CN115689315A - Curve health assessment method based on vehicle body vibration and noise response - Google Patents
Curve health assessment method based on vehicle body vibration and noise response Download PDFInfo
- Publication number
- CN115689315A CN115689315A CN202110783247.5A CN202110783247A CN115689315A CN 115689315 A CN115689315 A CN 115689315A CN 202110783247 A CN202110783247 A CN 202110783247A CN 115689315 A CN115689315 A CN 115689315A
- Authority
- CN
- China
- Prior art keywords
- curve
- noise
- health
- vehicle body
- effective value
- 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
Images
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 provides a curve health assessment method based on vehicle body vibration and noise response, which is characterized by comprising the following steps of: collecting vehicle body vibration response and noise signal data on a plurality of different curve devices, wherein the vehicle body vibration response and noise signal data comprise vehicle body vibration horizontal acceleration data, vertical acceleration data and noise data in a compartment; calculating and constructing an index system by using the collected data, wherein the index system comprises a horizontal acceleration effective value E H And effective value of vertical acceleration E V Sum noise effective value E P (ii) a Effective value E based on horizontal acceleration H Effective value of vertical acceleration E V And noise effective value E P Using clustering algorithms to construct said plurality of different curve devicesRespective curve health index CHI, wherein CHI = f (E) V +E H ,E P )＝f(E A ,E P )，E A ＝E V +E H Represents an effective value of acceleration, f represents E A And E P Determining the mapping relation with the CHI by adopting a clustering algorithm; the health of the curve device was assessed based on the values of CHI. Corresponding apparatus, computer-readable storage media, and systems are also provided.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a curve health evaluation method based on vehicle body vibration and noise response.
Background
The curve refers to a line which is connected between the railways and turns from one direction to the other direction on a plane, and the basic function of the curve is to change the running direction of a train on a railway line. In the urban rail transit line, the curve ratio is high, and the curve radius is relatively small. The curve health status assessment results are important basis for managers to formulate curve maintenance strategies, and unscientific health assessment methods will result in unreasonable maintenance activities. How to scientifically and comprehensively evaluate the curve health state has important significance on optimization compilation of maintenance plans.
According to the section 4 of the urban rail transit facility operation monitoring and evaluation method: rails and roadbed (GB/T39559.4-2020), in the actual operation and maintenance production management, the evaluation of curve quality state is to evaluate the quality of the curve from 4 dimensions of static irregularity (or static geometric dimension overrun), dynamic irregularity (or dynamic geometric dimension overrun), curve roundness (or curve normal vector) and curve abrasion. The data sources for evaluating the curve state comprise rail inspection vehicle detection data, rail inspection instrument detection data, rail corrugation detector detection data, manual line inspection data, curve versine inspection data and the like.
The above prior art methods, however, have some significant disadvantages:
1. section 4 of the urban rail transit facility operation monitoring and evaluation method: the detection period of the curve specified in the track and roadbed (GB/T39559.4-2020) is longer, and the detection periods of different detection modes are different, and the detailed description is shown in the following table 1. This makes the 4-dimensional state assessment indicator data more difficult to obtain at the same time intercept.
TABLE 1 detection period of different states of curve device
2. The disease grades of the state indexes of the curve equipment with different dimensions are different, and the comprehensive evaluation of the curve health based on the four-dimension state evaluation indexes is difficult. The dynamic and unsmooth disease grades are divided into 4 grades, i grade, II grade, III grade and IV grade; the static irregularity grade is classified into 3 grades, work acceptance, planned maintenance and temporary repair. The disease grade of the curve circularity (or curve versine) is divided into 2 grades, and the operation acceptance and daily maintenance are carried out. The curvilinear abrasion mainly comprises different forms such as side surface abrasion of an upper strand steel rail, head crushing of a lower strand steel rail, waveform abrasion and the like. The curve abrasion belongs to a rail damage type, the rail damage grade is divided into 3 grades, namely light damage, heavy damage and fracture.
