CN114386451A - Contact net dropper fault diagnosis and alarm method based on sensor information perception - Google Patents
Contact net dropper fault diagnosis and alarm method based on sensor information perception Download PDFInfo
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Abstract
The invention relates to a contact net dropper fault diagnosis method based on an acceleration sensor, which comprises the following steps: a sensor for measuring vertical acceleration and horizontal acceleration is arranged in a catenary dropper; preprocessing the data collected by the sensor, then calculating the time domain and frequency domain statistical characteristics of each cross-preprocessed data, and selecting the significant characteristics to establish a support vector machine model; and then, carrying out fault diagnosis on the group of data by using the trained support vector machine model, judging whether the hanger is in fault or not, if the hanger is in fault, carrying out independent component analysis on fault data, comparing the fault data with a threshold value under normal conditions, judging an abnormal time period, and judging a fault section and a fault position according to the running speed of the train. According to the scheme, the dropper can be monitored in real time through the background, fault diagnosis and positioning are performed, fault alarm information can be sent out in time, and maintenance personnel are informed to carry out maintenance work.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring of a contact network, in particular to a contact network dropper fault diagnosis and alarm method based on sensor information perception.
Background
The pantograph and the contact network system are one of important equipment of the electrified railway, the proportion of the contact network fault in the equipment fault of the electrified railway is large, the dropper is an important part of high-speed railway contact network equipment and is arranged between the carrier cable and the contact wire, the main function of the dropper is to control the height of the contact wire, the safety and the good current receiving quality of the pantograph-catenary relation are ensured, and the phenomenon that the integral dropper is broken or stranded can occur due to the influence of weather or external factors in the operation process of the high-speed railway contact network. Once the hanger breaks down, operation interruption can be caused, and great loss is brought to national economy and even life safety of people can be threatened.
The current contact network fault detection is mainly developed in the aspects of contact network reliability and maintenance strategies: in the aspect of contact network reliability, by analyzing the service life characteristics of the dropper and the reliability of the contact network strength, a corresponding failure model is designed to optimize a maintenance strategy, so that ideal early warning can be basically realized, but the influence of actual weather and the like is not considered; in the aspect of a contact network maintenance strategy, a corresponding maintenance strategy mathematical model is designed by analyzing the existing contact network maintenance strategy and organization mode, so that the maintenance strategy can be optimized, but faults cannot be well diagnosed and notified. In the existing research starting from the reliability of a contact network, a failure model needs to be established from the aspects of physical structure, chemical performance and the like, the external influences of weather, friction and the like of a dropper in operation are not considered, and certain practicability is lacked; and starting from a contact network maintenance strategy, the timeliness and systematicness of maintenance can be realized, but certain functions are lacked for fault diagnosis.
Disclosure of Invention
The invention aims to provide a contact net dropper fault diagnosis and alarm method based on sensor information perception, which can monitor, diagnose and position dropper states in real time, timely send out faults and inform maintenance personnel of timely maintenance.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a contact net dropper fault diagnosis method based on an acceleration sensor comprises the following specific steps:
1) a sensor for measuring vertical acceleration and horizontal acceleration is arranged in a catenary dropper;
2) preprocessing the data acquired by the sensor by adopting a maximum and minimum value method, calculating the time domain and frequency domain statistical characteristics of the preprocessed data in each span, and selecting a significant characteristic;
3) carrying out fault diagnosis by using a model trained by a support vector machine method, judging whether the dropper is in fault, and carrying out independent component analysis on fault data if the dropper is in fault;
4) and comparing the fault data with a threshold value (control limit) in the normal condition by using an independent component analysis method, judging abnormal time, and judging a fault section and a fault position according to the running speed of the train.
In the step 1), the catenary dropper is as follows: at the messenger support point, the messenger span, the contact line location point and the contact line span.
In the step 2), the pretreatment mode is normalization elimination dimension.
In step 2), the calculating the time domain and frequency domain statistical characteristics of the preprocessed data in each span includes:
the wave form factor, kurtosis factor, root mean square frequency and frequency standard deviation of vertical acceleration in the span of the carrier cable;
maximum, minimum, peak, standard deviation, kurtosis, root mean square, form factor, peak factor, kurtosis factor, pulse factor, margin, center of gravity frequency, mean square frequency, and frequency variance of horizontal acceleration across the messenger wire;
maximum value, minimum value, mean value of absolute value, peak value, standard deviation, kurtosis, root mean square, wave form factor, peak value factor, kurtosis factor, pulse factor, margin, barycentric frequency, mean square frequency and frequency variance of the vertical acceleration across the contact line;
standard deviation, root mean square, waveform factor, kurtosis factor of horizontal acceleration across the contact line.
