A camshaft fault simulation analysis system
By designing a system that includes fault simulation, signal acquisition, prediction verification, and data storage, the multi-faceted challenges of camshaft fault detection in existing technologies have been solved, achieving high-precision fault prediction and acquisition of optimal operating parameters.
Patent Information
- Application Number
- CN202311196739.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-15
- Publication Date
- 2026-08-25
- Estimated Expiration
- 2043-09-15
AI Technical Summary
Existing camshaft fault detection solutions are difficult to detect faults from multiple perspectives and are also difficult to obtain parameter settings for the camshaft under optimal operating conditions.
A system was designed that includes fault simulation, signal acquisition, fault prediction, prediction verification, data storage and comprehensive analysis. By simulating the operation of the camshaft, state parameters are collected, the fault time is predicted using a convolutional neural network, and the prediction results are verified by spectrum correction technology, so as to obtain the optimal operating parameters of the camshaft.
It improves the accuracy of camshaft fault prediction, obtains the optimal operating parameter settings for the camshaft, and provides data support for the factors influencing faults.
Smart Images
Figure CN117168803B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of engine technology, and in particular relates to a camshaft fault simulation and analysis system. Background Technology
[0002] The continuous rotation of the camshaft in a car drives the valve pushrod to move up and down, thereby controlling the opening and closing of the valves. By changing the curve of the camshaft, the opening and closing time of the valves can be precisely adjusted. Since the valve movement pattern is related to the power and operating characteristics of an engine, the camshaft plays a very important role in the operation of the engine.
[0003] Camshaft failures are often caused by a variety of factors, such as the engine oil not being changed for a long time, chain wear caused by high-power operation, and camshaft wear and loosening caused by insufficient oil supply. Existing camshaft failure detection solutions often cannot detect failures from multiple perspectives and are difficult to obtain the parameter settings for the camshaft under optimal operating conditions. Summary of the Invention
[0004] The purpose of this invention is to provide a camshaft fault simulation and analysis system to solve the problems existing in the prior art.
[0005] To achieve the above objectives, the present invention provides a camshaft fault simulation and analysis system, including a fault simulation subsystem, a signal acquisition subsystem, a fault prediction subsystem, a prediction verification subsystem, a data storage subsystem, and a comprehensive analysis subsystem;
[0006] The fault simulation subsystem and the signal acquisition subsystem are connected in sequence. The fault prediction subsystem and the prediction verification subsystem are respectively connected to the signal acquisition subsystem and the data storage subsystem. The data storage subsystem and the comprehensive analysis subsystem are connected in sequence.
[0007] The fault simulation subsystem is used to simulate camshaft operation and set fault parameters during the simulation process.
[0008] The signal acquisition subsystem is used to acquire state parameters during the camshaft operation simulation process;
[0009] The fault prediction subsystem is used to predict the time when the camshaft will fail based on the state parameters.
[0010] The prediction and verification subsystem is used to detect camshaft faults in real time based on the state parameters, and to verify the prediction results of the fault prediction subsystem based on the detection results.
[0011] The data storage subsystem is used to store the state parameters, the prediction results of the fault prediction subsystem, and the detection results of the prediction verification subsystem.
[0012] The comprehensive analysis subsystem is used to obtain the optimal operating parameters of the camshaft based on the data stored in the data storage subsystem.
[0013] Optionally, the fault simulation subsystem includes a specification storage module, a fault setting module, and a fault simulation module. The specification storage module stores the camshaft specification parameters to be simulated. The fault setting module is used to set fault parameters during the camshaft operation simulation process according to the camshaft specification parameters. The fault simulation module is used to perform several camshaft operation simulation processes according to the fault factors.
[0014] Optionally, the fault parameters include the rotational speed and acceleration of the camshaft, the oil concentration, and the fuel quantity. The fault setting module sets numerical differences for the rotational speed and acceleration, the oil concentration, and the fuel quantity according to the specifications of the wheel axle, and divides the rotational speed and acceleration, the oil concentration, and the fuel quantity into several groups according to the numerical differences.
