阅读说明：本技术 基于锁相值的轨道异常匹配的诊断方法 (Phase-locked value-based diagnosis method for track abnormity matching ) 是由 魏晖 胡志华 于 2021-09-27 设计创作，主要内容包括：本发明提供了一种基于锁相值的轨道异常匹配的诊断方法,包括：导入第一预设长度的已匹配的两组轨道几何状态数据作为分析样本,其中轨道几何状态数据T、R分别为测量序列和参考序列；将所述分析样本按第二预设长度L分为K组,并分别计算各组子样本的锁相值PLV(k)；设定阈值PLV-(lim),将各组的锁相值PLV(k)与阈值PLV-(lim)比较,若PLV(k)<PLV-(lim),则判定子样本P-(k)在k处存在异常匹配。本发明利用锁相值刻画轨道异常匹配,可定量描述参考序列与测量序列间的相位同步性,有助于及时发现异常匹配征兆。(The invention provides a phase-locked value-based track abnormity matching diagnosis method, which comprises the following steps: importing two groups of matched track geometric state data with first preset length As analysis samples, where the orbit geometry data T, R are a measurement sequence and a reference sequence, respectively; dividing the analysis samples into K groups according to a second preset length L, and respectively calculating each group of subsamples The phase-locked value plv (k); setting threshold PLV lim The phase-locked value PLV (k) of each group is compared with the threshold value PLV lim Comparison, if PLV (k)<PLV lim Then, the subsample P is determined k There is an anomalous match at k. The invention uses the phase-locked value to describe the track abnormal matching, and canThe phase synchronism between the reference sequence and the measurement sequence is quantitatively described, and the abnormal matching sign can be found in time.)

1. A method for diagnosing track anomaly matching based on phase-locked values is characterized by comprising the following steps:

importing two groups of matched track geometric state data with first preset lengthAs analysis samples, where the track geometry data T ═ T₁,t₂,…,t_N}，R＝{r₁,r₂,…,r_NThe sequence of measurement and the reference sequence of the analysis sample are respectively;

dividing the analysis samples into K groups according to a second preset length L, and respectively calculating each group of subsamples Where K ═ {1,2, …, K };

setting threshold PLV_limThe phase-locked value PLV (k) of each group is compared with the threshold value PLV_limComparison, if PLV (k)<PLV_limThen, the subsample P is determined_kThere is an anomalous match at k.

2. The method as claimed in claim 1, wherein the analysis samples are divided into K groups by a second preset length L, and each group of subsamples is calculated separately The step of locking the phase value plv (k) includes:

dividing the analysis samples into K groups according to a second preset length L, wherein each group of subsamplesIndependent of each other or overlapping each other;

calculating T_k(i)、R_k(i) Hilbert transform ofT_k(i) Denotes the kth measurement sequence subsample, R_k(i) Represents the kth reference sequence subsample, as follows:

calculating T_k(i)、R_k(i) Instantaneous phase ofAndthe following formula:

calculating a phase-locked value plv (k) as follows:

where i is 1,2, …, L, τ denotes an integral variable, and j denotes a virtual root.

3. The method of claim 1, wherein the set threshold PLV is set based on a phase-locked value for diagnosing track anomaly matching_limThe phase-locked value PLV (k) of each group is compared with the threshold value PLV_limThe step of comparing comprises:

setting a threshold PLV according to the influence degree of noise pollution, track disturbance and slippage and rotation of a measuring wheel on the data matching of the geometric state of the track or according to big data_limIf PLV (k)<PLV_limThen, the subsample P is determined_kThere is an anomalous match at k.

4. The method for diagnosing track anomaly matching based on phase-locked values as claimed in claim 1, wherein said matched two sets of track geometry data are collected by a track inspection machine, or by a track gauge, or by a track inspection vehicle.

Technical Field

The invention relates to the technical field of track detection, in particular to a diagnosis method for track abnormity matching based on a phase-locked value.

