CN111707351A - Abnormal position positioning method and system based on noise vibration source of truck chassis - Google Patents
Abnormal position positioning method and system based on noise vibration source of truck chassis Download PDFInfo
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- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
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Abstract
The invention discloses an abnormal position positioning method and system based on a noise vibration source of a truck chassis, which comprises the steps of constructing a truck chassis structure model by utilizing a truck chassis to be tested; inputting dynamic parameters of a truck chassis, driving a truck chassis structure model to perform simulation operation, and outputting vibration source frequency data and noise decibel data of the truck chassis; training and parameter optimization are carried out on the LSSVM, accuracy requirements and threshold values are set, and a prediction model is output after training is finished; and importing the vibration source frequency data and the noise decibel data into a prediction model to obtain the abnormal position of the noise vibration source of the truck chassis. According to the invention, noise decibel data and vibration source frequency data of each part of the truck chassis are obtained through a modeling simulation technology, and the position of the chassis part which is possibly abnormal in NVH is visually analyzed, so that the method has great significance for researching chassis NVH, saves manpower, material resources and financial resources for constructing the abnormal NVH chassis, is favorable for eliminating a noise pollution source, and improves the driving comfort level.
Description
Technical Field
The invention relates to the technical field of automobile engineering, in particular to an abnormal position positioning method and system based on a chassis noise vibration source of a truck.
Background
With the improvement of living standard, people have higher and higher requirements on noise, vibration and comfort of trucks, in order to improve the comfort of vehicles, all large automobile companies in the world set strict control standards for noise level in automobiles, NVH (noise, vibration and harshness) of vehicles is one of the concerns of manufacturing industries and part enterprises of all international automobiles, according to statistics, about one third of fault problems of trucks are related to NVH (noise, vibration and harshness) of vehicles, and nearly 20% of research and development cost of all large companies is consumed in solving the NVH problems of vehicles.
For a truck, the NVH problem exists everywhere, and the truck is limited by a plurality of factors such as a road surface and an engine during a road surface driving process, so that a whole vehicle or a local vehicle body vibrates, if the vibration frequency exceeds a certain standard, the driving comfort of a driver and the safety of materials loaded on the truck are seriously affected, wherein the problems are mainly generated by the engine NVH, the vehicle body NVH and a chassis NVH, the truck chassis supports and mounts the engine and parts and assemblies thereof to form the integral shape of the truck, and receives the power of the engine, so that the truck moves to ensure normal driving, and the vibration source noise of the truck chassis is extremely important for the influence of the integral comfort of the truck. For improving the NVH characteristics of automobiles, vibration sources, noise sources and transmission paths thereof need to be controlled, for example, when some trucks run, the noise of a carriage is large, and a checking source is in an engine, so the noise problem can involve three parts, namely the large noise of the engine, the poor vibration damping effect of engine suspension parts, the poor sound insulation technology of the front wall and the floor of the carriage, and a system problem which is related to each other.
When a truck is in a driving process, the noise in the truck is about 55-70 dB, if the noise exceeds 70dB, conversation among front and rear row persons can be influenced, noise below 80dB cannot cause noise deafness, noise 80-85 dB can cause slight hearing damage, noise 85-100 dB can cause a certain amount of noise deafness, and when the noise exceeds 100dB, a great amount of noise deafness can be caused; without thought preparation, the extremely high level of explosive noise can cause a permanent hearing loss in a moment, i.e., sudden deafness, in which case the human auditory organs are severely injured. The noise of the truck is an important index for measuring the overall quality of the truck, and effective vibration source noise reduction is the only way for ensuring the NVH performance of the vehicle, so that the vibration source noise can be quickly and accurately reduced only by correctly identifying the position influencing the vibration source noise and the position of abnormal noise.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the abnormal position positioning method based on the chassis noise vibration source of the truck can solve the problem that the driving comfort of the manufactured truck is influenced by the fact that the existing chassis noise vibration source noise of the truck cannot be accurately positioned.
