Method and device for compiling random vibration fatigue load spectrum of high-speed motor train unit structure

Abstract

The application provides a high-speed motor train unit structure random vibration fatigue load spectrum compiling method and device, and relates to the technical field of locomotive vehicles. The method comprises collecting vibration acceleration signals and stress signals, and preprocessing the vibration acceleration signals and the stress signals; obtaining principal component loads and trend loads according to the preprocessed vibration acceleration signals, determining a judgment threshold according to the principal component loads, and extracting a plurality of signal components according to the judgment threshold; superimposing the plurality of signal components and the trend loads to obtain high-precision acceleration time-domain signals, and extrapolating and reconstructing according to the high-precision acceleration time-domain signals, the preprocessed stress signals and preset working condition information to obtain multi-working condition acceleration vibration load time-domain spectra; and obtaining high-precision full-life acceleration vibration load spectra. The device executes the above method. The method and device provided in the application can obtain higher-precision high-speed motor train unit structure random vibration load spectra.

Description

Technical Field

[0001] This invention relates to the field of locomotive and rolling stock technology, specifically to a method and apparatus for compiling a random vibration fatigue load spectrum for a high-speed train structure. Background Technology

[0002] Key components of high-speed trains (including but not limited to traction transformers, traction converters, gearboxes, and suspension structures) are subjected to complex multi-source coupled vibration loads during operation. These loads exhibit significant non-stationary characteristics, and their dynamic features are mainly influenced by three factors:

[0003] Operating conditions: including train speed (especially ultra-high speed conditions of 400km / h and above) and track conditions (track irregularities, curve radius, gradient angle, etc.).

[0004] Environmental factors: temperature gradient changes, humidity fluctuations, and lateral wind loads, etc.

[0005] Structural coupling effect: dynamic interaction between car body, bogie, and components.

[0006] However, existing load spectrum compilation techniques have the following problems:

[0007] Insufficient data reliability: Existing load spectrum compilation methods mainly rely on limited historical data or simulation results, lacking a database of measured vibrations at speeds of 400 km / h and above. Data acquisition methods are limited, using only single-mode stress or acceleration sensing, making it difficult to comprehensively characterize actual load conditions. Furthermore, the existing sample size is insufficient to effectively cover extreme conditions under ultra-high-speed operation, leading to significant biases in fatigue life assessment.

[0008] Insufficient accuracy of extrapolation methods: Traditional load spectrum extrapolation methods (such as linear extrapolation or simple statistical models) cannot accurately capture the nonlinear characteristics of vibration loads. Threshold selection relies on subjective experience (such as the fixed percentile method), lacking objective standards, leading to distortion in extreme load reconstruction. In addition, commonly used statistical models such as the Weibull distribution have poor adaptability to non-stationary loads and are difficult to meet the accuracy requirements of full life cycle load prediction.

[0009] Poor engineering applicability: Existing methods are computationally time-consuming (single-condition analysis requires several hours) and lack effective multi-source data fusion algorithms, resulting in limited load spectrum accuracy. Large life-cycle prediction errors prevent the provision of reliable fatigue life assessment data for key components of high-speed trains. Summary of the Invention

[0010] To address the problems in the prior art, embodiments of the present invention provide a method and apparatus for compiling a random vibration fatigue load spectrum for a high-speed train structure, which can at least partially solve the problems existing in the prior art.

[0011] On the one hand, this invention proposes a method for compiling a random vibration fatigue load spectrum for a high-speed train structure, including:

[0012] Simultaneously acquire vibration acceleration and stress signals from high-speed trains, and preprocess the vibration acceleration and stress signals;

[0013] The principal component load and trend load are obtained from the preprocessed vibration acceleration signal. The judgment threshold is determined based on the principal component load, and multiple signal components are extracted based on the judgment threshold.

[0014] Multiple signal components are superimposed with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information, extrapolation and reconstruction are performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum.

[0015] High-precision full-life acceleration vibration load spectrum is obtained from the time-domain spectrum of acceleration vibration load under multiple working conditions.

[0016] The preprocessing of the vibration acceleration signal and stress signal includes:

[0017] Bandpass filtering is applied to the vibration acceleration signal and the stress signal.

[0018] The step of obtaining the principal component load and trend load based on the preprocessed vibration acceleration signal includes:

[0019] Empirical mode decomposition is performed on the preprocessed vibration acceleration signal, and feature analysis is conducted on the decomposed features to obtain principal component loads and trend loads.

