CN110793789A - Dynamic detection system under high-speed running state of train - Google Patents
Dynamic detection system under high-speed running state of train Download PDFInfo
- Publication number
- CN110793789A CN110793789A CN201910947782.2A CN201910947782A CN110793789A CN 110793789 A CN110793789 A CN 110793789A CN 201910947782 A CN201910947782 A CN 201910947782A CN 110793789 A CN110793789 A CN 110793789A
- Authority
- CN
- China
- Prior art keywords
- train
- signal
- processing
- detection system
- information
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/08—Railway vehicles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
Abstract
The invention discloses a dynamic detection system for a train in a high-speed running state, and belongs to the field of dynamic detection of trains. A dynamic detection system under the high-speed running state of a train, the data acquisition method adopted has the characteristics of simple installation, easy networking, convenient maintenance and the like, and can improve the energy of characteristic information quantity concerned by an observer while inhibiting noise, such as periodic impact caused by rail joints, periodic impact caused by gear abrasion and the like; the method can change the non-periodic impact caused by train defects into the periodic characteristic in a certain local time period, and can obviously distinguish various excitement such as the periodic impact caused by rail gaps, the non-periodic impact caused by the rail defects, the periodic impact caused by trains, the interference impact caused by trains, the periodic impact caused by train faults and the like in a time frequency spectrum.
Description
Technical Field
The invention relates to the field of train dynamic detection, in particular to a dynamic detection system for a train in a high-speed running state.
Background
With the new period of the leap-type development of railways in China, high-speed motor train units are started to be operated in large quantities, more than 2000 motor train units are put on line first, and the daily bearing passenger capacity of the motor train units is also increased in a festival mode. With the increase of years, the failure rate of the high-speed rail is also rising, and how to ensure the continuous, reliable and effortful operation of the high-speed rail is a problem which the high-speed rail in China has to overcome.
Some TEDS systems are installed in a plurality of railway train sections under the railway bureau, but the TEDS systems do not meet the functional indexes required by corresponding railway standards, and if reliable and stable products capable of replacing the existing systems appear, the TEDS systems still have a large market space.
The dynamic fault detection system of the motor train unit can find faults in the running of the motor train unit, is beneficial to improving the safety factor of the running of the motor train unit, can automatically detect each part of the motor train unit and improve the working efficiency of an overhaul facility; the high-speed railway has a large demand on the aspect of dynamic detection of the motor train unit, and the safety factor of the high-speed railway during operation is expected to be improved as much as possible; although a dynamic image detection system (TEDS) for the operation faults of the motor train unit exists at present, the dynamic detection system applied at present is single in identification method, low in identification efficiency and poor in stability, the system is hardly available, and a set of alternative system is urgently needed at present.
Disclosure of Invention
The invention aims to solve the problems of single identification method, low identification efficiency and poor stability of the conventional dynamic detection system, and provides a dynamic detection system in a high-speed running state of a train.
In order to achieve the purpose, the invention adopts the following technical scheme:
a dynamic detection system under a high-speed running state of a train comprises the following steps:
s1, vertically installing a plurality of vibration impact sensors above axle boxes of train bogie axles, installing wireless sensor nodes at the front end and the rear end of a train carriage, and networking the installed wireless sensors;
s2, preprocessing information acquired by the multiple sensors, wherein the preprocessing mainly comprises threshold processing, equidistant resampling and signal group classification;
s3, performing information alignment translation processing on the information subjected to the signal group classification processing in the S2;
s4, performing energy enhancement superposition processing on the signal obtained after the information alignment translation processing in the S3 in a space domain, and enabling g to bek(s) is the signal obtained after the signal group k is processed by energy enhancement and superposition, and then the signal can be obtained：
k＝1,2,3,4，m＝2,3,......
S5, signal g obtained after energy enhancement and superposition processing in S4k(s) processing by a modern time-frequency analysis method to obtain a spatial time-frequency spectrogram;
s6, the characteristic information of various vibration impact sources after the S5 impact reconstruction processing is reserved in a space time-frequency spectrogram, and the characteristic information of train defects is identified from the characteristic information of a plurality of vibration impact sources.
Preferably, the networking manner of the wireless sensor network in S1 adopts a hybrid networking scheme combining a mesh network and a string network.
