CN115615711A - Skylight fault detection method and device, vehicle and computer-readable storage medium - Google Patents

Skylight fault detection method and device, vehicle and computer-readable storage medium Download PDF

Info

Publication number
CN115615711A
CN115615711A CN202210951819.0A CN202210951819A CN115615711A CN 115615711 A CN115615711 A CN 115615711A CN 202210951819 A CN202210951819 A CN 202210951819A CN 115615711 A CN115615711 A CN 115615711A
Authority
CN
China
Prior art keywords
information
skylight
visual data
current
vehicle
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
Application number
CN202210951819.0A
Other languages
Chinese (zh)
Inventor
董永衡
翟增广
刘雪松
刘琪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Great Wall Motor Co Ltd
Original Assignee
Great Wall Motor Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Great Wall Motor Co Ltd filed Critical Great Wall Motor Co Ltd
Priority to CN202210951819.0A priority Critical patent/CN115615711A/en
Publication of CN115615711A publication Critical patent/CN115615711A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/007—Wheeled or endless-tracked vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60J—WINDOWS, WINDSCREENS, NON-FIXED ROOFS, DOORS, OR SIMILAR DEVICES FOR VEHICLES; REMOVABLE EXTERNAL PROTECTIVE COVERINGS SPECIALLY ADAPTED FOR VEHICLES
    • B60J7/00—Non-fixed roofs; Roofs with movable panels, e.g. rotary sunroofs
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
    • B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
    • B60R16/0232—Circuits relating to the driving or the functioning of the vehicle for measuring vehicle parameters and indicating critical, abnormal or dangerous conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/1752—Masking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Mechanical Engineering (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Algebra (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Evolutionary Biology (AREA)
  • Automation & Control Theory (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Traffic Control Systems (AREA)

Abstract

The embodiment of the invention provides a skylight fault detection method, a skylight fault detection device, a vehicle and a computer storage medium, which are used for acquiring current position information of skylight glass, current rotating speed information of a skylight motor, current vibration information of the vehicle and current noise information of the vehicle; detecting whether the skylight is abnormal or not based on the current position information, the current rotating speed information, the current vibration information and the current noise information to obtain a detection result; and if the detection result is abnormal, determining solution information corresponding to the detection result based on the trained fault detection model. In the embodiment of the invention, the abnormal detection of the skylight is carried out on the basis of various information by acquiring various information of the vehicle, and the skylight abnormal detection result is obtained. According to the invention, a big data fitting technology is adopted, so that the accuracy of detecting the reason of the skylight fault is improved, the skylight fault problem is solved remotely, and the vehicle production line detection efficiency and the accuracy of vehicle after-sale fault detection are further improved.

