A desorption fault diagnosis method, device, storage medium, module and vehicle

Abstract

Embodiments of the present application disclose a kind of evaporative system carbon tank desorption flow fault diagnosis method, device, storage medium, module and vehicle;The method and related device module of the present application, non-insertion type detection process is adopted, without interrupting carbon tank flushing process, can avoid the interference to control system;Test process does not need to set up complex enabling condition, improves the diagnostic efficiency and robustness;Test process is suitable for different system topological structures, flexible and convenient, maintainability is strong, easy to maintain upgrade.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent vehicles, and particularly relates to a desorption fault diagnosis method and device, a storage medium, a module and a vehicle. BACKGROUND

[0002] The monitoring of the evaporative system by the vehicle-mounted control unit includes desorption flow monitoring and leakage monitoring, and the OBD (On-Board Diagnostic) of the diagnosis unit becomes a key factor for improving the reliability and maintainability of the intelligent vehicle.

[0003] Active fault diagnosis of the carbon can unit will interfere with the related system, and normal carbon can flushing needs to be interrupted before diagnosis, which will lose a part of the carbon can flushing opportunity.

[0004] In addition, in the existing diagnosis process, the oil gas entering the engine cannot be sensed by the control system and will also interfere with the control system.

[0005] In order to ensure the robustness of diagnosis, complex enabling conditions need to be set, which brings about corresponding calibration workload; the robustness of diagnosis and the acquisition of its diagnosis opportunity are a pair of contradictions, in order to achieve satisfactory diagnosis robustness, the diagnosis window is usually relatively narrow; if the diagnosis window is enlarged, too much noise may be introduced, which reduces the robustness of diagnosis. SUMMARY

[0006] The application discloses a passive desorption fault diagnosis method and device, and related storage medium, module and vehicle.

[0007] When the working condition information is acquired and it is confirmed that the working condition information meets the preset condition, the possible existing fault is diagnosed through acquisition of a related component feature signal.

[0008] Among them, the working conditions of interest mainly include engine working conditions and / or carbon can flushing working conditions; different diagnoses can be selected to be performed in different specific working conditions, but the method of the application is not limited by the above working conditions, and the diagnosis process can be performed in a wider range of preset, controllable working conditions or even most of the randomly occurring working conditions.

[0009] Furthermore, the interference of the method disclosed by the application on the system to be diagnosed is minimized, and the method disclosed by the application only acquires information and does not attempt to introduce interference to the system to be diagnosed.

[0010] The diagnostic method selectively acquires diagnostic information by acquiring start-stop and / or on-off states of a first execution unit; wherein the first execution unit is excited and / or controlled by a periodic first trigger signal; the first trigger signal is issued by the system to be diagnosed according to actual control needs; the period of the first trigger signal is measured, and a sampling window matching the first trigger signal is thus selected; the sampling window refers to a sampling time period meeting a preset length and / or a set of sampling points meeting a preset number.

[0011] The diagnostic method selectively acquires diagnostic information by acquiring start-stop and / or on-off states of a first execution unit; wherein the first execution unit is excited and / or controlled by a periodic first trigger signal; the first trigger signal is issued by the system to be diagnosed according to actual control needs; the period of the first trigger signal is measured, and a sampling window matching the first trigger signal is thus selected; the sampling window refers to a sampling time period meeting a preset length and / or a set of sampling points meeting a preset number.

[0012] In order to adapt to the related feature extraction algorithm, the above-mentioned first feature vector is periodically acquired; generally, the first feature vector can be a time sequence or function of the pressure signal.

[0013] Further, the feature value and / or feature data can be the frequency and / or period of the pressure signal fluctuation.

[0014] The frequency and / or period of the above-mentioned signal can also be acquired, and then the diagnostic conclusion can be acquired by comparing the relationship between the frequency, period and reference value or preset value, such as difference.

