CN109910949B - Method, device and system for rail breakage detection - Google Patents
Method, device and system for rail breakage detection Download PDFInfo
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- CN109910949B CN109910949B CN201910191991.9A CN201910191991A CN109910949B CN 109910949 B CN109910949 B CN 109910949B CN 201910191991 A CN201910191991 A CN 201910191991A CN 109910949 B CN109910949 B CN 109910949B
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Abstract
The invention provides a method, a device and a system for rail breakage detection, wherein the method comprises the following steps: acquiring real-time vibration data of a train running on a steel rail; processing the real-time vibration data to obtain rail gap data; aligning the real-time vibration data with historical vibration data according to the rail gap data; and determining the position of the broken steel rail through comparative analysis with the historical vibration data. The rail breakage detection is simpler, more convenient and more accurate.
Description
Technical Field
The invention relates to the technical field of rail transit equipment, in particular to a method, a device and a system for rail breakage detection.
Background
In recent years, domestic rail transit and high-speed railways are rapidly developed, and with the requirements of high-speed running of trains and the like, the problems of abrasion, even breakage and the like of rails are inevitable after the trains run on the rails for a long time.
At present, the rail fracture detection is mainly carried out by adopting methods such as manual rail finding of a handcart flaw detection vehicle, a rail circuit and the like. The hand cart type rail flaw detection vehicle is used for detecting rail cracks and nuclear injuries by an ultrasonic pulse reflection method and a penetration method. When the steel rails are complete and no train passes through the track circuit, two steel rails and the track relay form a current loop, and the receiver can receive signals sent by the signal source.
However, the manual rail patrol of the hand-push type rail flaw detection vehicle has the disadvantages of slow detection speed, manual operation, high labor intensity during field operation, long rail occupation time and hysteresis in detection. When the steel rail is broken by the rail circuit detection method, a loop cannot be formed, and a receiver cannot receive a signal of a signal source; the track circuit is greatly influenced by the parameter condition of the track bed, and fault conditions such as short circuit between tracks, false alarm of red light bands and the like often occur in areas with small track bed leakage impedance and abundant rainwater, so the detection effect is poor.
Disclosure of Invention
The invention provides a broken rail detection method, a broken rail detection device and a broken rail detection system, and aims to realize simpler, more convenient and more accurate broken rail detection.
In a first aspect, a method for detecting rail break provided in an embodiment of the present invention includes:
acquiring real-time vibration data of a train running on a steel rail;
processing the real-time vibration data to obtain rail gap data;
aligning the real-time vibration data with historical vibration data according to the rail gap data;
and determining the position of the broken steel rail through comparative analysis with the historical vibration data.
In one possible design, obtaining real-time vibration data of a train traveling on a rail includes:
acquiring original vibration data corresponding to p steel rail points on the steel rail according to a preset rule;
and calculating an average value of the original vibration data to obtain real-time vibration data of the steel rail, wherein the real-time vibration data is used for representing vibration energy of the train running on the steel rail.
In one possible design, processing the real-time vibration data to obtain rail gap data includes:
filtering the real-time vibration data according to a preset lower limit threshold, and removing the real-time vibration data smaller than the lower limit threshold to obtain at least one multi-section continuous vibration data;
according to a first preset fixed length S, obtaining peak data every S steel rail distance from the current first vibration data of the multiple sections of continuous vibration data, wherein S is smaller than the distance W between two wheels of the train;
searching in the distance of the L1 steel rail at two sides of the peak data according to a first preset length L1, judging whether at least one first peak data exists, if one first peak data exists, integrating 1 score, traversing the peak data, obtaining the accumulated integral corresponding to each peak data, wherein the initial integral corresponding to the peak data is 0,
if the accumulated integral is equal to 0, rejecting peak data corresponding to the accumulated integral;
if the accumulated integral is greater than 0, retaining peak data corresponding to the accumulated integral;
according to a second preset length l2, searching from the reserved current first peak data to the right side, searching within the distance of the l2 steel rail of the current first peak data, judging whether at least one second peak data exists, if so, integrating 1 score, and continuously searching within the distance of the l2 steel rail from the second peak data to judge whether at least one third peak data exists until the peak data does not exist;
and traversing the reserved peak data in sequence to obtain rail gap data.
In one possible design, aligning the real-time vibration data with historical vibration data based on the rail gap data includes:
extracting historical vibration data from a preset database, respectively selecting the first N vibration data of the real-time vibration data and the first M vibration data of the historical vibration data as a data combination to obtain a plurality of data combinations, and calculating a correlation coefficient r of each data combination, wherein the absolute value of r is used for expressing the magnitude of correlation strength;
and selecting the data combination corresponding to the maximum absolute value, determining an alignment starting position, and aligning the vibration data with the historical vibration data in sequence according to the rail gap data.
In one possible design, aligning the real-time vibration data with historical vibration data based on the rail gap data further comprises:
and stretching or shrinking the real-time vibration data by taking the historical vibration data as a reference so as to align the real-time vibration data with the historical vibration data.