Preferably, the preprocessing in S2 includes the steps of:
a1, selecting the amplitude of a noise background signal as a expounding value, acquiring multiple segments of data through a vibration impact sensor under the condition that both a track and a vehicle are good and have no fault, removing partial maximum value points and partial minimum value points, then carrying out multiple segments of superposition average processing to obtain an average value, and taking the value as a threshold value;
a2, combining the train speed information to perform equidistant resampling processing on the non-equidistant signals, converting the time domain signals into space domain signals, and deriving the corresponding periods and frequencies into concepts such as space periods, space frequencies and the like;
and A3, taking monitoring points at the same position of each carriage as a group, and classifying all the sensor nodes on the left side and the right side.
Preferably, the information alignment translation process in S3 includes the following steps:
b1, first node L of signal group kkCollected signal lk(s) the spatially periodic impacts caused by the rail joint in(s) are referenced;
b2, connecting other nodes L in the groupiCollected signal li(S) left-hand phase shift Sc(i-k)/4, all node information in the signal group kSynchronizing over distance;
b3, connecting other nodes L in the groupiCollected signal li(s) Right-hand phase Shift mSD(i-k)/4, so that the signal l collected by each nodeiThe phases of the spatial periodic impacts caused by train connection fluctuation in(s) are different from each other by mSDThe phases of the spatial aperiodic impacts caused by train defects also differ from each other by plus mSD。
Preferably, after the information alignment translation processing, the spatial domain signal l isi(s) is derived as:
ui(s)＝li(s+SC(i-k)/4-mSD(i-k)/4)＝li(s+(SC-mSD)(i-k)/4)
wherein k is a signal group number, and k is 1,2,3, 4; i is the node number in the group, i is k +4, k +8,. m is a translation coefficient, and m is 2, 3.. the.; u. ofiAnd(s) is a signal obtained after the information alignment translation processing.
Preferably, in the S5, the modern time-frequency analysis performs time-frequency analysis on the vertical vibration signal of the train by using the short-time fourier transform in the first-class nuclear decomposition analysis method and the Wigner-Ville distribution in the second class, and extracts the characteristic information of the train defect.
Compared with the prior art, the invention provides a dynamic detection system in a high-speed running state of a train, which has the following beneficial effects:
1. the data acquisition method adopted by the invention has the characteristics of simple installation, easy networking, convenient maintenance and the like, can inhibit noise and simultaneously improve the energy of characteristic information quantity concerned by an observer, such as periodic impact caused by rail joints, periodic impact caused by gear abrasion and the like; the method can change the non-periodic impact caused by train defects into the periodic characteristic in a certain local time period, and can obviously distinguish various excitement such as the periodic impact caused by rail gaps, the non-periodic impact caused by the rail defects, the periodic impact caused by trains, the interference impact caused by trains, the periodic impact caused by train faults and the like in a time frequency spectrum.
Drawings
Fig. 1 is a schematic flow chart of a dynamic detection system in a high-speed train running state according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Example 1:
a dynamic detection system under a high-speed running state of a train comprises the following steps:
s1, vertically installing a plurality of vibration impact sensors above axle boxes of train bogie axles, installing wireless sensor nodes at the front end and the rear end of a train carriage, and networking the installed wireless sensors;
s2, preprocessing information acquired by the multiple sensors, wherein the preprocessing mainly comprises threshold processing, equidistant resampling and signal group classification;
s3, performing information alignment translation processing on the information subjected to the signal group classification processing in the S2;
s4, performing energy enhancement superposition processing on the signal obtained after the information alignment translation processing in the S3 in a space domain, and enabling g to bek(s) is the signal obtained after the energy enhancement superposition processing is carried out on the signal group k, and then the following can be obtained:
k＝1,2,3,4，m＝2,3,......
s5, signal g obtained after energy enhancement and superposition processing in S4k(s) processing by a modern time-frequency analysis method to obtain a spatial time-frequency spectrogram;
s6, the characteristic information of various vibration impact sources after the S5 impact reconstruction processing is reserved in a space time-frequency spectrogram, and the characteristic information of train defects is identified from the characteristic information of a plurality of vibration impact sources.
Further, preferably, the networking manner of the wireless sensor network in S1 adopts a hybrid networking scheme combining a mesh network and a string network.