[0015] If the above-mentioned difference meets a preset quantitative relationship with the preset threshold value, the related on-off quantity is set or the necessary signal or instruction is sent to the related system, or the corresponding fault handling process is started or the reminding, alarming and other operations are implemented.

[0016] In order to adapt to the actual scene of diagnosis, the efficiency of diagnosis can be improved by setting an array type diagnostic unit, and at least one of the second execution unit, the third execution unit and the Rth execution unit can be set, wherein R is a positive integer greater than or equal to two.

[0017] In order to acquire different feature vectors, different trigger signals can also be introduced to obtain different sampling windows.

[0018] Specifically, at least one of the second trigger signal, the third trigger signal and the Mth trigger signal can be set, wherein M is a positive integer greater than or equal to two.

[0019] As mentioned above, the second trigger signal, the third trigger signal and the Mth trigger signal can all cause the Rth execution unit to produce start-stop and / or on-off state changes, and due to the existence of the trigger process, the related comparison and judgment unit acquires enough analysis samples, thereby improving the reliability of analysis.

[0020] To adapt to the actual scene of diagnosis, one or more sensors can be arranged at different positions to monitor different desorption pipelines according to the actual configuration of the evaporation system, or the signals of the same sensor are used to monitor the desorption pipelines at different positions.

[0021] The trigger signal is controlled by the system to be diagnosed, and the diagnosis unit itself can not intervene. Here, the "different feature vectors" can be "signal amplitude", "signal frequency / period" or other state estimation results. During the normal control process of the system to be diagnosed, the diagnosis function can collect relevant signals in real time for calculation and judgment.

[0022] The acquisition of the signal can also be based on different application scenarios, and array sensors can be selected, or the same sensor can be placed at different observation points.

[0023] Specifically, a second sensor, a third sensor, and an Nth sensor can be provided, where N is a positive integer greater than or equal to two; the Nth sensor acquires an Nth feature vector, and the Nth feature vector is obtained periodically.

[0024] The Nth feature value and / or Nth feature data obtained from the Nth feature vector are used for the judgment output of the diagnosis result; the Nth feature value and / or Nth feature data are compared with a preset threshold value; and the diagnosis result and / or conclusion are obtained through the threshold relationship and / or correlation thereof.

[0025] Further, the acquisition of the feature value and / or feature data can be performed by a state estimation software and / or method; the state estimation method includes a Kalman filter method and other methods suitable for relevant signals.

[0026] The method of the present application is suitable for different application scenarios and hardware configurations, and the first sensor can be at least one of the following pressure sensors: tank pressure sensor, carbon canister upstream sensor, manifold pressure sensor, and Venturi tube upstream pressure sensor.

[0027] The fault diagnosis device corresponding to the method includes a signal acquisition unit, a sample construction unit, a feature extraction unit, and a comparison and judgment unit.

[0028] The signal acquisition unit acquires working condition information and confirms that the working condition information meets a preset condition; the working condition includes an engine working condition and / or a carbon canister flushing working condition.

[0029] The sample construction unit acquires the start-stop and / or on-off state of the first execution unit; the first execution unit is excited and / or controlled by a periodic first trigger signal.

[0030] By acquiring the period of the first trigger signal, a sampling window matching the first trigger signal is determined; in a sampling duration of a preset length and / or a set of sampling points of a preset number, the acquisition of relevant information is carried out.

[0031] Further, by periodically acquiring the first feature vector collected by the first sensor in the sampling window, the feature value and / or feature data of the first feature vector can be acquired in the feature extraction unit.

[0032] Further, by comparing the relationship and / or correlation between the feature value and / or feature data and the preset threshold value in the comparison judgment unit, a diagnosis result and / or conclusion is obtained.

[0033] Generally, the first feature vector is a time sequence or function of the pressure signal; the feature value and / or feature data can also be the frequency and / or period of the pressure signal fluctuation; by acquiring the difference between the frequency and / or period of the signal and the preset value, the state of the fault can be analyzed.