In one possible design, determining the location of the rail where the break exists by comparative analysis with the historical vibration data comprises:
according to a preset second fixed length d, respectively obtaining the average values corresponding to the real-time vibration data and the historical vibration data within the distance of every d steel rails in the real-time vibration data and the historical vibration data, if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is larger than a preset threshold value, judging that the steel rails corresponding to the real-time vibration data are broken, and if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is smaller than or equal to the preset threshold value, judging that the steel rails are not broken;
if the steel rail is broken, extracting historical vibration data corresponding to the broken steel rail from the database, and drawing the historical vibration data into a trend curve graph according to the time sequence;
and determining the position of the broken steel rail according to the trend curve graph, the train running speed, the station spacing and the sampling frequency information.
In a second aspect, an apparatus for detecting rail break provided in an embodiment of the present invention includes:
the acquisition module is used for acquiring real-time vibration data of the train running on the steel rail;
the obtaining module is used for processing the real-time vibration data to obtain rail gap data;
the alignment module is used for aligning the real-time vibration data with historical vibration data according to the rail gap data;
and the determining module is used for determining the position of the broken steel rail through comparative analysis with the historical vibration data.
In one possible design, the obtaining module is specifically configured to:
acquiring original vibration data corresponding to p steel rail points on the steel rail according to a preset rule;
and calculating an average value of the original vibration data to obtain real-time vibration data of the steel rail, wherein the real-time vibration data is used for representing vibration energy of the train running on the steel rail.
In one possible design, a module is obtained, in particular for:
filtering the real-time vibration data according to a preset lower limit threshold, and removing the real-time vibration data smaller than the lower limit threshold to obtain at least one multi-section continuous vibration data;
according to a first preset fixed length S, obtaining peak data every S steel rail distance from the current first vibration data of the multiple sections of continuous vibration data, wherein S is smaller than the distance W between two wheels of the train;
searching in the distance of the L1 steel rail at two sides of the peak data according to a first preset length L1, judging whether at least one first peak data exists, if one first peak data exists, integrating 1 score, traversing the peak data, obtaining the accumulated integral corresponding to each peak data, wherein the initial integral corresponding to the peak data is 0,
if the accumulated integral is equal to 0, rejecting peak data corresponding to the accumulated integral;
if the accumulated integral is greater than 0, retaining peak data corresponding to the accumulated integral;
according to a second preset length l2, searching from the reserved current first peak data to the right side, searching within the distance of the l2 steel rail of the current first peak data, judging whether at least one second peak data exists, if so, integrating 1 score, and continuously searching within the distance of the l2 steel rail from the second peak data to judge whether at least one third peak data exists until the peak data does not exist;
and traversing the reserved peak data in sequence to obtain rail gap data.
In one possible design, the alignment module is specifically configured to:
extracting historical vibration data from a preset database, respectively selecting the first N vibration data of the real-time vibration data and the first M vibration data of the historical vibration data as a data combination to obtain a plurality of data combinations, and calculating a correlation coefficient r of each data combination, wherein the absolute value of r is used for expressing the magnitude of correlation strength;
and selecting the data combination corresponding to the maximum absolute value, determining an alignment starting position, and aligning the vibration data with the historical vibration data in sequence according to the rail gap data.
In one possible design, aligning the real-time vibration data with historical vibration data based on the rail gap data further comprises:
and stretching or shrinking the real-time vibration data by taking the historical vibration data as a reference so as to align the real-time vibration data with the historical vibration data.
In one possible design, the determining module is specifically configured to:
according to a preset second fixed length d, respectively obtaining the average values corresponding to the real-time vibration data and the historical vibration data within the distance of every d steel rails in the real-time vibration data and the historical vibration data, if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is larger than a preset threshold value, judging that the steel rails corresponding to the real-time vibration data are broken, and if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is smaller than or equal to the preset threshold value, judging that the steel rails are not broken;
if the steel rail is broken, extracting historical vibration data corresponding to the broken steel rail from the database, and drawing the historical vibration data into a trend curve graph according to the time sequence;
and determining the position of the broken steel rail according to the trend curve graph, the train running speed, the station spacing and the sampling frequency information.
In a third aspect, the system for detecting a rail break provided in the embodiment of the present invention includes a memory and a processor, where the memory stores executable instructions of the processor; wherein the processor is configured to perform the method of rail break detection of any of the first aspect via execution of the executable instructions.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is configured to, when executed by a processor, implement the method for rail break detection in any one of the first aspect.
