Knock abnormal sound recognition method and device and medium
By collecting and analyzing the engine's vibration acceleration, noise and KPEAK images, combined with the setting and analysis of the threshold interval, the correctness and efficiency of knock noise recognition in engine performance calibration is solved, and the quantification of knock noise is achieved. Analyze and accurately identify.
Patent Information
- Application Number
- CN202411665022.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-20
- Publication Date
- 2025-02-18
AI Technical Summary
During the engine performance calibration process, the accuracy of identifying knock noise is not high and the efficiency is low. This is mainly because the existing technology relies on subjective auditory and combustion simulation, and it is impossible to fully determine whether the noise and vibration during knocking are acceptable.
By collecting vibration acceleration images, noise images and KPEAK images of the engine at different knock levels, combining the set noise mutation threshold interval, the vibration acceleration peak threshold interval is determined, and a KPEAK threshold interval is generated, and knock noise identification is performed. .
Quantitative analysis of engine knock noise is realized, the accuracy and efficiency of identification is improved, knock noise can be avoided early in calibration, and performance calibration development efficiency is improved.
Smart Images
Figure CN119469782A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of engine technology, and in particular to a method, device and medium for identifying abnormal knock noise. Background Art
[0002] Engine performance calibration refers to the precise adjustment and optimization of the engine's operating parameters to ensure that it can achieve the best performance and efficiency under different working conditions. Usually, during the performance calibration process of a gas engine, in order to pursue the engine's power and economy, the engine is often in a state of slight knock. Engine knock can often cause a series of problems such as power loss, increased fuel consumption, increased noise, and worsening emissions. Severe knock can also cause knocking, engine flameout, and reliability failures. Therefore, if the slight knock control is not appropriate, even if it does not cause reliability problems, it will cause abnormal noise problems, and even affect driving comfort after the engine is matched with the vehicle.
[0003] At present, in the process of performance calibration, calibration is carried out through calibration software and combustion test software. The identification of abnormal knock noise mostly relies on subjective listening to identify the abnormal knock noise.
[0004] However, during the calibration process of the calibration software and combustion test software, the combustion simulation situation is mainly considered. It is impossible to determine whether the noise generated during detonation is acceptable, and different people's judgments will produce subjective differences. In addition, the calibration boundary cannot take into account the dimensions of vibration and noise. Vibration and noise problems often require engineers to evaluate, and the calibration data needs to be repeatedly modified according to the acceptable degree of vibration and noise. Ultimately, the process of identifying abnormal knock noise is not accurate and efficient. Summary of the invention
[0005] The embodiments of the present application provide a method, device and medium for identifying abnormal knock noise, which are used to solve the problem of low accuracy and low efficiency in the process of identifying abnormal knock noise.
[0006] The present application embodiment adopts the following technical solutions:
[0007] On the one hand, an embodiment of the present application provides a method for identifying abnormal knock noise, the method comprising: collecting vibration acceleration images, noise images and KPEAK images of the engine at different knock levels; determining the vibration acceleration peak threshold interval of the engine according to a set noise mutation threshold interval, the noise image and the vibration acceleration image; generating the KPEAK threshold interval of the engine according to the vibration acceleration peak threshold interval and the KPEAK image; and identifying abnormal knock noise of the engine according to the KPEAK threshold interval.
[0008] In one example, determining the vibration acceleration peak threshold interval of the engine based on the set noise mutation threshold interval, the noise image and the vibration acceleration image specifically includes: aligning the horizontal coordinates of the noise image and the vibration acceleration image; the horizontal coordinate is a time point; superimposing the aligned noise image and the vibration acceleration image along the horizontal coordinate to obtain a first merged image; and extracting the vibration acceleration peak threshold interval corresponding to the noise mutation threshold interval based on the first merged image.
[0009] In one example, extracting the vibration acceleration peak threshold interval corresponding to the noise mutation threshold interval based on the first merged image specifically includes: in the first merged image, counting the vibration acceleration peak value at each time point within the noise mutation threshold interval to obtain the vibration acceleration peak value at each time point; generating a first knock relationship image based on the noise value and the vibration acceleration peak value at each time point; the coordinate axes of the first knock relationship image are the noise value and the vibration acceleration peak value; and determining the vibration acceleration peak threshold interval corresponding to the noise mutation threshold interval based on the first knock relationship image.