Description

Skylight fault detection method and device, vehicle and computer-readable storage medium
Technical Field
The invention relates to the technical field of vehicle skylight detection, in particular to a skylight fault detection method and device, a vehicle and a computer readable storage medium.
Background
The reason for the abnormal noise of the skylight is not independent but is caused by the mixture of various faults. For example: the problems of poor top sealing, water leakage, poor appearance and the like caused by excessive abrasion and installation deviation can cause abnormal sound of the skylight.
However, the conventional method for detecting abnormal skylight sound only aims at one piece of information of the vehicle, that is, only when the information appears, the abnormal skylight sound can be detected. The accuracy of skylight anomaly detection is low, and the vehicle production line detection efficiency and the vehicle after-sale fault repair efficiency are low.
Disclosure of Invention
In view of the above problems, embodiments of the present invention are proposed to provide a sunroof failure detection method and a sunroof detection device that overcome or at least partially solve the above problems.
In a first aspect, an embodiment of the present invention discloses a method for detecting a failure of a vehicle sunroof, where the method includes: acquiring current position information of skylight glass, current rotating speed information of a skylight motor, current vibration information of a vehicle and current noise information of the vehicle; detecting whether the skylight is abnormal or not based on the current position information, the current rotating speed information, the current vibration information and the current noise information to obtain a detection result; and if the detection result is abnormal, determining solution information corresponding to the detection result based on the trained fault detection model.
Optionally, the detecting whether the skylight is abnormal or not based on the current position information, the current rotating speed information, the current vibration information and the current noise information to obtain a detection result includes: generating first visualization data based on the current location information; generating second visual data based on the current rotating speed information; generating third visual data based on the current vibration information; generating fourth visual data based on the current noise information; and detecting whether the skylight is abnormal or not based on the first visual data, the second visual data, the third visual data and the fourth visual data to obtain a detection result.
Optionally, the trained fault detection model is deployed in a server; the determining of the solution information corresponding to the detection result based on the trained fault detection model includes: acquiring related information related to the detection result; the associated information comprises current weather information, current geographical position information and vehicle running frequency information; and sending the detection result and the associated information to the server so that the server processes the detection result by adopting the trained fault detection model to obtain corresponding solution information.
Optionally, the current position information includes skylight glass real-time position information and skylight motor real-time hall signal information; the skylight glass real-time position information comprises skylight glass position information and first time information; the skylight motor real-time Hall signal information comprises skylight motor Hall signal information and second time information; the generating of the first visual data based on the current position information and the generating of the second visual data based on the current rotating speed information comprises: generating a first graph by using the skylight glass position information and the first time information, and taking the first graph as the first visual data; and generating a second curve graph by adopting the skylight motor Hall signal information and the second time information, and taking the second curve graph as the second visual data.
Optionally, the vibration information includes vehicle vibration information and third time information; generating third visualization data based on the current vibration information, comprising: and generating a third graph by using the vehicle vibration information and the third time information, and taking the third graph as the third visual data. Optionally, the current noise information includes vehicle exterior noise frequency information, vehicle exterior noise amplitude information, vehicle interior noise frequency information, vehicle interior noise amplitude information, and fourth time information; generating fourth visualization data based on the current noise information, comprising: denoising the interior noise frequency information and the interior noise amplitude information based on the exterior noise frequency information and the exterior noise amplitude information to obtain denoised interior noise frequency information and denoised interior noise amplitude information; and generating a fourth curve graph by using the processed in-vehicle noise frequency information, the processed in-vehicle noise amplitude information and the fourth time information, and taking the fourth curve graph as the fourth visual data.
Optionally, the detecting whether the skylight is abnormal based on the first visual data, the second visual data, the third visual data and the fourth visual data to obtain a detection result includes: detecting information change values of any three items of visual data in the first visual data, the second visual data, the third visual data and the fourth visual data, and whether the information change values exceed a change value threshold value in a preset time period; and if so, generating a detection result of the abnormal skylight.
Optionally, the detecting whether the skylight is abnormal based on the first visual data, the second visual data, the third visual data, and the fourth visual data to obtain a detection result further includes: detecting information change values of any two items of visual data in the first visual data, the second visual data, the third visual data and the fourth visual data, whether the information change values exceed the change value threshold value in a preset time period, and whether amplitude values exceeding the change value threshold value exceed an amplitude value threshold value; and if the information change values of any two items of visual data exceed the change value threshold value within a preset time period, and the amplitude value exceeding the change value threshold value exceeds the amplitude value threshold value, generating a detection result that the skylight is abnormal.
In a second aspect, an embodiment of the present invention discloses a sunroof detecting device, including:
the acquisition module is used for acquiring current position information of skylight glass, current rotating speed information of a skylight motor, current vibration information of a vehicle and current noise information of the vehicle; the detection module is used for detecting whether the skylight is abnormal or not based on the current position information, the current rotating speed information, the current vibration information and the current noise information to obtain a detection result; and the determining module is used for determining solution information corresponding to the detection result based on the trained fault detection model if the detection result is abnormal.
Optionally, the detection module further includes: the first generation submodule is used for generating first visual data based on the current position information; the second generation submodule is used for generating second visual data based on the current rotating speed information; a third generation submodule, configured to generate third visual data based on the current vibration information; a fourth generation submodule, configured to generate fourth visualization data based on the current noise information; the first detection submodule is used for detecting whether the skylight is abnormal or not based on the first visual data, the second visual data, the third visual data and the fourth visual data to obtain a detection result.
