CN118025278A - Railway vehicle instability state diagnosis method based on rail edge device - Google Patents
Railway vehicle instability state diagnosis method based on rail edge device Download PDFInfo
- Publication number
- CN118025278A CN118025278A CN202410178767.7A CN202410178767A CN118025278A CN 118025278 A CN118025278 A CN 118025278A CN 202410178767 A CN202410178767 A CN 202410178767A CN 118025278 A CN118025278 A CN 118025278A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- wheel
- instability
- displacement
- frequency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000003745 diagnosis Methods 0.000 title claims abstract description 8
- 238000006073 displacement reaction Methods 0.000 claims description 37
- 238000004458 analytical method Methods 0.000 claims description 24
- 230000001133 acceleration Effects 0.000 claims description 14
- 229910000831 Steel Inorganic materials 0.000 claims description 9
- 239000010959 steel Substances 0.000 claims description 9
- 238000004088 simulation Methods 0.000 claims description 7
- 241001669679 Eleotris Species 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 4
- 230000000368 destabilizing effect Effects 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 abstract description 14
- 230000008901 benefit Effects 0.000 abstract description 4
- 230000001687 destabilization Effects 0.000 abstract description 4
- 238000009394 selective breeding Methods 0.000 abstract description 2
- 238000001514 detection method Methods 0.000 description 18
- 238000010586 diagram Methods 0.000 description 6
- 238000005096 rolling process Methods 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 5
- WYTGDNHDOZPMIW-RCBQFDQVSA-N alstonine Natural products C1=CC2=C3C=CC=CC3=NC2=C2N1C[C@H]1[C@H](C)OC=C(C(=O)OC)[C@H]1C2 WYTGDNHDOZPMIW-RCBQFDQVSA-N 0.000 description 4
- 230000005284 excitation Effects 0.000 description 3
- 230000007774 longterm Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 102000016550 Complement Factor H Human genes 0.000 description 2
- 108010053085 Complement Factor H Proteins 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000013524 data verification Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 206010012411 Derailment Diseases 0.000 description 1
- 241000270295 Serpentes Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention relates to a rail vehicle instability state diagnosis method based on a rail side device, which provides an instability coefficient to judge whether a vehicle is unstable or not. The performance index can be used for adjusting parameters according to actual railway lines and operation requirements. In the aspect of calculating the destabilization frequency, a judging method based on the energy ratio P E is provided. Compared with the traditional calculation aspect, the method has the advantages that artificial selection is not needed, the judgment is carried out through the duty ratio, the value of P E can be changed according to the requirement, and the method is high in precision and accuracy.
Description
Technical Field
The invention relates to the field of railway vehicles, in particular to the field of state monitoring of vehicles under long-term service operation.
Background
The rolling stock is a self-excited vibration phenomenon caused by the special contact relationship between wheels and steel rails and the creep force, and is an inherent characteristic of a rolling stock system. The stable and fast convergent hunting has no obvious effect on the dynamic performance of the vehicle, but the slow or severe periodic hunting has obvious effect on the vibration and operation safety of the vehicle system, and the hunting may generate resonance with vehicle parts, and the excessive wheel set motion displacement may even cause derailment risk. The vehicle can wear wheels and steel rails to different degrees under long-term operation, so that the equivalent taper of wheel-rail matching is increased, the suspension parameters of the bogie can be subjected to performance degradation and the like under long-term service and complex and changeable operation environments, and the phenomena can cause instability of a vehicle system, namely, the phenomenon of hunting instability of the vehicle, so that the wheel set moves violently and transversely. Therefore, the problem of vehicle hunting stability is always an important direction in the research field of railway vehicles, and timely and effectively detecting the transverse movement of wheel sets is very important for the vehicle hunting stability state, so that the safety and stable running of the vehicle are ensured.