[0034] In terms of system configuration, if the difference and the preset threshold value meet the preset magnitude relationship, the relevant switch value is set or the comparison signal or warning is sent to the relevant system.

[0035] Similarly, the device of the method is suitable for the use scene of multiple sensors, and accordingly, multiple sensors need to be set, and the acquisition of relevant signals is also periodic as in the method of the application.

[0036] Further, by acquiring different feature values and / or feature data, and comparing the relationship and / or correlation between different feature values and / or feature data and the preset threshold value, different diagnostic targets can be diagnosed or monitored simultaneously, so that the applicability of the system is further improved.

[0037] The application also improves the efficiency of fault diagnosis by introducing the acquisition of feature values and / or feature data, and corresponding filtering and / or digital filtering can be carried out according to different fault signal characteristics to improve the feature extraction efficiency and the effectiveness of the signal. For low-load desorption pipeline or carbon tank valve faults, Kalman filtering is a recommended method.

[0038] For the desorption flow detection of a vehicle, the method is suitable for the following various layout modes of pressure sensors: oil tank pressure sensor, carbon tank upstream sensor, manifold pressure sensor, Venturi tube upstream pressure sensor, etc. or similar sensors.

[0039] Therefore, the test process or system using the method or device of the application can be suitable for different topological structures, so that the system layout is flexible and convenient, has strong maintainability, and is easy to upgrade and modify.

[0040] Further, by using the method or device disclosed in the present application on the computer storage medium and the special desorption diagnosis module or vehicle, effective fault diagnosis and analysis of the related system can be obtained.

[0041] Since the non-insertion detection process is adopted, the carbon tank flushing process does not need to be interrupted, and interference to the control system is avoided; the test process does not need to set complex enable conditions, and thus the diagnosis efficiency and robustness are improved.

[0042] It should be noted that the terms "first", "second", and the like similar terms used in the present document are only used to describe the elements in the technical solutions, and do not constitute a limitation on the technical solutions, nor can they be understood as an indication or suggestion of the importance of the corresponding elements; the elements with "first", "second", and the like similar terms represent that the element at least contains one in the corresponding technical solution. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the present application, and to facilitate further understanding of the technical effects, technical features and purposes of the present application, the present application will be described in detail below in conjunction with the drawings, which constitute an essential part of the specification and together with the embodiments of the present application serve to illustrate the technical solutions of the present application, but do not constitute a limitation on the present application.

[0044] The same reference numerals in the drawings represent the same components, specifically:

[0045] Figure 1 The flowchart of the method embodiment of the present application;

[0046] Figure 2 The structural block diagram of the device embodiment of the present application;

[0047] Figure 3 The functional block diagram of the signal acquisition unit of the embodiment of the present application;

[0048] Figure 4 The layout schematic diagram of the diagnosis system of the embodiment of the present application;

[0049] Figure 5 The diagnosis flowchart based on the pressure signal amplitude of the embodiment of the present application;

[0050] Among them:

[0051] 1-fault diagnosis device;

[0052] 100-preprocessing flow, 200-signal acquisition flow, 300-feature extraction flow, 400-comparison output flow;

[0053] 101-signal acquisition unit, 201-sample construction unit, 301-feature extraction unit, 401-comparison and judgment unit;

[0054] 111 - first sensor, 121 - second sensor, 131 - third sensor, 141 - fourth sensor,

[0055] 151 - fifth sensor, 161 - sixth sensor, 171 - seventh sensor, 1N1 - Nth sensor, N is a positive integer;

[0056] 881 - first trigger signal, 882 - second trigger signal,

[0057] 883 - third trigger signal, 88M - Mth trigger signal, M is a positive integer. DETAILED DESCRIPTION

[0058] The application will be further described below in conjunction with the drawings and examples. Of course, the following specific examples described are only to explain the technical solutions of the application, but not to limit the application. In addition, the parts expressed in the examples or drawings are only examples of the relevant parts of the application, but not the whole of the application.