Optionally, the determining module includes: the first acquisition sub-module is used for acquiring the associated information related to the detection result; the associated information comprises current weather information, current geographical position information and vehicle running frequency information; and the sending submodule is used for sending the detection result and the associated information to a server so that the server processes the detection result by adopting the trained fault detection model to obtain corresponding solution information.
Optionally, the first generation submodule includes: a first generating unit configured to generate a first graph using the sunroof glass position information and the first time information, and to use the first graph as the first visualization data.
In an embodiment of the present invention, the second generation submodule includes: and the second generating unit is used for generating a second curve graph by adopting the hall signal information of the skylight motor and the second time information, and taking the second curve graph as the second visual data.
In an embodiment of the present invention, the third generation submodule includes: a third generating unit configured to generate a third graph using the vehicle vibration information and the third time information, and to use the third graph as the third visualization data.
Optionally, the fourth generation submodule includes: the first processing unit is used for denoising the in-vehicle noise frequency information and the in-vehicle noise amplitude information based on the outside noise frequency information and the outside noise amplitude information to obtain denoised in-vehicle noise frequency information and denoised in-vehicle noise amplitude information; and a fourth generating unit, configured to generate a fourth graph by using the processed in-vehicle noise frequency information, the processed in-vehicle noise amplitude information, and the fourth time information, and use the fourth graph as the fourth visualization data.
Optionally, the detection module further includes: the second detection submodule is used for detecting information change values of any three items of visual data in the first visual data, the second visual data, the third visual data and the fourth visual data, and whether the information change values exceed a change value threshold value in a preset time period; and the fifth generation submodule is used for generating a detection result that the skylight is abnormal if the skylight is abnormal.
Optionally, the detection module further includes: a third detection sub-module, configured to detect information change values of any two items of visual data in the first visual data, the second visual data, the third visual data, and the fourth visual data, whether the information change values all exceed the change value threshold within a preset time period, and detect whether a magnitude value exceeding the change value threshold exceeds a magnitude value threshold; and a sixth generation sub-module, configured to generate a detection result that the skylight is abnormal if the information change value of any two items of visual data exceeds the change value threshold within a preset time period, and the amplitude value exceeding the change value threshold exceeds the amplitude value threshold.
In a third aspect, an embodiment of the present invention discloses a vehicle, including: a processor, a memory and a computer program stored on the memory and being executable on the processor, the computer program, when executed by the processor, implementing the steps of the skylight fault detection method embodiment as described in the first aspect.
In a fourth aspect, an embodiment of the present invention discloses a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the skylight fault detection method embodiment according to the first aspect.
The embodiment of the invention has the following advantages:
acquiring current position information of skylight glass, current rotating speed information of a skylight motor, current vibration information of a vehicle and current noise information of the vehicle; detecting whether the skylight is abnormal or not based on the current position information, the current rotating speed information, the current vibration information and the current noise information to obtain a detection result; and if the detection result is abnormal, determining solution information corresponding to the detection result based on the trained fault detection model. In the embodiment of the invention, the abnormal detection of the skylight is carried out on the basis of various information by acquiring various information of the vehicle, and the skylight abnormal detection result is obtained. According to the invention, a big data fitting technology is adopted, so that the accuracy of detecting the reason of the skylight fault is improved, the skylight fault problem is solved remotely, and the vehicle production line detection efficiency and the accuracy of vehicle after-sale fault detection are further improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
FIG. 1 is a flowchart illustrating steps of a method for detecting a sunroof failure according to an embodiment of the present invention;
fig. 2 is a block diagram of a vehicle sunroof detecting device according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to fig. 1, a flow chart of steps of a vehicle sunroof fault detection method according to the present invention is shown, which may specifically include the following steps:
step 101, obtaining current position information of a skylight glass, current rotating speed information of a skylight motor, current vibration information of a vehicle and current noise information of the vehicle.
In the embodiment of the invention, the noise problem of the vehicle skylight is not independent, but is caused by mixing various faults together. Therefore, in order to accurately detect the abnormal sound problem of the vehicle skylight, the Hall sensor is arranged at the skylight motor of the vehicle and used for collecting the current position information of the skylight glass and the current rotating speed information of the skylight motor; the vibration collector is arranged at the vehicle body, and the vibration collector and the skylight vibration collector of the vehicle body are used for collecting the current vibration information of the vehicle because the existing skylight is provided with the vibration collector; the noise sensors are arranged inside and outside the vehicle, wherein the noise sensors in the vehicle are in-vehicle microphones and are used for acquiring current noise information of the vehicle, and the noise sensors comprehensively determine the reason of abnormal sound of the vehicle skylight and other accompanying problems by analyzing various information such as road conditions, noise, skylight motor rotating speed, time and the like of the vehicle skylight.
And 102, detecting whether the skylight is abnormal or not based on the current position information, the current rotating speed information, the current vibration information and the current noise information to obtain a detection result.
In an embodiment of the present invention, a vehicle controller is installed in a vehicle and is configured to receive current position information, current rotational speed information, current vibration information, and current noise information. After receiving the information, the vehicle controller detects the information, comprehensively analyzes the detection condition of the information, and obtains the detection result of whether the skylight is abnormal or not.
Further, in an embodiment of the present invention, the detecting whether the skylight is abnormal based on the current position information, the current rotation speed information, the current vibration information, and the current noise information to obtain a detection result includes:
generating first visualization data based on the current location information; generating second visual data based on the current rotating speed information; generating third visual data based on the current vibration information; generating fourth visual data based on the current noise information; and detecting whether the skylight is abnormal or not based on the first visual data, the second visual data, the third visual data and the fourth visual data to obtain a detection result.