The invention provides a method, a device and a system for rail breakage detection, wherein the method comprises the following steps: acquiring real-time vibration data of a train running on a steel rail; processing the real-time vibration data to obtain rail gap data; aligning the real-time vibration data with historical vibration data according to the rail gap data; and determining the position of the broken steel rail through comparative analysis with the historical vibration data. The rail breakage detection is simpler, more convenient and more accurate.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of an application scenario of the present invention;
fig. 2 is a flowchart of a method for rail break detection according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a train bogie for detecting rail break according to an embodiment of the present invention;
fig. 4(a) is a schematic diagram of a method for detecting rail break according to an embodiment of the present invention before filtering a lower limit;
fig. 4(b) is a schematic diagram of the method for detecting rail break according to the first embodiment of the present invention after filtering the lower limit;
fig. 5 is a schematic diagram of peak data in a method for detecting track break according to an embodiment of the present invention;
fig. 6 is a first broken line schematic diagram of rail break detection according to a first embodiment of the present invention;
fig. 7 is a schematic view of a broken line for rail break detection according to the first embodiment of the present invention;
fig. 8 is a schematic view showing a broken line of rail break detection according to the first embodiment of the present invention;
fig. 9 is a fourth schematic view illustrating broken-line detection according to the first embodiment of the present invention;
fig. 10 is a schematic diagram illustrating correlation coefficient analysis in a method for detecting rail break according to an embodiment of the present invention;
fig. 11 is a schematic diagram illustrating alignment of real-time vibration data and historical vibration data in a rail break detection method according to an embodiment of the present invention;
fig. 12 is a schematic diagram illustrating a method for detecting rail break according to an embodiment of the present invention;
fig. 13 is a schematic diagram of a trend graph in a rail break detection method according to an embodiment of the present invention;
fig. 14 is a schematic view of a broken rail detection apparatus according to a second embodiment of the present invention;
fig. 15 is a schematic structural diagram of a rail break detection system according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
The rail gap is the joint gap between two rails, which is the gap between two ends of the rail when the rail expands with heat and contracts with cold. The rail breakage generally means that when a train runs on a steel rail, the train is easy to break at the weak and defective positions of the steel rail due to the action of impact force.
Fig. 1 is a schematic diagram of an application scenario of the present invention, as shown in fig. 1, the embodiment may include: a train 11 and a broken rail detection system 12, the broken rail detection system 12 being configured to collect vibration energy in a direction perpendicular to the rails during actual operation of the train, the vibration energy being represented in an alternative embodiment by suitably quantized vibration data. When a train runs at a high speed at a rail fracture position, the train is subjected to strong impact force to generate strong vibration energy, and when the train runs at a rail gap between train stations, the train also generates strong vibration energy, which is similar to the vibration condition of the train at the rail fracture position, so that the rail fracture detection system needs to eliminate the rail gap position and accurately judge the rail fracture position. The rail breakage detection system acquires real-time vibration data of the train running on the steel rail; processing the real-time vibration data to obtain rail gap data; aligning the real-time vibration data with historical vibration data according to the rail gap data; and determining the position of the broken steel rail through comparative analysis with historical vibration data. Not only covers the range of train rail detection for all lines of daily operation, but also realizes more simple, convenient and accurate rail breakage detection. In a possible embodiment, the method for detecting broken rails has a good detection effect on the positions of broken rail seams larger than 4 mm.
Fig. 2 is a flowchart of a method for detecting rail break according to an embodiment of the present invention, as shown in fig. 1, the method in this embodiment may include:
s101, acquiring real-time vibration data of a train running on a steel rail.
Specifically, acquiring original vibration data corresponding to p steel rail points on a steel rail according to a preset rule; and calculating an average value of the original vibration data to obtain real-time vibration data of the steel rail, wherein the real-time vibration data is used for representing vibration energy of the train running on the steel rail.
The interrupted rail detection system of the embodiment collects real-time vibration data within a proper equal interval distance of a steel rail, the direction of the data collection is perpendicular to the steel rail, original vibration data corresponding to p steel rail points, such as a1 and a2 … … ap, are collected on the steel rail, the p original vibration data are averaged to obtain real-time vibration data of the steel rail, and the real-time vibration data are used for representing vibration energy of a train running on the steel rail.
In the embodiment, the real-time vibration data is obtained by utilizing the average value of the original vibration data, so that the deviation influence of the extreme real-time vibration data is eliminated, and more accurate real-time vibration data is obtained.
The rail gap is the joint gap between two rails, which is the gap between two ends of the rail when the rail expands with heat and contracts with cold. When a train passes through a rail gap, stronger vibration energy can be generated, and the vibration condition of the train at the rail break position is similar, so that real-time vibration data of the rail gap needs to be eliminated, and the position of a broken rail can be obtained more accurately.
In an optional embodiment, fig. 3 is a schematic diagram of a train bogie in rail break detection according to an embodiment of the present invention, as shown in fig. 3, a distance between two train wheels on the same side is Wlen (abbreviated as W), and due to the structure of a train and the rigid connection between the train wheels and the bogie, vibrations of the two train wheels passing through a rail gap are mutually transmitted, theoretically, a larger peak should exist in vibration at the rail gap, and a small peak appears at a distance between W rails on two sides.
