Performance detection method for mining steel wire braided sheath connector

Abstract

The invention relates to the technical field of connector performance detection, in particular to a mining steel wire braided sheath connector performance detection method. The method comprises the following steps: acquiring a contact resistance data sequence and a corresponding vibration data sequence of a connector; respectively sliding on the contact resistance data sequence and the vibration data sequence by using a sliding window; according to the contact resistance data and the vibration data in one sliding window, respectively obtaining a contact resistance performance characteristic value and a vibration performance characteristic value, and obtaining an anti-seismic performance performance coefficient corresponding to the sliding window; obtaining a first sequence; and according to the forward adjacent data point of one data point in the first sequence and the STL decomposition result of the first sequence, obtaining an abnormal possibility coefficient of the data point, according to the abnormal possibility coefficient of the last data point and the STL decomposition result, obtaining an early warning index of the last data point, and carrying out early warning on the current performance of the connector. According to the invention, the performance of the connector can be accurately detected.

Description

Technical Field

[0001] The present invention relates to the technical field of connector performance detection, and particularly to a method for detecting the performance of a mining wire braided sheath connector. Background Art

[0002] With the continuous improvement of the electrification level of mines, as a key connecting component in the cable system, the performance of the mining wire braided sheath connector is directly related to the safety and stability of power and signal transmission. However, in the mine environment with frequent vibrations and strong impacts, existing connector performance detection methods mostly focus on static items such as contact resistance, insulation strength, and sealing performance, and it is difficult to effectively reflect their actual working states under dynamic vibration loads, and there are problems such as the inability to identify potential failures such as virtual connections and looseness. It is necessary to detect the performance of the mining wire braided sheath connector, conduct a dynamic assessment of its seismic performance, so as to improve the pertinence of detection and the adaptability in the mining environment, and ensure the long-term stable operation of the connector.

[0003] Existing seismic performance detection methods for mining wire braided sheath connectors mostly adopt static tests or single vibration tests, usually applying standard vibration loads in a laboratory environment to evaluate their structural and electrical performance. Such methods can only reflect the instantaneous performance of the connector under ideal working conditions, and it is difficult to cover the performance changes under complex working conditions such as frequent vibrations, impacts, and displacements during long-term operation in mines. In fact, during underground operations, the connector is subjected to mechanical disturbances for a long time, and deterioration phenomena such as structural looseness and unstable contacts may occur, and its contact resistance will gradually fluctuate or even suddenly become abnormal. Due to the lack of a continuous monitoring and analysis mechanism for these dynamic changes, existing detection means are difficult to timely discover potential risks, nor can they accurately evaluate the operation reliability and remaining life of the connector, affecting the safety guarantee and maintenance efficiency of the mining system. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method for detecting the performance of a mining wire braided sheath connector, and the specific technical solution adopted is as follows: An embodiment of the present invention provides a method for detecting the performance of a mining wire braided sheath connector, and the method includes: Obtain the contact resistance data sequence of the connector and its corresponding vibration data sequence; use a sliding window to slide on the contact resistance data sequence and the vibration data sequence respectively; Obtain the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window according to the contact resistance data and the vibration data within a sliding window respectively; Obtain the seismic performance performance coefficient corresponding to the sliding window according to the normalized values of the contact resistance data and the vibration data within a sliding window and the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window; The seismic performance coefficient corresponding to each sliding window forms a first sequence; the anomaly possibility coefficient of a data point is obtained according to the forward neighboring data points of the data point in the first sequence and the STL decomposition result of the first sequence; The early warning index of the last data point is obtained according to the anomaly possibility coefficient of the last data point in the first sequence and the STL decomposition result; the current performance of the connector is warned based on the early warning index.

[0005] Preferably, the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window are respectively obtained according to the contact resistance data and the vibration data within a sliding window, including: The first-order difference is respectively performed on the contact resistance data and the vibration data within a sliding window to obtain the difference sequence corresponding to the contact resistance data within the sliding window and the difference sequence corresponding to the vibration data within the sliding window, which are respectively denoted as the contact resistance difference sequence and the vibration difference sequence; the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window are respectively obtained according to the contact resistance data, the contact resistance difference sequence, the vibration data, and the vibration difference sequence of a sliding window.