[0015] Optionally, the signal acquisition subsystem includes an angular velocity sensor, a vibration sensor, a temperature sensor, an oxygen flow sensor, and a timer, which are used to acquire the angular velocity of the camshaft, vibration signal, temperature data, oxygen content, and the time required for the operation simulation, respectively.
[0016] Optionally, the fault prediction subsystem uses a convolutional neural network to construct a fault prediction model, and takes the state parameters and the camshaft specification parameters as input data. The input data is divided into a training set and a test set. The fault prediction model is trained based on the training set, and the trained fault prediction model is input into the test set to obtain the time when the camshaft fails.
[0017] Optionally, the prediction verification subsystem uses discrete spectrum correction technology to extract frequency domain features from the vibration signal in the state parameters, constructs the vibration equation of the camshaft based on the frequency domain features, obtains the vibration frequency based on the vibration equation, and sets a maximum threshold for the vibration frequency. At the same time, it sets an angular velocity threshold based on the operating angular velocity in the state parameters. When both the vibration frequency and the operating angular velocity exceed the threshold, it is determined that the camshaft has a fault, and the operating simulation time when the camshaft has a fault is obtained. The operating simulation time is calibrated with the prediction time of the fault prediction subsystem. When the times are inconsistent, the fault prediction subsystem is optimized based on the operating simulation time when the fault is detected.
[0018] Optionally, the data storage subsystem constructs a data storage framework based on the temporal relationship of the data acquired in each simulation process;
[0019] The data storage architecture is an XML architecture. In the XML architecture, the data is stored in a progressive order of acquisition time, with the acquisition time as a constraint and the state parameters as a detailed description under the time. At the same time, the prediction results and detection results with the same acquisition time are stored accordingly based on the constraints.
[0020] Optionally, the comprehensive analysis subsystem constructs a state curve diagram of the camshaft under different fault parameters based on the state parameters and detection results of several operation simulations stored in the data storage subsystem, and obtains the fault parameters set in the optimal state of the camshaft based on the state curve diagram, and uses them as the optimal operating parameters.
[0021] The technical effects of this invention are as follows:
[0022] This invention conducts several simulation experiments on the camshaft by setting different influencing parameters, predicts camshaft faults based on the state parameters during the simulation process, and verifies the prediction results through fault detection methods, thereby improving the accuracy of fault prediction. At the same time, the optimal operating parameter settings of the camshaft are obtained based on the experimental results, providing data support for the analysis and research of factors affecting camshaft faults. Attached Figure Description
[0023] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:
[0024] Figure 1 This is a schematic diagram of the camshaft fault simulation and analysis system in an embodiment of the present invention. Detailed Implementation
[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.
[0026] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0027] Example 1
[0028] like Figure 1 As shown, this embodiment provides a camshaft fault simulation and analysis system, including a fault simulation subsystem, a signal acquisition subsystem, a fault prediction subsystem, a prediction verification subsystem, a data storage subsystem, and a comprehensive analysis subsystem;
[0029] Among them, the fault simulation subsystem and the signal acquisition subsystem are connected in sequence, the fault prediction subsystem and the prediction verification subsystem are respectively connected to the signal acquisition subsystem and the data storage subsystem, and the data storage subsystem and the comprehensive analysis subsystem are connected in sequence.
[0030] The functions of each subsystem are as follows:
[0031] The fault simulation subsystem is used to simulate camshaft operation and set fault parameters during the simulation process. Specifically, the fault simulation subsystem includes a specification storage module, a fault setting module, and a fault simulation module. The specification storage module stores the camshaft specification parameters to be simulated. The fault setting module is used to set fault parameters during the camshaft operation simulation process based on the camshaft specification parameters. The fault simulation module is used to perform several camshaft operation simulation processes based on fault factors.