And S102, processing the real-time vibration data to obtain rail gap data.
Specifically, the real-time vibration data are filtered according to a preset lower limit threshold, and the real-time vibration data smaller than the lower limit threshold are removed to obtain at least one multi-section continuous vibration data; according to a first preset fixed length S, obtaining peak data every S steel rail distance from current first vibration data of multiple continuous vibration data, wherein S is smaller than the distance W between two wheels of the train; searching in the distance of the L1 steel rail on two sides of peak data according to a first preset length L1, judging whether at least one first peak data exists, if so, integrating for 1, traversing the peak data, and then obtaining an accumulated integral corresponding to each peak data, wherein the initial integral corresponding to the peak data is 0, and if the accumulated integral is equal to 0, rejecting the peak data corresponding to the accumulated integral; and if the accumulated integral is greater than 0, retaining peak data corresponding to the accumulated integral.
In this embodiment, in order to remove the real-time vibration data at the non-rail gap, the collected real-time vibration data is filtered by using a lower threshold, the real-time vibration data lower than the threshold is removed, and the real-time vibration data at the rail gap and the WLen positions on the two sides of the rail gap are retained. In an alternative embodiment, fig. 4(a) is a schematic diagram before filtering the lower limit in the rail break detection method provided by the first embodiment of the present invention, and fig. 4(b) is a schematic diagram after filtering the lower limit in the rail break detection method provided by the first embodiment of the present invention, for example, the real-time vibration data in fig. 4(a) is divided into an upper broken line and a lower broken line, and the rail break detection system filters the real-time vibration data in fig. 4(a) by using the lower limit threshold to obtain at least one piece of multi-segment continuous vibration data as shown in fig. 4(b), where the rail gap data is necessarily included in the multi-segment continuous vibration data. In an alternative embodiment, each piece of continuous vibration data may include a plurality of pieces of real-time vibration data, and in order to facilitate finding the rail gap data, the filtering is performed again on the plurality of pieces of continuous vibration data. Fig. 5 is a schematic diagram of peak data in the rail break detection method according to an embodiment of the present invention, referring to fig. 5, for example, starting from current first vibration data of multiple pieces of continuous vibration data, one peak data is obtained every S rail distances according to a first preset fixed length S, where S is smaller than a distance W between two wheels of a train, as shown in a circle in fig. 5.
In a possible embodiment, all real-time vibration data within the distance of every S steel rail is replaced by the obtained peak data, and the peak data is continuously searched by using an accumulative integral method according to the small peak existing at the distance of the W steel rail on two sides of the rail gap by combining a decision tree analysis method. Fig. 6 is a first broken line schematic diagram of rail break detection provided in the first embodiment of the present invention, and fig. 7 is a second broken line schematic diagram of rail break detection provided in the first embodiment of the present invention. And traversing all peak data, and searching whether at least one peak data point exists at the W steel rail distance position on two sides of each peak data point. Because of the error in the actual situation, the search range L1(Wlen-a < L1< Wlen + a) of each peak data point can be selected according to the actual needs. Referring to fig. 6, according to a first preset length L1, searching is performed within a distance of an L1 steel rail on both sides of peak data, and it is determined whether at least one first peak data exists, if there is one first peak data, the first peak data is integrated into 1 score, referring to fig. 7, after traversing all peak data, an accumulated integral corresponding to each peak data is obtained, wherein an initial integral corresponding to each peak data is 0, and if the accumulated integral is equal to 0, the peak data corresponding to the accumulated integral is rejected; if the cumulative integral is greater than 0, the peak data corresponding to the cumulative integral is retained, and only one peak data cumulative integral is 0 with reference to fig. 7, and needs to be eliminated. In this embodiment, a is not specifically limited, and those skilled in the art may limit a according to actual situations to obtain better effects.
Further, according to a second preset length l2, searching from the reserved current first peak data to the right side, searching within the distance of the l2 steel rail of the current first peak data, judging whether at least one second peak data exists, if so, integrating 1 score, and continuously searching within the distance of the l2 steel rail from the second peak data to judge whether at least one third peak data exists until no peak data exists; and traversing the reserved peak data in sequence to obtain the rail gap data.
In this embodiment, the distance between every two rail gaps is the length of each section of steel rail, in an optional embodiment, the length Plen (abbreviated as P) of each section of steel rail may be fixed, assuming that all the above-mentioned retained peak data are rail gap data, searching whether there is at least one peak data at the distance P on both sides of the assumed rail gap data, and continuously using the above-mentioned integration method to detect the retained peak data, so as to obtain the rail gap data more accurately. In an alternative embodiment, the search range L2 for each peak data point can be selected according to actual needs (Plen-a < L2< Plen + a) because of the deviation in actual search. Fig. 8 is a broken-line schematic diagram of rail break detection provided in the first embodiment of the present invention, referring to fig. 8, according to a second preset length L2, search is performed from the retained current first peak data to the right side, search is performed within a distance from the current first peak data L2 to determine whether there is at least one second peak data, if there is one second peak data, 1 score is integrated, and search is continued from the L2 steel rail distance range from the second peak data to determine whether there is at least one third peak data, until there is no peak data, the search of the current first peak data is ended, and an accumulated integral of the current first peak data is obtained, where an initial integral corresponding to the retained peak data is 0. And searching from the current second peak data to the right side, repeating the process, obtaining the accumulated integral of the second peak data, and sequentially traversing all the retained peak data to obtain the accumulated integral corresponding to the current peak data.
