Flexible sensor array and decoupling method for synchronous monitoring of bogie structure / environment

Abstract

The present invention belongs to the technical field of intelligent monitoring of rail transit vehicles, and mainly relates to a flexible multi-modal sensing device and a signal decoupling method for on-line measurement of wind speed and structural vibration of a bogie. The flexible multi-modal sensing device includes a hot film sensing module for measuring the air flow velocity on the surface of the bogie and a piezoelectric module for measuring vibration. The flexibility feature can ensure that it closely and seamlessly adheres to the surface of the curved frame of the bogie without affecting the surface flow field distribution of the bogie. Both the surface wind pressure and the structural vibration of the bogie affect the piezoelectric sensing module, while the hot film sensor is insensitive to vibration. A decoupling method for the wind pressure and structural vibration of the bogie based on empirical mode decomposition is proposed to achieve the decoupling of the wind pressure and structural vibration signals of the bogie and improve the intelligent monitoring level of the structural health of the bogie.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring of rail transit vehicles, and mainly relates to a flexible multi-modal sensing device and a signal decoupling method for online measurement of bogie wind speed and structural vibration. Background Art

[0002] The bogie is a key device to ensure the stable and safe operation of the train. During the operation of the train, the bogie bears the weight of the train, provides buffering, vibration reduction, guides the train, and brakes the train. With the continuous increase in railway transportation volume and the continuous increase in train speed, the impact of wind on the bogie cannot be easily ignored. The anti-vibration force generated by the collision between the high-speed rolling wheels and the track and transmitted to the bogie is also increasing, aggravating the loss of the bogie. The higher the performance requirements of the bogie are, the more directly affecting the operation safety of the bogie.

[0003] During the high-speed operation of the train, the bogie is mainly affected by the strong wind environment and the collision between the wheelset and the track. The wind environment in the bogie chamber is complex and changeable, which is related to the environment in the chamber and the running speed of the train. The change in the wind speed can be used to obtain the change in the vibration force of the wind environment on the bogie. Zhong Zhixin, Hu Junhai and others from Shijiazhuang Railway University invented a new type of wind speed and direction sensor (CN207649678U), which is applied to the universal pendulum and the rotating frame. When the wind blows towards this type of wind speed and direction sensor, the universal pendulum of the sensor will swing against the wind. The direction of the universal pendulum swing is the wind direction, and the angle of the universal pendulum swing along the wind translates the size of the wind speed. However, the airflow on the surface of the bogie is complex, changeable and irregular, and the detection sensitivity is high. It may happen that the wind speed has changed before the universal pendulum has time to swing, and the universal pendulum structure is complex and bulky, which is difficult to attach to the surface of the bogie, and it will also affect the flow field distribution on the surface of the bogie. Tian Zhongshan, Yi Lin and others invented a piezoelectric vibration sensor with a compression sensitive element (CN113884174A), which integrates a compression piezoelectric sensitive element and a conditioning circuit (amplification and signal processing). When installed on the object to be tested, an inner shell and an aviation plug need to be installed. However, due to the existence of the outer shell and the need to connect to the object to be tested through a threaded hole, the installation process is cumbersome and the device is too bulky. It also affects the flow field distribution on the surface of the bogie. The detection system of the traditional bogie often collects various types of data on the bogie by installing various types of sensors. It has the disadvantages of too many sensors, low integration, susceptibility to interference, cumbersome installation, and high cost.

[0004] The flexible multimodal sensing device of the present invention uses a thermal film sensor to measure wind speed and a piezoelectric film sensor to measure vibration, which simplifies the installation process of the sensor, reduces mutual interference between sensors, and improves anti-interference ability. The flexible multimodal device is soft and thin, and can be tightly attached to the complex structure of the bogie without affecting the functional characteristics and surface flow field distribution of the bogie. It collects wind speed information on the surface of the bogie and the vibration information it is subjected to. The conformal mounting performance improves the accuracy of the flexible multimodal sensor device in collecting airflow and structural vibration signals on the surface of the bogie. A method for decoupling surface airflow and structural vibration based on multi-parameter information of the bogie is proposed to achieve refined perception and health monitoring of the bogie. Summary of the invention

[0005] The first purpose of the present invention is to provide a flexible multi-modal sensing device for synchronously monitoring the surface airflow velocity and structural vibration of a bogie, the device comprising a thermal mold sensing module for measuring the surface airflow velocity of the bogie and a piezoelectric module for vibration measurement, the thermal mold sensing module comprising a wind speed thermistor unit and two heating resistor units applied to the thermistor unit, so as to realize the measurement of the wind speed and structural vibration of the bogie, all functional units are integrated on the same flexible substrate and conformally mounted on the surface of the curved frame of the H-type bogie, the softness characteristics of the flexible sensor can ensure that it is tightly and seamlessly attached to the surface of the curved frame of the bogie without affecting the surface flow field distribution of the bogie, and the wind speed and vibration force voltage change signals of the bogie are collected in real time, and the relationship between the real-time wind speed and the output signal of the thermal mold sensor can be calculated; the mapping relationship between the piezoelectric output voltage and the structural vibration is calibrated, so as to realize the real-time monitoring of multiple parameters of the bogie, and the synchronous monitoring of the bogie structural vibration and environmental signals is the most effective and direct method to ensure the safety of train operation, and it plays an important role in promoting the safety monitoring and intelligent operation and maintenance of train operation.

