preset lower limit value;
if the characteristic variable is larger than the upper limit value or smaller than the lower limit value, a fault alarm is sent out;
if the characteristic variable is greater than or equal to the lower limit value and less than or equal to the upper limit value, calculating the fault degree of the part according to the characteristic variable, the upper limit value and the lower limit value, and then judging whether the fault degree of the part meets the condition of sending out a fault early warning alarm according to a preset fault early warning strategy.
2. The OBD system-based fault diagnosis method of claim 1, wherein the preset fault pre-warning strategy comprises: if the fault degree of the part is continuously greater than a preset alarm lower limit for y times in x driving cycles, a fault early warning alarm is sent out, wherein x and y are natural numbers, x is greater than or equal to 1, and y is greater than 1; wherein x and y are changed depending on the number of times the failure diagnosis of the component part is diagnosed in one driving cycle.
3. The OBD system-based fault diagnosis method according to claim 1, wherein the following formula is adopted when calculating the fault degree of the component according to the characteristic variable, the upper limit value and the lower limit value:
wherein, faultlvl represents the fault degree of the part, faultsig represents the characteristic variable, upperthres represents the upper limit value, and lowerthres represents the lower limit value.
4. The OBD system-based fault diagnosis method of claim 1, wherein after calculating the fault degree of the component, the method further comprises:
and performing low-pass filtering processing on the calculated fault degree of the part, and then judging whether the fault degree of the part subjected to low-pass filtering processing meets the condition of sending out a fault early warning alarm according to a preset fault early warning strategy.
5. The OBD system-based fault diagnosis method according to claim 4, wherein the calculated fault degree of the component is low-pass filtered by using the following formula:
wherein, faultlvl represents the fault degree of the parts, faultlvlm is the fault degree after low-pass filtering, m is the coefficient of low-pass filtering, and n is the signal serial number of the discrete time sequence.
6. The OBD system-based fault diagnosis method of claim 1, wherein the method further comprises:
and if the characteristic variable is smaller than the lower limit value or larger than the upper limit value, outputting a first identifier to trigger sending out a fault alarm.
7. The OBD system-based fault diagnosis method of claim 1, wherein the method further comprises:
and setting a fault degree valid bit, outputting a second identifier by the fault degree valid bit when the fault diagnosis of the part does not reach the diagnosis enabling condition, outputting a third identifier by the fault degree valid bit when the fault diagnosis of the part reaches the diagnosis enabling condition, and triggering and comparing the characteristic variable of the fault diagnosis of the part with the preset upper limit value and the preset lower limit value.
8. A failure diagnosis system for performing failure diagnosis of a component of a vehicle, comprising: an on-board electronic controller and a cloud server, wherein,
the vehicle-mounted electronic controller is configured to acquire a characteristic variable of a vehicle part acquired in real time, compare the acquired characteristic variable with a preset upper limit value and a preset lower limit value, send a first identifier to the cloud server when the characteristic variable is smaller than the lower limit value or larger than the upper limit value, calculate a fault degree of the part according to the characteristic variable, the upper limit value and the lower limit value when the characteristic variable is larger than or equal to the lower limit value and smaller than or equal to the upper limit value, and send a calculation result to the cloud server;
the cloud server is configured to send out a fault alarm after receiving the first identification, and judge whether to send out a fault early warning alarm according to a preset fault early warning strategy after receiving the calculation result.
9. The fault diagnosis system of claim 8, wherein the preset fault pre-warning strategy comprises: if the fault degree of the part is continuously greater than a preset alarm lower limit for y times in x driving cycles, a fault early warning alarm is sent out, wherein x and y are natural numbers, x is greater than or equal to 1, and y is greater than 1; wherein x and y are changed depending on the number of times the failure diagnosis of the component part is diagnosed in one driving cycle.