[0006] Preferably, the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window are respectively obtained according to the contact resistance data, the contact resistance difference sequence, the vibration data, and the vibration difference sequence of a sliding window, including: Calculate the product of the average value of the absolute values of the contact resistance data within a sliding window and the entropy value of the contact resistance difference sequence of the sliding window to obtain the contact resistance performance characteristic value corresponding to the sliding window; Calculate the product of the average value of the absolute values of the vibration data within a sliding window and the entropy value of the vibration difference sequence of the sliding window to obtain the vibration performance characteristic value corresponding to the sliding window.

[0007] Preferably, the seismic performance coefficient corresponding to the sliding window is obtained according to the normalized values of the contact resistance data and the vibration data within a sliding window and the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window, including: Normalize the contact resistance data within a sliding window to obtain a first normalized sequence, and normalize the vibration data within the sliding window to obtain a second normalized sequence; obtain the distance matching value of the first normalized sequence and the second normalized sequence through the DTW algorithm; obtain the seismic performance coefficient corresponding to the sliding window based on the distance matching value and the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window.

[0008] Preferably, obtaining the seismic performance coefficient corresponding to the sliding window based on the distance matching value, the contact resistance performance eigenvalue, and the vibration performance eigenvalue corresponding to the sliding window includes: Dividing the vibration performance eigenvalue corresponding to a sliding window by the sum of the contact resistance performance eigenvalue corresponding to the sliding window and the hyperparameter, and multiplying by the distance matching value to obtain the seismic performance coefficient corresponding to the sliding window.

[0009] Preferably, obtaining the anomaly possibility coefficient of a data point according to the forward neighboring data point of the data point in the first sequence and the STL decomposition result of the first sequence includes: The STL decomposition result of the first sequence includes a trend term, a seasonal term, and a residual term; Denote a preset number of adjacent data points before a data point as the forward neighboring data points of the data point, and the forward neighboring data points and the data point form the local segment corresponding to the data point; Use the exponential function with the natural constant as the base to perform a negative correlation mapping on the slope of the data points in the local segment corresponding to a data point in the trend term, and normalize to obtain the trend feature term; Calculate the average value of the absolute values of the amplitudes of the data points in the residual term in the local segment corresponding to a data point, and normalize to obtain the residual feature term; Perform a weighted sum of the trend feature term and the residual feature term to obtain the anomaly possibility coefficient of the data point.

[0010] Preferably, obtaining the warning index of the last data point according to the anomaly possibility coefficient of the last data point in the first sequence and the STL decomposition result includes: Denote the other data points except the data points in the local segment corresponding to the last data point as historical data points, obtain the absolute value of the difference between the anomaly possibility coefficient of the last data point and the mean value of the anomaly possibility coefficients of each historical data point, and divide by the sum of the hyperparameter and the standard deviation of the anomaly possibility coefficients of each historical data point to obtain the historical deviation degree; Divide the amplitude of the first data point in the trend term of the first sequence by the sum of the amplitude of the last data point in the trend term and the hyperparameter to obtain the trend deviation degree; Calculate the average value of the normalized value of the historical deviation degree and the normalized value of the trend deviation degree to obtain the warning index of the last data point.

[0011] Preferably, warning the current performance of the connector based on the warning index includes: When the warning index is greater than the judgment threshold, a warning prompt is given The embodiments of the present invention have at least the following beneficial effects: This application obtains the contact resistance data sequence of the connector and its corresponding vibration data sequence, and uses a sliding window to perform sliding analysis on the contact resistance data sequence and the vibration data sequence respectively to obtain the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to each sliding window; furthermore, based on the normalized values of the contact resistance data and the vibration data within the sliding window, as well as the corresponding contact resistance performance characteristic value and vibration performance characteristic value, the seismic performance coefficient corresponding to the sliding window is obtained and composed into a sequence to obtain the first sequence. By combining each data point in the first sequence with adjacent data points and the STL decomposition result of the sequence, the abnormal possibility coefficient of each data point is obtained, and then the warning index of the last data point in the first sequence is obtained. Finally, based on the warning index, the current performance of the connector is warned to obtain a more accurate warning result, thereby reducing the safety hazards brought by potential accidents such as poor contact of the wire braided sheath connector during operations such as mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0013] Figure 1 It is a method flow chart of a performance detection method for a mining wire braided sheath connector provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific implementation manner, structure, characteristics, and effects of a performance detection method for a mining wire braided sheath connector proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0016] The following specifically describes the specific solution of a performance detection method for a mining wire braided sheath connector provided by the present invention in conjunction with the drawings.