Background

The track geometric state data is the basis of track state evaluation and maintenance decision. According to the track inspection principle of 'dynamic inspection is mainly implemented and dynamic and static combination' is implemented on high-speed rails in China, massive repetitive track geometric dimension data are obtained through periodic dynamic inspection (dynamic inspection for short) and static inspection (static inspection for short), and the state of the track is determined through data samples. Although the railway department of industry has a huge amount of repetitive data, in engineering, the data is often only used for section analysis of the track state, such as overrun analysis or balance analysis according to the latest data.

The mining of deeper knowledge hidden in historical data, such as the establishment of a track degradation model, the evaluation of maintenance operation effect, the diagnosis of track diseases and the like, all depend on the establishment of a matching relationship between data samples. This match can come from external features as well as from the similarity of the data itself. The matching effect can be evaluated by using Pearson Correlation Coefficients (PCCs), Integral Absolute Error (IAE) and Mean Absolute Error (MAE), and the indexes can integrally explain the Correlation or precision of matching. However, due to noise pollution, track disturbance, slippage and rotation of the measuring wheel, local abnormal matching may occur between the measuring sequence and the reference sequence, and such abnormal matching often appears as desynchronization of instantaneous phase. Regarding local abnormal matching, a correlation method is needed to diagnose timely.

Disclosure of Invention

In view of the above, it is necessary to provide a method for diagnosing abnormal matching of a track based on a Phase Locking Value (PLV), which is also called Phase stability, Phase Locking factor or same Phase coherence index, describing synchronicity of different signals and has a Value range of [0,1 ] for solving the problem of abnormal matching that local Phase asynchronization may occur between a track geometry state data measurement sequence and a reference sequence in the prior art]E.g. T_kAnd R_kIf the phase is synchronized, plv (k) is 1.

A method for diagnosing track anomaly matching based on phase-locked values comprises the following steps:

importing two groups of matched track geometric state data P with a first preset length:as analysis samples, where the track geometry data T ═ T₁,t₂,…,t_N}，R＝{r₁,r₂,…,r_NThe sequence of measurement and the reference sequence of the analysis sample are respectively;

dividing the analysis samples into K groups according to a second preset length L, and respectively calculating each group of subsamples P_k: Where K ═ {1,2, …, K };

setting threshold PLV_limThe phase-locked value PLV (k) of each group is compared with the threshold value PLV_limComparison, if PLV (k)<PLV_limThen, the subsample P is determined_kThere is an anomalous match at k.

Further, in the above method for diagnosing track anomaly matching based on a phase-locked value, the analysis samples are divided into K groups according to a second preset length L, and each group of subsamples P is calculated respectively_k:The step of locking the phase value plv (k) includes:

dividing the analysis sample into K groups according to a second preset length L, wherein the groups can be mutually independent or mutually overlapped;

calculating T_k(i)、R_k(i) Hilbert transform ofT_k(i) Denotes the kth measurement sequence subsample, R_k(i) Represents the kth reference sequence subsample, as follows

Calculating T_k(i)、R_k(i) Instantaneous phase ofAndthe following formula

Calculating the phase-locked value PLV (k) as follows

Where i is 1,2, …, L, τ denotes an integral variable, and j denotes a virtual root.

Further, the method for diagnosing track anomaly matching based on the phase-locked value is characterized in that the set threshold PLV_limThe phase-locked value PLV (k) of each group is compared with the threshold value PLV_limThe step of comparing comprises:

the threshold PLV can be set according to the influence degree of noise pollution, track disturbance and slippage and rotation of the measuring wheel on the data matching of the track geometric state or according to big data_lim；

The subsample phase-locked value PLV (k) is compared with a threshold value PLV_limComparison, if PLV (k)<PLV_limThen, the subsample P is determined_kThere is an abnormal match at k, otherwise, the subsample P is determined_kThere is no anomalous match at k.