[0010] In one example, determining the vibration acceleration peak threshold interval corresponding to the noise mutation threshold interval based on the first knock relationship image specifically includes: dividing the first contour area corresponding to the noise mutation threshold interval according to the data point distribution of the first knock relationship image to remove random invalid points; determining the vibration acceleration peak threshold interval based on the first contour area.
[0011] In one example, the KPEAK threshold interval of the engine is generated based on the vibration acceleration peak threshold interval and the KPEAK image, specifically including: aligning the horizontal coordinates of the vibration acceleration image and the KPEAK image; the horizontal coordinate is the time point; superimposing the aligned vibration acceleration image and the KPEAK image along the horizontal coordinate to obtain a second merged image; and extracting the KPEAK threshold interval corresponding to the vibration acceleration peak threshold interval based on the second merged image.
[0012] In one example, extracting the KPEAK threshold interval corresponding to the vibration acceleration peak threshold interval based on the second merged image specifically includes: in the second merged image, counting the KPEAK value at each time point within the vibration acceleration peak threshold interval to obtain the KPEAK value at each time point; generating a second knock relationship image based on the vibration acceleration peak value and the KPEAK value at each time point; the coordinate axes of the second knock relationship image are the vibration acceleration peak value and the KPEAK value; and determining the KPEAK threshold interval corresponding to the vibration acceleration peak threshold interval based on the second knock relationship image.
[0013] In one example, determining the KPEAK threshold interval corresponding to the vibration acceleration peak threshold interval based on the second knock relationship image specifically includes: dividing the second contour area corresponding to the vibration acceleration peak threshold interval according to the data point distribution of the second knock relationship image to remove random invalid points; determining the KPEAK threshold interval based on the second contour area.
[0014] In one example, the knock noise identification of the engine according to the KPEAK threshold interval specifically includes: when there are multiple KPEAK threshold intervals, determining that the engine has a knock noise through the first KPEAK threshold interval, determining the engine knock noise warning through the second KPEAK threshold interval to adjust the performance calibration data, and determining that the knock noise of the engine is acceptable through the third KPEAK threshold interval; collecting the KPEAK image to be identified of the engine; when the data points whose KPEAK values in the KPEAK image to be identified are in the first KPEAK threshold interval exceed the first preset proportion of the total number of sampled KPEAKs, generating knock noise alarm information of the engine and limiting the engine torque increase; when the data points whose KPEAK values in the KPEAK image to be identified are in the second KPEAK threshold interval exceed the second preset proportion of the total number of sampled KPEAKs, generating knock noise warning information of the engine to prompt the adjustment of the performance calibration data; when the KPEAK value in the KPEAK image to be identified is in the third KPEAK threshold interval, determining that the engine can continue the performance calibration work.
[0015] On the other hand, an embodiment of the present application provides a knock noise identification device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the knock noise identification methods described above.
[0016] On the other hand, an embodiment of the present application provides a non-volatile computer storage medium for abnormal knock noise identification, which stores computer executable instructions, and the computer executable instructions can execute any of the above-mentioned methods for abnormal knock noise identification.
[0017] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:
[0018] By collecting a variety of image data, the state of the engine at different knock levels is more comprehensively reflected, providing more intuitive judgment information for the staff. Secondly, the vibration acceleration peak threshold is determined according to the set noise threshold, and the KPEAK threshold is generated based on this. The KPEAK threshold is derived by taking noise and vibration factors into consideration. When performing knock abnormality identification based on the KPEAK threshold, it can not only objectively determine whether the user's requirements for noise and vibration are met, but also promote performance calibration work, and realize quantitative analysis of engine knock abnormality. It can not only avoid knock abnormality in the early stage of calibration, but also improve the efficiency of performance calibration development, and also improve the accuracy and efficiency of knock abnormality identification. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] In order to more clearly illustrate the technical solution of the present application, some embodiments of the present application will be described in detail below in conjunction with the accompanying drawings, in which:
[0020] Figure 1 A schematic diagram of a process flow of a knock abnormal noise identification method provided in an embodiment of the present application;
[0021] Figure 2 A schematic diagram of superimposing a vibration acceleration image and a KPEAK image provided in an embodiment of the present application;
[0022] Figure 3 A knock relationship diagram between a vibration acceleration peak value and KPEAK provided in an embodiment of the present application;
[0023] Figure 4 A schematic diagram of the structure of a knock noise identification device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0024] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.
[0025] Some embodiments of the present application are described in detail below with reference to the accompanying drawings.