Therefore, new techniques and methods are needed to at least partially address the deficiencies of the prior art and to enable scientific assessment of the health of curvilinear devices.
Disclosure of Invention
The invention provides a method for comprehensively evaluating the health state of curve equipment from the vibration response of a passenger car body in the curve passing process and the noise in a carriage, which is different from the traditional method for evaluating the health state of a curve from the dynamic and static smoothness, the roundness and the abrasion condition of the curve and the like, and at least partially solves the problems in the prior art.
In order to achieve the above object, an embodiment of the present application provides a curve health assessment method based on vehicle body vibration and noise response, including:
a. acquiring vehicle body vibration response and noise signal data on a plurality of different curve devices, wherein the vehicle body vibration response and noise signal data comprise vehicle body vibration horizontal acceleration data, vertical acceleration data and noise data in a carriage;
b. calculating and constructing an index system by using the collected data, wherein the index system comprises a horizontal acceleration effective value E and a vertical acceleration effective value E which are respectively constructed by using horizontal acceleration data and vertical acceleration data H And vertical acceleration hasEffective value E V (ii) a Construction of a noise effective value E using noise data P ；
c. Effective value E based on horizontal acceleration H Effective value of vertical acceleration E V And an effective value of noise E P To construct a discretized quantitative index for measuring the health state of curve equipment, namely a curve health index CHI, which belongs to [1,2,3,4 ]]Wherein
CHI＝f(E V +E H ,E P )＝f(E A ,E P )，
E A ＝E V +E H Represents an effective value of acceleration, f represents E A And E P Determining the mapping relation with the CHI by adopting a clustering method;
d. and assessing the health of the curve device based on the value of the CHI.
According to the embodiment of the invention, the clustering method comprises a hierarchical clustering algorithm and a K-means clustering algorithm.
According to an embodiment of the present invention, in step b, the effective value E of the vertical acceleration is calculated by using a formula and respectively V And effective value of horizontal acceleration E H ：
Wherein, y V Representing the vertical acceleration signal value, y H Representing the horizontal acceleration signal value, x representing the signal sampling interval, and L representing the full length of the curve;
calculating the noise effective value E by using a formula P ：
Wherein L is p Representing the sound pressure level, the sound pressure p representing the difference in atmospheric pressure between sound-over and sound-out, p 0 Is the reference sound pressure.
According to an embodiment of the present invention, in step c, the clustering method includes:
step 1: in the reaction of E A ,E P Forming a two-dimensional space, wherein the curve health indexes CHI of the plurality of curve devices are different, and the plurality of curve devices are respectively a cluster;
step 2: calculating a curved device cluster D using equation (7) v And D e Distance D between each two ve ：
Wherein t is v Representing a curved cluster of devices D v The number of curve devices in (1); t is t e Representing a curved cluster of devices D e The number of curve devices in (1); d ij Representing curves in clusters device G i And G j The distance between each two is calculated by using the formula (6):
wherein E is i Is a curve device G i A state attribute variable of E i ＝(E i,A ,E i,P ) T ，E j Is a curve device G j A state attribute variable of E j ＝(E j,A ,E j,P ) T ；
And 3, step 3: will D ve The smallest two clusters are merged into a new cluster;
and 4, step 4: and (4) repeating the steps (2) and (3) until the curve devices are divided into 4 clusters, and obtaining a health characteristic clustering result of the curve devices.
According to an embodiment of the invention, the clustering method further comprises:
determining a possible value set of the CHI and a curve device cluster corresponding to each CHI value according to the health feature clustering result of the curve devices; and
determining CHI values of the plurality of curve devices according to the dependency relationship of the curve devices and the clusters.
According to an embodiment of the invention, the curve health assessment method based on the vehicle body vibration and noise response further comprises the step of determining a curve device set which needs to be finally maintained based on the CHI values of the curve devices and the result of the cluster analysis.