[0020] The judgment thresholds include a main frequency threshold, an energy percentage threshold, and a correlation coefficient threshold; correspondingly, the extraction of multiple signal components based on the judgment thresholds includes:

[0021] Signal components that satisfy at least two of the following threshold conditions—dominant frequency threshold, energy percentage threshold, and correlation coefficient threshold—are extracted as multiple signal components.

[0022] The judgment threshold also includes a kurtosis threshold; correspondingly, the method for compiling the random vibration fatigue load spectrum of a high-speed train structure also includes:

[0023] Each signal component that does not meet at least two types of threshold conditions is subjected to kurtosis determination using a kurtosis threshold;

[0024] The signal components that meet the kurtosis threshold condition are used as the extracted signal components.

[0025] The step of obtaining a high-precision full-life acceleration vibration load spectrum based on the time-domain spectrum of multi-condition acceleration vibration load includes:

[0026] The time-domain spectrum of multi-condition acceleration vibration load is used as the excitation signal and applied to a single-degree-of-freedom system with different natural frequencies.

[0027] The stress response of the single-degree-of-freedom system is solved by Duhamel integration. Based on the stress response of the single-degree-of-freedom system, the fatigue damage spectrum is calculated using the rainflow counting method.

[0028] Equivalent vibration load spectrum is calculated from the fatigue damage spectrum to obtain a high-precision full-life acceleration vibration load spectrum that is equivalent to the fatigue damage spectrum.

[0029] On the one hand, the present invention proposes a device for compiling a random vibration fatigue load spectrum of a high-speed train structure, comprising:

[0030] The acquisition unit is used to synchronously acquire vibration acceleration signals and stress signals of high-speed trains and to preprocess the vibration acceleration signals and stress signals.

[0031] The extraction unit is used to obtain the principal component load and trend load based on the preprocessed vibration acceleration signal, determine the judgment threshold based on the principal component load, and extract multiple signal components based on the judgment threshold.

[0032] The reconstruction unit is used to superimpose multiple signal components with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information, extrapolation reconstruction is performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum.

[0033] The acquisition unit is used to acquire a high-precision full-life acceleration vibration load spectrum based on the time-domain spectrum of acceleration vibration load under multiple working conditions.

[0034] In another aspect, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the following method:

[0035] Simultaneously acquire vibration acceleration and stress signals from high-speed trains, and preprocess the vibration acceleration and stress signals;

[0036] The principal component load and trend load are obtained from the preprocessed vibration acceleration signal. The judgment threshold is determined based on the principal component load, and multiple signal components are extracted based on the judgment threshold.

[0037] Multiple signal components are superimposed with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information, extrapolation and reconstruction are performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum.

[0038] High-precision full-life acceleration vibration load spectrum is obtained from the time-domain spectrum of acceleration vibration load under multiple working conditions.

[0039] This invention provides a computer-readable storage medium, comprising:

[0040] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method:

[0041] Simultaneously acquire vibration acceleration and stress signals from high-speed trains, and preprocess the vibration acceleration and stress signals;

[0042] The principal component load and trend load are obtained from the preprocessed vibration acceleration signal. The judgment threshold is determined based on the principal component load, and multiple signal components are extracted based on the judgment threshold.

[0043] Multiple signal components are superimposed with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information, extrapolation and reconstruction are performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum.

[0044] High-precision full-life acceleration vibration load spectrum is obtained from the time-domain spectrum of acceleration vibration load under multiple working conditions.

[0045] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the following method:

[0046] Simultaneously acquire vibration acceleration and stress signals from high-speed trains, and preprocess the vibration acceleration and stress signals;

[0047] The principal component load and trend load are obtained from the preprocessed vibration acceleration signal. The judgment threshold is determined based on the principal component load, and multiple signal components are extracted based on the judgment threshold.

[0048] Multiple signal components are superimposed with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information, extrapolation and reconstruction are performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum.

[0049] High-precision full-life acceleration vibration load spectrum is obtained from the time-domain spectrum of acceleration vibration load under multiple working conditions.

[0050] The method and apparatus for compiling random vibration fatigue load spectrum of high-speed train structure provided in this invention synchronously acquires vibration acceleration signals and stress signals of high-speed train, preprocesses the vibration acceleration signals and stress signals; obtains principal component loads and trend loads based on the preprocessed vibration acceleration signals, determines a judgment threshold based on the principal component loads, and extracts multiple signal components based on the judgment thresholds; superimposes the multiple signal components with the trend loads to obtain a high-precision acceleration time-domain signal; extrapolates and reconstructs based on the high-precision acceleration time-domain signal, the preprocessed stress signal, and preset working condition information to obtain a multi-working-condition acceleration vibration load time-domain spectrum; and obtains a high-precision full-life acceleration vibration load spectrum based on the multi-working-condition acceleration vibration load time-domain spectrum, which can obtain a higher-precision random vibration load spectrum of high-speed train structure and improve engineering applicability. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0052] Figure 1 This is a flowchart illustrating a method for compiling a random vibration fatigue load spectrum of a high-speed train structure according to an embodiment of the present invention.