In an optional embodiment, the range covered by the reserved peak data is found to be large, the corresponding accumulated integral is found to be high, and all the reserved peak data with the distance between every two reserved peak data closest to Plen are the rail gap data. Fig. 9 is a fourth schematic view of a broken line for rail break detection according to the first embodiment of the present invention. Referring to fig. 9, bold vertical lines and circles represent rail gap data.
And S103, aligning the real-time vibration data with historical vibration data according to the rail gap data.
Specifically, historical vibration data are extracted from a preset database, the first N vibration data of the real-time vibration data and the first M vibration data of the historical vibration data are respectively selected as a data combination to obtain a plurality of data combinations, and a correlation coefficient r of each data combination is calculated, wherein the absolute value of r is used for expressing the magnitude of the correlation strength; and selecting a data combination corresponding to the maximum absolute value, determining an alignment starting position, and aligning the vibration data with the historical vibration data in sequence according to the rail gap data.
In this embodiment, because the position of the rail gap of the same train station is fixed, historical vibration data is extracted from a preset database according to real-time vibration data collected between the same train station, wherein the preset database stores a plurality of historical vibration data, and the rail breakage detection system selects the current N vibration data of real-time vibration and the first M vibration data of the historical vibration data as a data combination, wherein M, N is not limited in this embodiment, and a person skilled in the art can limit the vibration data according to specific implementation conditions to achieve a better effect. In an alternative embodiment, the track break detection system performs multiple selections to obtain multiple data combinations, for example, (b1, b2), (b3, b4), and calculates a correlation coefficient r for each data combination, where an absolute value of r is used to indicate a magnitude of a correlation strength, where the magnitude of the absolute value of the correlation coefficient r indicates the correlation strength, where the correlation strength may be divided into three levels, which may include a strong correlation, a medium correlation, and a weak correlation. For example, the correlation coefficient for each data combination is equal to the difference between the real-time vibration data and the historical vibration data. Fig. 10 is a schematic diagram of correlation coefficient analysis in the rail break detection method according to an embodiment of the present invention, as shown in fig. 10, where a correlation coefficient r3 indicates that the correlation strength of the real-time vibration data corresponding to the combination of the correlation coefficient r3 data and the historical vibration data is high, for example, strong correlation; as another example, the correlation coefficient r1 indicates that the real-time vibration data corresponding to the combination of the correlation coefficient r1 data is less strongly, e.g., weakly, correlated with the historical vibration data. In an alternative embodiment, the data combination with the highest correlation strength is selected from a plurality of data combinations, the starting position of the data combination is confirmed, and the real-time vibration data and the historical vibration data are aligned. Referring to fig. 11, fig. 11 is a schematic diagram illustrating alignment of real-time vibration data and historical vibration data in a rail break detection method according to an embodiment of the present invention.
In an alternative embodiment, aligning the real-time vibration data with the historical vibration data based on the rail gap data further comprises: and stretching or shrinking the real-time vibration data by taking the historical vibration data as a reference so as to align the real-time vibration data with the historical vibration data.
In this embodiment, since the data of the train station is not acquired at the same time, in a possible embodiment, the lengths of the real-time vibration data and the historical vibration data after alignment may not be consistent, so that the historical vibration data is used as a reference to stretch or contract the real-time vibration data, so that the real-time vibration data and the historical vibration data are aligned and consistent. In an alternative embodiment, the real-time vibration data is stretched using cubic spline interpolation or shrunk using systematic sampling.
And S104, determining the position of the broken steel rail through comparative analysis with historical vibration data.
Specifically, according to a preset second fixed length d, average values corresponding to the real-time vibration data and the historical vibration data are respectively obtained in the distance of the steel rail every d in the real-time vibration data and the historical vibration data, if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is larger than a preset threshold value, it is judged that the steel rail corresponding to the real-time vibration data is broken, and if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is smaller than or equal to the preset threshold value, it is judged that the steel; if the steel rail is broken, taking out historical vibration data corresponding to the broken steel rail from the database, and drawing the historical vibration data into a trend curve chart according to the time sequence; and determining the position of the broken steel rail according to the trend curve graph, the train running speed, the station spacing and the sampling frequency information.