[0026] Figure 1 A schematic diagram of a process flow of a knock noise identification method provided in an embodiment of the present application. The method can be applied to different business fields, and some input parameters or intermediate results in the process can be manually adjusted to help improve accuracy.
[0027] The analysis method involved in the embodiments of the present application can be implemented by a terminal device or a server, and the present application does not impose any special restrictions on this. For the convenience of understanding and description, the following embodiments are described in detail by taking a server as an example.
[0028] It should be noted that the server may be a single device or a system consisting of multiple devices, that is, a distributed server, and this application does not make any specific limitation on this.
[0029] Figure 1 The process in includes the following steps:
[0030] S101: Collect vibration acceleration images, noise images and KPEAK images of the engine at different knock levels.
[0031] It should be noted that detonation refers to the natural occurrence of the end mixture gas ahead of the flame front in the cylinder. When detonation occurs, the end mixture gas of the combustion chamber burns rapidly and releases a large amount of heat sharply, resulting in a very high pressure rise rate. The released heat causes the generation of pressure waves and oscillations back and forth, transmitting noise through the engine body to the external environment.
[0032] KPEAK refers to the knock peak value, which is the maximum value of the cylinder pressure signal after high-pass filtering.
[0033] In some embodiments of the present application, in order to obtain the KPEAK threshold range, data must first be collected, and the KPEAK threshold range is finally obtained based on the collected noise, vibration, and KPEAK data.
[0034] Based on this, the vibration and noise data under different knock levels are collected through the joint data acquisition platform, and the engine vibration noise and in-cylinder combustion are monitored at the same time. The noise is based on the 1m noise on the left side of the engine as the reference value, the vibration level of the cylinder head is used as the reference value, and the knock peak value KPEAK is used as the reference value for in-cylinder combustion. By collecting the three reference values at the same time, the corresponding relationship between the three reference values is established. The KPEAK reference limit value during calibration is established according to the vibration and noise limit value.
[0035] Among them, the joint data acquisition platform is a system that integrates multiple sensors and data acquisition technologies, which is used to simultaneously monitor the vibration, noise and in-cylinder combustion of the engine. By collecting the vibration data of the engine, the platform can analyze the mechanical operation status of the engine, such as the vibration level of the cylinder head, so as to determine whether the engine has abnormal vibration. At the same time, the platform also collects the noise data of the engine, and uses the 1m noise on the left side of the engine as a reference value to evaluate whether the noise generated by the engine is within an acceptable range. In addition, the platform also evaluates whether the combustion state of the engine is stable and whether there is a risk of knock by monitoring the in-cylinder combustion, especially the knock peak value KPEAK.
[0036] In addition, based on the noise standard, the developers set the noise mutation threshold range and controlled the engine's operating parameters to generate different knock levels for vibration, noise and in-cylinder combustion conditions, which are divided into abnormal knock noise that has occurred and this degree of knock noise is obvious. The abnormal knock noise has not yet reached an unacceptable level, but it is close to or may reach it soon, and the noise is smaller than the former. The abnormal knock noise is acceptable and will not affect driving comfort, and the noise can be ignored. The engine is tested according to three different knock levels. As time changes, noise data, vibration data and KPEAK data of different knock levels are collected to obtain 1 noise image, 1 vibration acceleration image and 1 KPEAK image.
[0037] In addition, in some embodiments of the present application, for collecting noise data, vibration data and KPEAK data at different knock levels, a single vibration acceleration image, noise image and KPEAK image at each knock level can also be collected, that is, there are 3 vibration acceleration images, 3 noise images and 3 KPEAK images.
[0038] S102: Determine a vibration acceleration peak threshold interval of the engine according to a set noise mutation threshold interval, the noise image, and the vibration acceleration image.
[0039] In some embodiments of the present application, an image superposition method is used for image processing. First, the horizontal coordinates of the noise image and the vibration acceleration image are aligned. The horizontal coordinate is the time point. Then, the aligned noise image and the vibration acceleration image are superimposed along the horizontal coordinate to obtain a first merged image. Then, in the first merged image, the vibration acceleration peak value at each time point within the noise mutation threshold interval is counted to obtain the vibration acceleration peak value at each time point. Then, according to the noise value and the vibration acceleration peak value at each time point, a first knock relationship image is generated. The coordinate axes of the first knock relationship image are the noise value and the vibration acceleration peak value. Then, according to the data point distribution of the first knock relationship image, the first contour area corresponding to the noise mutation threshold interval is divided to remove random invalid points. Finally, according to the first contour area, the vibration acceleration peak threshold interval is determined.