[0053] Figure 2 This is a flowchart illustrating a method for compiling a random vibration fatigue load spectrum for a high-speed train structure, provided in another embodiment of the present invention.

[0054] Figure 3 This is a schematic diagram illustrating the adaptive threshold filtering and filtering information provided in an embodiment of the present invention.

[0055] Figure 4 This is a schematic diagram illustrating the fatigue damage spectrum (FDS) provided in an embodiment of the present invention.

[0056] Figure 5 This is a schematic diagram of the structure of a high-speed train structure random vibration fatigue load spectrum compilation device provided in an embodiment of the present invention.

[0057] Figure 6 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0059] Figure 1 This is a flowchart illustrating a method for compiling a random vibration fatigue load spectrum for a high-speed train structure according to an embodiment of the present invention. Figure 1 As shown, the method for compiling the random vibration fatigue load spectrum of a high-speed train structure provided in this embodiment of the invention includes:

[0060] Step S1: Synchronously acquire vibration acceleration signals and stress signals of the high-speed train, and preprocess the vibration acceleration signals and stress signals.

[0061] Step S2: Obtain principal component load and trend load based on the preprocessed vibration acceleration signal, determine the judgment threshold based on the principal component load, and extract multiple signal components based on the judgment threshold.

[0062] Step S3: Superimpose multiple signal components with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal, and the preset working condition information, perform extrapolation and reconstruction to obtain the multi-working-condition acceleration vibration load time-domain spectrum.

[0063] Step S4: Obtain a high-precision full-life acceleration vibration load spectrum based on the time-domain spectrum of acceleration vibration load under multiple working conditions.

[0064] In step S1 above, the device synchronously acquires the vibration acceleration and stress signals of the high-speed train and preprocesses them. The device can be a computer that executes this method. The acquisition, storage, use, and processing of data in this application's technical solution all comply with relevant regulations. Sensors can be pre-arranged, as explained below:

[0065] Triaxial accelerometers (to collect random vibration loads) and strain gauges (to measure stress response) are installed in key components of the EMU (such as traction transformers, traction converters, gearboxes, and motors) as well as in suspension and thin-walled structures.

[0066] Using a comprehensive vehicle testing system, vibration acceleration and stress signals are collected simultaneously at a sampling frequency of 10kHz to ensure data real-time performance and accuracy.

[0067] The preprocessing of the vibration acceleration signal and stress signal includes:

[0068] Bandpass filtering is applied to the vibration acceleration and stress signals. This process removes high-frequency noise and low-frequency drift from the raw data, ensuring its validity and reliability.

[0069] In step S2 above, the device acquires the principal component load and trend load based on the preprocessed vibration acceleration signal, determines a judgment threshold based on the principal component load, and extracts multiple signal components based on the judgment threshold. The acquisition of the principal component load and trend load based on the preprocessed vibration acceleration signal includes:

[0070] Empirical mode decomposition (EMD) is performed on the preprocessed vibration acceleration signal. Feature analysis is then conducted on the decomposed features to obtain principal component loads and trend loads. For example... Figure 2 As shown, Empirical Mode Decomposition (EEMD) is a method where principal component loads reflect the main characteristics of the vibration signal, while trend loads reflect the long-term trend of the signal.

[0071] The determination of the judgment threshold based on principal component loadings is explained as follows:

[0072] Based on the obtained principal component loads, the optimal thresholds for the multi-dimensional upper and lower limits of the vibration signal are calculated and comprehensively judged. By calculating and statistically analyzing the principal frequency, energy proportion, and correlation coefficient between the vibration signal and the original signal of the principal component loads, judgment threshold information is formed.

[0073] For the main frequency threshold, the 10th percentile of the main frequency of all vibration signals is taken as the lower limit of the low frequency threshold, and the 90th percentile of the main frequency of all vibration signals is taken as the upper limit of the high frequency threshold (not exceeding 80% of the original signal's Nyquist frequency).

[0074] For the energy percentage threshold, calculate the energy (sum of squares of component amplitudes) and average energy of each vibration signal, and take 20% of the average energy as the lower limit threshold (at least 0.5% of the energy is reserved).

[0075] For the correlation coefficient threshold, calculate the correlation coefficient between each vibration signal and the original signal, and calculate the median and standard deviation of all correlation coefficients. The lower limit threshold is the median minus 1.5 times the standard deviation.

