Train instability monitoring method, device and medium

Abstract

The present application discloses a train instability monitoring method, device and medium, which relates to the technical field of train instability monitoring and is used to monitor whether the frame is unstable. In view of the problem that the accuracy and monitoring range of the current instability monitoring scheme need to be further improved, a train instability monitoring method is provided. By utilizing the existing vibration-temperature composite sensor in the train axle box, the lateral acceleration of the train axle box side is collected; the lateral acceleration signal collected on the axle box side is further processed; the lateral acceleration on the axle box side is converted to the frame side through the suspension parameters between the wheelset and the axle frame to meet the instability monitoring needs of the train frame. Since the train is equipped with axle boxes on all 8 wheel positions, the monitoring range of the train frame instability can be expanded. In addition, since the source of train instability is wheel-rail excitation and wheel-rail profile matching, the vibration information collected on the wheelset side can also improve the accuracy of monitoring train instability based on the source of vibration information.

Description

Technical Field

[0001] The present application relates to the technical field of train instability monitoring, and in particular to a train instability monitoring method, device and medium. Background Art

[0002] During train operation, the geometry of the wheelset tread determines the train's stability on the track. This is achieved by ensuring the train's range of motion remains within permitted limits through "snaking" motion. However, excessive wheel-rail equivalent conicity and excessive wheel-rail excitation can exacerbate lateral motion, leading to snaking instability and compromising operational safety.

[0003] At present, the train instability monitoring program is mainly implemented by collecting the lateral acceleration of the train frame. Data is collected by installing lateral acceleration sensors at the 1st, 4th, 5th, and 8th frame ends of the EMU. The corresponding evaluation methods include the judgment of the acceleration amplitude and the judgment of the number of waves that continuously exceed the amplitude threshold.

[0004] However, due to cost and benefit considerations, the above scheme does not install frame lateral acceleration sensors at the ends of the 2-position, 3-position, 6-position, and 7-position frames, so its accuracy and reliability in monitoring train instability need to be further improved.

[0005] Therefore, technicians in this field are in urgent need of a train instability monitoring method to improve the accuracy and reliability of train instability monitoring while controlling implementation costs. Summary of the Invention

[0006] The purpose of this application is to provide a train instability monitoring method, device and medium to improve the accuracy and reliability of train instability monitoring while controlling implementation costs.

[0007] To solve the above technical problems, the present application provides a train instability monitoring method, comprising:

[0008] Obtaining the axle box lateral acceleration signal collected by the vibration temperature composite sensor installed on the train axle box;

[0009] The axle box lateral acceleration signal is transformed by Fourier method to obtain the corresponding Fourier spectrum;

[0010] The Fourier spectrum is integrated by frequency domain integration algorithm to obtain the integral displacement signal corresponding to the axle box lateral acceleration signal;

[0011] Determine and estimate the frame lateral acceleration signal based on the suspension parameters and the integrated displacement signal between the wheelset and the frame;

[0012] A frame instability index is determined according to the estimated frame lateral acceleration signal, so as to judge whether the train frame is laterally unstable according to the instability index.

[0013] In a possible embodiment, before determining the frame instability index according to the estimated frame lateral acceleration signal, the method further includes:

[0014] Obtaining the actual frame lateral acceleration signal collected by the vibration acceleration sensor installed on the train frame;

[0015] The actual frame lateral acceleration signal is fused with the estimated frame lateral acceleration signal through the Kalman filter algorithm to obtain a fused frame lateral acceleration signal.

[0016] Accordingly, determining the frame instability index based on the estimated frame lateral acceleration signal includes:

[0017] The frame instability index is determined based on the fused frame lateral acceleration signal.

[0018] In a possible embodiment, before determining the frame instability index based on the fused frame lateral acceleration signal, the method further includes:

[0019] The data of the lateral acceleration signal of the fusion frame is fitted by the least square method to obtain a fitting function;

[0020] According to the fitting function, the trend term in the fusion frame lateral acceleration signal is removed to obtain a new fusion frame lateral acceleration signal.