[0040] It should be noted that the first contour area corresponding to the noise mutation threshold interval can be divided according to the set distribution characteristics, and on the basis of the distribution characteristics, it can also be divided according to the principle of minimizing the discarded points in each noise mutation threshold interval.
[0041] It should also be noted that, through the above method, for example, the noise mutation threshold interval and the vibration acceleration peak threshold interval are:
[0042] Noise mutation ≥ 2dBA, vibration acceleration peak ≥ 80g is the threshold for abnormal knock noise. In this state, abnormal knock noise is unacceptable; 1dBA≤noise mutation <2dBA, 40g≤vibration acceleration peak <80g is the abnormal knock noise warning threshold. In this state, the calibration data needs to be adjusted; noise mutation <1dBA, vibration acceleration peak <40g is the acceptable threshold for abnormal knock noise. In this state, the vibration and noise requirements are met.
[0043] S103: Generate a KPEAK threshold interval of the engine according to the vibration acceleration peak threshold interval and the KPEAK image.
[0044] To determine the KPEAK threshold interval, the image superposition method is still used. First, the vibration acceleration image and the KPEAK image are aligned along the horizontal axis. The horizontal axis is the time point. Then, the aligned vibration acceleration image and the KPEAK image are superimposed along the horizontal axis to obtain a second merged image, as shown in FIG. Figure 2 In the second merged image, the KPEAK value at each time point within the vibration acceleration peak threshold interval is counted to obtain the KPEAK value at each time point; according to the vibration acceleration peak value and the KPEAK value at each time point, a second knock relationship image is generated, as shown in Figure 3The coordinate axes of the second knock relationship image are the vibration acceleration peak value and the KPEAK value; according to the data point distribution of the second knock relationship image, the second contour area corresponding to the vibration acceleration peak threshold interval is divided to remove random invalid points; according to the second contour area, the KPEAK threshold interval is determined.
[0045] It should be noted that when dividing the second contour area corresponding to the vibration acceleration peak threshold interval, it can be divided according to the set distribution characteristics, and on the basis of the distribution characteristics, it can also be divided according to the principle of minimizing the discarded points in each vibration acceleration peak threshold interval.
[0046] S104: Identifying abnormal knock noise of the engine according to the KPEAK threshold range.
[0047] In some embodiments of the present application, when there are multiple KPEAK threshold intervals, the first KPEAK threshold interval is used to determine whether the engine has abnormal knock noise, the second KPEAK threshold interval is used to determine the engine abnormal knock noise warning to adjust the performance calibration data, and the third KPEAK threshold interval is used to determine whether the engine abnormal knock noise is acceptable.
[0048] It should be noted that, for example, the KPEAK threshold range is: KPEAK value ≥ 0.8 bar, in this state, the knock noise is unacceptable; 0.4 bar ≤ KPEAK value < 0.8 bar, in this state, the calibration data needs to be adjusted; KPEAK value < 0.4 bar meets the vibration and noise requirements of knock.
[0049] Once the KPEAK threshold is defined, it can be used in the calibration phase. The engine is operated at a fixed speed and load, and data is collected within a certain period of time. For example, the collection time is set to 60 seconds, and the collection interval is set to 0.1 seconds to collect vibration acceleration images, noise images, and KPEAK images of the engine at different knock levels, and the data are statistically analyzed.
[0050] Statistics are performed based on the collected KPEAK values and the corresponding data point proportions, specifically: when the data points whose KPEAK values in the KPEAK image to be identified are in the first KPEAK threshold interval exceed the first preset proportion of the total number of sampled KPEAKs, a knock noise alarm message of the engine is generated, and the engine torque increase is limited; when the data points whose KPEAK values in the KPEAK image to be identified are in the second KPEAK threshold interval exceed the second preset proportion of the total number of sampled KPEAKs, a knock noise warning message of the engine is generated to prompt adjustment of the performance calibration data; when the KPEAK value in the KPEAK image to be identified is in the third KPEAK threshold interval, it is determined that the engine can continue the performance calibration work.
[0051] It should be noted here that the first preset proportion set in the embodiment of the present application is that when the data points with KPEAK value ≥ 0.8bar exceed 5% of the total number of samples, the system will issue an alarm and limit the engine torque increase; the second preset proportion is when the data points with 0.4bar≤KPEAK value<0.8bar exceed 10% of the total number of samples, a pop-up window will be displayed to generate knock and abnormal sound warning information of the engine to prompt the adjustment of performance calibration data; when the KPEAK value is in the third KPEAK threshold range, specifically when the KPEAK values of the collected data are all <0.4ba, the vibration and noise requirements are met and the calibration work continues.