In recent years, with rapid development of high-speed railways and progress of various detection technologies, related researches for detection of a vehicle hunting unstable state are layered endlessly, and can be roughly classified into two kinds, namely, vehicle-mounted detection and rail-side detection methods, according to the installation positions of detection devices. The vehicle-mounted detection method is characterized in that detection equipment is arranged on a vehicle, sensors such as displacement, acceleration, strain and the like are arranged on components such as an axle box, a framework and a vehicle body, a data acquisition system and a data processor are also arranged in the vehicle, the vehicle instability state is diagnosed based on the mapping relation between the vehicle hunting instability state and the detection physical quantity, the method has the advantage of high precision, and the vehicle instability state can be detected on the whole route or the overline. However, the vehicle-mounted equipment has limitations that firstly, the vehicle-mounted equipment needs to be independently powered, which is not a problem for passenger vehicles, but has poor feasibility for railway wagons; in addition, the vehicle-mounted equipment can only monitor the state of the tested vehicle, other vehicle states cannot be detected, and the running states of different vehicles have larger randomness, and the cost is overlarge if the vehicle-mounted equipment is mounted on each vehicle, so that the vehicle-mounted equipment can only monitor the running states of typical vehicles. Another detection method is a rail side detection method, in which detection devices are installed at both sides of a rail to detect the running state of vehicles in a train, and although such detection devices have the disadvantages of not being able to detect partial areas directly, they have the advantages of low cost, no need of vehicle-mounted power supply, capability of detecting all vehicles passing through a test area, and the like, and are widely used for railway vehicle state monitoring, such as a 5T system, and the like.
For the stability of the vehicle in the hunting state based on the rail edge detection device, the most internationally adopted method is to reversely push whether the vehicle is unstable or not through the stress of the steel rail. In order to diagnose and identify the damage states such as wheel scratch, scientific researchers attach and install sensors on the steel rail to obtain a force measuring steel rail, and on the basis, whether the vehicle is unstable or not is judged through the transverse force and the vertical force of the force measuring steel rail, but the reliability of the result is still to be further verified, and the method belongs to an indirect method and is not analyzed from the angle of a vehicle meandering mechanism.
The important manifestation of the vehicle hunting instability is that the wheel pair transversely reciprocates on the steel rail through comprehensive analysis, and if the wheel pair transverse movement displacement can be obtained through testing based on rail edge equipment, whether the vehicle is unstable or not can be judged based on the displacement. In view of this, how to design a railway vehicle instability state diagnosis method and an intelligent monitoring system based on rail side wheel pair transverse displacement detection is a key technical problem to be solved by scientific researchers in the field.
Disclosure of Invention
Aiming at the characteristics of rolling instability of railway vehicles, the characterization relation between the transverse movement of a wheel set and the instability state is researched, the mapping relation between the rolling instability state and the transverse movement quantity and frequency of the wheel set is established, and a vehicle rolling instability judging method is provided for a wheel set transverse displacement monitoring device with a non-contact rail side. By formulating the vehicle hunting instability judging flow, an intelligent monitoring system for the vehicle instability state is formed, and the recognition efficiency and recognition accuracy are improved.
The vehicle is unstable in hunting and reacts on the wheel set transverse movement displacement, the transverse movement of all past wheel sets can be monitored by detecting the wheel set transverse displacement condition through the rail edge, and the wheel sets with poor dynamic state can be timely identified, so that the further deterioration of the vehicle dynamic performance is avoided, and the transportation efficiency is improved.
The lateral displacement of the wheel set at the measuring point can be obtained by installing a certain number of displacement sensors on the side of the steel rail along the rail and detecting the distance between the inner side of the wheel set and the non-contact displacement sensors such as the eddy current sensor, the laser displacement sensor and the like when the wheel set passes. The number of sensors n required to detect a vehicle instability is based on detecting at least one complete roll wavelength L of the vehicle, which is related to vehicle speed, frequency of instability, etc. The sensors are arranged between the two sleepers, and the minimum number of the sensors is n is larger than or equal to L/L b and n is a positive integer, provided that the sleeper distance is L b.
And converting the transverse movement quantity of m wheel sets acquired by each sensor into transverse displacement of each wheel set, and unfolding analysis based on the transverse displacement. As shown below, when the wheel set is running on a track, the wheel set lateral equation of motion can be expressed as:
Wherein y w is the wheel set lateral movement displacement, A is the displacement amplitude of the wheel set lateral movement, w is the angular velocity, f is the movement frequency, Is the phase difference.