[0021] In a possible embodiment, transforming the axle box lateral acceleration signal by the Fourier method to obtain the corresponding Fourier spectrum includes:

[0022] The axle box lateral acceleration signal within a preset unit time is transformed by the Fourier method to obtain the corresponding Fourier spectrum;

[0023] Among them, the Fourier spectrum is:

[0024]

[0025] X(ω) represents the Fourier spectrum; x(t) represents the axle box lateral acceleration signal.

[0026] In a possible embodiment, integrating the Fourier spectrum using a frequency domain integration algorithm to obtain an integrated displacement signal corresponding to the axle box lateral acceleration signal includes:

[0027] According to the first formula, the Fourier spectrum is integrated twice to obtain an integrated displacement signal;

[0028] Among them, the first formula is:

[0029]

[0030] s(t) represents the integrated displacement signal; S(ω) represents the frequency spectrum of the integrated displacement signal.

[0031] In a possible embodiment, determining the estimated frame lateral acceleration signal based on the suspension parameters and the integrated displacement signal between the wheelset and the frame includes:

[0032] Determine the integral displacement signal using a second formula based on the suspension parameters between the wheelset and the frame and the integral displacement signal;

[0033] Among them, the second formula is:

[0034]

[0035] represents the estimated lateral acceleration of the train frame; m represents the equivalent mass of the train frame; k1, k2, k3 and k4 represent the spring stiffness corresponding to different wheel positions of the train frame, respectively.

[0036] In a possible embodiment, the actual frame lateral acceleration signal and the estimated frame lateral acceleration signal are fused by a Kalman filter algorithm to obtain a fused frame lateral acceleration signal, which includes:

[0037] Constructing an estimated observed value of the frame lateral acceleration signal;

[0038] The observed values ​​are:

[0039]

[0040] z k represents the observed value; e represents the observation error;

[0041] Determining a fused frame lateral acceleration signal using a third formula based on the actual frame lateral acceleration signal, the estimated frame lateral acceleration signal, and the observed value;

[0042] Among them, the third formula is:

[0043]

[0044] represents the lateral acceleration signal of the fusion structure; K k-1 represents the Kalman gain during the kth iteration; where K k-1 =1, the iterative formula of Kalman gain is:

[0045]

[0046] R represents the Kalman augmented observation covariance; Represents the framework prediction covariance, and the iterative formula of the framework prediction covariance is:

[0047]

[0048] P k-1 represents the parameter value for measuring the uncertainty of the frame lateral acceleration during the kth iteration, P0 is the effective value of the integrated displacement data; Q k represents the covariance of the forecast errors.

[0049] To solve the above technical problems, the present application also provides a train instability monitoring device, comprising:

[0050] The axle box monitoring module is used to obtain the axle box lateral acceleration signal collected by the vibration temperature composite sensor installed on the train axle box;

[0051] A signal conversion module is used to convert the axle box lateral acceleration signal by Fourier method to obtain the corresponding Fourier spectrum;

[0052] The integral displacement module is used to integrate the Fourier spectrum through the frequency domain integration algorithm to obtain the integral displacement signal corresponding to the axle box lateral acceleration signal;

[0053] An acceleration estimation module, configured to determine an estimated frame lateral acceleration signal based on suspension parameters and an integrated displacement signal between the wheelset and the frame;

[0054] The instability judgment module is used to determine a frame instability index based on the estimated frame lateral acceleration signal, so as to judge whether the train frame is laterally unstable based on the instability index.

[0055] To solve the above technical problems, the present application also provides a train instability monitoring device, comprising:

[0056] memory for storing computer programs;

[0057] A processor is used to implement the steps of the train instability monitoring method as described above when executing a computer program.

[0058] In order to solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the train instability monitoring method as described above are implemented.