Further, in the embodiment of the present invention, the current position information includes skylight glass real-time position information and skylight motor real-time hall signal information; the skylight glass real-time position information comprises skylight glass position information and first time information; the skylight motor real-time Hall signal information comprises skylight motor Hall signal information and second time information;
the generating of the first visualization data based on the current position information and the generating of the second visualization data based on the current rotation speed information includes:
generating a first graph by using the skylight glass position information and the first time information, and taking the first graph as the first visual data; and generating a second curve graph by adopting the skylight motor Hall signal information and the second time information, and taking the second curve graph as the second visual data.
Specifically, in the embodiment of the invention, the skylight motor is a double-Hall motor and is in communication connection with the vehicle controller. When the double-Hall motor rotates for one circle, two Hall signals are generated, each signal can be set to be 0.4mm, and the signal length can be set according to the motor model. The Hall sensor acquires Hall signal number of the skylight motor and time data corresponding to the Hall signal number. In one embodiment of the invention, for example: when a vehicle leaves a factory, initialization is needed, and the foremost end of the skylight glass is set to be an initial zero position, namely S (0) =0; at this time, the number of hall signals corresponding to the numerical value of the position of the sunroof glass is 0. When the sun window motor records the Nth Hall signal, the position of the skylight glass is S =0.4N. Therefore, the first curve graph of the position and the time of the skylight glass can be generated by acquiring the Hall signal number of the skylight motor and the time data corresponding to the Hall signal number. The first visualization data may be a first graphical representation of skylight glass position versus time.
After the first visual data are obtained, the skylight motor needs to transmit the first visual data to the vehicle controller, so that the vehicle controller can judge abnormal sound according to the first visual data.
When the day window motor records the nth hall signal, the used time is the difference between the time of the (N + 1) th hall signal and the T-th hall signal, namely T (N) = T (N + 1) - (N). Since the length of the sunroof motor corresponding to the time length T (N) at the nth hall signal is H, the rotation speed of the sunroof motor 1 corresponding to the time length T (N) is V (N) = H/T (N). And H is the length of the Hall signal of 0.4mm, and the rotating speed of the skylight motor at the Nth Hall signal is V (N) =0.4/T (N). Similarly, when the skylight motor records the nth hall signal, the speed of the skylight motor corresponding to the signal is V (N + 1) =0.4/T (N + 1). From the calculated speeds of the skylight motors V (N) and V (N + 1), a speed change value of the motor in the unit time T (N), that is, an acceleration of the motor in the unit time T (N) can be obtained, and the acceleration formula is a (N) = [ V (N + 1) -V (N) ]/{ [ T (N) + T (N + 1) ]/2}. Therefore, by acquiring the number of hall signals of the skylight motor and the time data corresponding to the number of hall signals, a curve graph of the acceleration and the time of the skylight motor can be generated. According to the mechanics principle, under the condition that the driving force of the system is unchanged, the resistance borne by the motor is in inverse proportion to the acceleration of the system, and therefore a second curve graph of the system resistance of the skylight motor 1 and the corresponding time can be obtained. The second visualization data may be a second graphical representation of the system resistance versus time.
After the second visual data are obtained, the skylight motor needs to transmit the second visual data to the vehicle controller, so that the vehicle controller can perform abnormal sound judgment on the second visual data.
Further, in the embodiment of the present invention, the vibration information includes vehicle vibration information and third time information;
the generating third visual data based on the current vibration information comprises:
and generating a third graph by using the vehicle vibration information and the third time information, and taking the third graph as the third visual data.
Specifically, in the embodiment of the present invention, the vibration collector is in communication connection with the vehicle controller, collects current vibration frequency information and current vibration amplitude information from the vehicle body and the sunroof, and generates third visual data of the vehicle vibration frequency, the vibration amplitude and the time according to the collected information. The third visualization data may be a third graphical representation, i.e. a graph of vehicle vibration information versus time. The vibration collector compares the generated third curve graph with the vehicle condition curve graph in the preset information base to determine the current vehicle condition. The vehicle condition curve graph of the preset information base is as follows: and a vehicle condition curve graph which is collected from a user and generated according to the collected current vibration frequency information and the current vibration amplitude information of the vehicle under different road conditions. In one embodiment of the invention: and generating a third curve graph according to the acquired data, comparing the third curve graph with the curve graphs in the database, and determining that the current vehicle is in the high-speed road section when the similarity reaches 99.9% after the third curve graph is compared with the high-speed road condition curve graphs in the database. After the road section is determined, the vibration collector needs to transmit the third visual data to the vehicle controller, so that the vehicle controller can judge abnormal sound according to the third visual data.
Further, in the embodiment of the present invention, the current noise information includes frequency information of the noise outside the vehicle, amplitude information of the noise outside the vehicle, frequency information of the noise inside the vehicle, amplitude information of the noise inside the vehicle, and fourth time information;
the generating fourth visual data based on the current noise information comprises:
denoising the in-vehicle noise frequency information and the in-vehicle noise amplitude information based on the outside noise frequency information and the outside noise amplitude information to obtain denoised in-vehicle noise frequency information and processed in-vehicle noise amplitude information;
and generating a fourth curve graph by using the in-vehicle noise frequency information subjected to noise reduction, the in-vehicle noise amplitude information subjected to noise reduction and the fourth time information, and taking the fourth curve graph as fourth visual data.
Specifically, in the embodiment of the invention, the noise collector is arranged outside the vehicle, is in communication connection with the vehicle controller, and is used for collecting the frequency information of the noise outside the vehicle and the amplitude information of the noise outside the vehicle; the microphone is arranged in the vehicle, is in communication connection with the vehicle controller and is used for the frequency information of the noise in the vehicle and the amplitude information of the noise in the vehicle. Wherein, the noise collector can transmit the outer noise frequency information of car, the outer noise amplitude information of car that gathers to the microphone.