In order to solve the technical problems, the invention provides the following technical scheme: constructing a truck chassis structure model by using a truck chassis to be tested; inputting dynamic parameters of the truck chassis, driving the truck chassis structure model to run in a simulation mode, and outputting vibration source frequency data and noise decibel data of the truck chassis; training and parameter optimization are carried out on the LSSVM, accuracy requirements and threshold values are set, and a prediction model is output after training is finished; and importing the vibration source frequency data and the noise decibel data into the prediction model to obtain the abnormal position of the noise vibration source of the truck chassis.
As a preferable scheme of the method for locating the abnormal position based on the noise vibration source of the truck chassis, the method comprises the following steps: before the LSSVM is trained, acquiring relevant data of the truck chassis to respectively construct a training set, a testing set and a verification set.
As a preferable scheme of the method for locating the abnormal position based on the noise vibration source of the truck chassis, the method comprises the following steps: constructing the training set comprises collecting data related to the chassis noise vibration source of the truck in the last five years; constructing the test set comprises collecting data related to the chassis noise vibration source of the truck in the last two years; and constructing the verification set comprises acquiring data related to the noise vibration source of the truck chassis to be tested.
As a preferable scheme of the method for locating the abnormal position based on the noise vibration source of the truck chassis, the method comprises the following steps: the input dynamic parameters comprise the mass m of the whole vehicle, the moment of inertia IZ, the wheel base L, the distance a from the center of mass to the front axle, the distance b from the center of mass to the rear axle, the front wheel side deflection rigidity Kf and the rear wheel side deflection rigidity Kr.
As a preferable scheme of the method for locating the abnormal position based on the noise vibration source of the truck chassis, the method comprises the following steps: outputting the vibration source frequency data and the noise decibel data, wherein the vibration source frequency data and the noise decibel data comprise that a truck chassis structure model is constructed in a modeling format according to a truck chassis real structure; importing the truck chassis structure model into a simulation format from the modeling format, and sequentially inputting relevant dynamic parameters of the truck chassis; the simulation format utilizes the input dynamic parameters to adjust corresponding constraint side and acts on the structural model of the truck chassis needing to be simulated; starting a checking instruction in software, and respectively detecting whether the truck chassis structural model meets the simulation operation requirement condition; if not, popping up an error indication in the window to remind the error content which cannot realize the simulation, and adjusting the error content to be modified to meet the requirement, if so, directly driving the truck chassis structural model by the simulation program to perform simulation calculation; the simulation calculation simulates the operation of the truck chassis structure model, and simultaneously utilizes a virtual technology to count NVH data in the operation process in real time and position the NVH data to a noise vibration source generating the NVH; and after the truck chassis structure model is simulated and operated for 10 minutes, respectively outputting the vibration source frequency data and the noise decibel data which generate the NVH data in the interval.
As a preferable scheme of the method for locating the abnormal position based on the noise vibration source of the truck chassis, the method comprises the following steps: selecting a radial basis function as a kernel function of the prediction model, wherein the kernel function is as follows:
wherein x ═ { x ═ x1；x2；…；x14}: a frequency characteristic matrix formed by frequency characteristic vectors of abnormal position data of the chassis noise vibration source of the truck, wherein y: the frequency characteristic vector, σ: kernel width, reflecting the distribution, range characteristics of the training set.
As a preferable scheme of the method for locating the abnormal position based on the noise vibration source of the truck chassis, the method comprises the following steps: outputting the prediction model comprises initializing a penalty parameter C and the sigma, training the LSSVM by using the training set, and testing by using the test set; setting the precision requirement, and if the precision of the LSSVM model does not meet the requirement, carrying out assignment optimization on the C and the sigma according to errors until the precision of test data meets the precision requirement; and setting the threshold value and outputting the prediction model.
As a preferable scheme of the method for locating the abnormal position based on the noise vibration source of the truck chassis, the method comprises the following steps: positioning by utilizing the prediction model, wherein the positioning comprises the step of introducing the vibration source frequency data and the noise decibel data into the prediction model; and if the vibration source frequency data or the noise decibel data of a certain component in the truck chassis structure exceed the threshold value, determining that the component is abnormal.