[0059] The present application provides a method for monitoring train instability. This method uses vibration and temperature composite sensors in the axle boxes at the eight wheel positions on the train frame to acquire lateral acceleration signals from the axle boxes. Furthermore, the method utilizes methods such as Fourier transform and frequency domain integration, combined with the suspension parameters between the wheelset and the frame, to convert the lateral acceleration signals collected at the axle boxes to the frame to meet the monitoring needs for whether the train frame is unstable. The train frame instability monitoring implemented by this solution, on the one hand, can monitor the snaking instability of the eight wheel positions of a single carriage by virtue of the fact that the axle boxes are installed at both frames of the single carriage, thereby increasing the scope of instability monitoring. On the other hand, since the source of train instability is wheel-rail excitation and wheel-rail profile matching, the wheelset vibration information collected by the axle boxes can also more accurately monitor train instability. Furthermore, this solution can be implemented using existing train axle boxes without the need for additional sensing equipment. This makes implementation simple and cost-effective, better meeting the needs of practical applications.

[0060] The train instability monitoring device and computer-readable storage medium provided in this application correspond to the above method and have the same effect as above. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0062] Figure 1 A flow chart of a train instability monitoring method provided by the present invention;

[0063] Figure 2 Schematic diagram of the connection structure between the train frame and the wheelset;

[0064] Figure 3 A flow chart of another train instability monitoring method provided by the present invention;

[0065] Figure 4 A structural diagram of a frame vibration monitoring module provided by the present invention;

[0066] Figure 5 A structural diagram of an axle box vibration monitoring module provided by the present invention;

[0067] Figure 6 A structural diagram of a train instability monitoring device provided by the present invention;

[0068] Figure 7 This is a structural diagram of another train instability monitoring device provided by the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0070] The core of this application is to provide a train instability monitoring method, device and medium.

[0071] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0072] In related technologies, to monitor train frame instability, accelerometers are typically installed on the train frame to collect lateral acceleration data. However, given the additional cost associated with installing accelerometers, a cost-effective approach currently favors diagonally placing lateral acceleration sensors on the frames. Specifically, for the eight wheel positions on the two frames of a train car, lateral acceleration sensors are typically installed on the 1st, 4th, 5th, and 8th frames.

[0073] However, although this solution can meet the monitoring needs of train frame instability to a certain extent, there is still room for improvement in the accuracy and monitoring range of instability monitoring to adapt to the needs of ever-changing development.

[0074] In order to solve the above problems, this application provides a train instability monitoring method, such as Figure 1 As shown, including:

[0075] S11: Acquire the axle box lateral acceleration signal collected by the vibration temperature composite sensor installed on the train axle box;

[0076] S12: transforming the axle box lateral acceleration signal by using the Fourier method to obtain a corresponding Fourier spectrum;

[0077] S13: Integrate the Fourier spectrum using a frequency domain integration algorithm to obtain an integrated displacement signal corresponding to the axle box lateral acceleration signal;

[0078] S14: determining an estimated frame lateral acceleration signal based on the suspension parameters and the integrated displacement signal between the wheelset and the frame;

[0079] S15: determining a frame instability index according to the estimated frame lateral acceleration signal, so as to judge whether the train frame is laterally unstable according to the instability index.

[0080] Among them, for step S11, the lateral acceleration data at the axis is collected by a vibration-temperature composite sensor installed in the axle box. The axle box is a component on railway locomotives and vehicles that is mounted on the axle neck to connect the wheelset and the bogie frame or the body of a two-axle vehicle. Its function is to transfer the weight and load of the vehicle body to the wheelset, lubricate the axle neck, reduce friction, and reduce running resistance. Therefore, the axle box is set on the 8 wheel positions of a single carriage. The vibration-temperature composite sensor is also a common sensor component in the axle box, which is used to collect the temperature and vibration conditions at the axle box. Therefore, the present method uses the vibration-temperature composite sensor installed in the axle box to collect the lateral acceleration at the axle box, which can be implemented using existing devices without the need for additional sensor equipment.

[0081] Furthermore, since the axle box lateral acceleration signal collected in step S11 reflects the vibration information at the axle box (i.e., the wheelset) (i.e., the collected lateral acceleration signal), the relationship between the train frame and the wheelset structure is as follows: Figure 2 shown. Figure 2 Shown is a frame, two wheel pairs, and four wheel positions, and each wheel pair is connected to the frame through a spring suspension.