Then, an expression of the wheel-set lateral vibration acceleration a w can be obtained:
Further can be expressed as:
At present, the standard specifications at home and abroad are not limited to the transverse vibration acceleration a w lim of the wheel set, and have the following main reasons. The wheel set lateral acceleration a w is greatly affected by rail excitation, the limit values of different vehicle types are obviously different, and the rail irregularity under different lines has larger randomness; in addition, the vibration acceleration of the wheel set is related to the contact position of the wheel rail, under the same condition, the contact of the wheel rail at the tread position or the contact of the wheel rail at the rim position of the wheel, the lateral acceleration can have a multiple difference, and the frequency response range of the wheel set is wide, so that the method for selecting a reasonable filtering range is very challenging. At present, a roll stability evaluation method of a railway vehicle is to perform band-pass filtering of 0.5 to 10Hz on transverse vibration acceleration of the end part of a frame, but the corresponding relation between the transverse vibration acceleration of the end part of the frame and the transverse vibration acceleration a w of a wheel set is not clear, and a specific relation is difficult to achieve. Therefore, it is theoretically possible to evaluate the stability of rolling motion of the railway wheel by preparing the wheel set lateral vibration acceleration limit a w lim, but there are many problems in practical use.
In view of this, a probability of the instability factor H C is proposed to measure whether the vehicle is subject to hunting, and the instability factor H C is a performance index related to the amplitude a of the wheel set lateral displacement and the wheel set lateral movement frequency f, which is proportional to Af 2.
HC＝Af2 (4)
The wheel set transverse movement quantity A can be obtained through testing based on the rail edge device, and the wheel set transverse movement frequency f can be obtained through fast Fourier transformation, so that the instability coefficient H C is obtained.
In this case, the frequency f is determined by using the concept of the vibration energy duty ratio P E, that is, the duty ratio of the analysis section frequency [ f 1,f2 ] in the whole frequency domain, the whole frequency domain range is considered to be 0.5-10 Hz, and each analysis section frequency range df can be changed as required, preferably 0.25-1 Hz, and the error is larger after exceeding 1 Hz. Then, the 1 st analysis segment is [0.5,0.5+df ], the 2 nd is [0.5+2 x df,0.5+3 x df ], and so on, the last analysis segment is [10-df,10]. The ratio of the energy of each analysis segment to the energy of all analysis segments is the value of P E, which can be expressed as:
Wherein E is the vibration energy of the wheel in the whole frequency domain range for transverse movement, and E i is the vibration energy of the wheel in the ith frequency analysis section for transverse movement. For the recommended analysis frequency range of vehicle lateral hunting, the sum of the energies E i for each frequency analysis segment is equal to E. Here, the limit value of the recommended P E is 30% -40%, that is, when a certain analysis segment P E exceeds the limit value, the frequency (the average value of the analysis frequency ranges f 1 and f 2 or the maximum value of the analysis frequency ranges f 1 and f 2 can be selected according to the need) is judged to be the vibration main frequency of the wheel pair transverse motion; if P E of all analysis sections is smaller than the limit value, the transverse motion of the wheel set can be considered to be random vibration, and the main vibration frequency is not considered, and at the moment, the frequency is considered to be 0.5Hz, so that the calculation of the instability coefficient H C is facilitated.
After the value of the instability coefficient H C is obtained through calculation, a reasonable instability coefficient limit value is required to be formulated to judge the vehicle snaking state, and for this purpose, a large number of dynamics simulation calculation preliminary sketching limit values are firstly provided, then the preliminary sketching limit values are applied to initial data verification of the rail side detection device, and finally the judgment limit value of the instability coefficient is corrected according to the actual measurement data of the rail side device. Different railway vehicle types have certain difference in the limit value of the instability coefficient, and the different railway vehicle types need to be cooperatively considered when in use.
Three judging states, namely normal, early warning and alarming, are given to the instability state of the railway vehicle according to the vehicle instability diagnosis and identification flow. The normal state is that the vehicle is not unstable, and the type of transverse vibration which is exhibited by the wheel set is random movement; the alarm state means that the vehicle is diagnosed as unstable and needs subsequent overhauling and maintenance; the early warning state is that the vehicle is in the two states, and the conditions that the vehicle is not unstable, but the transverse movement of the wheel set is large, obvious instability signs exist, the amplitude is small and the like possibly exist, and the wheel set state needs to be monitored.