As a preferable solution of the system for locating an abnormal position based on a noise vibration source of a chassis of a truck according to the present invention, wherein: the system comprises an information acquisition module 100, a data acquisition module and a data processing module, wherein the information acquisition module 100 is used for acquiring relevant data of a chassis of the truck to construct a data set and acquiring various parameters; the data processing center module 200 is connected to the information acquisition module 100, and is configured to receive input parameters and store the input parameters in a database, and includes a calculation unit 201, a detection unit 202, an extraction unit 203, and an input/output management unit 204, where the calculation unit 201 is configured to process the dynamic parameters, calculate an average value and a comparison value of each parameter, integrate the kinematic pair of the truck chassis structural model and the NVH parameters generated during operation, the detection unit 202 is configured to detect and compare whether the NVH parameters obtained by the calculation unit 201 exceed standard truck NVH values, and determine whether the remaining parameters are abnormal, the extraction unit 203 is configured to extract abnormal parameters in the detection module 202, the input/output management unit 204 is configured to transmit the parameters required by each item, and output the vibration source frequency data and the noise decibel data, storing into the database; the positioning module 300 is connected to the extracting unit 203, and is configured to receive the abnormal parameter in the extracting unit 203 and position the abnormal parameter to a position in the truck chassis structural model.
The invention has the beneficial effects that: according to the method, the noise decibel data and the vibration source frequency data of the positions of all parts of the truck chassis are obtained through a modeling simulation technology, the positions of chassis parts which are possibly abnormal in NVH are visually analyzed, the method has great significance for researching chassis NVH, the manpower, material resources and financial resources for constructing the abnormal NVH chassis are saved, and a large amount of time is saved; meanwhile, the chassis part is subjected to abnormal position positioning again by constructing a prediction model meeting the precision requirement and the threshold value, so that the method is more accurate, quicker, more suitable, more economical and more competitive, the problem that the chassis NVH cannot be accurately positioned in the design and manufacture of the conventional truck is solved, the noise pollution source can be eliminated, and the driving comfort level is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a schematic flow chart illustrating a method for locating an abnormal position based on a noise vibration source of a truck chassis according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of the frequency of a vibration source under test of two methods of the abnormal position locating method based on the noise vibration source of the truck chassis according to the first embodiment of the present invention;
FIG. 3 is a schematic block diagram of an abnormal position locating system based on a chassis noise vibration source of a truck according to a second embodiment of the present invention;
fig. 4 is a schematic network structure distribution diagram of an abnormal position locating system based on a noise vibration source of a truck chassis according to a second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
In recent years, the truck industry has been developed dramatically, and the vibration source noise of the truck is a problem to be solved while bringing people with abundant modern material civilization, and the truck noise is an important index of riding comfort of users and reflects the design level and the manufacturing process level of the truck to a great extent, so that controlling the noise of the truck to improve market competitiveness and reduce harm to environment and people becomes one of the main tasks facing global vehicle engineers.
Referring to fig. 1, a first embodiment of the present invention provides a method for locating an abnormal position based on a noise vibration source of a truck chassis, including:
s1: and constructing a truck chassis structure model by using the truck chassis to be tested.
S2: inputting dynamic parameters of the truck chassis, driving the truck chassis structure model to perform simulation operation, and outputting vibration source frequency data and noise decibel data of the truck chassis. It should be noted that the input kinetic parameters include:
the vehicle body mass m, the moment of inertia IZ, the wheel base L, the distance a from the center of mass to the front axle, the distance b from the center of mass to the rear axle, the front wheel side deflection rigidity Kf and the rear wheel side deflection rigidity Kr.
Further, outputting vibration source frequency data and noise decibel data, including:
constructing a truck chassis structure model in a modeling format according to the real structure of the truck chassis;
importing the truck chassis structure model into a simulation format from a modeling format, and sequentially inputting relevant dynamic parameters of a truck chassis;
the simulation format utilizes the input dynamic parameters to adjust corresponding constraint side and acts on a truck chassis structure model to be simulated;
starting a checking instruction in the software, and respectively detecting whether the structural model of the truck chassis meets the requirement conditions of simulation operation;
if not, popping up an error indication in the window to remind the error content incapable of realizing simulation, and adjusting the error content to be modified to meet the requirement, if so, directly driving the truck chassis structure model by the simulation program to perform simulation calculation;
simulating the operation of a chassis structure model of a truck by using simulation calculation, simultaneously carrying out real-time statistics on NVH data in the operation process by using a virtual technology, and positioning to a noise vibration source generating NVH;
and respectively outputting vibration source frequency data and noise decibel data of NVH data generated in the interval after the truck chassis structure model is simulated and operated for 10 minutes.