Further, in step 2), the selection manner of the salient features is as follows: and selecting the characteristic capable of obviously distinguishing normal and fault conditions in the time-frequency domain characteristics as the obvious characteristic through time-frequency analysis.
In step 3), the fault diagnosis method is as follows: and classifying and identifying normal and fault data by using the trained support vector machine model, and judging whether the hanger is in fault.
In step 4), Independent Component Analysis (ICA) is carried out on the data diagnosed as the fault, the ICA is used for carrying out unmixing on the original signal to obtain an independent component signal, and HotellingT is used2And a Square Prediction Error (SPE) statistical method for fault diagnosis, wherein the SPE statistical method introduces a main model statistic I2And auxiliary model statistics
The latter introduces SPE statistics.
In step 4), the determining manner of the threshold in the normal condition is as follows: performing kernel density estimation on the statistic by using a kernel function density estimation method to obtain fault detection statistic I2、I2 eAnd distributing the SPEs, and determining a threshold value in a normal condition.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the statistics of the acceleration signals of the monitoring points of the contact network are calculated from the two aspects of time domain and frequency domain, the characteristics are screened, then a support vector machine model is established for fault diagnosis, and the fault identification accuracy can reach more than 94.1%. And when the occurrence of the dropper fault is determined after diagnosis, calculating fault statistics by using an independent component analysis method, estimating a probability density function of the fault statistics by using a kernel function, determining a control limit, and finally determining a fault site. The method provided by the invention has high fault identification accuracy; not only can judge whether the failure rate of the broken hanging string exists in the test section, but also can position the specific failure position.
Drawings
FIG. 1 is a flow chart of a fault diagnosis warning method of the present invention;
FIG. 2 is a layout diagram of an acceleration sensor;
FIG. 3 is a comparison graph of average accuracy of different cross-SVM results;
in FIG. 4, (a), (b) and (c) are each I2、I2 eAnd the SPE statistic kernel density estimation probability density graph.
Detailed Description
The present invention will be described in detail with reference to specific embodiments.
Aiming at the problem that the breakage fault of a catenary dropper is difficult to automatically diagnose, the invention provides a dropper fault diagnosis method based on an acceleration sensor, which specifically comprises the following steps:
(1) a sensor for measuring vertical acceleration and horizontal acceleration is arranged in a catenary dropper;
the contact net dropper is characterized in that: a catenary support point, a catenary span, a contact line location point, and a contact line span.
(2) Preprocessing acceleration data acquired by a sensor by using a maximum-minimum value method, then calculating time domain and frequency domain statistical characteristics of the preprocessed data in each span, and selecting significant characteristics;
the preprocessing means is to normalize and eliminate dimensions, calculate the time domain and frequency domain statistical characteristics of the preprocessed data of each span, and obtain significant characteristics through time-frequency analysis;
the calculating the time domain and frequency domain statistical characteristics of the preprocessed data in each span comprises the following steps:
maximum, minimum, peak, standard deviation, kurtosis, root mean square, form factor, peak factor, kurtosis factor, pulse factor, margin, center of gravity frequency, mean square frequency, and frequency variance of horizontal acceleration across the messenger wire;
maximum value, minimum value, mean value of absolute value, peak value, standard deviation, kurtosis, root mean square, wave form factor, peak value factor, kurtosis factor, pulse factor, margin, barycentric frequency, mean square frequency and frequency variance of the vertical acceleration across the contact line;
standard deviation, root mean square, waveform factor, kurtosis factor of horizontal acceleration across the contact line.
The selection mode of the salient features is as follows: and selecting the characteristic capable of obviously distinguishing normal and fault conditions in the time-frequency domain characteristics as the obvious characteristic through time-frequency analysis.
(3) Then, carrying out model training solution on the group of data by using a support vector machine method, then carrying out fault diagnosis by using the obtained model, judging whether the span has a dropper fracture fault, and if the fault is diagnosed, switching to the next step to analyze the fault position;
the fault diagnosis mode is as follows: and classifying and identifying normal and fault data by using the trained support vector machine model.