In this embodiment, according to a preset second fixed length d, average values corresponding to the real-time vibration data and the historical vibration data are respectively obtained in the distance between the real-time vibration data and the historical vibration data every other d, if a difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is greater than a preset threshold value, it is determined that the steel rail corresponding to the real-time vibration data is broken, and if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is less than or equal to the preset threshold value, it is determined that the steel. Referring to fig. 12, fig. 12 is a schematic diagram illustrating a method for detecting rail break according to an embodiment of the present invention.
In an alternative embodiment, if there is a rail break, the historical vibration data corresponding to the rail break is taken out from the database, and the historical vibration data is plotted into a trend graph according to a time sequence, fig. 13 is a schematic diagram of the trend graph in the rail break detection method provided in the embodiment of the present invention, and according to the trend graph, the train traveling speed, the station spacing, and the sampling frequency information, the rail break occurs at the rail corresponding to the data point showing the ascending trend in fig. 13 is referred to, that is, the position of the rail having the break is determined.
Fig. 14 is a schematic view of a broken rail detection apparatus according to a second embodiment of the present invention, and as shown in fig. 14, the broken rail detection apparatus according to the present embodiment may include:
the acquisition module 21 is used for acquiring real-time vibration data of the train running on the steel rail;
the obtaining module 22 is used for processing the real-time vibration data to obtain rail gap data;
the alignment module 23 is used for aligning the real-time vibration data with the historical vibration data according to the rail gap data;
and the determining module 24 is used for determining the position of the broken steel rail through comparative analysis with the historical vibration data.
In an alternative embodiment, the obtaining module 21 is specifically configured to:
acquiring original vibration data corresponding to p steel rail points on a steel rail according to a preset rule;
and calculating an average value of the original vibration data to obtain real-time vibration data of the steel rail, wherein the real-time vibration data is used for representing vibration energy of the train running on the steel rail.
In an alternative embodiment, the obtaining module 22 is specifically configured to:
filtering the real-time vibration data according to a preset lower limit threshold, and removing the real-time vibration data smaller than the lower limit threshold to obtain at least one multi-section continuous vibration data;
according to a first preset fixed length S, obtaining peak data every S steel rail distance from current first vibration data of multiple continuous vibration data, wherein S is smaller than the distance W between two wheels of the train;
searching in the distance of the L1 steel rail at two sides of the peak data according to a first preset length L1, judging whether at least one first peak data exists, integrating 1 point if one first peak data exists, traversing the peak data to obtain the accumulated integral corresponding to each peak data, wherein the initial integral corresponding to the peak data is 0,
if the accumulated integral is equal to 0, rejecting peak data corresponding to the accumulated integral;
if the accumulated integral is greater than 0, peak data corresponding to the accumulated integral is reserved;
according to a second preset length l2, searching from the reserved current first peak data to the right side, searching within the distance of the l2 steel rail of the current first peak data, judging whether at least one second peak data exists, if so, integrating 1 score, and continuously searching within the distance of the l2 steel rail from the second peak data to judge whether at least one third peak data exists until no peak data exists;
and traversing the reserved peak data in sequence to obtain the rail gap data.
In an alternative embodiment, the alignment module 23 is specifically configured to:
extracting historical vibration data from a preset database, respectively selecting the first N vibration data of the real-time vibration data and the first M vibration data of the historical vibration data as a data combination to obtain a plurality of data combinations, and calculating a correlation coefficient r of each data combination, wherein the absolute value of r is used for expressing the magnitude of the correlation strength;
and selecting a data combination corresponding to the maximum absolute value, determining an alignment starting position, and aligning the vibration data with the historical vibration data in sequence according to the rail gap data.
In an alternative embodiment, aligning the real-time vibration data with the historical vibration data based on the rail gap data further comprises:
and stretching or shrinking the real-time vibration data by taking the historical vibration data as a reference so as to align the real-time vibration data with the historical vibration data.
In an alternative embodiment, the determining module 24 is specifically configured to:
according to a preset second fixed length d, respectively obtaining the average values corresponding to the real-time vibration data and the historical vibration data in the distance of the steel rail every d in the real-time vibration data and the historical vibration data, if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is larger than a preset threshold value, judging that the steel rail corresponding to the real-time vibration data is broken, and if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is smaller than or equal to the preset threshold value, judging that the steel rail is not;
if the steel rail is broken, taking out historical vibration data corresponding to the broken steel rail from the database, and drawing the historical vibration data into a trend curve chart according to the time sequence;
and determining the position of the broken steel rail according to the trend curve graph, the train running speed, the station spacing and the sampling frequency information.
The device for detecting rail break in this embodiment may execute the technical solution in the method shown in fig. 2, and for the specific implementation process and technical principle, reference is made to the relevant description in the method shown in fig. 2, which is not described herein again.
Fig. 15 is a schematic structural diagram of a rail break detection system provided in the third embodiment of the present invention, and as shown in fig. 15, the rail break detection system 30 of the present embodiment may include: a processor 31 and a memory 32.