[0082] Depend on Figure 2 It is not difficult to see that the axle box lateral acceleration signal cannot be directly used for train frame instability monitoring. Therefore, this method further processes the axle box lateral acceleration signal through subsequent steps S12 to S14:

[0083] First, the axle box lateral acceleration signal is Fourier transformed through step S12 to convert the axle box lateral acceleration signal from time series to frequency domain, which is convenient for subsequent processing; then, step S13 uses the frequency domain integration algorithm to convert the axle box lateral acceleration signal to obtain the Fourier spectrum for integration. The acceleration signal is integrated once to obtain the velocity signal, and the displacement signal is obtained by integrating it twice; and when the integral displacement signal after the secondary integration of the axle box lateral acceleration signal is obtained, step S14 can convert the axle box lateral acceleration signal on the wheelset side to the frame side through the elastic suspension connection relationship between the wheelset and the frame (that is, according to the frame dynamic parameters and transfer equations). Specifically, the suspension parameters between the wheelset and the frame (such as Figure 2 The stiffness k1~k4 in the train is used to realize the conversion of the integral displacement signal so that it can be used to realize the instability monitoring of the train frame.

[0084] It should also be noted that, since the lateral acceleration signal on the frame side obtained in step S14 is obtained by converting the lateral acceleration signal on the axle box side, it is referred to as an estimated frame lateral acceleration signal in this method.

[0085] After obtaining the lateral acceleration signal on the frame side, the instability index can be calculated based on this signal, and finally the train frame can be judged whether it is unstable based on this instability index. It is easy to know that the difference between the train instability monitoring method provided by this application and the related art lies mainly in how to obtain the frame side lateral acceleration signal that is ultimately used to judge whether the train frame is unstable. This application does not limit how to calculate the instability index after obtaining the signal and how to judge whether the train frame is unstable based on the instability index. The solution of converting the directly collected frame lateral acceleration signal into the instability index and judging whether the train frame is unstable in the related art can be adopted, so this embodiment will not be described in detail here.

[0086] As can be seen from the foregoing, the train instability monitoring method provided in this application utilizes the existing vibration-temperature composite sensor in the train axlebox to acquire lateral acceleration from the axlebox side. The lateral acceleration signal acquired from the axlebox side is then further processed using methods such as Fourier transform and frequency domain integration algorithms. The lateral acceleration from the axlebox side is then transformed to the frame side using the suspension parameters between the wheelset and axle frame, thereby meeting the needs of monitoring train frame instability.

[0087] Because axleboxes are installed on all eight wheel positions of a single train car and two frames, this method can collect vibration information from all eight wheel positions, further expanding the scope of monitoring for train frame instability. Furthermore, since train instability is primarily due to wheel-rail excitation and wheel-rail profile matching, vibration information collected from the wheelset side can also improve the accuracy of monitoring train instability based on the source of vibration information.

[0088] Furthermore, the above embodiment uses a vibration-temperature composite sensor in the train axle box to collect vibration information. If there is no further requirement for the accuracy of train instability monitoring, there is no need to install a vibration sensor on the train frame, which can further save implementation costs compared with related technologies.

[0089] However, this embodiment also provides an implementation scheme for utilizing vibration sensors provided on the frame in the related art (i.e., four vibration sensors are provided on a single carriage) to achieve more accurate frame instability monitoring. Before step S15, the following steps are further included:

[0090] S16: Acquire an actual frame lateral acceleration signal collected by a vibration acceleration sensor installed on the train frame;

[0091] S17: fusing the actual frame lateral acceleration signal and the estimated frame lateral acceleration signal through a Kalman filter algorithm to obtain a fused frame lateral acceleration signal;

[0092] Correspondingly, step S15 is specifically as follows:

[0093] The frame instability index is determined based on the fused frame lateral acceleration signal.