Since the sound data collected in the vehicle includes noise outside the vehicle, the microphone receives frequency information of the noise outside the vehicle and amplitude information of the noise outside the vehicle, and performs noise reduction processing on the collected frequency information of the noise inside the vehicle and amplitude information of the noise inside the vehicle by using the frequency information of the noise outside the vehicle and the amplitude information of the noise outside the vehicle according to the inverse spectrum noise reduction principle. And generating a fourth curve graph based on the current in-vehicle noise frequency information subjected to noise reduction, the current in-vehicle noise amplitude information and fourth time information corresponding to the noise information. The fourth visualized data can be a fourth curve graph, namely a graph of the noise in the vehicle and the time.
After the fourth visual data is obtained, the microphone needs to transmit the fourth visual data to the vehicle controller, so that the vehicle controller performs abnormal sound determination on the fourth visual data.
Further, in an embodiment of the present invention, the detecting whether there is an abnormality in the skylight based on the first visual data, the second visual data, the third visual data, and the fourth visual data to obtain a detection result includes:
detecting information change values of any three items of visual data in the first visual data, the second visual data, the third visual data and the fourth visual data, and whether the information change values exceed a change value threshold value in a preset time period;
and if so, generating a detection result of the abnormal skylight.
Specifically, in the embodiment of the present invention, the vehicle controller receives the first visual data, the second visual data, the third visual data, and the fourth visual data, and detects the received data. The data change value of the vehicle in the preset time period can be obtained by calculating the tolerance value of the curve graph in the received data, if the generated curve graph calculation result shows that the data change of the vehicle in the preset time period exceeds the change value threshold value, abnormal sound of the skylight is shown, and if a certain time point in the preset time period exceeds the data change value threshold value, the time point is ignored, and detection is continued.
When the data changes of any three curve graphs within the preset time period exceed the preset threshold value, the skylight is indicated to be abnormal, and the abnormal data of the skylight needs to be stored. In an embodiment of the invention, when the data change values in the first curve graph, the second curve graph and the third curve graph within the preset time period are detected to be compared with the change value threshold, and the data change values exceed the change value threshold, it is indicated that abnormal sound occurs in the skylight.
Further, in this embodiment of the present invention, the detecting whether there is an abnormality in the skylight based on the first visual data, the second visual data, the third visual data, and the fourth visual data to obtain a detection result further includes:
detecting information change values of any two items of visual data in the first visual data, the second visual data, the third visual data and the fourth visual data, whether the information change values exceed the change value threshold value within a preset time period, and whether the amplitude values exceeding the change value threshold value exceed the amplitude value threshold value;
and if the information change values of any two items of visual data exceed the change value threshold value within a preset time period, and the amplitude value exceeding the change value threshold value exceeds the amplitude value threshold value, generating a detection result that the skylight is abnormal.
Specifically, in the embodiment of the present invention, when any two curve graphs in the four items of visual data exceed the change value threshold value within the preset time period, and the exceeding amplitude exceeds the amplitude value threshold value, it is indicated that the skylight is abnormal, and the data of the detection result of the skylight abnormality needs to be stored. At this time, the amplitude value exceeding the amplitude value threshold is used as a criterion for judging the occurrence of abnormal noise in the skylight. In one embodiment of the invention, when the data change values of the first curve graph and the third curve graph in the preset time period are detected to be compared with the change value threshold value, if the data change values exceed the preset threshold value by 50%, the abnormal sound of the skylight is indicated; when the data of the first curve graph is compared with a preset threshold value and exceeds the preset threshold value by 50%, and the data of other curve graphs do not exceed the preset threshold value, ignoring the data and not considering that abnormal sound occurs in the skylight
And 103, if the detection result is abnormal, determining solution information corresponding to the detection result based on the trained fault detection model.
Specifically, in the embodiment of the present invention, a technician trains a fault detection neural network model by using skylight abnormal data collected in a centralized manner, and detects the generalization ability of the trained fault detection neural network model by testing a centralized sample; and taking a fault detection neural network model with generalization capability meeting the requirement as the fault detection model. The vehicle controller will detect the visual data received. And when the detection result shows that the skylight is in the abnormal state, the skylight is in the abnormal state. Namely, the case one: detecting that any three visual data in the four visual data are abnormal; and a second condition: any two kinds of the visual data exceed the variation value threshold value, and the exceeding amplitude exceeds the amplitude value threshold value. When abnormal data are detected, the vehicle controller sends the detection result to the gateway, and the gateway is in communication connection with the server, so that the server can receive the detection result sent by the vehicle controller through the gateway.
And after receiving the data of the detection result, the server determines a fault solving method corresponding to the detection result based on the trained fault detection model. In the embodiment of the present invention, after determining the solution, the server may perform the following steps in the first manner: the solution is directly displayed on the server terminal as solution information, so that after-sales personnel directly check the solution through the server terminal; in the second mode, the solution is returned to the vehicle controller as the solution information, so that the screen connected with the vehicle controller displays the solution information, and when the skylight fails, a user can obtain the solution information by looking up the screen to solve the skylight failure.
Further, in the embodiment of the present invention, the trained fault detection model is deployed in a server;
the determining of the solution information corresponding to the detection result based on the trained fault detection model includes:
acquiring related information related to the detection result; the associated information comprises current weather information, current geographical position information and vehicle running frequency information; and sending the detection result and the associated information to the server so that the server processes the detection result by adopting the trained fault detection model to obtain corresponding solution information.
Specifically, in the embodiment of the present invention, the gateway is connected to the vehicle controller and the server, on one hand, receives the detection result sent by the vehicle controller, and on the other hand, collects the associated information related to the detection result. The associated information includes: current weather information, current vehicle geographical position information, and the number of times the customer vehicle travels. After the gateway collects all the associated information, the associated information and the detection result need to be sent to the server together. And after receiving the association information and the detection result, the server generates a fault detection model through the association information and the detection result, and compares the newly generated fault detection model with the trained fault detection model to determine a fault solution.