[0059] As Figure 1 is a flow chart of the method embodiment of the application, by obtaining working condition information, confirming that the working condition information meets the preset condition; and obtaining the start-stop and / or on-off state of the first execution unit and the period of the first trigger signal 881, so as to obtain a sampling window matched with the first trigger signal 881.

[0060] Among them, the first execution unit is excited and / or controlled by the periodic first trigger signal 881; here, the working condition includes but is not limited to engine working condition and / or carbon tank flushing working condition; here, the sampling window refers to a section of sampling time length meeting the preset length and / or a set of sampling points meeting the preset number.

[0061] Further, by obtaining the first feature vector collected by the first sensor 111 in the sampling window; and obtaining the feature value and / or feature data of the first feature vector; comparing the relationship and / or correlation between the feature value and / or feature data and the preset threshold, and then obtaining the diagnosis result and / or conclusion; wherein the first feature vector is also obtained periodically.

[0062] The application is based on the signals collected by multiple sensors for fault diagnosis, wherein, as shown in the embodiment of Figure 2 , 3 The first feature vector of the embodiment is the time sequence or function of the pressure signal; and the feature value and / or feature data is the frequency and / or period of the pressure signal fluctuation.

[0063] Further, the difference between the frequency and / or period of the pressure or other signals and the preset value can be used to determine; if the difference and the preset threshold value meet the preset quantitative relationship, the relevant switch value is set or the diagnostic result is transmitted according to other system accepted standards.

[0064] As Figure 3 For the signal acquisition unit of the embodiment of the application, at least one of the second execution unit, the third execution unit and the Rth execution unit can be further included, wherein R is a positive integer greater than or equal to two; at least one of the second trigger signal, the third trigger signal and the Mth trigger signal can be further included, wherein M is a positive integer greater than or equal to two.

[0065] The second trigger signal, the third trigger signal and the Mth trigger signal can all cause the Rth execution unit to change the start-stop and / or switch state.

[0066] Again as Figure 2 , 3 As the signal detection unit, the second sensor 121, the third sensor 131 and the Nth sensor 1N1 are used to obtain the Nth feature vector, and the Nth feature vector is obtained periodically, wherein N is a positive integer greater than or equal to two.

[0067] Further, the Nth feature value and / or the Nth feature data of the Nth feature vector are obtained; the relationship and / or correlation between the Nth feature value and / or the Nth feature data and the preset threshold value are compared to obtain the diagnostic result and / or conclusion.

[0068] As Figures 2-4 The feature value and / or feature data are obtained by a state estimation method; the state estimation method includes a Kalman filtering method or other state estimation algorithm.

[0069] As Figure 4 The sensor of the embodiment is one of the oil tank pressure sensors 111, 121 and 131.

[0070] Specifically, as Figure 2 As shown in the structural block diagram of the device embodiment of the application, the fault diagnosis device 1 includes a signal acquisition unit 101, a sample construction unit 201, a feature extraction unit 301 and a comparison and judgment unit 401.

[0071] The signal acquisition unit 101 obtains the working condition information and confirms that the working condition information meets the preset condition; at the same time, the device is particularly suitable for the carbon tank flushing scene of the fuel engine.

[0072] Further, the start-stop and / or on-off state of the first execution unit is obtained by the sample construction unit 201; wherein the first execution unit is excited and / or controlled by a periodic first trigger signal 881; by obtaining the period of the first trigger signal 881, a sampling window matched with the first trigger signal 881 is determined, and then the first feature vector collected by the first sensor 111 in the sampling window is obtained.

[0073] The sampling window refers to a sampling time period meeting a preset length and / or a set of sampling points meeting a preset number; and the first feature vector is periodically obtained.