[0017] Embodiment: The main application scenario of the present invention is to detect the performance of a mining wire braided sheath connector to ensure its safe use.

[0018] Please refer to Figure 1 , which shows a flowchart of a method for detecting the performance of a mining wire braided sheath connector provided by an embodiment of the present invention. The method includes the following steps: Step S1, obtain the contact resistance data sequence of the connector and its corresponding vibration data sequence; use a sliding window to slide on the contact resistance data sequence and the vibration data sequence respectively.

[0019] When monitoring the seismic performance of a mining wire braided sheath connector, first deploy a high-precision resistance detection unit and a triaxial vibration sensor near the connector to monitor its conduction state and stress state in real time. Among them, the resistance detection unit performs high-frequency sampling (such as 100 Hz) with a milliohm-level resolution, records the electrical contact resistance fluctuation of the connector under vibration interference, and obtains the contact resistance data of the connector; the triaxial vibration sensor obtains the mechanical disturbance vibration of the connector at the same sampling frequency, and obtains the corresponding vibration data of the connector.

[0020] The above multi-source data is uploaded to an edge computing node or a backend platform through an industrial interface (such as RS485, CAN or LoRa) to form a continuous data sequence for dynamic analysis. Among them, the contact resistance data is composed into a contact resistance data sequence according to the time sequence, and the vibration data corresponding to the connector is composed into a vibration data sequence according to the time sequence.

[0021] A mining wire braided sheath connector is a key structural component dedicated to underground working environments such as coal mines, used to connect cables, electrical equipment or control systems. Its exterior is usually covered with a wire braided sheath to enhance the overall mechanical strength, tensile and protective capabilities. Such connectors are widely used in underground power supply systems, communication signal transmission and automation control equipment, and are the core components to ensure stable electrical connection and operation safety.

[0022] Due to the dynamic loads such as equipment vibration, geological disturbance, and mechanical shock often present in the mine environment, during the long-term operation of the connector, it is extremely easy to cause problems such as poor contact, short circuit, and signal loss due to micro-displacement, loosening or sheath fatigue. For example, in the roadway transportation system, the cable connector continuously vibrates with the equipment shaking. If the seismic performance is insufficient, it is very easy to cause the internal conductive structure to become loose, bringing major safety hazards. Therefore, carrying out special detection of the seismic performance of mining wire braided sheath connectors not only helps to evaluate their structural stability and electrical reliability, but is also an important basis for ensuring the safe operation of mines, extending the equipment life and realizing intelligent monitoring and maintenance.

[0023] Therefore, in order to be able to perform more accurate and timely detection of the performance of the mining wire braided protective cover connector. First, align the contact resistance data sequence and the vibration data sequence in chronological order. Then set a sliding window, where the length of the sliding window is set to a preset length, and the sliding step is set to half of the preset length. The setting of the preset length is related to the detection accuracy. If higher detection accuracy is required, the preset length can be appropriately reduced. Then use the sliding window to slide on the contact resistance data sequence and the vibration data sequence respectively, and perform subsequent analysis based on the contact resistance data and vibration data in each sliding window after each sliding.

[0024] Step S2, obtaining contact resistance performance characteristic values ​​and vibration performance characteristic values ​​corresponding to a sliding window respectively according to the contact resistance data and vibration data in the sliding window.

[0025] When testing the seismic performance of mining wire braided protective sleeve connectors, in order to reduce the interference of contact resistance drift caused by slow changes in temperature, humidity and other factors in the application environment on the analysis results, it is necessary to perform first-order differential processing on the contact resistance data in the sliding window to remove possible trend effects, thereby highlighting the fluctuation characteristics and making the complexity of the contact resistance fluctuations more obvious. This processing method can more sensitively capture early signs of degradation or potential failures caused by changes in operating conditions such as vibration and corrosion. Similarly, performing first-order differentials on vibration data in the sliding window can also enhance the ability to express signal fluctuation characteristics, making its complexity and instability characteristics clearer, thereby achieving more accurate quantification and evaluation of the connector's seismic performance, and providing strong support for subsequent fault warnings and structural reliability analysis.