[0052] pass Figure 1 The method can more comprehensively reflect the state of the engine at different knock levels by collecting a variety of image data, thereby providing richer information for subsequent knock and abnormal sound identification.
[0053] In addition, the vibration acceleration peak threshold interval is determined according to the set noise mutation threshold interval, noise image and vibration acceleration image, and the KPEAK threshold interval is generated according to the vibration acceleration peak threshold interval and KPEAK image. Both steps make full use of the time points and numerical information in the image data. By merging images and extracting threshold intervals, quantitative analysis of engine knock noise is achieved, and the relationship between knock KPEAK value and knock noise is established to guide knock calibration.
[0054] Finally, when the engine knock noise is identified according to the KPEAK threshold interval, multiple KPEAK threshold intervals are set to determine the situations where the engine has knock noise, knock noise warning, and knock noise is acceptable, and corresponding alarm information or warning information is generated accordingly to guide the engine performance calibration work. This method not only improves the accuracy of knock noise identification, but also can detect problems in time and take corresponding measures to avoid further damage to the engine.
[0055] More intuitively, Figure 2 A schematic diagram of superimposing a vibration acceleration image and a KPEAK image provided in an embodiment of the present application.
[0056] exist Figure 2 In the figure, the horizontal axis is the time point, and the vertical axis is the vibration acceleration and KPEAK value. Figure 2 The KPEAK value corresponding to each time point within the vibration acceleration peak threshold range can be clearly and intuitively understood, which provides statistical convenience for the staff and is more helpful in obtaining the second knock relationship diagram, thereby increasing the efficiency of identification.
[0057] Figure 3A knock relationship diagram between a vibration acceleration peak value and KPEAK provided in an embodiment of the present application.
[0058] right Figure 2 The KPEAK value at each time point within the vibration acceleration peak threshold interval is counted to obtain the KPEAK value at each time point, and the second knock relationship diagram is obtained by plotting the vibration acceleration peak value and the KPEAK value at each time point.
[0059] exist Figure 3 In the figure, the horizontal axis is the KPEAK value, and the vertical axis is the vibration acceleration peak value. It can be seen intuitively from the image that when the vibration acceleration peak value is ≥80g, the corresponding KPEAK value increases, and the corresponding KPEAK value is mostly ≥0.8bar, that is, the higher the vibration acceleration peak value, the larger the KPEAK value. The more serious the knock noise is, the two contour areas of vibration acceleration peak value ≥80g and 40g≤vibration acceleration peak value<80g are shown in the figure. Therefore, according to the data point distribution of the second knock relationship image, the second contour area corresponding to the vibration acceleration peak threshold interval is divided. Since the actual situation is not ideal, random invalid points need to be removed, and based on this, the KPEAK threshold interval can be intuitively determined according to the second contour area, and the results can be intuitively displayed in the form of charts, tables, etc. It greatly reduces the technical threshold of the staff and improves the efficiency and accuracy of identifying knock noise during the calibration process. Solve the current problem of how to identify the risk of knock noise during the performance calibration process.
[0060] Figure 4 A schematic diagram of the structure of a knock abnormal sound identification device provided in an embodiment of the present application includes:
[0061] at least one processor; and,
[0062] a memory communicatively connected to the at least one processor; wherein,
[0063] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the above-mentioned methods for identifying abnormal knock and noise.
[0064] Some embodiments of the present application provide a non-volatile computer storage medium for abnormal knock noise identification, which stores computer executable instructions, and the computer executable instructions can execute any of the above-mentioned abnormal knock noise identification methods.
[0065] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.
[0066] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.
[0067] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0068] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0069] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0071] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0072] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.
[0073] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.
[0074] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0075] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the technical principle of the present application should all fall within the protection scope of the present application.
Claims
1. A method for identifying abnormal knock noise, characterized in that: The method comprises: Collect vibration acceleration images, noise images and KPEAK images of the engine at different knock levels; Determining a vibration acceleration peak threshold interval of the engine according to a set noise mutation threshold interval, the noise image and the vibration acceleration image; generating a KPEAK threshold interval of the engine according to the vibration acceleration peak threshold interval and the KPEAK image; According to the KPEAK threshold range, abnormal knock noise of the engine is identified.