S3: and training and parameter optimization are carried out on the LSSVM, the precision requirement and the threshold value are set, and the prediction model is output after the training is finished. It should be noted that, before training the LSSVM, the method further includes:
the related data of the acquisition card vehicle chassis respectively construct a training set, a test set and a verification set.
Specifically, the constructing of the training set, the testing set and the verification set includes:
acquiring relevant data of chassis noise vibration sources of the truck in the last five years;
collecting related data of chassis noise vibration sources of the truck in the last two years;
and collecting relevant data of the noise vibration source of the truck chassis to be tested.
More specifically, the method comprises the following steps:
selecting a radial basis function as a kernel function of the prediction model, wherein the kernel function is as follows:
wherein x ═ { x ═ x1；x2；…；x14}: a frequency characteristic matrix formed by frequency characteristic vectors of abnormal position data of the chassis noise vibration source of the truck, y: frequency characteristic vector of relevant data of truck chassis noise vibration source, sigma: the kernel width reflects the distribution and range characteristics of the training set.
Further, outputting the predictive model includes:
initializing punishment parameters C and sigma, training the LSSVM by using a training set, and testing by using a test set;
setting a precision requirement, and if the precision of the LSSVM model does not meet the requirement, carrying out assignment optimization on C and sigma according to errors until the precision of the test data meets the precision requirement;
and setting a threshold value and outputting a prediction model.
S4: and importing the vibration source frequency data and the noise decibel data into a prediction model to obtain the abnormal position of the noise vibration source of the truck chassis. Wherein, it should be further noted that, the positioning by using the prediction model includes:
leading the vibration source frequency data and the noise decibel data into a prediction model;
and if the vibration source frequency data or the noise decibel data of a certain component in the truck chassis structure exceed a threshold value, the position of the component is abnormal.
Generally speaking, when a truck runs on a road surface, the truck is limited by various factors such as the road surface, an engine and the like, so that the problem of local vibration of the whole truck or a truck body is caused, if the vibration frequency exceeds a certain standard, the driving comfort of a driver and the safety of loading substances on the truck are seriously influenced, according to the standard set by the environmental protection department of China, the noise of a load-carrying truck with the regulated power of more than 150KW is 80 decibels, the noise in the truck does not exceed 72 decibels when the load-carrying truck runs at a constant speed of 50km/h, and the limit value is basically consistent with the noise limit value of developed countries, but the noise reduction of the current heavy truck in China to 80 decibels is undoubtedly a great challenge; since the noise of the truck is an important index for measuring the overall quality of the truck, and effective reduction of the vibration source noise is the only way for ensuring the NVH performance of the vehicle, the vibration source noise can be reduced quickly and accurately only by correctly identifying the position of the vibration source noise and the position of abnormal noise.
In order to better illustrate the degree of damage of noise decibels to human body, this embodiment lists the corresponding embodiment descriptions of decibel values at different stages, as shown in the following table:
table 1: decibel value description table.
| Decibel value/dB | Decibel embodiment |
| 0.1 | |
| 1 | Sound of cat walking |
| 10 | Sound of breathing |
| 30 | Sound of striking keyboard |
| 40 | Normal AC sound |
| 60 | |
| 70 | Upper limit of ear comfort |
| 85 | Slight injury of |
| 100 | Certain degree of deafness |
Preferably, the existing vehicle noise detection technology generally adopts an external detection device or a sensor installed inside a vehicle body to perform fault detection, and inputs overlarge equipment resources and human resources aiming at the problem of vehicle NVH, and the obtained effect is often not in direct proportion, so that the promotion and development of automobile engineering are greatly hindered, and the huge consumption of national resources is also realized; compared with the prior art, the method provided by the invention has the advantages that the simulation model is adopted to simulate operation, and train the extremely-high-precision prediction model to realize the accurate positioning of the abnormal noise vibration source or the fault position of the truck part, so that the manpower and financial resources are saved, the technicians can adjust the positioned fault abnormal position, the loss is timely recovered, and a good foundation is laid for the popularization of the method in the truck market.