10. The fault diagnosis system of claim 8, wherein the cloud server is further configured to perform low-pass filtering on the calculated fault degree of the component, and further determine whether the fault degree of the component after the low-pass filtering satisfies a fault early warning alarm according to a preset fault early warning policy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010224969.2A CN113448301A (en) | 2020-03-26 | 2020-03-26 | Fault diagnosis method and system based on OBD system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010224969.2A CN113448301A (en) | 2020-03-26 | 2020-03-26 | Fault diagnosis method and system based on OBD system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113448301A true CN113448301A (en) | 2021-09-28 |
Family
ID=77807598
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010224969.2A Pending CN113448301A (en) | 2020-03-26 | 2020-03-26 | Fault diagnosis method and system based on OBD system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113448301A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114815771A (en) * | 2022-03-21 | 2022-07-29 | 三一专用汽车有限责任公司 | Vehicle fault diagnosis method and device and vehicle |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103455026A (en) * | 2013-08-23 | 2013-12-18 | 王绍兰 | Method and device for diagnosis and early warning of vehicle faults |
| CN104133467A (en) * | 2014-07-30 | 2014-11-05 | 浪潮集团有限公司 | OBDS long-distance fault diagnosis and recovery system based on cloud computation |
| CN104834303A (en) * | 2014-12-19 | 2015-08-12 | 北汽福田汽车股份有限公司 | Vehicle fault diagnosis method and system, and vehicle |
| CN106256633A (en) * | 2015-06-20 | 2016-12-28 | 曼卡车和巴士股份公司 | Method for the characteristic line of online adaptive mixed motor-car |
| CN205910555U (en) * | 2016-08-24 | 2017-01-25 | 河北工业大学 | Vehicle status foresees system based on thing networking |
-
2020
- 2020-03-26 CN CN202010224969.2A patent/CN113448301A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103455026A (en) * | 2013-08-23 | 2013-12-18 | 王绍兰 | Method and device for diagnosis and early warning of vehicle faults |
| CN104133467A (en) * | 2014-07-30 | 2014-11-05 | 浪潮集团有限公司 | OBDS long-distance fault diagnosis and recovery system based on cloud computation |
| CN104834303A (en) * | 2014-12-19 | 2015-08-12 | 北汽福田汽车股份有限公司 | Vehicle fault diagnosis method and system, and vehicle |
| CN106256633A (en) * | 2015-06-20 | 2016-12-28 | 曼卡车和巴士股份公司 | Method for the characteristic line of online adaptive mixed motor-car |
| CN205910555U (en) * | 2016-08-24 | 2017-01-25 | 河北工业大学 | Vehicle status foresees system based on thing networking |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114815771A (en) * | 2022-03-21 | 2022-07-29 | 三一专用汽车有限责任公司 | Vehicle fault diagnosis method and device and vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6830540B2 (en) | Cloud-based vehicle failure diagnostic methods, equipment and systems | |
| US8315829B2 (en) | Battery state-of-health monitoring system and method | |
| CN111753867A (en) | Monitoring and diagnosing vehicle system problems using machine learning classifiers | |
| CN111506048B (en) | Vehicle fault early warning method and related equipment | |
| CN112394282B (en) | Carbon tank electromagnetic valve diagnosis method, device, equipment and storage medium | |
| CN112946485A (en) | Vehicle battery detection method, storage medium and device | |
| CN105335599B (en) | A kind of detection method and system of automobile failure diagnosis rate | |
| CN114742157A (en) | Vehicle health state determination method, device, equipment and storage medium | |
| CN113341813A (en) | Urban gas medium-low pressure pipe network detection method and system | |
| CN113173104A (en) | New energy vehicle power battery early warning method and system | |
| CN113448301A (en) | Fault diagnosis method and system based on OBD system | |
| CN113232462B (en) | Tire pressure management method, device and computer storage medium | |
| KR101505975B1 (en) | Method and system for fault dignosis of engine | |
| CN111207932B (en) | Motor vehicle exhaust monitoring method and related equipment | |
| CN114962031B (en) | Method and system for detecting coking of pipeline of air inlet system of internal combustion engine and vehicle | |
| CN116577104A (en) | Engine fault detection method and equipment | |
| CN114488994A (en) | Optimization method and device for improving vehicle fault diagnosis robustness | |
| CN115061451A (en) | Automobile fault diagnosis method and device, intelligent terminal and storage medium | |
| CN115179767A (en) | Method for diagnosing charging abnormality of electric vehicle, and storage medium | |
| CN115079663A (en) | Vehicle power system monitoring method and device based on digital twin technology | |
| CN103324123B (en) | Vehicle-mounted data pre-processing method based on embedded system | |
| Czech | An Intelligent approach to wear of piston-cylinder assembly diagnosis based on entropy of wavelet packet and probabilistic neural networks | |
| CN113242815A (en) | Method for diagnosing safety components in a motor vehicle | |
| KR101387421B1 (en) | Vehicle Analysis Apparatus Using Earth Level Signal | |
| CN113581015B (en) | Safety early warning method and device for fuel cell system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210928 |