Further, preferably, the preprocessing in S2 includes the steps of:
a1, selecting the amplitude of a noise background signal as a expounding value, acquiring multiple segments of data through a vibration impact sensor under the condition that both a track and a vehicle are good and have no fault, removing partial maximum value points and partial minimum value points, then carrying out multiple segments of superposition average processing to obtain an average value, and taking the value as a threshold value;
a2, combining the train speed information to perform equidistant resampling processing on the non-equidistant signals, converting the time domain signals into space domain signals, and deriving the corresponding periods and frequencies into concepts such as space periods, space frequencies and the like;
and A3, taking monitoring points at the same position of each carriage as a group, and classifying all the sensor nodes on the left side and the right side.
Further, preferably, the information alignment translation process in S3 includes the steps of:
b1, first node L of signal group kkCollected signal lk(s) the spatially periodic impacts caused by the rail joint in(s) are referenced;
b2, connecting other nodes L in the groupiCollected signal li(S) left-hand phase shift Sc(i-k)/4, synchronizing the information of all nodes in the signal group k in distance;
b3, connecting other nodes L in the groupiCollected signal li(s) Right-hand phase Shift mSD(i-k)/4So that the signal l collected by each nodeiThe phases of the spatial periodic impacts caused by train connection fluctuation in(s) are different from each other by mSDThe phases of the spatial aperiodic impacts caused by train defects also differ from each other by plus mSD。
Further, preferably, after the information alignment shift processing, the spatial domain signal li(s) is derived as:
ui(s)＝li(s+SC(i-k)/4-mSD(i-k)/4)＝li(s+(SC-mSD)(i-k)/4)
wherein k is a signal group number, and k is 1,2,3, 4; i is the node number in the group, i is k +4, k +8,. m is a translation coefficient, and m is 2, 3.. the.; u. ofiAnd(s) is a signal obtained after the information alignment translation processing.
Further, preferably, in the S5, the modern time-frequency analysis performs time-frequency analysis on the vertical vibration signal of the train by using the short-time fourier transform in the first-class nuclear decomposition analysis method and the Wigner-Ville distribution in the second class, so as to extract the characteristic information of the train defect.
Example 2: based on example 1, but with the difference that:
starting a simulation design experiment according to the embodiment 1, and achieving the following effect, wherein 8 groups of motor cars finish image acquisition and automatic identification alarm transmission within 5 minutes after the motor cars pass at the speed of 5-300 KM/h; the acoustic detection realizes the automatic identification and alarm of typical faults within the vehicle speed range of 80-180 KM/h; image resolution: the resolution is less than 1 mm/pixel; automatically recognizing the target: bolts with the diameter of 5mm or more are lost; the oil stain area of the part with abnormal deformation displacement of 5mm or more and 50mm x 50mm or more; the automatic identification time of the 8-group motor train is less than 5 minutes, and the automatic identification alarm accuracy is not less than 50%. The calculation method comprises the following steps: the real fault alarm number/total alarm number of the train is equal to the accuracy. Information storage: the original collected information and images of the detection station are stored for no less than 30 days. Axle counting and vehicle counting errors: the axle and the vehicle can be automatically counted; the axle counting error is less than 3 x 10 < -6 >; the vehicle counting error is less than 3 x 10-5. Speed measurement error: the speed can be automatically measured; the speed measurement error does not exceed 5 x 10-2 in the normal running process; the vehicle number recognition rate is as follows: greater than 99.9%. And (3) vehicle orientation identification: automatically identifying the 1,2 bit ends, the 1,2 bit sides and the left and right sides of the running direction of the vehicle; the recognition rate is not lower than 99.9%. Identifying the size of the alarm frame: not more than 2 times the actual failure. And performing multi-domain analysis visual presentation on the sound data. An automatic alarm module: and independently deploying, operating and uploading verification data according to four parts of a bogie, a vehicle body skirt board, a vehicle body joint and a floor of different vehicle types. The terminal operation is simpler and more efficient.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (6)
1. A dynamic detection system under a high-speed running state of a train is characterized by comprising the following steps:
s1, vertically installing a plurality of vibration impact sensors above axle boxes of train bogie axles, installing wireless sensor nodes at the front end and the rear end of a train carriage, and networking the installed wireless sensors;
s2, preprocessing information acquired by the multiple sensors, wherein the preprocessing mainly comprises threshold processing, equidistant resampling and signal group classification;
s3, performing information alignment translation processing on the information subjected to the signal group classification processing in the S2;
s4, performing energy enhancement superposition processing on the signal obtained after the information alignment translation processing in the S3 in a space domain, and enabling g to bek(s) is the signal obtained after the energy enhancement superposition processing is carried out on the signal group k, and then the following can be obtained:
s5, signal g obtained after energy enhancement and superposition processing in S4k(s) processing by a modern time-frequency analysis method to obtain a spatial time-frequency spectrogram;
s6, the characteristic information of various vibration impact sources after the S5 impact reconstruction processing is reserved in a space time-frequency spectrogram, and the characteristic information of train defects is identified from the characteristic information of a plurality of vibration impact sources.