Further, according to the diagnosis method for track anomaly matching based on the phase-locked value, the two sets of matched track geometric state data can be acquired by a track inspection instrument, or acquired by a track measuring instrument, or acquired by a track inspection vehicle.

According to the method for diagnosing the track abnormal matching based on the phase locking value, provided by the invention, the sub-sample phase locking value PLV (k) of the two times of track geometric state data T and R is used as the feature vector of the abnormal matching, the abnormal matching of the data T and R is diagnosed from the aspect of synchronism, the diagnosis problem of the abnormal matching with the local phase asynchronization is solved, the physical significance is clear, the operation process is simple, and the method is favorable for finding abnormal matching signs in time.

Drawings

FIG. 1 is a flow chart of a method for diagnosing track anomaly matching based on phase-locked values according to a first embodiment of the present invention;

FIG. 2 is a flow chart of a method for diagnosing track anomaly matching based on phase-locked values according to a second embodiment of the present invention;

FIG. 3 is a schematic view of an analysis sample according to a second embodiment of the present invention;

fig. 4 is a diagram illustrating a phase-locked value according to a second embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

These and other aspects of embodiments of the invention will be apparent with reference to the following description and attached drawings. In the description and drawings, particular embodiments of the invention have been disclosed in detail as being indicative of some of the ways in which the principles of the embodiments of the invention may be practiced, but it is understood that the scope of the embodiments of the invention is not limited correspondingly. On the contrary, the embodiments of the invention include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.

Referring to fig. 1, the method for diagnosing track anomaly matching based on phase-locked values according to the first embodiment of the present invention is applied to track geometric state data analysis to diagnose whether there is anomaly matching in the data. The method for diagnosing the track anomaly matching based on the phase-locked value comprises the steps S11-S13.

Step S11, importing the matched two groups of track geometric state numbers with the first preset lengthAccording to P:as analysis samples, where the track geometry data T ═ T₁,t₂,…,t_N}，R＝{r₁,r₂,…,r_NAnd, the measurement sequence and the reference sequence of the analysis sample, respectively.

The track geometric state data analysis needs to avoid abnormal matching of data, and in order to guarantee rapidity of abnormal matching diagnosis, segmented diagnosis can be performed on analysis samples. Wherein, the track geometric status data can be collected by the existing detection equipment, and the detection equipment comprises but is not limited to a 0-grade track inspection instrument, a track measuring instrument and a track inspection vehicle.

Step S12, dividing the analysis samples into K groups according to a second preset length L, and respectively calculating each group of subsamples P_k:Where K is {1,2, …, K }.

Dividing the analysis sample into K groups according to a second preset length L, wherein the groups can be mutually independent or mutually overlapped; if the analysis sample is matched accurately, the measurement sequence T and the reference sequence R should have strict synchronism, and the PLV is 1; when an anomalous match occurs, the aforementioned tight phase synchronization will be disrupted, and therefore one of the diagnostic key features for anomalous matching of track geometry data is the instantaneous phase difference of T and R.

Step S13, setting threshold PLV_limThe phase-locked value PLV (k) of each group is compared with the threshold value PLV_limComparison, if PLV (k)<PLV_limThen, the subsample P is determined_kThere is an anomalous match at k.

Wherein a threshold value PLV is set_limThe method can be determined according to the influence degree of noise pollution, track disturbance and slippage and rotation of the measuring wheel on the track geometric state data matching, or according to big data.

In specific implementation, the phase-locked value PLV (k) of each group is respectively compared with the set threshold value PLV_limAnd calculating difference values to obtain a plurality of difference values. If PLV (k)<PLV_limThen, the subsample P is determined_kThere is an abnormal match at k, otherwise, the subsample P is determined_kThere is no anomalous match at k.

The embodiment of the invention diagnoses the abnormal matching of the data T and the data R from the angle of synchronism by using the sub-sample phase-locked values PLV (k) of the two times of track geometric state data T and R as the feature vectors of the abnormal matching, solves the diagnosis problem of the local abnormal matching, has clear physical significance and simple operation process, and is beneficial to finding abnormal matching signs in time.