In order to verify and explain the technical effect adopted in the method, the embodiment selects the existing external noise source identification analysis method to perform a comparison test with the method, and compares the test result by means of scientific demonstration to verify the real effect of the method; the existing external noise source identification analysis method is long in test time, complex in working steps, prone to errors due to manual statistics, expensive in measuring instruments and capable of only detecting the noise of an existing finished product, and cannot achieve a good analysis effect on an undeveloped and newly manufactured vehicle.
And (3) testing environment:
(1) in the existing method for identifying and analyzing the external noise source, microphones are arranged around a chassis, the working condition with the largest accelerated noise is repeated, noise signals at all positions are collected, sensors are respectively arranged above a speed changer and a middle rear axle to carry out the same working condition test, the microphones are arranged at an inlet and an outlet of a silencer to carry out the full accelerated test, the maximum noise position at all positions of the chassis is analyzed by sound pressure level contrast analysis, and a spectrum analyzer is combined to analyze the signals, and the final result data is manually collected;
(2) according to the method, a simulation model is established according to a heavy truck tested by a method in the prior art, the same constraint pair is given, driving is simulated on a simulation platform, vibration source frequency data and noise decibel data are output and led into a prediction model for calculation processing, and abnormal position data of a noise vibration source of a chassis component are output;
the data obtained are shown in the following table:
table 2: part of the test results (chassis) are in comparison with the table.
Referring to a table 2 and a figure 2, under the propulsion of 1-5 gears, vibration source frequencies are increased in the same ratio, compared with the existing vehicle exterior noise identification and analysis method, the difference between noise decibel data and vibration source frequency data detected and output is obvious, abnormal intervals are easier to distinguish and compare if the difference is obvious, a component with higher frequency and higher decibel indicates that the noise vibration source is abnormal in simulation operation, and the data result output by the method is closer to reality compared with the existing method manual statistical data, so that the noise vibration source can be accurately positioned; the difference between the noise decibel data and the vibration frequency data output by the method in the prior art is not large, a manual statistic has certain errors, and the judgment of the noise fault position by means of an analytical instrument in the later period also has certain error factors, so that the noise decibel data and the vibration frequency data cannot be analyzed and judged well.
Preferably, in this embodiment, while verifying that the method of the present invention has a higher accurate positioning function than the prior art, the method of the present invention and the prior vehicle exterior noise identification and analysis method respectively perform verification test to output time of results, and further verify that the method of the present invention has higher efficiency, in order to better illustrate the real effect of the method of the present invention, all the test procedures are performed by only one person, as shown in the following table:
table 3: test time comparison table.
| Method of producing a composite material | Time/h | Efficiency/%) |
| Existing method for recognizing noise outside vehicle | 14 | 42％ |
| The method of the |
4 | 83％ |
The method in the prior art has complex operation steps, more measuring instruments are arranged and installed (see test condition I), and the test result is counted manually (one person independently installs and collects data), so the test time is longer; the method only needs to spend 2.5 hours for modeling, 0.5 hours for simulation operation, and 1 hour for importing simulation output data into a trained prediction model for abnormal positioning (one-person modeling), only the modeling stage needs all manual operations in the whole process, the artificial error is lower in risk, smaller in error and less in integral testing time compared with the prior art, and the comparison of efficiency data can obviously show that the method really shortens the time for positioning the abnormal position of the noise vibration source of the vehicle chassis, and has a certain positive effect on the research and development of a new stage of automobile engineering.