A memory 32 for storing a computer program (e.g., an application program, a functional module, etc. implementing the above-described method of rail break detection), computer instructions, etc.;
the computer programs, computer instructions, etc. described above may be stored in one or more memories 32 in partitions. And the above-mentioned computer program, computer instructions, data, etc. can be called by the processor 31.
A processor 31 for executing the computer program stored in the memory 32 to implement the steps of the method according to the above embodiments.
Reference may be made in particular to the description relating to the preceding method embodiment.
The processor 31 and the memory 32 may be separate structures or may be integrated structures integrated together. When the processor 31 and the memory 32 are separate structures, the memory 32 and the processor 31 may be coupled by a bus 33.
The server in this embodiment may execute the technical solution in the method shown in fig. 2, and for the specific implementation process and the technical principle, reference is made to the relevant description in the method shown in fig. 2, which is not described herein again.
The invention provides a method, a device and a system for rail breakage detection, wherein the method comprises the following steps: acquiring real-time vibration data of a train running on a steel rail; processing the real-time vibration data to obtain rail gap data; aligning the real-time vibration data with historical vibration data according to the rail gap data; and determining the position of the broken steel rail through comparative analysis with the historical vibration data. Not only covers the range of train rail detection for all lines of daily operation, but also realizes more simple, convenient and accurate rail breakage detection. In a possible embodiment, the method for detecting broken rails has a good detection effect on the positions of broken rail seams larger than 4 mm.
In addition, embodiments of the present application further provide a computer-readable storage medium, in which computer-executable instructions are stored, and when at least one processor of the user equipment executes the computer-executable instructions, the user equipment performs the above-mentioned various possible methods.
Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in a communication device.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of rail break detection, comprising:
acquiring real-time vibration data of a train running on a steel rail;
processing the real-time vibration data to obtain rail gap data;
aligning the real-time vibration data with historical vibration data according to the rail gap data;
determining the position of the broken steel rail through comparative analysis with the historical vibration data;
aligning the real-time vibration data with historical vibration data according to the rail gap data, comprising:
extracting historical vibration data from a preset database, respectively selecting the first N vibration data of the real-time vibration data and the first M vibration data of the historical vibration data as a data combination to obtain a plurality of data combinations, and calculating a correlation coefficient r of each data combination, wherein the absolute value of r is used for expressing the magnitude of correlation strength;
selecting the data combination corresponding to the maximum absolute value, determining an alignment starting position, and aligning the vibration data with the historical vibration data in sequence according to the rail gap data;
processing the real-time vibration data to obtain rail gap data, comprising:
filtering the real-time vibration data according to a preset lower limit threshold, and removing the real-time vibration data smaller than the lower limit threshold to obtain at least one multi-section continuous vibration data;
according to a first preset fixed length S, obtaining peak data every S steel rail distance from the current first vibration data of the multiple sections of continuous vibration data, wherein S is smaller than the distance W between two wheels of the train;
searching in the distance of L1 steel rails at two sides of peak data according to a first preset length L1, judging whether at least one first peak data exists, selecting a searching range L1(Wlen-a < L1< Wlen + a) of each peak data point according to actual needs, wherein Wlen is the distance between two train wheels at the same side, a is a value limited according to actual conditions, if one first peak data exists, integrating 1 point, traversing the peak data, obtaining the accumulated integral corresponding to each peak data, wherein the initial integral corresponding to the peak data is 0,
if the accumulated integral is equal to 0, rejecting peak data corresponding to the accumulated integral;
if the accumulated integral is greater than 0, retaining peak data corresponding to the accumulated integral;
according to a second preset length L2, searching from the reserved current first peak data to the right side, searching within the distance of the L2 steel rail of the current first peak data, judging whether at least one second peak data exists, selecting the searching range L2 of each peak data point according to actual needs (Plen-a < L2< Plen + a), wherein Plen is the length of each section of steel rail, a is a value limited according to actual conditions, if yes, integrating 1 point, and continuously searching within the distance of the L2 steel rail from the second peak data to determine whether at least one third peak data exists until the peak data does not exist;
and traversing the reserved peak data in sequence to obtain rail gap data.
2. The method of claim 1, wherein acquiring real-time vibration data of the train traveling on the rail comprises:
acquiring original vibration data corresponding to p steel rail points on the steel rail according to a preset rule;
and calculating an average value of the original vibration data to obtain real-time vibration data of the steel rail, wherein the real-time vibration data is used for representing vibration energy of the train running on the steel rail.
3. The method of claim 1, wherein aligning the real-time vibration data with historical vibration data based on the rail seam data further comprises:
and stretching or shrinking the real-time vibration data by taking the historical vibration data as a reference so as to align the real-time vibration data with the historical vibration data.