2. The dynamic detection system for the high-speed train running state according to claim 1, wherein: the networking mode of the wireless sensor network in S1 adopts a hybrid networking scheme combining a mesh network and a string network.
3. The dynamic detection system for the high-speed train running state according to claim 1, wherein: the preprocessing in S2 includes the steps of:
a1, selecting the amplitude of a noise background signal as a expounding value, acquiring multiple segments of data through a vibration impact sensor under the condition that both a track and a vehicle are good and have no fault, removing partial maximum value points and partial minimum value points, then carrying out multiple segments of superposition average processing to obtain an average value, and taking the value as a threshold value;
a2, combining the train speed information to perform equidistant resampling processing on the non-equidistant signals, converting the time domain signals into space domain signals, and deriving the corresponding periods and frequencies into concepts such as space periods, space frequencies and the like;
and A3, taking monitoring points at the same position of each carriage as a group, and classifying all the sensor nodes on the left side and the right side.
4. The dynamic detection system for the high-speed train running state according to claim 1, wherein: the information alignment translation process in S3 includes the steps of:
b1, first node L of signal group kkCollected signal lk(s) the spatially periodic impacts caused by the rail joint in(s) are referenced;
b2, connecting other nodes L in the groupiCollected signal li(S) left-hand phase shift Sc(i-k)/4, all nodes in the signal group k are informedSynchronizing information on distance;
b3, connecting other nodes L in the groupiCollected signal li(s) Right-hand phase Shift mSD(i-k)/4, so that the signal l collected by each nodeiThe phases of the spatial periodic impacts caused by train connection fluctuation in(s) are different from each other by mSDThe phases of the spatial aperiodic impacts caused by train defects also differ from each other by plus mSD。
5. The dynamic detection system for the high-speed train running state according to claim 1, wherein: after the information alignment translation processing, a space domain signal li(s) is derived as:
ui(s)＝li(s+SC(i-k)/4-mSD(i-k)/4)＝li(s+(SC-mSD)(i-k)/4)
wherein k is a signal group number, and k is 1,2,3, 4; i is the node number in the group, i is k +4, k +8,. m is a translation coefficient, and m is 2, 3.. the.; u. ofiAnd(s) is a signal obtained after the information alignment translation processing.