Example 2
Referring to fig. 3 and 4, a second embodiment of the present invention, which is different from the first embodiment, provides an abnormal position locating system based on a noise vibration source of a truck chassis, including an information acquisition module 100, a data processing center module 200 and a locating module 300, wherein the information acquisition module 100 is a bridge connecting a computer and the external physical world, the data processing center module 200 is an operation core for interpreting computer instructions and processing data in computer software, and the locating module 300 is a signal connector connected with the computer in an infinite or wired manner, specifically, as follows:
the information acquisition module 100 is used for constructing a data set and acquiring various parameters of related data of an acquisition card vehicle chassis, integrates a communication chip and a storage chip on a circuit board based on a communication module of a remote data acquisition platform, and has a function of transmitting data through the remote data acquisition platform, for example, a computer, a single chip microcomputer and an ARM can be connected with the remote data acquisition platform through an RS232 serial port, and various voice and data communication functions are realized through an AT instruction control module.
The data processing center module 200 is connected to the information acquisition module 100, and is configured to receive input parameters and store the input parameters in a database, and includes a calculation unit 201, a detection unit 202, an extraction unit 203, and an input/output management unit 204, where the calculation unit 201 is configured to process dynamic parameters, calculate an average value and a comparison value of each parameter, and integrate a kinematic pair of a truck chassis structure model and an NVH parameter generated during operation, the detection unit 202 is configured to detect whether the NVH parameter obtained by the comparison calculation unit 201 exceeds a standard NVH value of the truck, and determine whether other parameters are abnormal, the extraction unit 203 is configured to extract an abnormal parameter in the detection module 202, the input/output management unit 204 is configured to transmit each required parameter, and output vibration source frequency data and noise decibel data, and store the parameter in the database.
In popular terms, the data processing center module 200 is mainly divided into three layers, including a control layer, an operation layer and a storage layer, wherein the control layer is a command control center of the data processing center module 200 and is composed of an instruction register IR, an instruction decoder ID and an operation controller OC, the control layer can sequentially take out various instructions from a memory according to a program which is pre-programmed by a user, place the instructions in the instruction register IR, analyze and determine the instructions through the instruction decoder, inform the operation controller OC of operation, and send micro-operation control signals to corresponding components according to a determined time sequence; the operation layer is the core of the calculation unit 201, can execute arithmetic operation (such as addition, subtraction, multiplication, division and addition operation thereof) and logic operation (such as shift, logic test or two-value comparison), is connected to the control layer, and performs operation by receiving a control signal of the control layer; the storage layer is a database of the data processing center module 200, and can store data (data to be processed and data already processed).
The positioning module 300 is connected to the extracting unit 203, and is configured to receive the abnormal parameter in the extracting unit 203 and position the abnormal parameter in the truck chassis structural model, the positioning module 300 obtains a relevant input/output protocol format through a serial port communication protocol technology, mainly including a data type and an information format, and information content is in communication connection with the data processing center module 200 through a serial port, when the positioning module 300 receives the abnormal parameter in the extracting unit 203, the positioning module 300 calculates a pseudo distance between the abnormal parameter and the abnormal parameter through CPU operation of the system, and selects a distance intersection method to calculate a position of the abnormal parameter in the truck chassis structural model.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (9)
1. An abnormal position positioning method based on a noise vibration source of a truck chassis is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
constructing a truck chassis structure model by using a truck chassis to be tested;
inputting dynamic parameters of the truck chassis, driving the truck chassis structure model to run in a simulation mode, and outputting vibration source frequency data and noise decibel data of the truck chassis;
training and parameter optimization are carried out on the LSSVM, accuracy requirements and threshold values are set, and a prediction model is output after training is finished;
and importing the vibration source frequency data and the noise decibel data into the prediction model to obtain the abnormal position of the noise vibration source of the truck chassis.
2. The method of claim 1, wherein the method further comprises the step of: the training of the LSSVM may be preceded by,
and collecting related data of the truck chassis to respectively construct a training set, a testing set and a verification set.
3. The method of claim 2, wherein the method further comprises: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
constructing the training set comprises collecting data related to the chassis noise vibration source of the truck in the last five years;
constructing the test set comprises collecting data related to the chassis noise vibration source of the truck in the last two years;
and constructing the verification set comprises acquiring data related to the noise vibration source of the truck chassis to be tested.