[0076] The judgment thresholds include a main frequency threshold, an energy percentage threshold, and a correlation coefficient threshold; correspondingly, the extraction of multiple signal components based on the judgment thresholds includes:

[0077] Signal components that satisfy at least two of the following threshold conditions—dominant frequency threshold, energy percentage threshold, and correlation coefficient threshold—are extracted as multiple signal components. Referring to the example above, the signal component satisfying the dominant frequency threshold condition is the signal component falling between the lower and upper limits of the threshold corresponding to the dominant frequency.

[0078] Signal components that meet the energy percentage threshold condition are those whose energy percentage is greater than the lower limit of the threshold corresponding to the energy percentage.

[0079] Signal components that meet the correlation coefficient threshold condition are those whose threshold is greater than the lower limit of the threshold corresponding to the correlation coefficient.

[0080] The judgment threshold also includes a kurtosis threshold; correspondingly, the method for compiling the random vibration fatigue load spectrum of high-speed train structures also includes:

[0081] Each signal component that does not meet at least two types of threshold conditions is subjected to kurtosis determination using a kurtosis threshold;

[0082] Signal components that meet the kurtosis threshold condition are extracted as multiple signal components. Referring to the example above, kurtosis judgment is performed separately for all signals that do not meet the above judgment threshold condition. First, the kurtosis (fourth-order statistic) of each signal is calculated, and the 75th percentile of all kurtosis is taken as the lower limit threshold. If the kurtosis detection is passed, it is retained to ensure that the impulse information in the original signal is preserved.

[0083] like Figure 3 As shown, this automatic threshold calculation and filtering process can be implemented without manual parameter setting and is effective for different types of vibration / impact signals. Simultaneously, it retains key transient features through kurtosis detection and effectively filters out irrelevant components through multi-dimensional joint judgment.

[0084] In step S3 above, the device superimposes multiple signal components with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal, and preset working condition information, extrapolation reconstruction is performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum. The selected signal components and the decomposed trend load are then linearly superimposed to obtain a high-precision acceleration time-domain signal suitable for subsequent extrapolation reconstruction.

[0085] Taking the station entry operation of a certain type of EMU as an example, the collected frequency domain stress data is input into the simulation extrapolation model, and the acceleration is simulated and extrapolated using the Monte Carlo simulation method to generate corresponding time domain and frequency domain information. The obtained extrapolated acceleration spectrum is compared with the original stress spectrum by the response frequency to ensure the reliability and accuracy of the results.

[0086] In step S4 above, the device obtains a high-precision full-life acceleration vibration load spectrum based on the time-domain spectrum of multi-condition acceleration vibration load. The process of obtaining the high-precision full-life acceleration vibration load spectrum based on the time-domain spectrum of multi-condition acceleration vibration load includes:

[0087] The time-domain spectrum of multi-condition acceleration vibration load is used as the excitation signal and applied to a single-degree-of-freedom system with different natural frequencies.

[0088] The stress response of the single-degree-of-freedom system is solved by Duhamel integration. Based on the stress response of the single-degree-of-freedom system, the fatigue damage spectrum is calculated using the rainflow counting method.

[0089] Equivalent vibration load spectrum is calculated from the fatigue damage spectrum to obtain a high-precision full-life acceleration vibration load spectrum that is equivalent to the fatigue damage spectrum.

[0090] For the time-domain spectrum of multi-condition acceleration vibration loads (i.e., non-stationary loads) obtained by extrapolation and reconstruction, a series of single-degree-of-freedom (SDOF) systems with fixed damping ratios and different natural frequencies are constructed to replace the actual auxiliary mounting base. For a single-degree-of-freedom system at a certain natural frequency, the time-domain solution of the relative displacement response of the non-stationary load acting on the SDOF system can be solved by Duhamel integration. The time-domain solution of the stress response is obtained through the material elastic modulus. The damage value caused by the stress response at that natural frequency is calculated by combining the rainflow counting method. By changing the natural frequency, the damage value varying with the natural frequency can be obtained, forming a fatigue damage spectrum with frequency on the horizontal axis and damage value on the vertical axis, such as... Figure 4 As shown.

[0091] Equivalent vibration load spectrum calculation: Theoretically, both stationary and non-stationary loads have fatigue damage spectra. Therefore, based on the principle of equal damage, the equivalent stationary vibration load spectrum that causes the same fatigue damage as the non-stationary load can be derived in reverse, and this spectrum can be used to replace the original non-stationary load to obtain the acceleration vibration load spectrum that has the same damage capability to the auxiliary mounting base as the original non-stationary load.