Referring to fig. 2, the method for diagnosing track anomaly matching based on phase-locked values according to the second embodiment of the present invention includes steps S21-S23.

Step S21, importing two sets of matched track geometric state data P with a first preset length:as analysis samples, where the track geometry data T ═ T₁,t₂,…,t_N}，R＝{r₁,r₂,…,r_NAnd, the measurement sequence and the reference sequence of the analysis sample, respectively.

In this embodiment, the right height data of the track geometric status data of the first preset length is collected to be used as an analysis sample. Specifically, considering both the calculation efficiency and the convenience of track unit management, the first preset length may be set to 1000 m. The right height data of the track geometric state data can be acquired through existing detection equipment, and the detection equipment comprises but is not limited to a 0-level track inspection instrument, a track measuring instrument and a track inspection vehicle. The analysis samples are shown in figure 3.

Step S22, dividing the analysis samples into K groups according to a second preset length L, and respectively calculating each group of subsamples P_k:Where K is {1,2, …, K }.

In a specific implementation, to ensure the mileage resolution, the second preset length may be set to 10m, 20m or 50m, and for a sampling step of 0.125m, i.e., L is 80, 160 or 400. Meanwhile, the K groups of sub-samples can be mutually independent or mutually overlapped. In this embodiment, L is 400, and K is 20 when the sampling step distance is 0.125m and the sub-samples are independent of each other for 1000m of track geometry state data.

Wherein the step of obtaining the phase-locked value comprises:

step S221, calculating T_k(i)、R_k(i) Hilbert transform ofThe following formula

Step S222, calculating T_k(i)、R_k(i) Instantaneous phase ofAndthe following formula

In practice, to avoid solvingAndwhen the phase is wound, the jump is generated, and the instantaneous phase difference is overlarge, so that the phase difference can be adjustedAndand (3) performing unwrapping processing, or eliminating the influence of phase jump through wavelet decomposition, empirical mode decomposition, moving average filtering and median filtering.

Step S223, calculating a phase-locked value PLV (k) as follows

Where i is 1,2, …, L, τ denotes an integral variable, and j denotes a virtual root.

The phase lock values for each subsample are shown in fig. 4.

Step S23, setting threshold PLV_limThe phase-locked value PLV (k) of each group is compared with the threshold value PLV_limComparison, if PLV (k)<PLV_limThen, the subsample P is determined_kThere is an anomalous match at k.

Wherein a threshold value PLV is set_limThe method can be determined according to the influence degree of noise pollution, track disturbance and slippage and rotation of the measuring wheel on the track geometric state data matching, or according to big data. As can be appreciated, the threshold PLV is set_limThe threshold may be a hard threshold or a soft threshold, and may be determined based on line conditions, measured conditions, or environmental conditions, or based on big data.

In specific implementation, the phase-locked value PLV (k) of each group is respectively compared with the set threshold value PLV_limAnd calculating difference values to obtain a plurality of difference values. If PLV (k)<PLV_limThen, the subsample P is determined_kThere is an abnormal match at k, otherwise, the subsample P is determined_kThere is no anomalous match at k. For example, set the threshold value PLV_limWhen the sub-sample phase-locked value plv (k) is less than 0.80, the sub-sample P is diagnosed_kThere is an anomalous match at K, which requires a critical check or process, where K is 1,2, …, K. At K444+450 miles, plv (K) 0.749, as shown in fig. 4, the lock-in value is out of range, which can be diagnosed as an anomalous match.

In the above steps, the track geometric state data includes track left high and low, left track direction, left vector, right vector, track gauge change rate, level, superelevation, distortion, track long wave irregularity, etc., and may be acquired by using existing detection equipment, including but not limited to a 0-grade track inspection tester, a track measuring instrument, and a track inspection vehicle.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.