According to another aspect of the present invention, there is provided a curve health assessment apparatus based on vehicle body vibration and noise response, comprising:
the vibration response and noise data acquisition module is used for acquiring vibration response data and noise data;
the calculation and construction module of the index system is used for calculating and constructing the index system;
the comprehensive evaluation algorithm building module for curve health is used for building a discretization quantitative index for measuring the health state of curve equipment; and
and the health evaluation module of the curve device evaluates the health of the curve device based on the CHI value.
According to yet another aspect of the invention, the invention further includes a computer-readable storage medium storing executable instructions that, when executed by a processor, cause performance of the aforementioned curve health assessment method based on vehicle body vibration and noise response. The readable storage medium may be a nonvolatile memory such as a hard disk or a magnetic disk, and may be applied to various terminals, such as a computer, a server, and the like.
According to still another aspect of the present invention, the present invention further includes a system for evaluating the health of a curve based on vibration and noise response of a vehicle body, the system including a processor and a storage device for storing executable instructions, wherein when the executable instructions are executed by the processor, the method for evaluating the health of a curve based on vibration and noise response of a vehicle body can be implemented.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof taken in conjunction with the accompanying drawings.
Drawings
Some specific embodiments of the invention will be described in detail hereinafter, by way of illustration and not limitation, with reference to the accompanying drawings. The objects and features of the present invention will become more apparent in view of the following description taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a method for assessing the health of a curve based on vehicle body vibration and noise response according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a curve health assessment device based on vehicle body vibration and noise response according to an embodiment of the invention.
Fig. 3 is a block diagram of a vehicle body vibration and noise response based curve health assessment system constructed by a processor 201 and a memory 202 according to an embodiment of the present invention.
Fig. 4 is a clustering result graph of the curve equipment health index CHI obtained by analysis by using the curve health assessment method based on vehicle body vibration and noise response according to the embodiment of the invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort shall fall within the protection scope of the present application.
As shown in fig. 1, the curve health assessment method based on vehicle body vibration and noise response according to the present embodiment includes:
the method comprises the following steps: vibration response and noise data acquisition
In order to acquire the horizontal acceleration data of the vibration of the train body, the vertical acceleration data and the noise data in the carriage, the hardware structure of the line quality inspection tester is expanded, and a noise measurement module is added on the basis of the original acceleration measurement module. The improved line quality detector can be carried by personnel to be placed or installed on an electric bus, and the latest train body vibration horizontal acceleration data, the latest train body vibration vertical acceleration data and the latest train body noise data in a carriage are obtained.
Step two: construction and calculation of index system
(1) Effective value of horizontal acceleration
Research discovers that poor track smoothness, roundness and curved steel rail abrasion are main reasons for aggravating contact vibration, and cause overlarge impact acceleration, aggravate the dynamic action of a wheel rail of a vehicle, so that the vehicle vibrates abnormally, and the rail elastic strip is broken off, a fastener falls off, and the impact damage is caused to key parts and the like at the bottom of the vehicle. Therefore, the effective value of the vibration acceleration can be adopted to measure the vibration response of the vehicle body, and the calculation formula is shown in the formula.
Wherein E is V Representing effective value of vertical acceleration, E H Representing the effective value of the horizontal acceleration. y is V Representing the value of the vertical acceleration signal, y H Represents the horizontal acceleration signal value, x represents the signal sampling interval (e.g., 0.25m, etc.), and L represents the full length of the curve.
(2) Effective value of noise
The noise in the carriage has positive correlation with the severity of curve abrasion, and the severe rail abrasion can obviously increase the noise in the carriage. The harsh acoustic environment in the vehicle cabin can degrade service quality. Instead of the formula defining the effective value of the acceleration, the mean value E of the sound pressure level may be used P Description of the overall noise of a car as it passes through a curveThe sound conditions are shown in the formula.