The invention has the beneficial effects that:
compared with the prior art, the method for diagnosing the instability state of the railway vehicle is mainly characterized in that a performance index, namely an instability coefficient is provided for judging whether the vehicle is unstable or not. The performance index can be used for adjusting parameters according to actual railway lines and operation requirements.
In the aspect of calculating the destabilization frequency, a judging method based on the energy ratio P E is provided. Compared with the traditional calculation aspect, the method has the advantages that artificial selection is not needed, the judgment is carried out through the duty ratio, the value of P E can be changed according to the requirement, and the method is high in precision and accuracy.
In some documents, the vibration acceleration of the bearing saddle is directly compared with the vibration acceleration, but the vibration acceleration is directly related to rail excitation and the like, so that the rail excitation is possibly large, and the vehicle is easy to exceed 1m/s 2 and is misjudged as unstable although not unstable; secondly, for the actual measured data of the line, the difficulty of screening useful data is high, the data interference is great, and the difficulty of establishing connection with the unstable state of the vehicle is high.
Drawings
FIG. 1 is a schematic illustration of lateral displacement detection within one wavelength of wheel set hunting;
FIG. 2 is a schematic diagram of the calculation of the instability coefficient H C at different P E;
FIG. 3 is a schematic diagram of a simulation model-based instability coefficient limit calculation process;
FIG. 4 is a schematic diagram of the results of calculating the instability coefficients under different conditions;
Fig. 5 is a vehicle instability status diagnosis and identification flow.
Detailed Description
Fig. 1 shows a schematic diagram of lateral displacement detection within a wavelength of wheel set hunting instability, wherein 1-20 are sensors installed between rail sleepers. The basic principle is to be able to accurately capture at least one cycle of serpentine motion wavelength in the event of a vehicle serpentine instability. If the highest speed per hour of the vehicle is considered to be 120km/h and the lowest destabilizing frequency is considered to be 3Hz, the serpentine wavelength is 11.11m, if considered in terms of a span per sleeper of 0.6m, 20 sensors are required to cover 12m. As the velocity decreases, the serpentine wavelength decreases and the 12m interval may also capture one or more wavelengths. Because the destabilization frequency, the highest running speed and the sleeper span of different types of railway vehicles are different, the number of the sensors can be increased or decreased according to actual conditions. In addition, the sensors are at least required to be arranged on one side of the track, and if the sensors are arranged on two sides simultaneously, the reliability of data after partial sensor failure is better.
Fig. 2 is a schematic calculation diagram of the instability coefficient H C under different P E, in which different color areas correspond to different analysis frequency bands, the frequency band interval df taken here is 0.5Hz, the amplitude a of the wheel set lateral motion is 14mm, the amplitude is irrelevant to the value of P E, and the result belongs to the time-frequency result of the wheel set lateral motion. When P E is 25%, f i is the maximum value 2Hz in the interval, and the calculated value of H C is 56; when P E was taken to be 35%, f i was taken to be the maximum value within the interval of 2hz and the calculated value of h C was 56. The energy ratio P E in the interval of 1.5-2Hz is obviously more than 25% and 35% in the graph, so that f i is obtained as 2Hz. The calculated instability coefficients are correspondingly the same. In other cases, different P E limits may result in different values taken by f i, which in turn results in different destabilization coefficients.
Fig. 3 is a schematic diagram of a calculation flow of the instability coefficient limit based on a simulation model. Different input parameters are selected, parameters which have great influence on the transverse stability of the vehicle are mainly considered, including a wheel track contact relationship, cross pull rod rigidity, side bearing friction coefficients, vehicle running speed and the like, dynamic analysis is carried out according to the input parameters, the mapping relationship between the vehicle snake motion state and the instability coefficient is calculated, and then the limit value of the instability coefficient is provided. For example, the side bearing friction coefficient varies in the range of [ -50%,50% ], the wheel track equivalent taper varies in the range of [0.05,0.7], the cross tie stiffness varies in the range of [ -100%,100% ], the core friction coefficient varies in the range of [ -50%,50% ], and the operating speed varies in the range of [50km/h,120km/h ]. During calculation, a parameter is randomly selected from each input physical quantity, and a plurality of physical quantities form a parameter set which is a calculation working condition.