[0094] That is, this embodiment takes into account that when the vibration information collected on the axle box side is converted to the frame side, there may be a certain deviation between the result and the actual value due to factors such as the algorithm or processing equipment; therefore, the vibration information at the frame is directly collected by the vibration sensor provided at the frame; and the vibration information collected by the two sensors are fused to obtain a fused frame lateral acceleration signal for comprehensive judgment of frame instability; compared with the related art of monitoring frame instability only by using the vibration information directly collected on the frame side, the method of this embodiment fuses the vibration information collected on the frame side and the axle box side, and uses the fused signal to judge whether the train frame is unstable or not, which has higher accuracy.

[0095] At the same time, the method provided in this embodiment does not require the installation of any additional sensors, and does not increase the implementation cost and difficulty compared to related technologies, and better meets the needs of frame instability monitoring in actual train operation scenarios.

[0096] Furthermore, based on the train frame instability fusion monitoring method provided in the above embodiment, this embodiment also provides a possible implementation plan for the specific realization of data processing involved in the entire method flow.

[0097] First, the Fourier transform performed on the axle box lateral acceleration signal in step S12 can be specifically:

[0098] The axle box lateral acceleration signal within a preset unit time is transformed by the Fourier method to obtain the corresponding Fourier spectrum;

[0099] Among them, the Fourier spectrum is:

[0100]

[0101] X(ω) represents the Fourier spectrum; x(t) represents the axle box lateral acceleration signal; illustratively, the above-mentioned preset unit time may be 1 second.

[0102] Afterwards, since the time domain signal can be regarded as the sum of harmonic functions, the amplitude of each harmonic component is the value of the signal spectrum at each frequency point, that is:

[0103]

[0104] Therefore, the integration of x(t) can be expressed as:

[0105]

[0106] Where v(t) represents the integrated velocity signal; V(ω) is the frequency spectrum of the integrated velocity signal.

[0107] Similarly, in step S13, the axle box lateral acceleration signal is integrated twice (that is, the integrated speed signal is integrated again), and the result is:

[0108] According to the first formula, the Fourier spectrum is integrated twice to obtain an integrated displacement signal;

[0109] Among them, the first formula is:

[0110]

[0111] s(t) represents the integrated displacement signal; S(ω) represents the frequency spectrum of the integrated displacement signal.

[0112] After the integral displacement signal is obtained, it can be converted to the frame side through step S14 to obtain an estimated frame lateral acceleration signal. Step S14 is:

[0113] Determine the integral displacement signal using a second formula based on the suspension parameters between the wheelset and the frame and the integral displacement signal;

[0114] Among them, the second formula is:

[0115]

[0116] represents the estimated frame lateral acceleration; m represents the equivalent mass of the train frame; k1, k2, k3 and k4 are as follows Figure 2 As shown, they represent the spring stiffness corresponding to the four different wheel positions of a train frame, that is, the suspension parameters between the wheelset and the frame mentioned above; Figure 2 The solid circle where the spring and wheelset are connected is the spring displacement point, and the displacement distance is the value of the integral displacement signal.

[0117] In addition, the equivalent mass m of the train frame can be specifically expressed as:

[0118]

[0119] Among them, m c is the mass above the secondary suspension, m t The quality of the train frame.

[0120] After obtaining the estimated frame lateral acceleration signal based on the above method, it is necessary to fuse the estimated frame lateral acceleration signal and the actual frame lateral acceleration signal through the Kalman filter algorithm. That is, for the above step S17, it can be specifically as follows:

[0121] S171: constructing an estimated observation value of the frame lateral acceleration signal;

[0122] The observed values ​​are:

[0123]

[0124] z k represents the observation value; e represents the observation error. For example, the observation error can be 1 / 10 of the acceleration test accuracy resolution.

[0125] S172: Determine a fused frame lateral acceleration signal using a third formula based on the actual frame lateral acceleration signal, the estimated frame lateral acceleration signal, and the observed value;

[0126] Among them, the third formula is:

[0127]

[0128] represents the lateral acceleration signal of the fusion structure; K k-1 represents the Kalman gain during the kth iteration; where K k-1 =1, the iterative formula of Kalman gain is:

[0129]

[0130] R represents the Kalman augmented observation covariance. For example, the Kalman augmented observation covariance can be taken as the sensitivity of the framework acceleration sensor; Represents the framework prediction covariance, and the iterative formula of the framework prediction covariance is:

[0131]

[0132] P k-1 represents the parameter value for measuring the uncertainty of the frame lateral acceleration during the kth iteration, P0 is the effective value of the integrated displacement data; Q k It represents the covariance of the prediction error. For example, it can be taken as the mean of the effective value of the axle box acceleration.