[0006] The second purpose of the present invention is to provide a method for decoupling wind pressure and structural vibration of a bogie surface based on flexible multimodal sensing data. The wind pressure directly affects the surface flow field distribution of the bogie and affects the structural vibration of the bogie at the same time. The anti-vibration force of the wheel-rail vibration will affect the structural vibration signal of the bogie. The thermal mode sensor in the flexible multimodal sensing array will collect the surface flow field information of the bogie. The piezoelectric sensor in the flexible multimodal sensing array will collect the bogie structural vibration signal caused by the coupling of wind pressure and wheel-rail vibration. The thermal mode sensor is only sensitive to airflow, and the influence of the anti-vibration force of wheel-rail vibration on the thermal mode sensor can be ignored. A method for decoupling wind pressure and structural vibration of a bogie based on empirical mode decomposition is proposed to evaluate the influence of wind pressure environment and bogie structural vibration on its structural health and safe driving. The vibration signal x(t) and wind speed signal y(t) recorded by the sensor. The proposed bogie structure and environment perception decoupling algorithm mainly includes: IMF component calculation, empirical mode decomposition, autocorrelation and covariance coefficient calculation. The specific steps are as follows:

[0007] (1) The sensor collects the original signal x(t), let h0 = x(t), g0(t) = y(t);

[0008] (2) Get the original signal h k The extreme points of g(t) and g0(t);

[0009] (3) The upper and lower envelopes are obtained by connecting the spline curves, e xmax (t), e xmin (t) and e ymax (t), e ymin (t);

[0010] (4) Sum the envelope curves and take the average value to obtain:

[0011] The median function of x(t):

[0012] The median function of y(t):

[0013] (5) Subtract m from h(t) x (t), subtract m from g(t) y (t):

[0014] h k (t) = h k-1 (t)-m x(k-1) (t)

[0015] g k (t) = g k-1 (t)-m y(k-1) (t)

[0016] (6) Determine hk (t) and g k (t) Whether the IMF conditions are met:

[0017] h that meets IMF conditions k (t) then we get the IMF component: Imf x(i) (t) = h k (t);

[0018] h that does not meet the IMF conditions k (t) Repeat steps 2, 3, 4, 5, and 6;

[0019] g that meets IMF conditions k (t) then we get the IMF component: Imf y(i) (t) = g k (t);

[0020] g that does not meet IMF conditions k (t) Repeat steps 2, 3, 4, 5, and 6;

[0021] (7) Subtract the Imf obtained by the Shifting algorithm from the original signal k (t) Quantity:

[0022] Vibration signal x(t) decomposition:

[0023] u x(0) (t) = x(t), u x(i) (t) = u x(i-1) (t)-Imf x(i) (t);

[0024] Wind speed signal y(t) decomposition:

[0025] u y(0) (t) = y(t), u y(i) (t) = u y(i-1) (t)-Imf y(i) (t);

[0026] (8) Determine the decomposition u of the vibration signal x(t) x(i) (t) Whether there are more than two poles, if yes, repeat steps 1, 2, 3, 4, 5, 6, 7, 8. If the IMF component is not obtained for the first time in step 1, then h0(t) = u x(i) (t) If satisfied, EMD decomposition ends;

[0027] Determine the decomposition of the wind speed signal y(t) y(i) (t) Whether there are more than two poles, if yes, repeat steps 1, 2, 3, 4, 5, 6, 7, 8. If the IMF component is not obtained for the first time in step 1, then h0(t) = u y(i)(t), if satisfied, EMD decomposition ends;

[0028] (9) After the EMD decomposition is completed, the formula for decomposing the original signal is obtained:

[0029] Residual component of vibration signal x(t): r x (t) = u x(i) (t);

[0030] Vibration signal x(t) decomposition:

[0031] Residual component of wind speed signal y(t): r y (t) = u y(i) (t);

[0032] Wind speed signal y(t) decomposition:

[0033] (10) Find the autocorrelation function and Imf of the vibration signal x(t) x Autocorrelation function of (i)(t) component:

[0034]

[0035]

[0036] Find the autocorrelation function and Imf of the wind speed signal y(t) y(i) (t) The autocorrelation function of the component:

[0037]

[0038]

[0039] (11) Solve the mutual correlation coefficient between the vibration signal x(t) and the wind speed signal y(t):

[0040]

[0041] Set ρ to the Imf of the filtered vibration signal x(t) x(i) (t) threshold;

[0042] (12) Solve the Imf of the vibration signal x(t) x(i) The correlation coefficient between the (t) component and the wind speed signal y(t):

[0043]

[0044] (13) Filter out Imf with ρ(j) less than ρ x(i) (t) component, discard ρ(j) greater than ρImf x(i)(t) component, because ρ refers to a coefficient of the correlation between the vibration signal x(t) and the wind speed signal y(t), and ρ(j) refers to the Imfx of the vibration signal x(t). (i) A coefficient of the correlation between the (t) component and the wind speed signal y(t):

[0045] When ρ(j)>ρ, it means that the wind speed signal y(t) has a great influence on the vibration signal x(t) in this frequency band;

[0046] When ρ(j)<ρ, it means that the wind speed signal y(t) has little influence on the vibration signal x(t) in this frequency band;

[0047] Eliminate the IMF Imf with large influence of wind speed signal y(t) x(i) The purpose of the (t) component is to infinitely reduce the influence of wind speed on vibration, so as to obtain a vibration signal that is almost unaffected by wind speed.