[0074] As shown in Figure 2 , the device further comprises a feature extraction unit 301 for obtaining feature values and / or feature data of the first feature vector; and further comprises a comparison and judgment unit 401 for comparing the relationship and / or correlation between the feature values and / or feature data and a preset threshold, and then obtaining a diagnosis result and / or conclusion.

[0075] Further, the first feature vector can be a time sequence or function of the pressure signal; the feature values and / or feature data can be the frequency and / or period of the pressure signal fluctuation; the difference between the frequency and / or period and a preset value is obtained; if the difference and the preset threshold meet a preset quantitative relationship, the related switch quantity is set or a preset operation or control is performed.

[0076] As shown in Figure 4 , the layout diagram of the diagnosis system of the embodiment of the present application; wherein the second sensor 121, the third sensor 131, and the seventh sensor 171 can be used to collect detection signals, and periodically obtain corresponding feature vectors.

[0077] Further, the feature values and / or feature data corresponding to the feature vectors are obtained; by comparing the relationship and / or correlation between the feature values and / or feature data and a preset threshold, a diagnosis result and / or conclusion is obtained.

[0078] As shown in Figure 4 , 5 , the feature values and / or feature data are obtained by a state estimation method; and the state estimation method comprises a Kalman filtering method.

[0079] Specifically, the above-mentioned sensors can generally include tank pressure sensors 111, 121, 131; or can be implemented by using carbon can upstream sensors 141, manifold pressure sensors 171, and Venturi tube upstream pressure sensors 151; wherein the signal of the supercharging pressure sensor (161) is generally used for judging the engine working condition.

[0080] Generally, the method of the present application can be implemented in a microprocessor or micro memory unit, including a storage medium body for storing a computer program and necessary peripheral circuits; when the computer program is executed by the microprocessor, the relevant method of the present application can be implemented.

[0081] Further, the desorption diagnosis module and / or the vehicle protecting the above-mentioned microprocessor and / or micro memory unit and / or the above-mentioned device also naturally adopt the same inventive concept as the present application, solve the corresponding technical problems, and the relevant contents will not be described again.

[0082] It should be noted that the above embodiments are only for more clearly illustrating the technical solutions of the present application, and those skilled in the art can understand that the embodiments of the present application are not limited to the above content, and the obvious changes, replacements or substitutions based on the above content do not exceed the scope covered by the technical solutions of the present application; other embodiments will also fall within the scope of the present application without departing from the inventive concept of the present application.

Claims

1. A method for diagnosing desorption faults, characterized in that... include: Acquire operating condition information and confirm that the operating condition information meets preset conditions; the operating conditions include engine operating conditions and / or carbon canister flushing operating conditions. The start / stop and / or switch status of the first execution unit and the period of the first trigger signal (881) are obtained to obtain a sampling window that matches the first trigger signal (881); wherein, the sampling window refers to a sampling duration of a preset length and / or a set of sampling points of a preset number; the first execution unit is excited and / or controlled by the periodic first trigger signal (881); The first feature vector collected by the first sensor (111) used to detect the desorption pipeline within the sampling window is obtained; wherein the first feature vector is acquired periodically; Obtain the feature values ​​and / or feature data of the first feature vector; compare the relationship and / or correlation between the feature values ​​and / or feature data and a preset threshold to obtain the diagnostic results and / or conclusions.

2. The method of claim 1, wherein: The first feature vector is a time series or function of the pressure signal; The characteristic value and / or characteristic data are the frequency and / or period of the pressure signal fluctuation.

3. The method of claim 2, further comprising: Obtain the difference between the frequency and / or period and the preset value; If the difference matches the preset threshold value, the relevant switch value will be set.

4. The method according to any one of claims 1 to 3, further comprising: At least one of the second execution unit, the third execution unit, up to the Rth execution unit, where R is a positive integer greater than or equal to two; The method further includes at least one of a second trigger signal, a third trigger signal, up to an Mth trigger signal, where M is a positive integer greater than or equal to two; The second trigger signal, the third trigger signal, up to the Mth trigger signal, and the first trigger signal can all cause the Rth execution unit to start / stop and / or change its switching state.