(4) And comparing the fault data with a statistic control limit (threshold) determined in normal conditions by using an independent component analysis method, judging an abnormal time period, and judging that the fault time period is a fault section according to the running speed of the train.
The independent component analysis process comprises the following steps: unmixing the original signal to obtain independent component signal, and using HotellingT2And performing fault diagnosis by using a Square Prediction Error (SPE) statistical method, performing kernel density estimation on the statistical quantity by using a kernel function density estimation method, and obtaining fault detection statistical quantity I2、I2 eAnd the control limit of the SPE.
And detecting fault data based on the control limit, judging a time period when the fault data exceeds the control limit, further determining a time point when the fault occurs, and determining a fault position based on the intermediate time point and the train speed per hour.
The above method is specifically illustrated by the following specific examples:
in the embodiment, the method is used for fault diagnosis of the dropper of the high-speed rail contact network, all the data are vibration signal data collected by the 6C system of the high-speed rail contact network, and fault diagnosis and positioning of the high-speed rail contact network are carried out by utilizing the signal data feature extraction method in the invention and combining a support vector machine model classification method and an independent component analysis method. The fault diagnosis and alarm method of the invention is shown in a flow chart in fig. 1, and mainly comprises the steps of installing monitoring equipment, obtaining data, solving a monitoring model and diagnosing faults. The specific operation steps are as follows:
step 1) installing sensors at a catenary supporting point 1, a supporting point 2 and a midspan position of a contact line, and a positioning point 2 and a midspan position of the contact line, and monitoring mechanical characteristic parameters between the contact line and a pantograph in a high-speed rail running process in real time: the vertical acceleration and the horizontal acceleration of a catenary support point 1, the vertical acceleration and the horizontal acceleration of a catenary support point 2, the vertical acceleration and the horizontal acceleration of a catenary span, the vertical acceleration and the horizontal acceleration of a contact line span, and the vertical acceleration and the horizontal acceleration of a contact line positioning point 2.
Referring to fig. 2, by using the vibration measuring equipment for monitoring the catenary and the contact line, mechanical characteristics such as acceleration at different positions of the catenary and the contact line are collected, wherein 1#, 2#, and 3# are vibration measuring equipment for supporting points 1 and mid-span and supporting points 2 in sequence, and 4#, and 5# are vibration measuring equipment for positioning points 2 in the mid-span and mid-span of the contact line in sequence.
Step 2) preprocessing the data, eliminating dimensional influence, calculating time domain and frequency domain statistical characteristic parameters, taking pulsating wind 1 as an example, and selecting significant characteristics after time-frequency analysis, wherein the significant characteristics comprise:
the wave form factor, kurtosis factor, root mean square frequency and frequency standard deviation of vertical acceleration in the span of the carrier cable; maximum, minimum, peak, standard deviation, kurtosis, root mean square, form factor, peak factor, kurtosis factor, pulse factor, margin, center of gravity frequency, mean square frequency, and frequency variance of horizontal acceleration across the messenger wire; maximum value, minimum value, mean value of absolute value (rectified mean value), peak value, standard deviation, kurtosis, root mean square, wave form factor, peak value factor, kurtosis factor, impulse factor and margin of the vertical acceleration in the span of the contact line; center of gravity frequency, mean square frequency, frequency variance; the standard deviation, root mean square, waveform factor and kurtosis factor of the horizontal acceleration across the contact line span are connected by 21, and the characteristics are 38.
And 3) dividing the data into a test set and a training set, wherein the test set accounts for 25 percent (10 samples) (5 positive examples and 5 negative examples), and the training set accounts for 75 percent (30 samples) (15 positive examples and 15 negative examples). Taking the significant features as attributes, using training set data to train a support vector machine model, and testing the classification accuracy on a test set; fig. 3 is a comparison of results of 30 times of SVM classification accuracy averages of different span data, and it can be seen that normal and fault data can be classified with an accuracy of 94.44% at 21 spans, and the accuracy of separating normal and fault data is low at other spans, so that 21 spans can be judged as broken sections of the hanger.