[0048] (14) The filtered Imf x(i) The (t) signal is reconstructed to obtain the decoupled vibration signal X(t).

[0049] Furthermore, the present invention provides a flexible multimodal sensing device for measuring the wind speed and structural vibration of a bogie, which has soft characteristics and a small membrane thickness, and comprises two vibration force piezoelectric units, a wind speed thermistor unit and two heating resistor units. All units are integrated into a flexible substrate, which can ensure that the flexible sensing device is conformally attached to the curved surface structure of the bogie, has no effect on the air flow field of the surface structure of the bogie, is beneficial to the sensing detection of the bogie in a complex fluid environment, and improves the accuracy and precision of fluid detection. At the same time, the conformal attachment also improves the sensitivity and precision of the bogie structural vibration monitoring.

[0050] Furthermore, the flexible multimodal sensing device includes 12 sensors of different types, which are placed on the H-shaped frame surface of the bogie. Each single arm is divided into three sections, and the sensors are evenly distributed in pairs. The thermistor unit and the heating resistor unit construct a constant temperature difference mode control circuit containing a Wheatstone bridge. Due to the convection heat loss principle and the thermal sensitivity of the thermistor resistor, a voltage signal that is only affected by wind speed is output; the two vibration sensor units will output a voltage signal affected by vibration due to their pressure-sensitive characteristics. The output vibration voltage signal is mainly affected by wind pressure and wheel-rail rebound force, and both voltage signals are irregular non-stationary signals. In the sensing device, the wind speed signal is output by a hot film sensor. Due to the detection principle of the hot film sensor, the output signal is minimally affected by the vibration of the bogie structure. Therefore, the signal is mainly directly determined by the air velocity on the bogie surface. However, since the vibration voltage signal detected by the sensor is affected by two factors: air flow and (pressure) and wheel-rail vibration anti-vibration force, the corresponding relationship between the hot film sensor and the air velocity (pressure) on the bogie surface can be used to decouple the influence of wind pressure and wheel-rail vibration anti-vibration force on the bogie structural vibration, providing technical support for the integrated monitoring of bogie environment-structure vibration.

[0051] Furthermore, the present invention is a flexible multimodal sensing device for measuring wind speed and vibration force of a bogie, wherein the wind speed detection sensing unit includes one wind speed thermistor unit and two heating resistor units. The overall size of the thin film sensor device is 300mm*60mm, the thickness is 250nm, the thermistor unit is located at a length of 150mm and a width of 30mm, and the placement direction is perpendicular to the width of the entire film. The heating resistor unit is placed parallel to the wind speed thermistor unit 10mm above and below. The thermistor unit uses a platinum resistor as the thermistor material, and its temperature coefficient of resistance (TCR) is 1.75*10 3 ppm / ℃. Resistivity is 2.01*10 5 Ω·cm, and the heating resistance unit is 200Ω.

[0052] Furthermore, the present invention is a flexible multi-modal sensing device for measuring wind speed and vibration force of a bogie, wherein the vibration sensing unit includes two vibration force pressure sensitive units. The two pressure sensitive units are respectively arranged at 50 mm inside the two widths and 30 mm inside the two lengths of the film sensor, and the two units are 200 mm apart. The pressure sensitive units are all made of PVDF piezoelectric film, with a size of 20 mm*15 mm, a thickness of 0.24 mm, and a piezoelectric constant d 33 is 24±1pC / N, and the elastic modulus Ep is 1.2*10 3 MPa, resistivity is 10 13 Ω·cm.

[0053] Furthermore, the bogie environment and structural vibration decoupling method of the present invention performs empirical mode decomposition (EMD) on the voltage signals output by the thermal film and piezoelectric sensors, and then calculates the autocorrelation functions and correlation coefficients of the IMF components decomposed from the multiple sensor signals and the original collected signals, obtains the correlation coefficients of each IMF through the wind speed signal, sets thresholds for each IMF component of the composite vibration signal, obtains effective IMF components of the vibration signal through filtering and reconstructs them, and obtains the influence of wind pressure and wheel-rail reaction force on the bogie structural vibration signal, respectively, realizes the solution of the wind pressure and structural vibration signals on the bogie surface, and provides support for the accurate perception and safety assessment of the bogie state information.

[0054] Furthermore, the thermal film sensor in the flexible multimodal sensing device for bogie environment and structural vibration of the present invention is used to sense the airflow information on the bogie surface. The airflow directly acts on the bogie surface and chamber. The thermistor of the thermal film sensor loses a part of heat due to convection, which causes the resistance temperature to decrease and the resistance value to change. At this time, the bridge on the constant temperature difference control circuit (CTD) will be unbalanced, thereby outputting a voltage U that changes with the wind speed. The output voltage U not only has a certain correlation with the wind speed, but also is fed back to the heating resistor to control the heating power to ensure that the temperature Tr of the thermistor unit and the temperature difference with the ambient temperature T0 are constant, that is, ΔT=T r -T0. After the CTD circuit ensures that ΔT is constant, the power P of the thermal sensor unit and the wind speed V have a single-value relationship, and the formula is as follows:

[0055]