4. The method of any one of claims 1 to 3, wherein determining the location of a rail with a break by comparative analysis with the historical vibration data comprises:
according to a preset second fixed length d, respectively obtaining the average values corresponding to the real-time vibration data and the historical vibration data within the distance of every d steel rails in the real-time vibration data and the historical vibration data, if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is larger than a preset threshold value, judging that the steel rails corresponding to the real-time vibration data are broken, and if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is smaller than or equal to the preset threshold value, judging that the steel rails are not broken;
if the steel rail is broken, extracting historical vibration data corresponding to the broken steel rail from the database, and drawing the historical vibration data into a trend curve graph according to the time sequence;
and determining the position of the broken steel rail according to the trend curve graph, the train running speed, the station spacing and the sampling frequency information.
5. A device for rail break detection, comprising:
the acquisition module is used for acquiring real-time vibration data of the train running on the steel rail;
the obtaining module is used for processing the real-time vibration data to obtain rail gap data;
the alignment module is used for aligning the real-time vibration data with historical vibration data according to the rail gap data;
the determining module is used for determining the position of the broken steel rail through comparative analysis with the historical vibration data;
the alignment module is specifically configured to:
extracting historical vibration data from a preset database, respectively selecting the first N vibration data of the real-time vibration data and the first M vibration data of the historical vibration data as a data combination to obtain a plurality of data combinations, and calculating a correlation coefficient r of each data combination, wherein the absolute value of r is used for expressing the magnitude of correlation strength;
selecting the data combination corresponding to the maximum absolute value, determining an alignment starting position, and aligning the vibration data with the historical vibration data in sequence according to the rail gap data;
an obtaining module, specifically configured to:
filtering the real-time vibration data according to a preset lower limit threshold, and removing the real-time vibration data smaller than the lower limit threshold to obtain at least one multi-section continuous vibration data;
according to a first preset fixed length S, obtaining peak data every S steel rail distance from the current first vibration data of the multiple sections of continuous vibration data, wherein S is smaller than the distance W between two wheels of the train;
searching in the distance of L1 steel rails at two sides of peak data according to a first preset length L1, judging whether at least one first peak data exists, selecting a searching range L1(Wlen-a < L1< Wlen + a) of each peak data point according to actual needs, wherein Wlen is the distance between two train wheels at the same side, a is a value limited according to actual conditions, if one first peak data exists, integrating 1 point, traversing the peak data, obtaining the accumulated integral corresponding to each peak data, wherein the initial integral corresponding to the peak data is 0,
if the accumulated integral is equal to 0, rejecting peak data corresponding to the accumulated integral;
if the accumulated integral is greater than 0, retaining peak data corresponding to the accumulated integral;
according to a second preset length L2, searching from the reserved current first peak data to the right side, searching within the distance of the L2 steel rail of the current first peak data, judging whether at least one second peak data exists, selecting the searching range L2 of each peak data point according to actual needs (Plen-a < L2< Plen + a), wherein Plen is the length of each section of steel rail, a is a value limited according to actual conditions, if yes, integrating 1 point, and continuously searching within the distance of the L2 steel rail from the second peak data to determine whether at least one third peak data exists until the peak data does not exist;
and traversing the reserved peak data in sequence to obtain rail gap data.
6. The apparatus of claim 5, wherein the obtaining module is specifically configured to:
acquiring original vibration data corresponding to p steel rail points on the steel rail according to a preset rule;
and calculating an average value of the original vibration data to obtain real-time vibration data of the steel rail, wherein the real-time vibration data is used for representing vibration energy of the train running on the steel rail.
7. The apparatus of claim 5, wherein aligning the real-time vibration data with historical vibration data based on the rail gap data further comprises:
and stretching or shrinking the real-time vibration data by taking the historical vibration data as a reference so as to align the real-time vibration data with the historical vibration data.
8. The apparatus according to any one of claims 5 to 7, wherein the determining module is specifically configured to:
according to a preset second fixed length d, respectively obtaining the average values corresponding to the real-time vibration data and the historical vibration data within the distance of every d steel rails in the real-time vibration data and the historical vibration data, if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is larger than a preset threshold value, judging that the steel rails corresponding to the real-time vibration data are broken, and if the difference value between the average value corresponding to the real-time vibration data and the average value corresponding to the historical vibration data is smaller than or equal to the preset threshold value, judging that the steel rails are not broken;
if the steel rail is broken, extracting historical vibration data corresponding to the broken steel rail from the database, and drawing the historical vibration data into a trend curve graph according to the time sequence;
and determining the position of the broken steel rail according to the trend curve graph, the train running speed, the station spacing and the sampling frequency information.
9. The system for detecting the rail breakage is characterized by comprising a memory and a processor, wherein the memory stores executable instructions of the processor; wherein the processor is configured to perform the method of rail break detection of any of claims 1-3 via execution of the executable instructions.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of rail break detection according to any one of claims 1 to 3.
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| CN111332338A (en) * | 2020-03-24 | 2020-06-26 | 北京交通大学 | Track disease detection system |
| CN115805973A (en) * | 2022-11-30 | 2023-03-17 | 中国铁路昆明局集团有限公司 | A system and method for monitoring the breakage of a point rail of a turnout |
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