By adopting the technical scheme of the embodiment of the application, the visual data can be generated based on various information through acquiring various information of the vehicle, the generated visual data is subjected to abnormal detection, the abnormal detection result of the skylight is obtained, the big data fitting technology is adopted for the abnormal detection result of the skylight, the accuracy of skylight fault reason detection is improved, the skylight fault problem can be remotely solved, and the vehicle production line detection efficiency and the accuracy of vehicle after-sale fault detection are improved.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Referring to fig. 2, a block diagram of a vehicle sunroof detecting system device according to an embodiment of the present invention is shown, which may specifically include the following modules: the device comprises an acquisition module 201, a detection module 202 and a storage module 203, wherein:
the acquisition module 201 is configured to acquire current position information of a sunroof glass, current rotation speed information of a sunroof motor, current vibration information of a vehicle, and current noise information of the vehicle;
a detection module 202, configured to detect whether the skylight is abnormal based on the current position information, the current rotation speed information, the current vibration information, and the current noise information, and obtain a detection result;
a determining module 203, configured to determine, if the detection result is abnormal, solution information corresponding to the detection result based on the trained fault detection model.
In an embodiment of the present invention, the detection module further includes:
the first generation submodule is used for generating first visual data based on the current position information;
the second generation submodule is used for generating second visual data based on the current rotating speed information;
a third generation submodule, configured to generate third visual data based on the current vibration information;
a fourth generation submodule, configured to generate fourth visual data based on the current noise information;
and the first detection submodule is used for detecting whether the skylight is abnormal or not based on the first visual data, the second visual data, the third visual data and the fourth visual data to obtain a detection result.
In an embodiment of the present invention, the determining module includes:
the first obtaining submodule is used for obtaining the relevant information related to the detection result; the associated information comprises current weather information, current geographical position information and vehicle running frequency information;
and the sending submodule is used for sending the detection result and the associated information to a server so that the server processes the detection result by adopting the trained fault detection model to obtain corresponding solution information.
In an embodiment of the present invention, the first generation submodule includes:
a first generating unit configured to generate a first graph using the sunroof glass position information and the first time information, and to use the first graph as the first visualization data.
In an embodiment of the present invention, the second generation submodule includes:
and the second generating unit is used for generating a second curve graph by adopting the hall signal information of the skylight motor and the second time information, and taking the second curve graph as the second visual data.
In an embodiment of the present invention, the third generation submodule includes:
a third generating unit configured to generate a third graph using the vehicle vibration information and the third time information, and to use the third graph as the third visualization data.
In an embodiment of the present invention, the fourth generation submodule includes:
the first processing unit is used for denoising the in-vehicle noise frequency information and the in-vehicle noise amplitude information based on the outside noise frequency information and the outside noise amplitude information to obtain denoised in-vehicle noise frequency information and denoised in-vehicle noise amplitude information;
and a fourth generating unit, configured to generate a fourth graph by using the processed in-vehicle noise frequency information, the processed in-vehicle noise amplitude information, and the fourth time information, and use the fourth graph as the fourth visualization data.
In an embodiment of the present invention, the detection module further includes:
the second detection submodule is used for detecting information change values of any three items of visual data in the first visual data, the second visual data, the third visual data and the fourth visual data, and whether the information change values exceed a change value threshold value in a preset time period;
and the fifth generation submodule is used for generating a detection result that the skylight is abnormal if the skylight is abnormal.
In an embodiment of the present invention, the detection module further includes:
a third detection sub-module, configured to detect information change values of any two items of visual data in the first visual data, the second visual data, the third visual data, and the fourth visual data, whether the information change values all exceed the change value threshold within a preset time period, and detect whether a magnitude value exceeding the change value threshold exceeds a magnitude value threshold;
and a sixth generation submodule, configured to generate a detection result that the skylight is abnormal if the information change values of any two items of visual data exceed the change value threshold within a preset time period, and the amplitude value exceeding the change value threshold exceeds the amplitude value threshold.
For the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
By adopting the technical scheme of the embodiment of the application, the visual data can be generated based on various information by acquiring various information of the vehicle, the generated visual data is subjected to anomaly detection, and the detection result of the anomaly of the skylight is obtained. According to the invention, a big data fitting technology is adopted for the detection result of the skylight abnormity, so that the accuracy of detecting the skylight fault reason is improved, the skylight fault problem is remotely solved, and the vehicle production line detection efficiency and the accuracy of vehicle after-sale fault detection are further improved.
An embodiment of the present invention further provides a vehicle, including:
the skylight fault detection method comprises a processor, a memory and a computer program which is stored on the memory and can run on the processor, wherein when the computer program is executed by the processor, each process of the skylight fault detection method embodiment is realized, the same technical effect can be achieved, and in order to avoid repetition, the description is omitted here.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the processes of the skylight fault detection method embodiment, and can achieve the same technical effects, and in order to avoid repetition, the descriptions are not repeated here.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable information processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable information processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable information processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable information processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or terminal device that comprises the element.
The method and the apparatus provided by the present invention are described in detail, and the principle and the embodiment of the present invention are explained by applying specific examples, and the description of the above examples is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (11)