[0056] C1 and C2 are constants, V is the fluid velocity T r is the temperature of the thermistor unit, and T0 is the ambient temperature. The ΔT constant temperature difference is determined by setting a difference between the thermistor on a bridge and its bridge resistance initially, and the calculation formula is as follows:

[0057] ΔR m =αR m ΔT

[0058] α is the temperature coefficient of the thermistor unit, R m is the thermistor unit resistance, ΔR mIt is the difference between the thermistor unit and its bridge resistance calculated by setting ΔT. Furthermore, the piezoelectric film sensor in the flexible multimodal sensing device for bogie environment and structural vibration of the present invention is affected by the wind pressure generated by the flow field on the surface of the bogie and the wheel-rail anti-vibration force. At this time, the piezoelectric film generates an induced voltage due to the piezoelectric effect, which is amplified by the conditioning circuit to output the voltage U. This output voltage U is directly related to the composite vibration force collected by the bogie, and the two satisfy a linear relationship. The formula is as follows:

[0059] P=C·U

[0060] Where U is the voltage generated by the piezoelectric effect of the piezoelectric unit after being amplified and output by the conditioning circuit, P is the vibration force received on the bogie, and C is the proportional coefficient of the relationship between the vibration force and the output voltage calculated from multiple sets of experimental data.

[0061] Furthermore, the flexible multimodal sensing device of the present invention synchronously collects the bogie environment and structural vibration information through the thermal film and piezoelectric, thereby solving the problem of strong coupling between the environment and structural vibration signals. The ability of environment-structure integrated detection realizes the accurate perception and safety monitoring of the bogie. The current monitoring device and method can also be expanded to the monitoring of the wheel-rail vibration on the bogie, which is meaningful for the health monitoring of the bogie, wheel-rail system and track, and can comprehensively improve the intelligent monitoring level of the bogie.

[0062] In summary, the present invention provides a flexible multimodal sensing device and a signal decoupling method for synchronously monitoring the wind pressure and structural vibration on the bogie surface. The device mainly adopts two sensors, PVDF piezoelectric film and platinum resistance thermistor unit, to realize the online monitoring of the vibration force and wind speed of the bogie during the operation of the train, thereby reducing the potential safety hazards during the operation of the train. The vibration force monitoring is based on the piezoelectric effect principle of the piezoelectric film, and the wind speed measurement is based on the resistance thermal sensitivity of the thermistor unit under the condition of heat loss. The decoupling method of the vibration signal detected and output on the bogie utilizes the characteristics that both the wind speed signal and the vibration signal are affected by the wind environment at the same time, and there must be a certain similarity in the changes of the two signals to decouple the signal of the wheel-rail recoil force in the output vibration signal. The decoupling of wind speed vibration and wheel-rail recoil force is achieved by using EMD decomposition and calculating autocorrelation function and correlation coefficient, and a single signal of the vibration force transmitted to the bogie by the wheel-rail collision is obtained. The influence of wheel-rail movement on bogie loss is estimated through this signal. At the same time, the abnormal state of a certain section of track during train operation can be judged through the abnormal signal segment of the signal, and the railway maintenance personnel can be promptly reminded to conduct abnormal inspections on the abnormal section of track to reduce the safety hazards of train operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 a is a 3D rendering of the flexible multimodal sensing device attached to the double arms of the bogie. Figure 1 b is the physical quantity effect diagram of the pressure on the bogie in the complex wind environment during the operation of the train and the vibration force fed back to the bogie by the collision between the wheelset and the track. Figure 1 c is a structural diagram of the flexible multimodal sensing device, including: 2 vibration force pressure sensitive units, a platinum resistance wind speed thermistor unit, and heating units of two thermistor units. Figure 1 d is the principle diagram of piezoelectric effect of PVDF piezoelectric film. Figure 1 e is a schematic diagram showing that the thermistor unit loses heat in a wind convection environment, causing a temperature change and thus a resistance change.

[0064] Figure 2 It is a schematic diagram of a wind speed thermistor unit for measuring wind speed, a combined application of a Wheatstone bridge and a control circuit for ensuring a constant temperature difference.

[0065] Figure 3 It is a flowchart of the overall system using the flexible multimodal sensing device.

[0066] Figure 4 It is a scenario diagram of the overall system where a flexible multimodal sensing device is applied to a bogie to monitor wind speed and vibration force online.

[0067] Figure 5 It is a flowchart of using the EMD method to decompose the signal and calculate the autocorrelation function and correlation coefficient of the IMF component.

[0068] Figure 6 It is a flowchart of vibration detection calibration process.

[0069] Figure 7 It is a flowchart of wind speed detection calibration process.

[0070] Figure 8 It is a flow chart of the application of flexible multimodal sensing device to real-time detection on train bogies.

[0071] Fig. 9 It is a decoupling flow chart of the output signal of the flexible multimodal sensing device.

[0072] Fig.10 It is the bogie structure vibration state data.

[0073] Fig.11 It is the modal decomposition result (IMF component) of the bogie vibration signal based on the empirical mode method (EMD).

[0074] Meaning of symbols in the figure:

[0075] 1-bogie; 2-flexible multimodal sensing device; 3-ZigBee node; 4-multi-channel charge amplifier; 5-data acquisition device; 6-host computer; 7-(BS mode) browser; 21-pressure sensitive unit; 22-heating resistor unit; 23-thermal sensitive unit; 211, 212, 213, 214, 221, 222, 223, 224, 231, 232-metal leads. DETAILED DESCRIPTION

[0076] In order to make the technical solution and problem solved by the present invention clearer, the following is a further detailed explanation of the drawings. The technology and methods involved here are not limited to the present invention, but the specific implementation scheme can only explain the satisfaction of the present invention.