In the formula, E P Representing an effective value of noise; l is p The sound pressure level is expressed and describes the intensity of noise in the vehicle cabin, and the unit is dB (decibel). The sound pressure p represents the difference in atmospheric pressure between when sound is passing and when sound is not passing. p is a radical of 0 The reference sound pressure may be, for example, 2 × 10 -5 Pa。
Step three: construction of comprehensive evaluation algorithm for curve health
In order to evaluate the health status of the curve as a whole, the present embodiment proposes a curve health index CHI (curvehealth index) from the viewpoint of vibration and noise response of the train during the running curve, and comprehensively evaluates the health of the curve. CHI is a discretization quantitative index for measuring the health state of curve equipment, and CHI belongs to [1,2,3,4 ]]Is a pair of state evaluation indexes E V 、E H And E P The different CHI values represent different health characteristics of the curve device, and the calculation formula is as follows:
CHI＝f(E V +E H ,E P )＝f(E A ,E P )\*MERGEFORMAT (5)
wherein E is A Representing effective value of acceleration, E A ＝E V +E H . f represents E V 、E H And E P The mapping relationship with the CHI may be determined by, for example, hierarchical clustering (Hierarchicalclustering). Alternatively, the hierarchical clustering algorithm in the present embodiment may be replaced with other clustering algorithms, such as the K-means clustering algorithm.
Clustering analysis is carried out on a plurality of curve equipment samples, the curve equipment samples are divided into 4 clusters (or classes), the health states of the curve equipment belonging to the same cluster have great similarity, the health state difference of the curve equipment belonging to different clusters is large, and the algorithm flow is as follows.
Step 1: in the reaction of E A ,E P In the two-dimensional space formed, the health characteristics of the U curve devices are different, and the U curve device samples are respectively a cluster.
Step 2: and calculating the distance between every two curve equipment clusters, and judging the similarity of the health characteristics of the curve equipment clusters. Curve equipment sample G i And G j A distance d between ij The Euclidean distance definition is used, and the calculation is shown in a formula. Distance d between curve device samples ij Used to measure the similarity of health characteristics between curve device samples. Wherein E is i Is a curve device G i A state attribute variable of E i ＝(E i,A ,E i,P ) T 。E j Is a curve device G j A state attribute variable of (E) j ＝(E j,A ,E j,P ) T 。
Curve device cluster D v And D e Distance D of ve The Average Linkage policy is used for definition, as shown in the formula, that is, the distance between the curve device clusters is the Average distance between all curve device sample pairs in the clusters. Similarity of health features between clusters of curvilinear devices using D ve Measurement is made, D ve Smaller indicates higher similarity of healthy features. Wherein, t v Representing a curved device Cluster D v The number of samples in (1). t is t e Represents a cluster D e The number of samples in (1).
And 3, step 3: will D ve The smallest two clusters are combined into a new cluster, i.e. the two curve devices with the most similar health characteristics are combined into a new cluster.
And 4, step 4: and repeating Step 2 and Step 3 until the U curve devices are divided into 4 clusters, and obtaining the health characteristic clustering result of the curve devices.
And 5: and determining the possible value set of the CHI and the curve device cluster corresponding to each CHI value according to the health feature clustering result of the CHI.
Step 6: and determining U values of the curve device samples CHI according to the dependency relationship between the curve device samples and the clusters, and ending.
The curve health characteristics for the different CHI values and the proposed maintenance strategy are shown in table 2.
TABLE 2 Curve Equipment different health characteristics and proposed maintenance strategies
Step four: CHI-based curve maintenance decision
According to different health characteristics of the curve equipment shown in the table 2, the curve equipment with CHI =4 is recommended to be preferentially considered to arrange maintenance activities, and meanwhile, the maintenance and maintenance capabilities of management departments are considered to determine the curve equipment set which needs to be finally maintained.