[0133] It is not difficult to see from this embodiment that the signal fusion based on the Kalman filter algorithm is an iterative optimization process. The number of iterative optimization times can be determined according to the actual accuracy requirements of instability monitoring to obtain the final fusion frame lateral acceleration signal for instability monitoring of the train frame.

[0134] Furthermore, after obtaining the fused frame lateral acceleration signal based on the method provided in the above embodiment, before formally determining whether the train frame is instability in step S15, this embodiment also provides a possible implementation scheme, wherein the above method further includes:

[0135] S18: fitting the data of the fusion frame lateral acceleration signal by a least square method to obtain a fitting function;

[0136] S19: removing the trend term in the fusion frame lateral acceleration signal according to the fitting function to obtain a new fusion frame lateral acceleration signal.

[0137] During the data acquisition process of the lateral acceleration signal, the signal will inevitably deviate from the baseline due to factors such as the zero drift of the amplifier with temperature changes, unstable low-frequency performance outside the sensor frequency range, and environmental interference around the sensor. This phenomenon is called a trend term.

[0138] The aforementioned embodiment of the fused train frame instability monitoring method involves collecting lateral acceleration signals from both the axlebox and frame sides, so the resulting fused frame lateral acceleration signal inevitably contains a trend term. To eliminate the adverse effects of this trend term on the accuracy of frame instability monitoring, this embodiment uses the least squares method to fit the fused signal data. The resulting fitted signal is a quadratic polynomial function. Subtracting the fitted signal from the fused signal removes the trend term from the fused signal. The resulting signal is then used as an evaluation basis to eliminate the interference of the trend term on instability monitoring, further improving accuracy.

[0139] Based on the above embodiments, a train instability monitoring method is provided, and its specific implementation process can be as follows: Figure 3 As shown, including:

[0140] S21: collecting the axle box lateral acceleration signal and the actual frame lateral acceleration signal;

[0141] S22: performing a second integration on the axle box lateral acceleration signal using a frequency domain integration algorithm to obtain an integrated displacement signal;

[0142] S23: converting the integrated displacement signal into an estimated frame lateral acceleration signal according to the frame dynamic parameters and the transfer equation;

[0143] S24: fusing the estimated frame lateral acceleration signal and the actual frame lateral acceleration signal through a Kalman filter algorithm to obtain a fused frame lateral acceleration signal;

[0144] S25: Remove the trend term in the lateral acceleration signal of the fusion frame through least squares fitting;

[0145] S26: Determine train frame instability based on the fused frame lateral acceleration signal.

[0146] In addition, this embodiment also provides a possible implementation scheme for the actual hardware architecture to which the above method is applied:

[0147] This embodiment provides a rail train, including one or more train carriages, each of which includes a frame vibration monitoring module and an axle box vibration monitoring module.

[0148] Among them, the frame vibration monitoring module is as follows Figure 4 As shown, it includes: a frame sensor and a first monitoring board; one frame vibration monitoring module can include four frame sensors and one first monitoring board; the frame sensors can be installed on the 1st, 4th, 5th and 8th wheel positions respectively.

[0149] The frame sensor is a sensor combination consisting of a frame lateral acceleration sensor, a connecting cable and a connector. Specifically, the frame lateral acceleration sensor can be installed on the outer web of the end of the frame side beam; further, the frame lateral vibration acceleration sensor can adopt a piezoelectric acceleration sensor with a range of 10g, and the sampling frequency can be set to 256Hz.

[0150] The axle box vibration monitoring module is as follows Figure 5 As shown, it includes: an axle box sensor and a second monitoring board; one axle box vibration monitoring module can include 8 axle box sensors and 1 second monitoring board; the axle box sensors are installed on 8 wheel positions respectively.