[0032] The aforementioned fault parameters include the camshaft rotation speed and acceleration, oil concentration, and fuel quantity. The fault setting module sets numerical differences for the rotation speed and acceleration, oil concentration, and fuel quantity according to the wheel shaft specifications. Based on these numerical differences, the rotation speed and acceleration, oil concentration, and fuel quantity are divided into several groups of simulated parameters.
[0033] The signal acquisition subsystem is used to acquire the state parameters during the camshaft operation simulation process. It includes an angular velocity sensor, a vibration sensor, a temperature sensor, an oxygen flow sensor, and a timer, which are used to acquire the camshaft's angular velocity, vibration signal, temperature data, oxygen content, and the time required for the operation simulation process, respectively.
[0034] The fault prediction subsystem is used to predict the time when the camshaft will fail based on the state parameters. The specific prediction principle includes: the fault prediction subsystem uses a convolutional neural network to build a fault prediction model, and takes the state parameters and the camshaft specification parameters as input data. The input data is divided into a training set and a test set. The fault prediction model is trained based on the training set, and the trained fault prediction model is input into the test set to obtain the time when the camshaft will fail.
[0035] The prediction and verification subsystem is used to detect camshaft faults in real time based on state parameters, and to verify the prediction results of the fault prediction subsystem based on the detection results. First, the prediction and verification subsystem uses discrete spectrum correction technology to extract frequency domain features from the vibration signal in the acquired state parameters, constructs the vibration equation of the camshaft based on the frequency domain features, obtains the vibration frequency based on the vibration equation, and sets a maximum threshold for the vibration frequency. At the same time, an angular velocity threshold is set for the operating angular velocity in the state parameters. When both the vibration frequency and the operating angular velocity exceed the threshold, it is determined that the camshaft has a fault.
[0036] When a fault is detected, the operating simulation time of the camshaft at the time of the fault is obtained by a timer. The operating simulation time is then calibrated with the prediction time of the fault prediction subsystem. If the times are inconsistent, the fault prediction subsystem is optimized based on the operating simulation time at the time of the fault detection.
[0037] The data storage subsystem is used to store the state parameters acquired in the experiment, the prediction results of the fault prediction subsystem, and the detection results of the prediction verification subsystem. Specifically, the data storage subsystem constructs an XML data storage framework based on the temporal relationship of the data acquired in each simulation process. In the XML framework, the state parameters are described in detail under the time according to the progressive order of data acquisition time, and the acquisition time is used as a constraint. At the same time, the prediction results and detection results with the same acquisition time are stored accordingly based on the constraint.
[0038] The comprehensive analysis subsystem is used to obtain the optimal operating parameters of the camshaft based on the data stored in the data storage subsystem. The specific working principle is as follows:
[0039] The comprehensive analysis subsystem constructs a state curve diagram of the camshaft under different fault parameters based on the state parameters and detection results of several operation simulations stored in the data storage subsystem. The fault parameters set under the optimal state of the camshaft are obtained from the state curve diagram and used as the optimal operating parameters.
[0040] This embodiment conducts several simulation experiments on the camshaft by setting different influencing parameters, predicts camshaft faults based on the state parameters during the simulation process, and verifies the prediction results through fault detection methods, thereby improving the accuracy of fault prediction. At the same time, the optimal operating parameter settings for the camshaft are obtained based on the experimental results.