As shown in fig. 2, the present embodiment also provides a curve health assessment apparatus based on vehicle body vibration and noise response, including:
a vibration response and noise data acquisition module 10 for acquiring vibration response data and noise data;
an index system calculation and construction module 20 for calculating and constructing an index system;
the comprehensive evaluation algorithm construction module 30 for curve health is used for constructing a discretization quantitative index for measuring the health state of curve equipment; and
and a health assessment module 40 of the curve device, which assesses the health of the curve device based on the CHI value.
According to yet another aspect of the invention, the invention also includes a computer-readable storage medium storing executable instructions that, when executed by a processor, cause performance of the aforementioned curve health assessment method based on vehicle body vibration and noise response. The readable storage medium may be a nonvolatile memory such as a hard disk or a magnetic disk, and may be applied to various terminals, such as a computer, a server, and the like. The readable storage medium may include a high-speed random access memory, and may further include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid state storage device, and may be applied to various terminals, which may be computers, servers, and the like.
The storage medium also includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs (Read-Only memories), RAMs (Random AcceSS memories), EPROMs (EraSable Programmable Read-Only memories), EEPROMs (Electrically EraSable Programmable Read-Only memories), flash memories, magnetic cards, or optical cards. That is, a storage medium includes any medium that can store or transmit information in a form readable by a device (e.g., a computer). The storage medium may also be a read-only memory, a magnetic or optical disk, or the like.
Embodiments of the present invention also provide a computer program product, which when run on a computer causes the computer to perform the relevant steps described above, so as to implement the method in the above embodiments.
The apparatus, the computer storage medium, the computer program product, or the chip provided by the present invention are all configured to execute the corresponding methods provided above, and therefore, the beneficial effects that can be achieved by the apparatus, the computer storage medium, the computer program product, or the chip can refer to the beneficial effects in the corresponding methods provided above, which are not described herein again.
In addition, as shown in fig. 3, a curve health assessment system based on vehicle body vibration and noise response is further provided, the system includes a processor 201 and a storage device 202, wherein the storage device 202 is used for storing computer-executable instructions, and when the device is operated, the processor 201 can execute the computer-executable instructions stored in the storage device 202, so that the chip can execute the curve health assessment method based on vehicle body vibration and noise response.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The method of the invention is used for detecting the No. 1 line of a certain subway in a certain northern city, and the total number of curve devices which are uplinked on the subway line is 61. The line quality detector can be used for measuring the vertical acceleration effective value, the horizontal acceleration effective value and the noise effective value of each curve device. By using the comprehensive evaluation algorithm of the curve health, the corresponding health index CHI value of each curve device can be obtained, and the result is shown in fig. 4. As can be seen from fig. 2 by analyzing (1) the health feature CHI values of the 61 curve devices ascending on the line 1 are set to {1,3,4}, and the curve devices with CHI =2 do not exist on the line 1, and the corresponding health features are that the noise effective value is small and the acceleration effective value is large; (2) There are 8 curve devices with CHI =4, and maintenance should be scheduled as soon as possible.
Compared with the prior art, the method of the invention can realize beneficial technical effects:
1) The method is based on the basic action of curve equipment, innovatively utilizes vehicle response indexes under the action of wheel-rail contact, evaluates the health condition of the curve equipment in real time, and is an effective supplement of the conventional curve quality general state evaluation method; the method can measure under the condition that the vehicle normally works, thereby being convenient and quick.
(2) Different from the traditional evaluation method, the 4-dimensional state evaluation index of the curve has longer detection period and different detection periods, so that the indexes are difficult to obtain at the same time intercept point.
(3) The detection device (improved line quality detector) provided by the invention can be carried by personnel or arranged on a subway electric bus, so that the real-time accurate evaluation on the full-line curve health state is realized, and a manager can master the curve state degradation process and master the state degradation rule.
Although the present application has been described in terms of embodiments, those of ordinary skill in the art will recognize that there are numerous variations and permutations of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and permutations without departing from the spirit of the application.