[0077] The flexible multi-modal sensing device of the present invention includes a thermal film and a piezoelectric sensing function, see Figure 1 a. During operation, the flow field and structural vibration information of the bogie surface are collected in real time to solve the problem that the vibration force transmitted to the bogie by the wheel-rail collision and the vibration force generated by the wind in the bogie chamber are coupled together and difficult to decouple, such as Figure 1 As shown in the figure: During the operation of the train, the bogie is subjected to two forces: one is the wind vibration force P 风 , let one be the wheel-rail collision vibration force P 轨 , both forces are irregular forces with uncertain directions and magnitudes acting on the bogie. In order to collect these irregular force signals, the present invention attaches flexible multi-modal sensing devices to the two arms of the bogie, such as Figure 1 As shown in a, 6 flexible multimodal sensing devices are placed at each single arm of the bogie, totaling 12 pieces, which are placed in pairs at the bends of the two arms of the bogie (a total of 8 pieces are attached to the 4 bends of the two arms) and the middle sections of the two arms of the bogie are also attached (4 pieces).

[0078] Figure 1 c explains the composition of the flexible multimodal sensing device. The film substrate of the device is 300mm long and 60mm wide. It consists of 1 platinum resistance thermistor, 2 heating resistor units, and 2 PVDF film pressure-sensitive units. The platinum resistance thermistor is located at a length of 150mm and a width of 30mm. The placement direction is perpendicular to the width of the entire film. The heating resistor unit is placed parallel to the wind speed thermistor 10mm above and below. The specific parameters of the platinum resistance thermistor are: TCR is 1.75*10 3 ppm / ℃, resistivity is 2.01*10 5 Ω·cm. The heating resistance unit is 200Ω, two heating units are connected in parallel, and the combined resistance is 100Ω.

[0079] The PVDF film pressure-sensitive unit is 20 mm long, 15 mm wide and 0.24 mm thick. It is located 50 mm inside the width and 30 mm inside the length of the film sensor. The two units are 200 mm apart. Its specific parameters are: piezoelectric constant d 33 is 24±1pC / N, elastic modulus Ep is 1.2*10 3 MPa, resistivity is 10 13 Ω·cm.

[0080] The output voltage of the pressure-sensitive unit reflects the principle of the vibration force. Figure 1 As shown in Figure d, when the piezoelectric film is subjected to a pressure P, the positive and negative charges inside it will move to the two poles, thereby generating a voltage output. The magnitude and speed of the generated voltage are positively correlated with the magnitude of the pressure P. The principle of the thermal sensing unit detection principle is as follows Figure 1 As shown in e, the thermal unit will lose a heat Q under convection in the working state. loss , so that the temperature of the thermistor changes by ΔT. Due to the thermal sensitivity of the thermistor, its resistance value will also change by ΔR, which will cause the output voltage to change based on the Wheatstone bridge.

[0081] Thermistor and heating resistor units detect wind speed also based on a Wheatstone bridge and feedback control circuit, such as Figure 2 As shown, two fixed resistors R and R r The Wheatstone bridge circuit is formed by the platinum thermistor, and the voltage output between the two bridges is fed back to the two parallel heating resistors P h It constitutes a feedback control circuit. The role of the Wheatstone bridge is to associate the wind speed with the output voltage. To put it simply, the wind speed changes, causing the heat lost by the thermistor to change, thereby changing its resistance, breaking the change in the middle potential of the two bridges of the Wheatstone bridge circuit, and then causing the output voltage to change, that is, the wind speed change will directly affect the change in the output voltage. The function of the feedback circuit is to ensure the constant temperature of the thermistor and the sensor temperature. Based on the King formula, it can be obtained that when the ambient temperature and the temperature difference of the thermistor are constant, the fluid flow rate and the current passing through the thermistor are in a single-valued function relationship. Therefore, in order to achieve a constant temperature difference, the initial parameters of Rr and Rm are first set to Rr=Rm+△Rm (△Rm>0, Rm increases with a positive coefficient). Therefore, in the initial case, Vout will output a positive voltage to provide heating to Rm, and when it increases to be equal to Rr, it provides a constant heating power to Rh, and Vout outputs a constant value. At this time, the power of the heating resistor Rh is guaranteed to be constant to keep the temperature of Rm unchanged. At this time, the circuit reaches a dynamic equilibrium state.

[0082] In order to realize the online detection of bogies during train operation, the hot film sensor (thermal unit) and the vibration sensor (pressure sensitive unit) are integrated on the flexible multimodal sensing device, and a detection system is built. First, the hot film sensor outputs a voltage signal affected by wind speed through the CTD control circuit and the vibration sensor outputs a voltage signal affected by vibration. After being processed and amplified by the conditioning circuit, it is input into the MCU chip by A / D. The MCU is connected to the Zigbee node network through the serial port and transmitted to the host computer for display. The overall system flow chart is shown as follows: Figure 3 As shown, the overall system scene effect is as follows Figure 4 shown.

[0083] Before the flexible multi-modal sensor device is officially applied, it is necessary to calibrate the sensor device. The required equipment includes: a wind tunnel, which provides a wind environment for the sensor device and can blow wind at a certain wind speed, that is, Figure 1 The wind and electromagnetic vibration test bench in Figure e provides a vibration source of specified size for the sensor device. Figure 1 The vibration force P in d.

[0084] After completing the calibration of the flexible multi-modal sensing device, an online detection system is built in a specific train bogie environment. During the operation of the train, the opposite wind flows into the bogie chamber. Based on the heat loss principle and the Wheatstone bridge, the integrated thermal film sensor will output a voltage signal. The wind speed signal and the calibrated relationship model between wind speed and voltage signal can be used to calculate the wind speed felt by the sensor on the bogie. Similarly, the vibration sensor integrated in the flexible multi-modal sensing device will use the established relationship model between voltage signal and vibration force to calculate the vibration force felt by the sensor on the bogie.

[0085] Finally, the obtained wind speed data and vibration force data are processed. Based on the characteristics of simultaneous detection of wind speed data and vibration force data and the similarity that both wind speed signal and vibration signal are affected by the wind environment at the same time, it can be known that the data change of vibration force at the same time completely includes the data change of wind speed, so the wind speed data can be used to decouple the two vibration sources of vibration force (wind vibration and wheel-rail feedback vibration). The specific steps are as follows:

[0086] (1) The sensor collects the original signal x(t), let h0(t) = x(t), g0(t) = y(t);

[0087] (2) Get the original signal h k The extreme points of g(t) and g0(t);

[0088] (3) The upper and lower envelopes are obtained by connecting the spline curves, e xmax (t), e xmin (t) and e ymax(t), e ymin (t);

[0089] (4) Sum the envelope curves and take the average value to obtain:

[0090] The median function of x(t):

[0091] The median function of y(t):

[0092] (5) Subtract m from h(t) x (t), subtract m from g(t) y (t)

[0093] h k (t) = h k-1 (t)-m x(k-1) (t)

[0094] g k (t) = g k-1 (t)-m y(k-1) (t)

[0095] (6) Determine h k (t) and g k (t) Whether the IMF conditions are met:

[0096] h that meets IMF conditions k (t) then we get the IMF component: Imf x(i) (t) = h k (t);

[0097] h that does not meet the IMF conditions k (t) Repeat steps 2, 3, 4, 5, and 6;

[0098] g that meets IMF conditions k (t) then we get the IMF component: Imf y(i) (t) = g k (t);

[0099] g that does not meet IMF conditions k (t) Repeat steps 2, 3, 4, 5, and 6;

[0100] (7) Subtract the Imf obtained by the Shifting algorithm from the original signal k (t) Quantity:

[0101] Vibration signal x(t) decomposition:

[0102] u x(0) (t) = x(t), u x(i )(t)=u x(i-1) (t)-Imfx(i) (t);

[0103] Wind speed signal y(t) decomposition:

[0104] u y(0) (t) = y(t), u y(i) (t) = u y(i-1) (t)-Imf y(i) (t);

[0105] (8) Determine the decomposition u of the vibration signal x(t) x(i) (t) Are there more than two poles? If yes, repeat steps 1, 2, 3, 4, 5, 6, 7, and 8. If the IMF component is not obtained for the first time in step 1, then h 0(t) =u x(i) (t) If satisfied, EMD decomposition ends;

[0106] Determine the decomposition of the wind speed signal y(t) y(i) (t) Whether there are more than two poles, if yes, repeat steps 1, 2, 3, 4, 5, 6, 7, 8. If the IMF component is not obtained for the first time in step 1, then h0(t) = u y(i) (t) If satisfied, EMD decomposition ends;

[0107] (9) After the EMD decomposition is completed, the formula for decomposing the original signal is obtained:

[0108] Residual component of vibration signal x(t): r x (t) = u x(i) (t);

[0109] Vibration signal x(t) decomposition:

[0110] Residual component of wind speed signal y(t): r y (t) = u y(i) (t);

[0111] Wind speed signal y(t) decomposition:

[0112] (10) Find the autocorrelation function of the vibration signal x(t) and its Imfx ( i ) (t) component autocorrelation function:

[0113]

[0114]

[0115] Find the autocorrelation function and Imf of the wind speed signal y(t) y(i) (t) component autocorrelation function:

[0116]

[0117]

[0118] (11) Solve the mutual correlation coefficient between the vibration signal x(t) and the wind speed signal y(t):

[0119]

[0120] Set ρ to the Imf of the filtered vibration signal x(t) x(i) (t) threshold;

[0121] (12) Solve the Imf of the vibration signal x(t) x(i) The mutual correlation coefficient between the (t) component and the wind speed signal y(t):

[0122]

[0123] (13) Filter out Imf with ρ(j) less than ρ x(i) (t) component, discard ρ(j) greater than ρImf x(i) (t) component, because ρ refers to a coefficient of the correlation between the vibration signal x(t) and the wind speed signal y(t), and ρ(j) refers to the ImF of the vibration signal x(t). x(i) A coefficient of the correlation between the (t) component and the wind speed signal y(t):

[0124] When ρ(j)>ρ, it means that the wind speed signal y(t) has a great influence on the vibration signal x(t) in this frequency band;

[0125] When ρ(j)<ρ, it means that the wind speed signal y(t) has little influence on the vibration signal x(t) in this frequency band;

[0126] Eliminate the IMF Imf with large influence of wind speed signal y(t) x(i) The purpose of the (t) component is to infinitely reduce the influence of wind speed on vibration, so as to obtain a vibration signal that is almost unaffected by wind speed.

[0127] (14) The filtered Imf x(i) The (t) signal is reconstructed to obtain the decoupled vibration signal X(t).

[0128] Before the flexible multi-modal sensor device is applied, its detection data needs to be calibrated. The vibration detection calibration experiment flow chart is as follows: Figure 6As shown, the flexible multimodal sensor is placed in a windless environment, and an electromagnetic vibration test bench is used to apply vibration forces of different magnitudes to the sensor device to obtain multiple groups of different vibration force output signals. These output signals are used to establish the relationship between the vibration force and the output signal, that is, the constant C is calculated. The above operation is repeated for multiple rounds to calculate multiple constants C, and the average value is taken to obtain the final vibration calibration constant to complete the vibration force detection calibration.

[0129] Wind speed detection calibration experiment process is as follows Figure 7 As shown, the flexible multimodal thin film sensor device is first placed in a wind tunnel, and winds of different wind speeds are blown to the sensor in the wind tunnel without other vibration sources to obtain multiple sets of different wind speed output signals. These data signals are used to inversely solve two constants C1 and C2 based on the King formula to establish the relationship between wind speed and output signal. Repeat the above operation for multiple rounds to obtain multiple sets of constants C1 and C2, take the average value to obtain the final wind speed calibration constant, and complete the wind speed detection calibration.

[0130] The application of multi-modal flexible sensor device in bogie is briefly described as follows: Figure 8 As shown, the multimodal flexible sensor device is placed in the application scenario, that is, the sensor device is attached to the surface of the bogie frame, and the monitoring system is set up. During the operation of the train, the thermal sensing unit of the sensor film will output the air flow velocity voltage signal on the bogie surface, and the pressure sensitive unit will output the bogie structure vibration voltage signal. Then, the wind speed value on the sensor surface is calculated based on the established model of air flow velocity and output voltage, and the vibration force value felt by the sensor surface is calculated based on the established model of vibration and output voltage. At this time, the multimodal flexible sensor device completes its monitoring and data collection function.

[0131] After the wind speed and vibration data collected by the device are obtained in the host computer, the vibration data is decoupled using the method. The decoupling process is as follows: Fig. 9 As shown, firstly, the obtained wind speed data and vibration data are subjected to EMD decomposition to obtain the IMF components of the wind speed data and vibration data from low frequency to high frequency, and then the IMF components and the autocorrelation functions of the wind speed data and vibration data are calculated. The correlation coefficient (reflecting the similarity between the two) ρ between the two is obtained by calculating the autocorrelation function of the wind speed data and vibration data, and a threshold is set. Then, the IMF component of the vibration data and the autocorrelation function of the wind speed data are used to solve the correlation coefficient ρ(j) between the two. Then, the vibration IMF component is filtered out through the relationship between the threshold ρ and the correlation coefficient ρ(j). Finally, these filtered IMF components are reconstructed to obtain the vibration signal of the wheel-rail vibration, and the decoupling is completed at this time.

[0132] For further verification, the flexible multifunctional sensing device is used to measure the bogie vibration signal (caused by wind pressure and wheel-rail anti-vibration coupling) online, such as Fig.10As shown, it can be seen that the steering gear vibration signal presents multi-frequency band characteristics. Fig.11 The decomposition results of the bogie structural vibration signal through the empirical mode method provide a basis for the decoupling of the effects of wind pressure and structural vibration on the bogie force, which is helpful for the accurate analysis of the bogie stress state.

[0133] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for decoupling wind pressure and structural vibration on a bogie surface based on flexible multimodal sensing data, characterized in that: Wind pressure directly affects the surface flow field distribution of the bogie, and also affects the structural vibration of the bogie; the anti-vibration force of wheel-rail vibration will affect the structural vibration signal of the bogie. The thermal model sensor will collect the surface flow field information of the bogie, and the piezoelectric sensor will collect the bogie structural vibration signal caused by the coupling of wind pressure and wheel-rail vibration. The thermal model sensor is only sensitive to airflow, and the influence of the anti-vibration force of wheel-rail vibration on the thermal model sensor can be ignored. The bogie wind pressure and structural vibration decoupling method based on empirical mode decomposition is adopted to evaluate the influence of wind pressure environment and bogie structural vibration on its structural health and safe driving. The vibration signal x(t) and wind speed signal y(t) recorded by the sensor. The proposed bogie structure and environment perception decoupling algorithm mainly includes: IMF component calculation, empirical mode decomposition, autocorrelation and covariance coefficient calculation. The specific steps are as follows: (1) The sensor collects the original vibration signal x(t) and wind speed y(t), and sets h0(t) = x(t) and g0(t) = y(t); (2) Obtain the extreme points of the original signals h0(t) and g0(t); (3) The spline curves are connected to obtain the upper and lower envelopes, e xmax (t), e xmin (t) and e ymax (t), e ymin (t); (4) Sum the envelope and take the average value to get: The median function of x(t): The median function of y(t): (5)h k-1 (t) is the vibration signal after k-1 rounds of EMD decomposition; g k-1 (t) is the wind speed signal obtained by performing k-1 rounds of EMD decomposition on the vibration signal. When performing k-1 rounds of EMD decomposition, h k-1 (t) and g k-1 (t) minus the corresponding median function m x(k-1) (t) and m y(k-1) (t): h k (t)=h k-1 (t)-m x(k-1) (t) g k (t)=g k-1 (t)-m y(k-1) (t) (6) Determine h k (t) and g k (t) Whether the IMF conditions are met: h that meets IMF conditions k (t) then we get the IMF component: Imf x(i) (t) = h k (t); h that does not meet the IMF conditions k (t) Repeat steps (2), (3), (4), (5), and (6); g that meets IMF conditions k (t) then we get the IMF component: Imf y(i) (t) = g k (t); g that does not meet IMF conditions k (t) Repeat steps (2), (3), (4), (5), and (6); (7) Subtract the Imf obtained by the Shifting algorithm from the original signal k (t) Quantity: Vibration signal x(t) decomposition: u x(0) (t) = x(t), u x(i) (t) = u x(i-1) (t)-Imf x(i) (t); Wind speed signal y(t) decomposition: u y(0) (t) = y(t), u y(i) (t) = u y(i-1) (t)-Imf y(i) (t); (8) Determine the u after the vibration signal x(t) is decomposed x(i) (t) has more than two poles. If so, repeat steps (1), (2), (3), (4), (5), (6), (7) and (8). If this is not the first time that the IMF component is obtained in step (1), then h0(t) = u x(i) (t), if satisfied, EMD decomposition ends; Determine the decomposition of the wind speed signal y(t) y(i) (t) has more than two poles. If so, repeat steps (1), (2), (3), (4), (5), (6), (7) and (8). If this is not the first time that the IMF component is obtained in step (1), then h0(t) = u y(i) (t) If satisfied, EMD decomposition ends; (9) After the EMD decomposition is completed, the formula for decomposing the original signal is obtained: Residual component of vibration signal x(t): r x (t) = u x(i) (t); Vibration signal x(t) decomposition: Residual component of wind speed signal y(t): r y (t) = u y(i) (t); Wind speed signal y(t) decomposition: (10) Find the autocorrelation function and Imf of the vibration signal x(t) x(i) (t) The autocorrelation function of the component: Find the autocorrelation function and Imf of the wind speed signal y(t) y(i) (t) The autocorrelation function of the component: (11) Solve the mutual correlation coefficient between the vibration signal x(t) and the wind speed signal y(t): Set ρ to the Imf of the filtered vibration signal x(t) x(i) (t) threshold; (12)Imf of vibration signal x(t) x(i) The mutual correlation coefficient between the (t) component and the wind speed signal y(t): (13) Filter out Imf with ρ(j) less than ρ x(i) (t) component, discard Imf with ρ(j) greater than ρ x(i) (t) component, because ρ refers to a coefficient of the correlation between the vibration signal x(t) and the wind speed signal y(t), and ρ(j) refers to the Imf of the vibration signal x(t). x(i) A coefficient of the correlation between the (t) component and the wind speed signal y(t): When ρ(j)>ρ, it means that the wind speed signal y(t) has a great influence on the vibration signal x(t) in this frequency band; When ρ(j)<ρ, it means that the wind speed signal y(t) in this frequency band has little influence on the vibration signal x(t); (14) Filtered Imf x(i) The (t) signal is reconstructed to obtain the decoupled vibration signal X(t).

2. A flexible multi-modal sensing device for online measurement of wind speed and structural vibration of a bogie, which is used to implement the decoupling method of claim 1, characterized in that: The device includes a thermal mold sensing module for measuring the airflow velocity on the bogie surface and a piezoelectric module for vibration measurement. The thermal mold sensing module includes a wind speed thermistor unit and two heating resistor units applied to the thermistor unit to realize the measurement of the wind speed and structural vibration of the bogie. All functional units are integrated on the same flexible substrate and conformally installed on the surface of the H-type bogie curved frame. The softness of the flexible sensor can ensure that it is tightly and seamlessly attached to the surface of the bogie curved frame without affecting the surface flow field distribution of the bogie. The wind speed and vibration force voltage change signals of the bogie are collected in real time, and the relationship between the real-time wind speed and the output signal of the thermal mold sensor is calculated.

3. The flexible multi-modal sensing device for online measurement of wind speed and structural vibration of a bogie according to claim 2, characterized in that: It has soft properties, a small membrane thickness, and includes two vibration force piezoelectric units, a wind speed thermistor unit and two heating resistor units. All units are integrated into a flexible substrate to ensure that the flexible sensing device is conformally attached to the curved surface structure of the bogie.

4. The flexible multi-modal sensing device for online measurement of wind speed and structural vibration of a bogie according to claim 2, characterized in that: The vibration information collected by the piezoelectric sensor in the flexible multimodal sensing device is generated by the superposition of wind-induced vibration and structural vibration, which are coupled with each other.

5. The flexible multi-modal sensing device for online measurement of wind speed and structural vibration of a bogie according to claim 2, characterized in that: The flexible multimodal sensing device can synchronously sense the wind pressure and structural vibration information on the bogie surface. The piezoelectric sensor in the flexible multimodal sensing device is affected by both wind pressure and structural vibration, while the thermal film sensor is only sensitive to wind pressure signals. Based on the current signal characteristics, the decoupling of the bogie affected by the environment and structural vibration is achieved.

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