6. The dynamic detection system for the high-speed train running state according to claim 1, wherein: and in the modern time-frequency analysis in the S5, the short-time Fourier transform in the first type of nuclear decomposition analysis method and the Wigner-Ville distribution in the second type of nuclear decomposition analysis method are used for carrying out time-frequency analysis on the vertical vibration signals of the train, and the characteristic information of the train defects is extracted.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910947782.2A CN110793789A (en) | 2019-10-08 | 2019-10-08 | Dynamic detection system under high-speed running state of train |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910947782.2A CN110793789A (en) | 2019-10-08 | 2019-10-08 | Dynamic detection system under high-speed running state of train |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110793789A true CN110793789A (en) | 2020-02-14 |
Family
ID=69438804
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910947782.2A Pending CN110793789A (en) | 2019-10-08 | 2019-10-08 | Dynamic detection system under high-speed running state of train |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110793789A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103487252A (en) * | 2013-09-24 | 2014-01-01 | 重庆市科学技术研究院 | Automobile transmission rack endurance test operation state monitoring method |
| CN104635270A (en) * | 2015-02-16 | 2015-05-20 | 中国地质大学（北京） | Converted wave static correction method based on synthetic record constraints |
| CN107837082A (en) * | 2017-11-27 | 2018-03-27 | 乐普（北京）医疗器械股份有限公司 | Electrocardiogram automatic analysis method and device based on artificial intelligence self study |
| CN108426713A (en) * | 2018-02-26 | 2018-08-21 | 成都昊铭科技有限公司 | Rolling bearing Weak fault diagnostic method based on wavelet transformation and deep learning |
| CN110263474A (en) * | 2019-06-27 | 2019-09-20 | 重庆理工大学 | A kind of cutter life real-time predicting method of numerically-controlled machine tool |
-
2019
- 2019-10-08 CN CN201910947782.2A patent/CN110793789A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103487252A (en) * | 2013-09-24 | 2014-01-01 | 重庆市科学技术研究院 | Automobile transmission rack endurance test operation state monitoring method |
| CN104635270A (en) * | 2015-02-16 | 2015-05-20 | 中国地质大学（北京） | Converted wave static correction method based on synthetic record constraints |
| CN107837082A (en) * | 2017-11-27 | 2018-03-27 | 乐普（北京）医疗器械股份有限公司 | Electrocardiogram automatic analysis method and device based on artificial intelligence self study |
| CN108426713A (en) * | 2018-02-26 | 2018-08-21 | 成都昊铭科技有限公司 | Rolling bearing Weak fault diagnostic method based on wavelet transformation and deep learning |
| CN110263474A (en) * | 2019-06-27 | 2019-09-20 | 重庆理工大学 | A kind of cutter life real-time predicting method of numerically-controlled machine tool |
Non-Patent Citations (1)
| Title |
|---|
| 赵洁等: "基于冲击重构技术的车辆_轨道耦合系统垂向振动分析", 《振动与冲击》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103852271B (en) | High-speed train running gear fault diagnosis and remote monitoring system based on Internet of Things | |
| CN106394606B (en) | A kind of railway car wheel loses circle detection method and detection device | |
| CN204556257U (en) | A kind of rolling stock rolling bearing acoustic diagnostic instrument | |
| CN111071291B (en) | Train wheel set monitoring system and train wheel set monitoring method | |
| CN103196682B (en) | Based on the train suspension system fault separating method of the information fusion of D-S evidence theory | |
| CN102998130B (en) | Fault detecting method for train suspension system of data driving based on acceleration measuring | |
| CN211401691U (en) | Different-sound rail bottom acoustic diagnosis device for driving mechanism of running part of motor train unit | |
| CN114997252B (en) | Vehicle-mounted detection method for wheel polygon based on inertia principle | |
| CN206307052U (en) | A kind of railway car wheel loses loop truss device | |
| CN113988326A (en) | Subway equipment maintenance optimization method and system | |
| CN113776760B (en) | Train wheel set out-of-round fault monitoring method and system based on whole-axis vibration analysis | |
| CN105403420A (en) | Bogie fault diagnosis method integrated with multiple sensors | |
| DE102012219111A1 (en) | Method for localization of rail vehicle in rail network, involves matching corresponding period of reference signal of route section of rail network with selected portion of actual time signal | |
| CN111444574A (en) | Sensor layout optimization method based on dynamic analysis | |
| CN107782548B (en) | Rail vehicle part detection system | |
| CN110793789A (en) | Dynamic detection system under high-speed running state of train | |
| CN110171442B (en) | Detection system and detection method for wheel flat scars | |
| CN110207810B (en) | Axle box vibration detection system | |
| CN115805971A (en) | Method for monitoring polygonal fault of train wheel on line | |
| CN113516159A (en) | Fault diagnosis method and system for cracks of pinion shaft of running part of railway vehicle | |
| Oba et al. | Condition monitoring for Shinkansen bogies based on vibration analysis | |
| CN208579937U (en) | A kind of vehicle-mounted axle box vibration monitor system of high-speed rail EMU | |
| CN212903311U (en) | ZD27 EMUs bogie vibration acceleration monitoring devices | |
| CN115931399A (en) | Method for real-time online detection of polygonal fault of high-speed train wheel | |
| CN108827665A (en) | Wheel flat fault detection method based on empirical mode decomposition and multi-scale entropy |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200214 |