Claims (9)
1. A method for assessing the health of a vehicle body based on vibration and noise response of the vehicle body, comprising:
a. collecting vehicle body vibration response and noise signal data on a plurality of different curve devices, wherein the vehicle body vibration response and noise signal data comprise vehicle body vibration horizontal acceleration data, vertical acceleration data and noise data in a compartment;
b. calculating and constructing an index system by using the collected data, wherein the index system comprises a horizontal acceleration effective value E and a vertical acceleration effective value E which are respectively constructed by using horizontal acceleration data and vertical acceleration data H And effective value of vertical acceleration E V (ii) a Construction of a noise effective value E using noise data P ；
c. Based on the effective value E of the horizontal acceleration H Effective value of vertical acceleration E V And noise effective value E P To construct a discretization quantitative index for measuring the health state of curve equipment, namely a curve health index CHI, which belongs to [1,2,3,4 ]]Wherein
CHI＝f(E V +E H ,E P )＝f(E A ,E P )，
E A ＝E V +E H Effective value of acceleration, f represents E A And E P Determining the mapping relation with the CHI by adopting a clustering method;
d. the health of the curve device was assessed based on the values of CHI.
2. The vehicle body vibration and noise response-based curve health assessment method according to claim 1, wherein said clustering method comprises hierarchical clustering algorithm and K-means clustering algorithm.
3. The method for evaluating the health of a curve based on vibration and noise response of a vehicle body according to claim 1, wherein in the step b, the vertical acceleration is calculated by using a formula and respectivelyDegree of validity E V And effective value of horizontal acceleration E H ：
Wherein, y V Representing the value of the vertical acceleration signal, y H Representing the horizontal acceleration signal value, x representing the signal sampling interval, and L representing the full length of the curve;
calculating the noise effective value E by using a formula P ：
Wherein L is p Representing the sound pressure level, the sound pressure p representing the difference in atmospheric pressure between sound-over and sound-out, p 0 Is the reference sound pressure.
4. The vehicle body vibration and noise response-based curve health assessment method according to claim 1, wherein in the step c, the clustering method comprises:
step 1: in the reaction of E A ,E P Forming a two-dimensional space, wherein the curve health characteristics of the curve devices are different, and the curve devices are respectively a cluster;
step 2: calculating a Curve device Cluster D by Using equation (7) v And D e Distance D between each two ve ：
Wherein t is v Representing a curved cluster of devices D v The number of curve devices in (1); t is t e Representing a curved device Cluster D e The number of curve devices in (1); d ij Representing curves in clusters device G i And G j The distance between each two is calculated using equation (6):
wherein, E i Is a curve device G i A state attribute variable of E i ＝(E i,A ,E i,P ) T ，E j Is a curve device G j A state attribute variable of E j ＝(E j,A ,E j,P ) T ；
And step 3: will D ve The smallest two clusters are merged into a new cluster;
and 4, step 4: and repeating the steps 2 and 3 until the curve devices are divided into 4 clusters, and obtaining a result of the healthy feature clustering of the curve devices.
5. The vehicle body vibration and noise response-based curve health assessment method according to claim 4, wherein the clustering method further comprises:
determining a possible value set of the CHI and a curve device cluster corresponding to each CHI value according to the health feature clustering result of the curve devices; and
determining CHI values of the plurality of curve devices according to the dependency relationship of the curve devices and the clusters.
6. The vehicle body vibration and noise response-based curve health assessment method according to claim 5, wherein: and determining a curve device set which needs to be finally maintained based on the CHI values of the curve devices and the result of the cluster analysis.
7. A vehicle body vibration and noise response based curve health assessment apparatus comprising:
the vibration response and noise data acquisition module is used for acquiring vibration response data and noise data;
the calculation and construction module of the index system is used for calculating and constructing the index system;
the comprehensive evaluation algorithm building module for curve health is used for building a discretization quantitative index for measuring the health state of curve equipment; and
and the health evaluation module of the curve device evaluates the health of the curve device based on the CHI value.