[0026] The contact resistance data and vibration data in each sliding window are respectively subjected to first-order differences to obtain a differential sequence corresponding to the contact resistance data in each sliding window and a differential sequence corresponding to the vibration data in each sliding window. The differential sequence corresponding to the contact resistance data in a sliding window and the differential sequence corresponding to the vibration data in the sliding window are respectively recorded as a contact resistance differential sequence and a vibration differential sequence.

[0027] According to the contact resistance data, contact resistance differential sequence, vibration data and vibration differential sequence of a sliding window, the contact resistance performance characteristic value and vibration performance characteristic value corresponding to the sliding window are obtained respectively. Specifically, the product of the average value of the absolute value of the contact resistance data in a sliding window and the entropy value of the contact resistance differential sequence of the sliding window is obtained to obtain the contact resistance performance characteristic value corresponding to the sliding window. Similarly, the product of the average value of the absolute value of the vibration data in a sliding window and the entropy value of the vibration differential sequence of the sliding window is obtained to obtain the vibration performance characteristic value corresponding to the sliding window.

[0028] Taking the contact resistance performance eigenvalue as an example, the specific calculation model is as follows: , wherein, represents the contact resistance performance eigenvalue corresponding to a sliding window, represents the value of the i-th contact resistance data within the sliding window, and N represents the number of contact resistance data within the sliding window; represents the mean value of the sum of the absolute values of the values of each contact resistance data within the sliding window. This value represents the overall size of the data within the sliding window. The larger this value is, the more likely it is that the abnormal performance characteristics within the sliding window are greater; represents the information entropy of the data within the contact resistance difference sequence of the sliding window. The information entropy calculation method is a prior art and will not be elaborated here. The larger the information entropy value is, the greater the complexity of the random variation of the contact resistance data within the sliding window is, and the greater its performance eigenvalue is. Similarly, based on the vibration data and vibration difference sequence of a sliding window with the same calculation method, the vibration performance eigenvalue of the sliding window can be obtained.

[0029] Step S3, obtain the seismic performance coefficient corresponding to the sliding window according to the normalized values of each contact resistance data and each vibration data within a sliding window, and the contact resistance performance eigenvalue and vibration performance eigenvalue corresponding to the sliding window.

[0030] If within the same time period, the vibration performance eigenvalue of the vibration data is larger, while the contact resistance performance eigenvalue of the corresponding connector contact resistance is smaller, and the contact resistance performance eigenvalue does not fluctuate significantly with the change of the vibration intensity, it may indicate that the connector has strong seismic resistance and good mechanical stability, and the contact performance is relatively stable. For example, in a high-intensity vibration environment, if the contact resistance of a certain connector always remains low and the fluctuation amplitude is small, it indicates that its internal contact structure is not affected by the vibration disturbance and can still maintain a reliable conduction state. On the contrary, if the resistance fluctuates violently when the vibration increases, it may mean that the structure is loose or the contact surface is unstable.

[0031] Therefore, further obtain the vibration data and contact resistance data collected by the connector within the sliding window of the same time for further analysis.

[0032] Normalize the contact resistance data within a sliding window to obtain a first normalized sequence, and normalize the vibration data within the sliding window to obtain a second normalized sequence. Immediately afterwards, take the first normalized sequence and the second normalized sequence as data inputs, and use the DTW algorithm to obtain the distance matching value between these two normalized sequences. If this distance matching value is smaller, it indicates that the changes in the vibration data and the contact resistance data are more similar at this time, which indirectly indicates that the change in the contact resistance at this time is more strongly related to the vibration change, that is, the seismic resistance performance ability of the connector at this time may be weaker. DTW (Dynamic Time Warping) is an algorithm used to measure the similarity between two time series. This algorithm is a well-known technology and will not be elaborated here).

[0033] Further, obtain the seismic resistance performance coefficient corresponding to the sliding window based on the distance matching value between the first normalized sequence and the second normalized sequence corresponding to a sliding window, as well as the contact resistance performance eigenvalue and the vibration performance eigenvalue corresponding to the sliding window. Specifically, divide the contact resistance performance eigenvalue corresponding to a sliding window by the sum of the vibration performance eigenvalue corresponding to the sliding window and a hyperparameter, and multiply it by the distance matching value to obtain the seismic resistance performance coefficient corresponding to the sliding window.