[0092] The method for compiling the random vibration fatigue load spectrum of a high-speed train structure provided in this embodiment of the invention has the following specific technical improvement directions:

[0093] Multi-source data fusion: Simultaneous acquisition of vibration acceleration and stress response data compensates for the limitations of single sensor data and improves the accuracy and reliability of load spectrum.

[0094] Data preprocessing optimization: EEMD mode decomposition technology is used to replace traditional filtering methods, which can more accurately separate the principal components and trend components of the signal and improve data accuracy.

[0095] Optimized extrapolation method: Based on statistical threshold screening and generalized Pareto distribution (GPD) extreme value extrapolation, the accuracy of extreme value load reconstruction is improved, ensuring the applicability of the full life cycle load spectrum.

[0096] Improved frequency domain analysis method: A vibration load spectrum compilation method based on fatigue damage spectrum is adopted. The stress response of non-stationary load is calculated by using a single degree of freedom (SDOF) model and Duhamel integral. A high-precision spectrum is generated by combining rainflow counting method, thus optimizing the calculation efficiency.

[0097] Improve data acquisition and processing efficiency: Introduce automated data acquisition and real-time processing technologies to meet the needs of large-scale testing of high-speed trains and improve overall analysis efficiency.

[0098] Existing technologies for compiling load spectra of high-speed trains suffer from key deficiencies such as insufficient multi-source data fusion, limited accuracy of extrapolation methods, and imperfect time-frequency conversion methods. This invention innovatively employs multi-sensor synchronous acquisition, EEMD mode decomposition, threshold screening and extrapolation reconstruction based on generalized Pareto distribution, and a fatigue damage spectrum generation method combining a single-degree-of-freedom model and Duhamel integral to construct a complete load spectrum compilation technology system, which significantly improves the accuracy and efficiency of fatigue life assessment of key components of high-speed trains with speeds of 400 km / h and above.

[0099] The method for compiling the random vibration fatigue load spectrum of high-speed train structures provided in this invention has the following beneficial technical effects:

[0100] Multi-source data fusion: By simultaneously acquiring vibration acceleration and stress response data, this invention overcomes the limitations of data acquisition from a single sensor, significantly improving the accuracy and reliability of the load spectrum.

[0101] Innovative Algorithm Combination: This invention organically combines Empirical Mode Decomposition (EEMD), threshold screening, and vibration load spectrum based on fatigue damage spectrum, as well as extrapolation and reconstruction generation, to form a complete load spectrum compilation method, which significantly improves the engineering applicability of the load spectrum.

[0102] Accurate threshold calculation: Utilizing statistical principles, the data modes are analyzed and judged from multiple dimensions, including the main frequency range, correlation coefficient, and energy proportion, to accurately extract the principal component loadings and achieve multiple data cleaning.

[0103] Efficiency and economy: By optimizing threshold selection and extrapolation algorithm, this invention reduces the test cycle and cost, and provides a highly reliable input for fatigue life assessment of key components of EMU trains.

[0104] The method for compiling a random vibration fatigue load spectrum for high-speed train structures provided in this invention involves simultaneously acquiring vibration acceleration and stress signals from the high-speed train, preprocessing the vibration acceleration and stress signals, obtaining principal component loads and trend loads based on the preprocessed vibration acceleration signals, determining a judgment threshold based on the principal component loads, and extracting multiple signal components based on the judgment thresholds. These multiple signal components are then superimposed with the trend loads to obtain a high-precision acceleration time-domain signal. Extrapolation and reconstruction are performed based on the high-precision acceleration time-domain signal, the preprocessed stress signal, and preset working condition information to obtain a multi-working-condition acceleration vibration load time-domain spectrum. This method obtains a high-precision full-life acceleration vibration load spectrum based on the multi-working-condition acceleration vibration load time-domain spectrum, enabling the acquisition of a more accurate random vibration load spectrum for high-speed train structures and improving engineering applicability.

[0105] Furthermore, the preprocessing of the vibration acceleration signal and stress signal includes:

[0106] Bandpass filtering is performed on the vibration acceleration signal and stress signal. This can be referred to the above embodiments for explanation, and will not be repeated here.

[0107] Further, the step of obtaining the principal component load and trend load based on the preprocessed vibration acceleration signal includes:

[0108] Empirical mode decomposition is performed on the preprocessed vibration acceleration signal, and feature analysis is conducted on the decomposed features to obtain principal component loads and trend loads. This can be referred to the above embodiments for further explanation and will not be repeated here.