1. A method of detecting a malfunction of a vehicle sunroof, the method comprising:
acquiring current position information of skylight glass, current rotating speed information of a skylight motor, current vibration information of a vehicle and current noise information of the vehicle;
detecting whether the skylight is abnormal or not based on the current position information, the current rotating speed information, the current vibration information and the current noise information to obtain a detection result;
and if the detection result is abnormal, determining solution information corresponding to the detection result based on the trained fault detection model.
2. The sunroof fault detection method according to claim 1, wherein the detecting whether the sunroof is abnormal based on the current position information, the current rotational speed information, the current vibration information, and the current noise information to obtain a detection result comprises:
generating first visual data based on the current position information, generating second visual data based on the current rotating speed information, generating third visual data based on the current vibration information, and generating fourth visual data based on the current noise information;
and detecting whether the skylight is abnormal or not based on the first visual data, the second visual data, the third visual data and the fourth visual data to obtain a detection result.
3. The skylight fault detection method of claim 1, wherein the trained fault detection model is deployed in a server;
the determining, based on the trained fault detection model, solution information corresponding to the detection result includes:
acquiring related information related to the detection result; the associated information comprises current weather information, current geographical position information and vehicle running frequency information;
and sending the detection result and the associated information to the server so that the server processes the detection result by adopting the trained fault detection model to obtain corresponding solution information.
4. The sunroof fault detection method according to claim 2, wherein the current position information includes sunroof glass real-time position information and sunroof motor real-time hall signal information; the skylight glass real-time position information comprises skylight glass position information and first time information; the skylight motor real-time Hall signal information comprises skylight motor Hall signal information and second time information;
the generating of the first visualization data based on the current position information and the generating of the second visualization data based on the current rotation speed information includes:
generating a first graph by using the skylight glass position information and the first time information, and taking the first graph as the first visual data;
and generating a second curve graph by adopting the skylight motor Hall signal information and the second time information, and taking the second curve graph as the second visual data.
5. The sunroof failure detection method according to claim 2, wherein the vibration information includes vehicle vibration information and third time information;
the generating third visual data based on the current vibration information comprises:
and generating a third graph by using the vehicle vibration information and the third time information, and taking the third graph as the third visual data.
6. The sunroof fault detection method of claim 2, wherein the current noise information includes outside noise frequency information, outside noise amplitude information, inside noise frequency information, inside noise amplitude information, and fourth time information;
the generating fourth visual data based on the current noise information comprises:
denoising the interior noise frequency information and the interior noise amplitude information based on the exterior noise frequency information and the exterior noise amplitude information to obtain denoised interior noise frequency information and processed interior noise amplitude information;
and generating a fourth curve graph by using the in-vehicle noise frequency information subjected to noise reduction, the in-vehicle noise amplitude information subjected to noise reduction and the fourth time information, and taking the fourth curve graph as fourth visual data.
7. The skylight fault detection method according to claim 2, wherein the detecting whether the skylight is abnormal or not based on the first visual data, the second visual data, the third visual data and the fourth visual data to obtain a detection result comprises:
detecting information change values of any three items of visual data in the first visual data, the second visual data, the third visual data and the fourth visual data, and whether the information change values exceed a change value threshold value in a preset time period;
and if so, generating a detection result of the abnormal skylight.
8. The sunroof fault detection method according to claim 2, wherein the detecting whether the sunroof has an abnormality based on the first visualization data, the second visualization data, the third visualization data, and the fourth visualization data to obtain a detection result further comprises:
detecting information change values of any two items of visual data in the first visual data, the second visual data, the third visual data and the fourth visual data, whether the information change values exceed the change value threshold value in a preset time period, and whether amplitude values exceeding the change value threshold value exceed an amplitude value threshold value;
and if the information change values of any two items of visual data exceed the change value threshold value within a preset time period, and the amplitude value exceeding the change value threshold value exceeds the amplitude value threshold value, generating a detection result that the skylight is abnormal.
9. A detection device for a vehicle sunroof, the device comprising:
the acquisition module is used for acquiring current position information of skylight glass, current rotating speed information of a skylight motor, current vibration information of a vehicle and current noise information of the vehicle;
the detection module is used for detecting whether the skylight is abnormal or not based on the current position information, the current rotating speed information, the current vibration information and the current noise information to obtain a detection result;
and the determining module is used for determining solution information corresponding to the detection result based on the trained fault detection model if the detection result is abnormal.
10. A vehicle, characterized by comprising: processor, memory and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of the sunroof failure detection method of any of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the skylight fault detection method according to any one of claims 1-6.
CN202210951819.0A 2022-08-09 2022-08-09 Skylight fault detection method and device, vehicle and computer-readable storage medium Pending CN115615711A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210951819.0A CN115615711A (en) 2022-08-09 2022-08-09 Skylight fault detection method and device, vehicle and computer-readable storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210951819.0A CN115615711A (en) 2022-08-09 2022-08-09 Skylight fault detection method and device, vehicle and computer-readable storage medium