[0034] The specific calculation model of the seismic resistance performance coefficient is as follows: , where X represents the seismic resistance performance coefficient corresponding to a sliding window, represents the distance matching value between the first normalized sequence and the second normalized sequence corresponding to the sliding window. The larger this value, the better the seismic resistance performance of the connector during this period of time, and respectively represent the first normalized sequence and the second normalized sequence corresponding to the sliding window; and respectively represent the vibration performance eigenvalue and the contact resistance performance eigenvalue corresponding to the sliding window. c is a hyperparameter used to prevent the denominator from being zero, and its value is 0.001; represents the ratio of the vibration performance eigenvalue to the contact resistance performance eigenvalue corresponding to the sliding window. The larger this value, the stronger the seismic resistance performance of the connector during this period of time. Thus, the seismic resistance performance coefficient corresponding to each sliding window can be obtained from the start of the sliding of the sliding window until the current moment.

[0035] Step S4, the seismic resistance performance coefficients corresponding to each sliding window form a first sequence; obtain the anomaly possibility coefficient of a data point based on the forward neighboring data points of the data point in the first sequence and the STL decomposition result of the first sequence.

[0036] Although the seismic performance coefficient calculated under any sliding window can reflect the contact stability of the connector to a certain extent, a single index is not sufficient to accurately determine whether there is looseness or potential failure, and it may be difficult to reveal the potential looseness of the connector.

[0037] Therefore, the seismic performance coefficients corresponding to each sliding window are combined into a sequence in chronological order, and this sequence is denoted as the first sequence. Further, the STL decomposition is performed on the first sequence to obtain the trend term, seasonal term, and residual term (this algorithm is a well-known calculation and will not be elaborated here).

[0038] Next, when analyzing the local potential abnormal performance of any decomposed data point, a preset number of data points before it and itself are selected as the local segment for analysis. The preset number of adjacent data points before a data point is denoted as the forward neighboring data points of this data point, and the forward neighboring data points and this data point form the local segment corresponding to this data point; where the preset number takes a value of 3, and the implementer can adjust it according to the actual situation. The length of the sequence after decomposition of the first sequence is equal to that before decomposition. The preset number of adjacent data points before here has the same meaning whether in the original sequence or the decomposed sequence.

[0039] Further, use the exponential function with the natural constant as the base to perform a negative correlation mapping on the slope of the data points in the trend term of the local segment corresponding to a data point, and normalize it to obtain the trend feature term; calculate the average value of the absolute values of the amplitudes of the data points in the residual term of the local segment corresponding to a data point, and normalize it to obtain the residual feature term; perform a weighted sum of the trend feature term and the residual feature term to obtain the abnormal possibility coefficient of this data point.

[0040] The specific calculation model of the abnormal possibility coefficient is: , where W is the abnormal possibility coefficient of a data point, indicating the local potential abnormal possibility of this data point, β represents the weight coefficient, and the weight value here is 0.6, which is an empirical value. Because the change trend of the seismic performance of the connector is an important indicator indicating possible connection abnormalities such as looseness, a higher weight is assigned to it; norm represents the linear normalization function; e represents the natural constant; represents the slope of the data points before this data point and itself in the trend term, that is, the slope of the data points in the corresponding local segment in the trend term. The slope here refers to the slope jointly determined by these points, that is, it represents its trend. If this value is smaller, it means that the seismic performance of the connector at this time drops sharply, that is the larger the value, the higher the possibility of potential abnormality at this point; Denotes the amplitude of the r-th data point in the residual term among the preset number of data points including this data point and the previous ones, that is, the amplitude of the r-th data point in the local segment corresponding to this data point in the residual term. Then it represents the mean of the absolute values of these amplitudes, which represents the degree of random variation of the seismic performance coefficient of the local connector at this point. The larger this value, the worse the stability of the connection at this time, and the greater the abnormal probability coefficient. Thus, the abnormal probability coefficients of each data point in the first sequence can be obtained.

[0041] Step S5, obtain the warning index of the last data point according to the abnormal probability coefficient of the last data point in the first sequence and the STL decomposition result; give a warning about the current performance of the connector based on the warning index.

[0042] In the first sequence, except for the last data point, other data points can be regarded as historical data. The last data point represents the sliding window obtained after the last sliding of the sliding window, and represents the current time in terms of time.