[0109] Furthermore, the judgment threshold includes a dominant frequency threshold, an energy percentage threshold, and a correlation coefficient threshold; correspondingly, the extraction of multiple signal components based on the judgment threshold includes:

[0110] Signal components that satisfy at least two of the following threshold conditions—dominant frequency threshold, energy proportion threshold, and correlation coefficient threshold—are extracted as multiple signal components. This can be referred to the above embodiments for further explanation, and will not be repeated here.

[0111] Furthermore, the judgment threshold also includes a kurtosis threshold; correspondingly, the method for compiling the random vibration fatigue load spectrum of the high-speed train structure also includes:

[0112] Each signal component that does not meet at least two types of threshold conditions is subjected to kurtosis determination using a kurtosis threshold; this can be referred to the above embodiments for explanation, and will not be repeated here.

[0113] The signal components that meet the kurtosis threshold condition are used as the extracted signal components. This can be referred to the above embodiments for further explanation, and will not be repeated here.

[0114] Furthermore, the step of obtaining a high-precision full-life acceleration vibration load spectrum based on the time-domain spectrum of multi-condition acceleration vibration load includes:

[0115] The time-domain spectrum of multi-condition acceleration vibration load is used as the excitation signal and applied to a single-degree-of-freedom system with different natural frequencies; the above embodiments can be referred to for explanation, and will not be repeated here.

[0116] The stress response of a single-degree-of-freedom system is solved by Duhamel integration. Based on the stress response of the single-degree-of-freedom system, the fatigue damage spectrum is calculated using the rainflow counting method. This can be referred to the above embodiments for explanation, and will not be repeated here.

[0117] An equivalent vibration load spectrum is calculated from the fatigue damage spectrum to obtain a high-precision full-life acceleration vibration load spectrum that is equivalent to the fatigue damage spectrum. This can be referred to the above embodiments for further explanation and will not be repeated here.

[0118] Figure 5 This is a schematic diagram of a high-speed train structure random vibration fatigue load spectrum compilation device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the high-speed train structure random vibration fatigue load spectrum compilation device provided in this embodiment of the invention includes a data acquisition unit 501, an extraction unit 502, a reconstruction unit 503, and an acquisition unit 504, wherein:

[0119] The acquisition unit 501 is used to synchronously acquire the vibration acceleration signal and stress signal of the high-speed train and preprocess the vibration acceleration signal and stress signal; the extraction unit 502 is used to obtain the principal component load and trend load based on the preprocessed vibration acceleration signal, determine the judgment threshold based on the principal component load, and extract multiple signal components based on the judgment threshold; the reconstruction unit 503 is used to superimpose the multiple signal components and the trend load to obtain a high-precision acceleration time-domain signal, and perform extrapolation reconstruction based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information to obtain the multi-working-condition acceleration vibration load time-domain spectrum; the acquisition unit 504 is used to obtain a high-precision full-life acceleration vibration load spectrum based on the multi-working-condition acceleration vibration load time-domain spectrum.

[0120] Specifically, the acquisition unit 501 in the device is used to synchronously acquire the vibration acceleration signal and stress signal of the high-speed train, and preprocess the vibration acceleration signal and stress signal; the extraction unit 502 is used to obtain the principal component load and trend load based on the preprocessed vibration acceleration signal, determine the judgment threshold based on the principal component load, and extract multiple signal components based on the judgment threshold; the reconstruction unit 503 is used to superimpose the multiple signal components and the trend load to obtain a high-precision acceleration time-domain signal, and perform extrapolation reconstruction based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information to obtain the multi-working-condition acceleration vibration load time-domain spectrum; the acquisition unit 504 is used to obtain a high-precision full-life acceleration vibration load spectrum based on the multi-working-condition acceleration vibration load time-domain spectrum.

[0121] The random vibration fatigue load spectrum compilation device for high-speed train structures provided in this invention synchronously acquires vibration acceleration signals and stress signals of high-speed trains, preprocesses the vibration acceleration signals and stress signals, obtains principal component loads and trend loads based on the preprocessed vibration acceleration signals, determines a judgment threshold based on the principal component loads, and extracts multiple signal components based on the judgment thresholds, superimposes the multiple signal components with the trend loads to obtain a high-precision acceleration time-domain signal, and performs extrapolation and reconstruction based on the high-precision acceleration time-domain signal, the preprocessed stress signal, and preset working condition information to obtain a multi-working-condition acceleration vibration load time-domain spectrum, and obtains a high-precision full-life acceleration vibration load spectrum based on the multi-working-condition acceleration vibration load time-domain spectrum. This enables the acquisition of a higher-precision random vibration load spectrum for high-speed train structures and improves engineering applicability.

[0122] Furthermore, the acquisition unit 501 is specifically used for:

[0123] Bandpass filtering is applied to the vibration acceleration signal and the stress signal.