[0151] The axlebox sensor is also a sensor combination, consisting of a vibration-temperature composite sensor, a connecting cable, and a connector. The sensor is mounted on the axlebox body. The axlebox lateral vibration acceleration sensor can use a piezoelectric accelerometer with a range of 500g and a sampling frequency of 10,000Hz.

[0152] In the above embodiment, a train instability monitoring method is described in detail. This application also provides a corresponding embodiment of a train instability monitoring device. It should be noted that this application describes the embodiments of the device from two perspectives: one is based on the functional module perspective, and the other is based on the hardware perspective.

[0153] Based on the perspective of functional modules, such as Figure 6 As shown, this embodiment provides a train instability monitoring device, including:

[0154] The axle box monitoring module 11 is used to obtain the axle box lateral acceleration signal collected by the vibration temperature composite sensor installed on the train axle box;

[0155] The signal conversion module 12 is used to convert the axle box lateral acceleration signal by Fourier method to obtain the corresponding Fourier spectrum;

[0156] The integral displacement module 13 is used to integrate the Fourier spectrum using a frequency domain integration algorithm to obtain an integral displacement signal corresponding to the axle box lateral acceleration signal;

[0157] An acceleration estimation module 14 is configured to determine an estimated frame lateral acceleration signal based on suspension parameters and an integrated displacement signal between the wheelset and the frame;

[0158] The instability judgment module 15 is used to determine a frame instability index according to the estimated frame lateral acceleration signal, so as to judge whether the train frame is laterally unstable according to the instability index.

[0159] Since the embodiments of the apparatus part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the apparatus part, and will not be repeated here.

[0160] Figure 7 This is a structural diagram of a train instability monitoring device provided in another embodiment of the present application, such as Figure 7 As shown, a train instability monitoring device includes: a memory 20 for storing a computer program;

[0161] The processor 21 is configured to implement the steps of a train instability monitoring method according to the above embodiment when executing a computer program.

[0162] The train instability monitoring device provided in this embodiment may include but is not limited to a mobile terminal, a personal computer, a workstation, etc.

[0163] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of a digital signal processor (DSP), a field programmable gate array (FPGA), and a programmable logic array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0164] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory, and non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201, wherein, after the computer program is loaded and executed by the processor 21, it can implement the relevant steps of a train instability monitoring method disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include but is not limited to a train instability monitoring method, etc.

[0165] In some embodiments, a train instability monitoring device may further include a display screen 22 , an input / output interface 23 , a communication interface 24 , a power supply 25 , and a communication bus 26 .

[0166] Those skilled in the art will understand that Figure 7 The structure shown in the figure does not constitute a limitation on a train instability monitoring device, and may include more or fewer components than shown in the figure.

[0167] A train instability monitoring device provided in an embodiment of the present application includes a memory and a processor. When the processor executes a program stored in the memory, it can implement the following method: a train instability monitoring method.

[0168] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiment.

[0169] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0170] The above is a detailed introduction to a train instability monitoring method, device and medium provided by the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of this application.

[0171] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A train instability monitoring method, characterized in that: include: Obtaining the axle box lateral acceleration signal collected by the vibration temperature composite sensor installed on the train axle box; Transforming the axle box lateral acceleration signal by a Fourier method to obtain a corresponding Fourier spectrum; Integrating the Fourier spectrum using a frequency domain integration algorithm to obtain an integrated displacement signal corresponding to the axle box lateral acceleration signal; Determining an estimated frame lateral acceleration signal based on suspension parameters between the wheelset and the frame and the integrated displacement signal; Obtaining the actual frame lateral acceleration signal collected by the vibration acceleration sensor installed on the train frame; fusing the actual frame lateral acceleration signal with the estimated frame lateral acceleration signal through a Kalman filter algorithm to obtain a fused frame lateral acceleration signal; A frame instability index is determined according to the fused frame lateral acceleration signal, so as to judge whether the train frame is laterally unstable according to the instability index.