[0043] Thus, through the abnormal probability coefficient of the last data point and the abnormal probability coefficients of each historical data point for analysis, and combined with the trend term in the STL decomposition result, the warning index at the current time can be obtained. Obtain the warning index of the last data point according to the abnormal probability coefficient of the last data point in the first sequence and the STL decomposition result. Specifically, record the data points other than those in the local segment corresponding to the last data point as historical data points, obtain the absolute value of the difference between the abnormal probability coefficient of the last data point and the mean of the abnormal probability coefficients of each historical data point, and divide it by the sum of the hyperparameter and the standard deviation of the abnormal probability coefficients of each historical data point to obtain the historical deviation degree; divide the amplitude of the first data point in the trend term in the first sequence by the sum of the amplitude of the last data point in the trend term and the hyperparameter to obtain the trend deviation degree; calculate the average value of the normalized value of the historical deviation degree and the normalized value of the trend deviation degree to obtain the warning index of the last data point.

[0044] The calculation model of the warning index of the last data point is specifically as follows: , Among them, Denotes the warning index of the n-th data point in the first sequence, that is, the warning index of the last data point, representing the warning index at the current time; Denotes the abnormal probability coefficient of the last data point, It represents the mean of the anomaly likelihood coefficients of each data point except for the last data point and its forward neighboring data points, that is, the mean of the anomaly likelihood coefficients of each historical data point. c represents a hyperparameter, and σ represents the standard deviation of the anomaly likelihood coefficients of each historical data point; norm represents a linear normalization function; It represents the amplitude of the first data point in the first sequence in the trend term. It represents the amplitude of the last data point in the trend term. It is the historical deviation degree, which represents the absolute value of the difference between the anomaly likelihood coefficient of the last data point and the mean of the anomaly likelihood coefficients of each historical data point, divided by the standard deviation of the anomaly likelihood coefficients of each historical data point. If this value is larger, it indicates that the potential anomaly likelihood coefficient of the connector performance at this time is larger compared to the historical deviation degree, and the probability of its potential anomaly is higher; It is the trend deviation degree, which represents the ratio of the amplitude of the first data point in the trend term to the amplitude of the last data point in the trend term, that is, it represents the deviation degree of the current seismic performance of the connector compared to its initial seismic performance. The larger this value is, the worse the seismic performance of the connector at this time, and the higher the probability of its potential anomaly.

[0045] Thus, the warning index at the current time can be obtained. Further, a judgment threshold is set. Here, the judgment threshold G is set to 0.2 (an empirical value, specifically determined through experimental tests according to the connector model, etc.). When the warning index is greater than the judgment threshold, a warning prompt is given, indicating that the connection performance of the connector may be abnormal and timely maintenance and troubleshooting are required.

[0046] In summary, the present application analyzes the contact resistance data and vibration data of the connector by using a sliding window to obtain the contact resistance performance characteristic values and vibration performance characteristic values corresponding to each sliding window, and then obtains the seismic performance coefficient corresponding to the sliding window. Then, the data points in the first sequence composed of the seismic performance coefficients corresponding to each sliding window are analyzed to obtain the anomaly likelihood coefficient of each data point, and finally the warning index of the last data point (the warning index at the current time) is obtained, achieving the purpose of more accurate detection of the mine-used wire braided protective sleeve connector.

[0047] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0048] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized.

[0049] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for detecting the performance of a mining wire braided sheath connector, characterized in that, The method includes: Obtaining the contact resistance data sequence of the connector and its corresponding vibration data sequence; sliding a sliding window respectively on the contact resistance data sequence and the vibration data sequence; Respectively obtaining the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window according to the contact resistance data and the vibration data within a sliding window; Obtaining the earthquake resistance performance coefficient corresponding to the sliding window according to the normalized values of the contact resistance data and the vibration data within a sliding window and the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window; The earthquake resistance performance coefficients corresponding to each sliding window form a first sequence; obtaining the anomaly possibility coefficient of a data point according to the forward neighboring data points of the data point in the first sequence and the STL decomposition result of the first sequence; Obtaining the warning index of the last data point according to the anomaly possibility coefficient of the last data point in the first sequence and the STL decomposition result; warning the current performance of the connector based on the warning index.