8. A computer readable storage medium storing executable instructions that when executed by a processor cause the performance of the vehicle body vibration and noise response based curve health assessment method of any one of the preceding claims 1-6.
9. A vehicle body vibration and noise response based curve health assessment system, the system comprising a processor and a storage device, the storage device being configured to store executable instructions that, when executed by the processor, enable performance of the vehicle body vibration and noise response based curve health assessment method of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110783247.5A CN115689315A (en) | 2021-07-12 | 2021-07-12 | Curve health assessment method based on vehicle body vibration and noise response |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110783247.5A CN115689315A (en) | 2021-07-12 | 2021-07-12 | Curve health assessment method based on vehicle body vibration and noise response |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115689315A true CN115689315A (en) | 2023-02-03 |
Family
ID=85044567
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110783247.5A Pending CN115689315A (en) | 2021-07-12 | 2021-07-12 | Curve health assessment method based on vehicle body vibration and noise response |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115689315A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115774652A (en) * | 2023-02-13 | 2023-03-10 | 浪潮通用软件有限公司 | Cluster control equipment health monitoring method, equipment and medium based on clustering algorithm |
-
2021
- 2021-07-12 CN CN202110783247.5A patent/CN115689315A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115774652A (en) * | 2023-02-13 | 2023-03-10 | 浪潮通用软件有限公司 | Cluster control equipment health monitoring method, equipment and medium based on clustering algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2020244288A1 (en) | Method and apparatus for evaluating truck driving behaviour based on gps trajectory data | |
| Attoh-Okine | Big data and differential privacy: analysis strategies for railway track engineering | |
| CN106960285B (en) | Subway train operation service quality detection device and method | |
| CN108665191B (en) | Subway station outside association area calculation method based on aggregation effect | |
| CN113420367B (en) | Subway steel rail wave grinding detection method and device based on vibration and noise response | |
| EP3994635A1 (en) | Monitoring, predicting and maintaining the condition of railroad elements with digital twins | |
| CN106638190A (en) | Railway line quality monitoring method and system | |
| Braga et al. | Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component | |
| CN113030450A (en) | Asphalt pavement full-period performance monitoring and evaluating method and system | |
| CN111055881A (en) | Wheel-rail interface damage evolution monitoring method based on noise signals | |
| CN115689315A (en) | Curve health assessment method based on vehicle body vibration and noise response | |
| Flintsch et al. | Probe vehicles used to measure road ride quality: Pilot demonstration | |
| CN113947130A (en) | Training and using method and device for regression prediction AI model of polygonal wear waveform of wheel | |
| Yao et al. | Rail weld defect prediction and related condition-based maintenance | |
| Zeng et al. | Prediction of mud pumping in railway track using in-service train data | |
| Wang et al. | A machine learning based methodology for broken rail prediction on freight railroads: A case study in the United States | |
| Dybeł et al. | Sensitivity of geometric parameters in the sustainability development of continuous welded rail | |
| Ghiasi et al. | An unsupervised anomaly detection framework for onboard monitoring of railway track geometrical defects using one-class support vector machine | |
| CN110143217B (en) | Track state measuring method, system and device | |
| Zhang et al. | Broken rail prediction with machine learning-based approach | |
| Tan et al. | Machine learning approaches for tamping effectiveness prediction | |
| CN116767307A (en) | Dynamic irregularity measuring system for rail transit and wheel track relation evaluation method | |
| Van Hauwermeiren et al. | A scalable, self‐supervised calibration and confounder removal model for opportunistic monitoring of road degradation | |
| CN115290133A (en) | Method and system for monitoring track structure at joint of light rail platform | |
| CN114358593A (en) | Bridge area track smoothness evaluation method based on PCA model |
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 |