[0124] Furthermore, the extraction unit 502 is specifically used for:

[0125] Empirical mode decomposition is performed on the preprocessed vibration acceleration signal, and feature analysis is conducted on the decomposed features to obtain principal component loads and trend loads.

[0126] Furthermore, the judgment thresholds include a dominant frequency threshold, an energy percentage threshold, and a correlation coefficient threshold; correspondingly, the extraction unit 502 is specifically used for:

[0127] Signal components that satisfy at least two of the following threshold conditions—dominant frequency threshold, energy percentage threshold, and correlation coefficient threshold—are extracted as multiple signal components.

[0128] Furthermore, the judgment threshold also includes a kurtosis threshold; correspondingly, the high-speed train structure random vibration fatigue load spectrum compilation device is also used for:

[0129] Each signal component that does not meet at least two types of threshold conditions is subjected to kurtosis determination using a kurtosis threshold;

[0130] The signal components that meet the kurtosis threshold condition are used as the extracted signal components.

[0131] Furthermore, the acquisition unit 504 is specifically used for:

[0132] The time-domain spectrum of multi-condition acceleration vibration load is used as the excitation signal and applied to a single-degree-of-freedom system with different natural frequencies.

[0133] The stress response of the single-degree-of-freedom system is solved by Duhamel integration. Based on the stress response of the single-degree-of-freedom system, the fatigue damage spectrum is calculated using the rainflow counting method.

[0134] Equivalent vibration load spectrum is calculated from the fatigue damage spectrum to obtain a high-precision full-life acceleration vibration load spectrum that is equivalent to the fatigue damage spectrum.

[0135] The embodiments of the present invention provide a device for compiling random vibration fatigue load spectrum of high-speed train structure, which can be used to execute the processing flow of the above method embodiments. Its function will not be repeated here, but can be referred to the detailed description of the above method embodiments.

[0136] Figure 6 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention, such as... Figure 6 As shown, the computer device includes: a memory 601, a processor 602, and a computer program stored in the memory 601 and executable on the processor 602. When the processor 602 executes the computer program, it implements the following method:

[0137] Simultaneously acquire vibration acceleration and stress signals from high-speed trains, and preprocess the vibration acceleration and stress signals;

[0138] The principal component load and trend load are obtained from the preprocessed vibration acceleration signal. The judgment threshold is determined based on the principal component load, and multiple signal components are extracted based on the judgment threshold.

[0139] Multiple signal components are superimposed with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information, extrapolation and reconstruction are performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum.

[0140] High-precision full-life acceleration vibration load spectrum is obtained from the time-domain spectrum of acceleration vibration load under multiple working conditions.

[0141] This embodiment discloses a computer program product, which includes a computer program that, when executed by a processor, implements the following method:

[0142] Simultaneously acquire vibration acceleration and stress signals from high-speed trains, and preprocess the vibration acceleration and stress signals;

[0143] The principal component load and trend load are obtained from the preprocessed vibration acceleration signal. The judgment threshold is determined based on the principal component load, and multiple signal components are extracted based on the judgment threshold.

[0144] Multiple signal components are superimposed with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information, extrapolation and reconstruction are performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum.

[0145] High-precision full-life acceleration vibration load spectrum is obtained from the time-domain spectrum of acceleration vibration load under multiple working conditions.

[0146] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the following method:

[0147] Simultaneously acquire vibration acceleration and stress signals from high-speed trains, and preprocess the vibration acceleration and stress signals;

[0148] The principal component load and trend load are obtained from the preprocessed vibration acceleration signal. The judgment threshold is determined based on the principal component load, and multiple signal components are extracted based on the judgment threshold.

[0149] Multiple signal components are superimposed with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information, extrapolation and reconstruction are performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum.

[0150] High-precision full-life acceleration vibration load spectrum is obtained from the time-domain spectrum of acceleration vibration load under multiple working conditions.

[0151] Compared with existing technologies, the present invention provides a method for compiling a random vibration fatigue load spectrum for high-speed train structures. This method simultaneously acquires vibration acceleration and stress signals from the high-speed train, preprocesses these signals, obtains principal component loads and trend loads from the preprocessed vibration acceleration signals, determines a judgment threshold based on the principal component loads, and extracts multiple signal components based on the judgment threshold. These multiple signal components are then superimposed with the trend load to obtain a high-precision acceleration time-domain signal. Extrapolation and reconstruction are performed based on the high-precision acceleration time-domain signal, the preprocessed stress signal, and preset working condition information to obtain a multi-working-condition acceleration vibration load time-domain spectrum. By obtaining a high-precision full-life acceleration vibration load spectrum from the multi-working-condition acceleration vibration load time-domain spectrum, the method can acquire a more accurate random vibration load spectrum for high-speed train structures and improve engineering applicability.