2. The performance detection method of a mining wire braided sheath connector according to claim 1, characterized in that The step of respectively obtaining the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window according to the contact resistance data and the vibration data within a sliding window includes: Performing first-order difference on the contact resistance data and the vibration data within a sliding window respectively, obtaining the difference sequence corresponding to the contact resistance data within the sliding window and the difference sequence corresponding to the vibration data within the sliding window, denoted as the contact resistance difference sequence and the vibration difference sequence respectively; respectively obtaining the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window according to the contact resistance data, the contact resistance difference sequence, the vibration data and the vibration difference sequence of a sliding window.

3. A performance detection method for a mining wire braided sheath connector according to claim 2, characterized in that, The step of respectively obtaining the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window according to the contact resistance data, the contact resistance difference sequence, the vibration data and the vibration difference sequence of a sliding window includes: Calculating the product of the average value of the absolute values of the contact resistance data within a sliding window and the entropy value of the contact resistance difference sequence of the sliding window to obtain the contact resistance performance characteristic value corresponding to the sliding window; Calculating the product of the average value of the absolute values of the vibration data within a sliding window and the entropy value of the vibration difference sequence of the sliding window to obtain the vibration performance characteristic value corresponding to the sliding window.

4. A performance detection method for a mining wire braided sheath connector according to claim 1, characterized in that, The step of obtaining the earthquake resistance performance coefficient corresponding to the sliding window according to the normalized values of the contact resistance data and the vibration data within a sliding window and the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window includes: Normalizing the contact resistance data within a sliding window to obtain a first normalized sequence, and normalizing the vibration data within the sliding window to obtain a second normalized sequence; obtaining the distance matching value of the first normalized sequence and the second normalized sequence through the DTW algorithm; obtaining the earthquake resistance performance coefficient corresponding to the sliding window based on the distance matching value and the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window.

5. A performance detection method for a mining wire braided sheath connector according to claim 4, characterized in that, Obtaining the seismic performance coefficient corresponding to the sliding window based on the distance matching value, the contact resistance performance characteristic value, and the vibration performance characteristic value corresponding to the sliding window includes: Dividing the vibration performance characteristic value corresponding to a sliding window by the sum of the contact resistance performance characteristic value corresponding to the sliding window and the hyperparameter, and multiplying by the distance matching value to obtain the seismic performance coefficient corresponding to the sliding window.

6. The performance detection method of a mining wire braided sheath connector according to claim 1, characterized in that, Obtaining the anomaly possibility coefficient of a data point according to the forward adjacent data points of a data point in the first sequence and the STL decomposition result of the first sequence includes: The STL decomposition result of the first sequence includes a trend term, a seasonal term, and a residual term; a preset number of adjacent data points before a data point are denoted as the forward adjacent data points of the data point, and the forward adjacent data points and the data point form a local segment corresponding to the data point; using the exponential function with the natural constant as the base to perform a negative correlation mapping on the slope of the data points in the local segment corresponding to a data point in the trend term, and normalizing to obtain a trend feature term; calculating the average value of the absolute values of the amplitudes of the data points in the residual term in the local segment corresponding to a data point, and normalizing to obtain a residual feature term; performing a weighted sum of the trend feature term and the residual feature term to obtain the anomaly possibility coefficient of the data point.

7. A performance detection method for a mining wire braided sheath connector according to claim 1, characterized in that, Obtaining the warning index of the last data point according to the anomaly possibility coefficient of the last data point in the first sequence and the STL decomposition result includes: Denoting the data points other than those in the local segment corresponding to the last data point as historical data points, obtaining the absolute value of the difference between the anomaly possibility coefficient of the last data point and the mean value of the anomaly possibility coefficients of each historical data point, and dividing by the sum of the hyperparameter and the standard deviation of the anomaly possibility coefficients of each historical data point to obtain the historical deviation degree; dividing the amplitude of the first data point in the trend term of the first sequence by the sum of the amplitude of the last data point in the trend term and the hyperparameter to obtain the trend deviation degree; calculating the average value of the normalized value of the historical deviation degree and the normalized value of the trend deviation degree to obtain the warning index of the last data point.

8. A performance detection method for a mining wire braided sheath connector according to claim 1, characterized in that, Warning the current performance of the connector based on the warning index includes: When the warning index is greater than the judgment threshold, a warning prompt is given.

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