2. The train instability monitoring method according to claim 1, characterized in that: Before determining the frame instability index according to the fused frame lateral acceleration signal, the method further includes: Fitting the data of the lateral acceleration signal of the fusion structure by a least square method to obtain a fitting function; According to the fitting function, the trend term in the fusion frame lateral acceleration signal is removed to obtain a new fusion frame lateral acceleration signal.

3. The train instability monitoring method according to claim 1, characterized in that: The transforming of the axle box lateral acceleration signal by the Fourier method to obtain a corresponding Fourier spectrum includes: Transforming the axle box lateral acceleration signal within a preset unit time by using the Fourier method to obtain the corresponding Fourier spectrum; Wherein, the Fourier spectrum is: ； represents the Fourier spectrum; Represents the axle box lateral acceleration signal.

4. The train instability monitoring method according to claim 3, characterized in that: The integrating the Fourier spectrum by a frequency domain integration algorithm to obtain an integrated displacement signal corresponding to the axle box lateral acceleration signal includes: According to the first formula, performing a second integration on the Fourier spectrum to obtain the integrated displacement signal; Among them, the first formula is: ； represents the integrated displacement signal; represents the frequency spectrum of the integrated displacement signal.

5. The train instability monitoring method according to claim 4, characterized in that: Determining the estimated frame lateral acceleration signal based on the suspension parameters between the wheelset and the frame and the integrated displacement signal includes: Determine the integral displacement signal using a second formula based on suspension parameters between the wheelset and the frame and the integral displacement signal; Wherein, the second formula is: ； represents the estimated frame lateral acceleration; represents the equivalent mass of the train frame; 、 、 and They respectively represent the spring stiffness corresponding to different wheel positions of the train frame.

6. The train instability monitoring method according to claim 5, characterized in that: The step of fusing the actual frame lateral acceleration signal with the estimated frame lateral acceleration signal through a Kalman filter algorithm to obtain a fused frame lateral acceleration signal comprises: Constructing an observation value of the estimation frame lateral acceleration signal; The observed values ​​are: ； represents the observed value; represents the observation error; determining the fused frame lateral acceleration signal by a third formula based on the actual frame lateral acceleration signal, the estimated frame lateral acceleration signal, and the observed value; Wherein, the third formula is: ； represents the lateral acceleration signal of the fusion frame; K k-1 represents the Kalman gain during the kth iteration; where K k-1 =1, the iterative formula of the Kalman gain is: ； R represents the Kalman augmented observation covariance; Denotes the framework prediction covariance, and the iterative formula of the framework prediction covariance is: ； P k-1 represents the parameter value for measuring the uncertainty of the frame lateral acceleration during the kth iteration, P0 is the effective value of the integral displacement signal; Q k represents the covariance of the forecast errors.

7. A train instability monitoring device, characterized in that: include: The axle box monitoring module is used to obtain the axle box lateral acceleration signal collected by the vibration temperature composite sensor installed on the train axle box; A signal conversion module, configured to convert the axle box lateral acceleration signal using a Fourier method to obtain a corresponding Fourier spectrum; an integral displacement module, configured to integrate the Fourier spectrum using a frequency domain integration algorithm to obtain an integral displacement signal corresponding to the axle box lateral acceleration signal; an acceleration estimation module, configured to determine an estimated frame lateral acceleration signal based on suspension parameters between the wheelset and the frame and the integrated displacement signal; An instability judgment module is used to determine a frame instability index based on a fused frame lateral acceleration signal, so as to judge whether the train frame is laterally unstable based on the instability index; wherein the fused frame lateral acceleration signal is obtained by: obtaining an actual frame lateral acceleration signal collected by a vibration acceleration sensor installed on the train frame; and fusing the actual frame lateral acceleration signal with the estimated frame lateral acceleration signal through a Kalman filter algorithm to obtain a fused frame lateral acceleration signal.

8. A train instability monitoring device, characterized in that: include: memory for storing computer programs; A processor is configured to implement the steps of the train instability monitoring method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the train instability monitoring method according to any one of claims 1 to 6 are implemented.

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