Publications (1)

Publication Number Publication Date
CN115615711A true CN115615711A (en) 2023-01-17

Family

ID=84857490

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210951819.0A Pending CN115615711A (en) 2022-08-09 2022-08-09 Skylight fault detection method and device, vehicle and computer-readable storage medium

Country Status (1)

Country Link
CN (1) CN115615711A (en)

Similar Documents

Publication Publication Date Title
Antoni et al. Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine
CN103674234B (en) State early warning method and system for abnormal vibration of wind generating set
CN110987502B (en) Shield equipment and monitoring and diagnosing system and method for online state of main drive of shield equipment
CN110779716A (en) Embedded mechanical fault intelligent diagnosis equipment and diagnosis method
Klausen et al. Multi-band identification for enhancing bearing fault detection in variable speed conditions
Guo et al. An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression
CN114371001B (en) Gear box fault defect detection system
US12061236B2 (en) Power conversion device, rotating machine system, and diagnosis method
Wu et al. A modified tacho-less order tracking method for the surveillance and diagnosis of machine under sharp speed variation
US20220187165A1 (en) Method for correcting a time-dependent measurement signal of a motor transmission unit as well as a method for detecting wear and/or damage of the same by means of this correction method
CN114739671A (en) Bearing fault diagnosis method based on improved generalized S transformation
CN111999087A (en) Vibration screen online state monitoring method based on LabVIEW
CN109934136B (en) Rolling bearing fault diagnosis method based on Duffing vibrator and eigen mode component
CN103914889B (en) A kind of system and method that the collection behavior of car speed is carried out to abnormality detection
CN115615711A (en) Skylight fault detection method and device, vehicle and computer-readable storage medium
CN112320599B (en) Health monitoring method and system for port hoisting equipment
CN113588260A (en) Electric automobile gear noise signal detecting system
CN112320520A (en) Elevator abnormal vibration detection method based on residual error analysis
CN112465068A (en) Rotating equipment fault feature extraction method based on multi-sensor data fusion
EP3600799B1 (en) Method, apparatus and system for monitoring industrial robot
CN105651512A (en) Bump detection method of main speed-reducing gear of automobile driving axle
CN115452420A (en) Locking fault detection method, device, equipment and storage medium
CN109029994A (en) A kind of equipment, method and automobile using sound detection automobile bearing
CN113960541A (en) Radar fault early warning method based on massive frequency domain data and moving average method
Beering et al. Estimation of rotational speeds based on gearbox vibrations via artificial neural networks

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