2. The method according to claim 1, characterized in that The step of determining the vibration acceleration peak threshold interval of the engine according to the set noise mutation threshold interval, the noise image and the vibration acceleration image specifically includes: Aligning the abscissas of the noise image and the vibration acceleration image; the abscissas are time points; Superimposing the aligned noise image and the vibration acceleration image along the horizontal axis to obtain a first combined image; A vibration acceleration peak value threshold interval corresponding to the noise mutation threshold interval is extracted according to the first combined image.
3. The method according to claim 2, characterized in that The step of extracting the vibration acceleration peak threshold interval corresponding to the noise mutation threshold interval according to the first merged image specifically includes: In the first merged image, the vibration acceleration peak value at each time point within the noise mutation threshold interval is counted to obtain the vibration acceleration peak value at each time point; Generate a first knock relationship image according to the noise value and the vibration acceleration peak value at each time point; the coordinate axes of the first knock relationship image are the noise value and the vibration acceleration peak value; A vibration acceleration peak value threshold interval corresponding to the noise mutation amount threshold interval is determined according to the first knock relationship image.
4. The method according to claim 3, characterized in that The determining, according to the first knock relationship image, a vibration acceleration peak value threshold interval corresponding to the noise mutation threshold interval specifically includes: Dividing a first contour area corresponding to a noise mutation threshold interval according to the data point distribution of the first knock relationship image to remove random invalid points; A vibration acceleration peak threshold interval is determined according to the first contour area.
5. The method according to claim 1, characterized in that Generating the KPEAK threshold interval of the engine according to the vibration acceleration peak threshold interval and the KPEAK image specifically includes: Align the abscissa of the vibration acceleration image with that of the KPEAK image; the abscissa is the time point; The aligned vibration acceleration image and the KPEAK image are superimposed along the horizontal axis to obtain a second merged image; A KPEAK threshold interval corresponding to the vibration acceleration peak threshold interval is extracted according to the second combined image.
6. The method according to claim 5, characterized in that Extracting the KPEAK threshold interval corresponding to the vibration acceleration peak threshold interval according to the second merged image specifically includes: In the second merged image, the KPEAK value at each time point within the vibration acceleration peak threshold interval is counted to obtain the KPEAK value at each time point; A second knock relationship image is generated according to the vibration acceleration peak value and the KPEAK value at each time point; the coordinate axes of the second knock relationship image are the vibration acceleration peak value and the KPEAK value; A KPEAK threshold interval corresponding to the vibration acceleration peak threshold interval is determined according to the second knock relationship image.
7. The method according to claim 6, characterized in that Determining the KPEAK threshold interval corresponding to the vibration acceleration peak threshold interval according to the second knock relationship image specifically includes: Dividing a second contour area corresponding to the vibration acceleration peak threshold interval according to the data point distribution of the second knock relationship image to remove random invalid points; A KPEAK threshold interval is determined according to the second contour area.
8. The method according to claim 1, characterized in that: The performing knock and abnormal noise identification on the engine according to the KPEAK threshold range specifically includes: When there are multiple KPEAK threshold intervals, determining that the engine has abnormal knock noise through the first KPEAK threshold interval, determining the engine abnormal knock noise warning through the second KPEAK threshold interval to adjust the performance calibration data, and determining that the abnormal knock noise of the engine is acceptable through the third KPEAK threshold interval; Collecting the KPEAK image to be identified of the engine; When the number of data points whose KPEAK values in the KPEAK image to be identified are within the first KPEAK threshold interval exceeds a first preset proportion of the total number of sampled KPEAK values, generating knock abnormal sound alarm information of the engine and limiting the engine torque increase; When the number of data points whose KPEAK values in the KPEAK image to be identified are within the second KPEAK threshold interval exceeds a second preset proportion of the total number of sampled KPEAKs, generating knock abnormal sound warning information of the engine to prompt adjustment of performance calibration data; When the KPEAK value in the to-be-identified KPEAK image is within a third KPEAK threshold interval, it is determined that the engine can continue the performance calibration work.
9. A knock noise identification device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for identifying abnormal knock and noise as described in any one of claims 1 to 8.
10. A non-volatile computer storage medium for knock and abnormal noise identification, storing computer executable instructions, characterized in that: The computer executable instructions can execute a method for identifying abnormal knock noise as described in any one of claims 1 to 8.