5. The method of claim 4, further comprising: The second sensor (121), the third sensor (131) up to the Nth sensor (1N1), where N is a positive integer greater than or equal to two; the Nth sensor (1N1) acquires the Nth feature vector, which is obtained periodically; Obtain the Nth feature value and / or Nth feature data of the Nth feature vector; compare the relationship and / or correlation between the Nth feature value and / or Nth feature data and a preset threshold to obtain the diagnostic result and / or conclusion.

6. The method according to any one of claims 1 to 3, wherein: The feature values ​​and / or feature data are obtained through a state estimation method; the state estimation method includes the Kalman filtering method.

7. The method of claim 6, wherein: The first sensor includes at least one of the following pressure sensors: the pressure sensor includes a tank pressure sensor (111, 121, 131), a carbon canister upstream sensor (141), a manifold pressure sensor (171), and a venturi upstream pressure sensor (151).

8. A fault diagnosis device (1), comprising: The system comprises a signal acquisition unit (101), a sample construction unit (201), a feature extraction unit (301), and a comparison and judgment unit (401); among which, The signal acquisition unit (101) acquires operating condition information and confirms that the operating condition information meets preset conditions; the operating conditions include engine operating conditions and / or carbon canister flushing operating conditions; The sample construction unit (201) acquires the start / stop and / or switch status of the first execution unit; the first execution unit is excited and / or controlled by a periodic first trigger signal (881); Based on the start / stop and / or switch status of the first execution unit and the period of the first trigger signal (881), a sampling window matching the first trigger signal (881) is obtained; wherein, the sampling window refers to a sampling duration of a preset length and / or a set of sampling points of a preset number; The first feature vector collected by the first sensor (111) used to detect the desorption pipeline within the sampling window is obtained; wherein the first feature vector is acquired periodically; The feature extraction unit (301) acquires the feature values ​​and / or feature data of the first feature vector; The comparison and judgment unit (401) compares the relationship and / or correlation between the feature value and / or feature data and the preset threshold to obtain the diagnosis result and / or conclusion.

9. The apparatus of claim 8, wherein: The first feature vector is a time series or function of the pressure signal; The characteristic value and / or characteristic data are the frequency and / or period of the pressure signal fluctuation; Obtain the difference between the frequency and / or period and the preset value; If the difference matches the preset threshold value, the relevant switch value will be set.

10. The apparatus of any one of claims 8-9, further comprising: The second sensor (121), the third sensor (131), and so on up to the Nth sensor (1N1), where N is a positive integer greater than or equal to two; the Nth sensor (1N1) acquires the Nth feature vector, which is obtained periodically; Obtain the Nth feature value and / or Nth feature data of the Nth feature vector; compare the relationship and / or correlation between the Nth feature value and / or Nth feature data and a preset threshold to obtain the diagnostic result and / or conclusion.

11. The apparatus of claim 8 or 9, wherein: The feature values ​​and / or feature data are obtained through a state estimation method; the state estimation method includes the Kalman filtering method. The first sensor includes at least one of the following pressure sensors: the pressure sensor includes a tank pressure sensor (111, 121, 131), a carbon canister upstream sensor (141), a manifold pressure sensor (171), and a venturi upstream pressure sensor (151).

12. A computer storage medium, comprising: The storage medium itself used to store computer programs; When executed by a microprocessor, the computer program implements the method as described in any of claims 1-7.

13. A desorption diagnostic module, comprising: Any of the apparatuses as described in claims 8-11; And / or the storage medium as described in claim 12.

14. A vehicle comprising: Any of the apparatuses as described in claims 8-11; And / or the storage medium as described in claim 12; And / or the module as described in claim 13.

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