[0152] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, 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.

[0153] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and 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.

[0154] 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.

[0155] 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.

[0156] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0157] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for compiling a random vibration fatigue load spectrum for a high-speed train structure, characterized in that, include: Simultaneously acquire vibration acceleration and stress signals from high-speed trains, and preprocess the vibration acceleration and stress signals; Empirical mode decomposition is performed on the preprocessed vibration acceleration signal, and feature analysis is performed on the decomposed features to obtain principal component loads and trend loads. The principal component loads reflect the main features of the vibration acceleration signal, and the trend loads reflect the long-term variation trend of the vibration acceleration signal. The judgment threshold is determined based on the principal component loading, and multiple signal components are extracted based on the judgment threshold. The judgment thresholds include the main frequency threshold, the energy proportion threshold, and the correlation coefficient threshold. The step of extracting multiple signal components based on a judgment threshold includes: taking signal components that satisfy at least two of the following threshold conditions: a dominant frequency threshold, an energy proportion threshold, and a correlation coefficient threshold, as the extracted multiple signal components; the judgment threshold also includes a kurtosis threshold; the method for compiling the random vibration fatigue load spectrum of a high-speed train structure further includes: performing kurtosis judgment on each signal component that does not satisfy at least two threshold conditions using a kurtosis threshold; and taking signal components that satisfy the kurtosis threshold conditions as the extracted multiple signal components; Multiple signal components are superimposed with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information, extrapolation and reconstruction are performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum. High-precision full-life acceleration vibration load spectrum is obtained from the time-domain spectrum of acceleration vibration load under multiple working conditions.

2. The method for compiling the random vibration fatigue load spectrum of a high-speed train structure according to claim 1, characterized in that, The preprocessing of the vibration acceleration signal and stress signal includes: Bandpass filtering is applied to the vibration acceleration signal and the stress signal.

3. The method for compiling the random vibration fatigue load spectrum of a high-speed train structure according to claim 1 or 2, characterized in that, The process of obtaining a high-precision full-life acceleration vibration load spectrum based on the time-domain spectrum of multi-condition acceleration vibration loads includes: The time-domain spectrum of multi-condition acceleration vibration load is used as the excitation signal and applied to a single-degree-of-freedom system with different natural frequencies. The stress response of the single-degree-of-freedom system is solved by Duhamel integration. Based on the stress response of the single-degree-of-freedom system, the fatigue damage spectrum is calculated using the rainflow counting method. Equivalent vibration load spectrum is calculated from the fatigue damage spectrum to obtain a high-precision full-life acceleration vibration load spectrum that is equivalent to the fatigue damage spectrum.

4. A device for compiling a random vibration fatigue load spectrum of a high-speed train structure, characterized in that, include: The acquisition unit is used to synchronously acquire vibration acceleration signals and stress signals of high-speed trains and to preprocess the vibration acceleration signals and stress signals. The extraction unit is used to perform empirical mode decomposition on the preprocessed vibration acceleration signal, perform feature analysis on the decomposed features, and obtain principal component loads and trend loads. The principal component loads reflect the main features of the vibration acceleration signal, and the trend loads reflect the long-term variation trend of the vibration acceleration signal. The judgment threshold is determined based on the principal component loads, and multiple signal components are extracted based on the judgment thresholds. The judgment thresholds include the main frequency threshold, the energy proportion threshold, and the correlation coefficient threshold. The step of extracting multiple signal components based on a judgment threshold includes: taking signal components that satisfy at least two of the following threshold conditions: a dominant frequency threshold, an energy proportion threshold, and a correlation coefficient threshold, as the extracted multiple signal components; the judgment threshold also includes a kurtosis threshold; the method for compiling the random vibration fatigue load spectrum of a high-speed train structure further includes: performing kurtosis judgment on each signal component that does not satisfy at least two threshold conditions using a kurtosis threshold; and taking signal components that satisfy the kurtosis threshold conditions as the extracted multiple signal components; The reconstruction unit is used to superimpose multiple signal components with the trend load to obtain a high-precision acceleration time-domain signal. Based on the high-precision acceleration time-domain signal, the preprocessed stress signal and the preset working condition information, extrapolation reconstruction is performed to obtain the multi-working-condition acceleration vibration load time-domain spectrum. The acquisition unit is used to acquire a high-precision full-life acceleration vibration load spectrum based on the time-domain spectrum of acceleration vibration load under multiple working conditions.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 3.

7. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 3.

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