4. The method of claim 1, wherein the method further comprises the step of: the input of said kinetic parameters comprises the input of,
the vehicle body mass m, the moment of inertia IZ, the wheel base L, the distance a from the center of mass to the front axle, the distance b from the center of mass to the rear axle, the front wheel side deflection rigidity Kf and the rear wheel side deflection rigidity Kr.
5. The method of locating an abnormal position based on a chassis noise vibration source of a truck according to claim 1 or 4, wherein: outputting the vibration source frequency data and the noise decibel data, including,
constructing the truck chassis structure model in a modeling format according to the real structure of the truck chassis;
importing the truck chassis structure model into a simulation format from the modeling format, and sequentially inputting relevant dynamic parameters of the truck chassis;
the simulation format utilizes the input dynamic parameters to adjust corresponding constraint side and acts on the structural model of the truck chassis needing to be simulated;
starting a checking instruction in software, and respectively detecting whether the truck chassis structural model meets the simulation operation requirement condition;
if not, popping up an error indication in the window to remind the error content which cannot realize the simulation, and adjusting the error content to be modified to meet the requirement, if so, directly driving the truck chassis structural model by the simulation program to perform simulation calculation;
the simulation calculation simulates the operation of the truck chassis structure model, and simultaneously utilizes a virtual technology to count NVH data in the operation process in real time and position the NVH data to a noise vibration source generating the NVH;
and after the truck chassis structure model is simulated and operated for 10 minutes, respectively outputting the vibration source frequency data and the noise decibel data which generate the NVH data in the interval.
6. The method of claim 5, wherein the method further comprises: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
selecting a radial basis function as a kernel function of the prediction model, which is as follows:
wherein x ═ { x ═ x1；x2；…；x14}: a frequency characteristic matrix formed by frequency characteristic vectors of abnormal position data of the chassis noise vibration source of the truck, wherein y: the frequency characteristic vector, σ: kernel width, reflecting the distribution, range characteristics of the training set.
7. The method for locating the abnormal position of the vibration source of the chassis noise of the truck according to claim 1 or 6, wherein: outputting the predictive model may include outputting the predictive model,
initializing a penalty parameter C and the sigma, training the LSSVM by using the training set, and testing by using the test set;
setting the precision requirement, and if the precision of the LSSVM model does not meet the requirement, carrying out assignment optimization on the C and the sigma according to errors until the precision of test data meets the precision requirement;
and setting the threshold value and outputting the prediction model.
8. The method of claim 7, wherein the method further comprises: and using the predictive model to perform positioning, including,
importing the vibration source frequency data and the noise decibel data into the prediction model;
and if the vibration source frequency data or the noise decibel data of a certain component in the truck chassis structure exceed the threshold value, determining that the component is abnormal.
9. The utility model provides an unusual position positioning system based on truck chassis noise shakes source which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the information acquisition module (100) is used for acquiring related data of the truck chassis to construct a data set and acquiring various parameters;
the data processing center module (200) is connected to the information acquisition module (100), is used for receiving input parameters and storing the input parameters into a database, and comprises a calculation unit (201), a detection unit (202), an extraction unit (203) and an input/output management unit (204), wherein the calculation unit (201) is used for processing the kinetic parameters, calculating the average value and the comparison value of each parameter, integrating the kinematic pair of the truck chassis structural model and the NVH parameters generated in operation, the detection unit (202) is used for detecting and comparing whether the NVH parameters acquired by the calculation unit (201) exceed the standard NVH value of the truck, and judging whether the other parameters are abnormal, the extraction unit (203) is used for extracting the abnormal parameters in the detection module (202), and the input/output management unit (204) is used for transmitting the parameters required by each item, outputting the vibration source frequency data and the noise decibel data, and storing the vibration source frequency data and the noise decibel data in the database;
the positioning module (300) is connected with the extracting unit (203) and is used for receiving the abnormal parameters in the extracting unit (203) and positioning the abnormal parameters to the position of the abnormal parameters in the truck chassis structure model.
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