Step 4) diagnosing the fault section of the span diagnosed as the fault by using an independent component analysis method, unmixing the original signal by using an ICA (independent component analysis) method to obtain an independent component signal, and then using Hotelling2And extracting fault statistic I by using sum Square Prediction Error (SPE) statistical method2、I2 eAnd performing fault diagnosis on the SPE, and performing kernel density estimation on the statistic by using a kernel function density estimation method, wherein the case that the pulsating wind is 1 is taken as an example, I2、I2 eThe SPE statistic kernel density estimation probability density is shown in fig. 4, and the control limits can be determined as follows: 62.11, 21.216, 265.48. The time periods when the fault data exceed the control limit can be judged to be 10.66-11.75, 11.21-11.71 and 10.668-11.394 respectively, and as the train runs at a constant speed, the value of the time statistic at the middle point of the time period exceeding the threshold value of the statistic is maximum, so that the time points of the fault occurrence can be considered to be 11.205s, 11.46s and 11.03s, the average time points among the three statistics is 11.2303s, and then the train speed per hour is 250km/h, so that the train at the time point is located at 29.884m from the beginning of the 21 span, the length of the 21 span is 50m, and the span dropper is located at 25m, namely the fault is determined to be located near the 21 span [ error 4.884m ] m]。
The invention is not limited to the examples, and any equivalent changes to the technical solution of the invention by a person skilled in the art after reading the description of the invention are covered by the claims of the invention.
Claims (6)
1. A contact net dropper fault diagnosis and alarm method based on sensor information perception is characterized in that:
the method comprises the following steps:
1) a sensor for measuring vertical acceleration and horizontal acceleration is arranged in a catenary dropper;
2) preprocessing the data acquired by the sensor by adopting a maximum and minimum value method, calculating the time domain and frequency domain statistical characteristics of the preprocessed data in each span, and selecting a significant characteristic;
3) carrying out fault diagnosis by using a model trained by a support vector machine method, judging whether the dropper is in fault, and carrying out independent component analysis on fault data if the dropper is in fault;
4) and comparing the fault data with a threshold value under a normal working condition by using an independent component analysis method, judging abnormal time, and judging a fault section and a fault position according to the running speed of the train.
2. The contact net dropper fault diagnosis and alarm method based on sensor information perception of claim 1, wherein:
the contact net dropper is characterized in that: a catenary support point, a catenary span, a contact line location point, and a contact line span.
3. The contact net dropper fault diagnosis and alarm method based on sensor information perception of claim 1, wherein:
in the step 2), the pretreatment mode is normalization elimination dimension; the selection mode of the salient features is as follows: and selecting the characteristic capable of obviously distinguishing normal and fault conditions in the time-frequency domain characteristics as the obvious characteristic through time-frequency analysis.
4. The contact net dropper fault diagnosis and alarm method based on sensor information perception of claim 1, wherein:
in step 3), the fault diagnosis method is as follows: and classifying and identifying normal and fault data by using the trained support vector machine model, and judging whether the hanger is in fault.
5. The contact net dropper fault diagnosis and alarm method based on sensor information perception of claim 1, wherein:
in the step 4), the independent component analysis process is as follows: unmixing the original signal by independent component analysis to obtain independent component signal, and using HotellingT2And SPE statistical method for fault diagnosis, wherein the SPE statistical method introduces main model statistics I2And auxiliary model statistics Ie 2The latter introduces the SPE statistics.
6. The contact net dropper fault diagnosis and alarm method based on sensor information perception of claim 1, wherein:
in step 4), the threshold determination mode under the normal working condition is as follows: performing kernel density estimation on the statistic by using a kernel function density estimation method to obtain fault detection statistic I2、I2 eAnd distributing the SPE, and determining a threshold value under a normal working condition.
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| CN114936487A (en) * | 2022-04-29 | 2022-08-23 | 中铁第一勘察设计院集团有限公司 | Method and system for positioning contact net positioner fault |
| CN115169405A (en) * | 2022-07-14 | 2022-10-11 | 北京威控科技股份有限公司 | Hotel guest room equipment fault diagnosis method and system based on support vector machine |
| CN117454262A (en) * | 2023-12-22 | 2024-01-26 | 华东交通大学 | Contact network fault identification method, system, storage medium and electronic equipment |
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| CN117454262B (en) * | 2023-12-22 | 2024-03-22 | 华东交通大学 | Contact network fault identification method, system, storage medium and electronic equipment |
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