[0041] Those skilled in the art will understand that embodiments of the present invention can be provided as systems or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0042] This invention is described with reference to flowchart illustrations and / or block diagrams of computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0043] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0044] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0045] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A camshaft fault simulation and analysis system, characterized in that, It includes a fault simulation subsystem, a signal acquisition subsystem, a fault prediction subsystem, a prediction verification subsystem, a data storage subsystem, and a comprehensive analysis subsystem; The fault simulation subsystem and the signal acquisition subsystem are connected in sequence. The fault prediction subsystem and the prediction verification subsystem are respectively connected to the signal acquisition subsystem and the data storage subsystem. The data storage subsystem and the comprehensive analysis subsystem are connected in sequence. The fault simulation subsystem includes a specification storage module, a fault setting module, and a fault simulation module. The specification storage module stores the camshaft specification parameters to be simulated, and the fault setting module is used to set fault parameters during the camshaft operation simulation process according to the camshaft specification parameters. The fault simulation module is used to simulate the operation of the camshaft several times based on the fault parameters. The fault parameters include the camshaft rotation speed and acceleration, oil concentration, and fuel quantity. The fault setting module sets numerical differences for the rotation speed and acceleration, oil concentration, and fuel quantity according to the wheel axle specifications, and divides the rotation speed and acceleration, oil concentration, and fuel quantity into several groups according to the numerical differences. The fault simulation subsystem is used to simulate camshaft operation and set fault parameters during the simulation process. The signal acquisition subsystem is used to acquire state parameters during the camshaft operation simulation process; The fault prediction subsystem is used to predict the time when the camshaft will fail based on the state parameters. The prediction and verification subsystem is used to detect camshaft faults in real time based on the state parameters, and to verify the prediction results of the fault prediction subsystem based on the detection results. The prediction and verification subsystem uses discrete spectrum correction technology to extract frequency domain features from the vibration signal in the state parameters, constructs the vibration equation of the camshaft based on the frequency domain features, obtains the vibration frequency based on the vibration equation, and sets a maximum threshold for the vibration frequency. At the same time, it sets an angular velocity threshold based on the operating angular velocity in the state parameters. When both the vibration frequency and the operating angular velocity exceed the threshold, it is determined that the camshaft has a fault, and the operating simulation time when the camshaft has a fault is obtained. The operating simulation time is calibrated with the prediction time of the fault prediction subsystem. When the time is inconsistent, the fault prediction subsystem is optimized based on the operating simulation time when the fault is detected. The data storage subsystem is used to store the state parameters, the prediction results of the fault prediction subsystem, and the detection results of the prediction verification subsystem. The comprehensive analysis subsystem is used to obtain the optimal operating parameters of the camshaft based on the data stored in the data storage subsystem.
2. The camshaft fault simulation and analysis system according to claim 1, characterized in that, The signal acquisition subsystem includes an angular velocity sensor, a vibration sensor, a temperature sensor, an oxygen flow sensor, and a timer, which are used to acquire the angular velocity of the camshaft, vibration signal, temperature data, oxygen content, and the time required for the operation simulation, respectively.
3. The camshaft fault simulation and analysis system according to claim 1, characterized in that, The fault prediction subsystem uses a convolutional neural network to construct a fault prediction model, and takes the state parameters and camshaft specifications as input data. The input data is divided into a training set and a test set. The fault prediction model is trained based on the training set, and the trained fault prediction model is input into the test set to obtain the time when the camshaft fails.
4. The camshaft fault simulation and analysis system according to claim 1, characterized in that, The data storage subsystem constructs a data storage framework based on the temporal relationship of the data acquired in each simulation process; The data storage architecture is an XML architecture. In the XML architecture, the data is stored in a progressive order of acquisition time, with the acquisition time as a constraint and the state parameters as a detailed description under the time. At the same time, the prediction results and detection results with the same acquisition time are stored accordingly based on the constraints.
5. The camshaft fault simulation and analysis system according to claim 1, characterized in that, The comprehensive analysis subsystem constructs a state curve diagram of the camshaft under different fault parameters based on the state parameters and detection results of several operation simulations stored in the data storage subsystem. Based on the state curve diagram, the fault parameters set in the optimal state of the camshaft are obtained and used as the optimal operating parameters.
Citation Information
Patent Citations
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CN111506043A
Intelligent drive axle health monitoring system and method based on cloud edge cooperative computing
CN114202198A