The distribution of the instability coefficients under a number of simulation conditions is depicted in fig. 4. From the figure, most of working conditions are distributed in the interval below 10, the small part is distributed in the interval between 10 and 50, and the working conditions exceed 50 in the very few. According to a large number of simulation calculations as shown in the figure, comprehensively considering the amplitude of the transverse movement of the wheel set and the recognition result of the instability frequency, and further obtaining the limit value of the instability coefficient H C. The appropriate instability limit value can be obtained after simulation calculation according to different actual operation lines. The limit value is applied to initial data verification of the track side detection device, and finally correction is carried out according to actual measurement data of the track side device. In an example, if H C is less than or equal to 10, then the vehicle is judged to be in a steady state; if H C is more than 25, judging that the vehicle is in an unstable state, alarming the stability of the vehicle, and confirming the abnormal state of the vehicle and overhauling and maintaining the vehicle; if H C is more than 10 and less than or equal to 25, although the vehicle is judged to be in a non-unstable state, the vehicle instability coefficient is large and the vehicle is possibly in an intermediate state between the unstable state and the non-unstable state, so that the vehicle is in an early warning state, and important attention is required in the follow-up state monitoring.
The vehicle instability status diagnosis and identification flow is shown in fig. 5. And according to the transverse displacement y wi of each wheel set of the vehicle body measured by the rail side detection device, calculating the average value of the transverse displacement of the wheel sets, and further obtaining the difference value between the transverse displacement of each wheel set and the average value, namely the displacement deviating from the balance position. In the process of calculating the wheel set transverse displacement average value, a maximum value and a minimum value are removed on the basis of the values acquired by the n sensors, and then y w0 is calculated according to a formula (6).
According to the calculated wheel set lateral displacement average y w0, subtracting y w0 on the basis of the original data, the corrected wheel set lateral displacement is still named y wi. Based on the corrected wheel set lateral displacement, the amplitude ai=max (y wi) is calculated, FFT conversion is performed, the proportion of each frequency band P E in the range of 0.5 to 10Hz is calculated, and the energy calculation formula of each analysis segment can be expressed as:
Where i is the number of frequency analysis segments, where there are 19 total analysis segments, i=1, 2, …,19; after FFT, the magnitude of the fourier transform corresponding to frequency f i is a fi. Then, energy E i is calculated in each frequency analysis section, and the ratio of each frequency band P E is calculated by combining formula (5).
When P E is more than or equal to 35%, the maximum corresponding frequency is f i, and when P E is less than 35%, f i is 0.5Hz. The instability coefficient H C＝Aifi 2 is further calculated. When H C is less than 10, the running condition of the vehicle body is stable, and the vehicle is in a normal running state; when H C is more than or equal to 10 and less than 25, judging that the vehicle is not unstable, but the instability coefficient is larger, and the vehicle is in an early warning state; when H C is more than or equal to 25, the vehicle is judged to be in a hunting instability state, and the abnormal state and overhaul and maintenance of the vehicle are required to be confirmed and are in an alarm state.
Claims (4)
1. A railway vehicle unstability state diagnosis method based on a rail edge device is characterized in that a certain number of displacement sensors are arranged on the side of a steel rail along a track, and the distance between the inner side of a wheel pair and a measuring point and the distance between the inner side of the wheel pair and the displacement sensors are detected when the wheel pair passes, so that the transverse displacement of the wheel pair at a measuring point is obtained; the complete snaking wavelength of the vehicle is L, a displacement sensor is arranged between two sleepers, the sleeper distance is L b, the number n of the displacement sensors is more than or equal to L/L b, and n is a positive integer;
Converting the transverse movement quantity of m wheel pairs acquired by each sensor into transverse displacement of each wheel pair, and unfolding analysis based on the transverse displacement; when the wheel set runs on the track, the wheel set lateral motion equation is expressed as:
Wherein y w is the wheel set lateral movement displacement, A is the displacement amplitude of the wheel set lateral movement, w is the angular velocity, f is the movement frequency, Is the phase difference, t is time;
Expression of wheel set lateral vibration acceleration a w:
Expression of the instability coefficient H C:
HC＝Af2
expression of the vibration energy ratio P E:
Wherein E is the vibration energy of the wheel in the whole frequency domain range for transverse movement, and E i is the vibration energy of the wheel in the ith frequency analysis section for transverse movement.
2. The rail side device-based railway vehicle instability status diagnosing method according to claim 1, wherein the displacement sensor is a non-contact sensor.
3. The method for diagnosing a destabilizing state of a railway vehicle based on a rail side unit according to claim 1, wherein when P E exceeds 30%, the frequency is judged as a main movement frequency of a wheel set lateral movement; and after calculating the value of the instability coefficient H C according to the main movement frequency, judging the vehicle hunting state.
4. The rail side device-based railway vehicle instability status diagnosing method according to claim 1, wherein the instability coefficient is calculated based on a simulation model; different input parameters are selected, wherein the input parameters comprise a wheel track contact relation, cross pull rod rigidity, side bearing friction coefficient and vehicle running speed, dynamic analysis is carried out according to the input parameters, the mapping relation between the vehicle hunting state and the instability coefficient is calculated, and then the limit value of the instability coefficient is provided.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410178767.7A CN118025278A (en) | 2024-02-14 | 2024-02-14 | Railway vehicle instability state diagnosis method based on rail edge device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410178767.7A CN118025278A (en) | 2024-02-14 | 2024-02-14 | Railway vehicle instability state diagnosis method based on rail edge device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118025278A true CN118025278A (en) | 2024-05-14 |
Family
ID=91001741
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410178767.7A Pending CN118025278A (en) | 2024-02-14 | 2024-02-14 | Railway vehicle instability state diagnosis method based on rail edge device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118025278A (en) |
-
2024
- 2024-02-14 CN CN202410178767.7A patent/CN118025278A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108515984B (en) | Wheel damage detection method and device | |
| US10753827B2 (en) | Method and system for measuring vertical wheel impact force in real-time based on tire pressure monitoring | |
| CN105923014B (en) | A kind of track transition Amplitude Estimation method based on evidential reasoning rule | |
| Tanaka et al. | Application of axle-box acceleration to track condition monitoring for rail corrugation management | |
| CN110209147B (en) | Bogie fault position identification method and system and mapping relation establishment method and device | |
| CN111071291B (en) | Train wheel set monitoring system and train wheel set monitoring method | |
| CN101377433A (en) | Method for measuring vehicle weight based on steel rail deformation / stress parameters | |
| CN113276905A (en) | Identification method and measurement method for distinguishing track corrugation and wheel polygon abrasion | |
| CN214028661U (en) | Subway vehicle operation quality monitoring system | |
| Chang et al. | Dynamic performance evaluation of an inspection wagon for urban railway tracks | |
| KR100946232B1 (en) | Apparatus and method for measuring derailment coefficient using vertical displacement and steady-state lateral acceleration | |
| Corni et al. | Real-time on-board condition monitoring of train axle bearings | |
| CN111444574A (en) | Sensor layout optimization method based on dynamic analysis | |
| Mosleh et al. | Approaches for weigh-in-motion and wheel defect detection of railway vehicles | |
| CN110171442B (en) | Detection system and detection method for wheel flat scars | |
| Wang et al. | A diagnostic method of freight wagons hunting performance based on wayside hunting detection system | |
| CN105128888B (en) | Track waveform abrasion method for quick based on many midpoint string synchro measures | |
| CN118025278A (en) | Railway vehicle instability state diagnosis method based on rail edge device | |
| Oba et al. | Condition monitoring for Shinkansen bogies based on vibration analysis | |
| CN108827665A (en) | Wheel flat fault detection method based on empirical mode decomposition and multi-scale entropy | |
| Cheng et al. | Vibration of axle box from wheel diameter difference in vehicle | |
| CN113624140A (en) | Online measuring device and method for wheel cake parameters of railway vehicle | |
| Peng et al. | Wayside wheelset lateral motion detection and vehicle hunting instability evaluation | |
| Wei et al. | Study on wheel-flat detection method based on vehicle system acceleration measurement | |
| CN111855055A (en) | Method for detecting wheel rail force based on rail bottom displacement of steel rail |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |