A performance testing method for mining steel wire braided sheath connector

Abstract

The present invention relates to the technical field of connector performance detection, and specifically to a method for detecting the performance of a mining steel wire braided sheath connector. The method comprises: obtaining a contact resistance data sequence and a corresponding vibration data sequence of a connector; sliding a sliding window on the contact resistance data sequence and the vibration data sequence respectively; obtaining a contact resistance performance characteristic value and a vibration performance characteristic value according to the contact resistance data and vibration data in a sliding window, and obtaining a seismic performance coefficient corresponding to the sliding window; and then obtaining a first sequence; obtaining an abnormality probability coefficient of a data point in the first sequence according to the forward adjacent data points of the data point and the STL decomposition result of the first sequence, obtaining a warning indicator of the last data point according to the abnormality probability coefficient of the last data point and the STL decomposition result, and issuing a warning on the current performance of the connector. The present application can accurately detect the performance of the connector.

Description

Technical Field

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

[0002] As the electrification level of mines continues to increase, the performance of mining steel wire braided sheath connectors, as key connecting components in cable systems, is directly related to the safety and stability of power and signal transmission. However, mine environments are subject to frequent vibrations and strong impacts. Existing connector performance testing methods mostly focus on static items such as contact resistance, insulation strength, and sealing. They are difficult to effectively reflect their actual working conditions under dynamic vibration loads, and there is a problem of being unable to identify potential failures such as false connections and looseness. It is necessary to test the performance of mining steel wire braided sheath connectors and conduct a dynamic evaluation of their seismic performance to improve the targeted testing and adaptability to mining environments, thereby ensuring the long-term and stable operation of the connectors.

[0003] Existing methods for testing the seismic performance of steel wire braided sheath connectors for mining use mostly static tests or single vibration tests, usually applying standard vibration loads in a laboratory environment to evaluate their structural and electrical performance. This type of method 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 vibration, impact, and displacement during long-term operation in mines. In fact, during underground operations, connectors are subjected to long-term mechanical disturbances, and may experience degradation phenomena such as loose structure and unstable contact. Their 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 methods are unable to detect potential risks in a timely manner, and it is also difficult to accurately assess the operating reliability and remaining life of the connector, affecting the safety and maintenance efficiency of mining systems. 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 testing the performance of a mining steel wire braided sheath connector. The technical solution adopted is as follows:

[0005] One embodiment of the present invention provides a method for testing the performance of a mining steel wire braided sheath connector, the method comprising:

[0006] Acquire a contact resistance data sequence and a corresponding vibration data sequence of the connector; and slide the contact resistance data sequence and the vibration data sequence using a sliding window.

[0007] According to the contact resistance data and vibration data in a sliding window, respectively obtain the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window;

[0008] Obtaining a seismic performance coefficient corresponding to a sliding window according to normalized values of each contact resistance data and each vibration data within a sliding window and a contact resistance performance characteristic value and a vibration performance characteristic value corresponding to the sliding window;

[0009] The seismic performance coefficients corresponding to each sliding window form a first sequence; the abnormal possibility coefficient of a data point in the first sequence is obtained based on the forward adjacent data points of the data point and the STL decomposition result of the first sequence;

[0010] An early warning indicator of the last data point is obtained based on the abnormal probability coefficient of the last data point in the first sequence and the STL decomposition result; and an early warning of the current performance of the connector is issued based on the early warning indicator.

[0011] Preferably, obtaining the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to a sliding window according to the contact resistance data and the vibration data in the sliding window respectively includes:

[0012] The contact resistance data and vibration data in a sliding window are respectively subjected to first-order differences to obtain a differential sequence corresponding to the contact resistance data in the sliding window and a differential sequence corresponding to the vibration data in the sliding window, which are respectively recorded as a contact resistance differential sequence and a vibration differential sequence; based on the contact resistance data, contact resistance differential sequence, vibration data and vibration differential sequence in a sliding window, the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window are respectively obtained.

[0013] Preferably, obtaining the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to a sliding window respectively according to the contact resistance data, the contact resistance differential sequence, the vibration data and the vibration differential sequence of the sliding window includes:

[0014] Calculate 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 difference sequence in the sliding window to obtain the contact resistance performance characteristic value corresponding to the sliding window;

[0015] 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 difference sequence of the sliding window is calculated to obtain the vibration performance characteristic value corresponding to the sliding window.

[0016] Preferably, obtaining the seismic performance coefficient corresponding to a sliding window according to the normalized values of each contact resistance data and each vibration data in a sliding window and the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window includes:

[0017] The contact resistance data within a sliding window are normalized to obtain a first normalized sequence, and the vibration data within the sliding window are normalized to obtain a second normalized sequence; the distance matching value of the first normalized sequence and the second normalized sequence is obtained through the DTW algorithm; and the seismic performance coefficient corresponding to the sliding window is obtained based on the distance matching value and the contact resistance performance characteristic value and vibration performance characteristic value corresponding to the sliding window.

[0018] Preferably, obtaining 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 includes:

[0019] The vibration performance eigenvalue corresponding to a sliding window is divided by the sum of the contact resistance performance eigenvalue corresponding to the sliding window and the hyperparameter, and multiplied by the distance matching value to obtain the seismic performance coefficient corresponding to the sliding window.

[0020] Preferably, obtaining the abnormality probability coefficient of a data point according to the forward adjacent data points of the data point in the first sequence and the STL decomposition result of the first sequence includes:

[0021] The STL decomposition results of the first sequence include trend terms, seasonal terms and residual terms; a preset number of data points adjacent to a data point are recorded as the forward neighboring data points of the data point, and the forward neighboring data points and the data point constitute the local segment corresponding to the data point; an exponential function with a natural constant as the base is used to negatively correlate the slopes of the data points in the trend term in the local segment corresponding to a data point, and normalized to obtain the trend characteristic term; the average value of the absolute value of the amplitude of each data point in the local segment corresponding to a data point in the residual term is calculated, and normalized to obtain the residual characteristic term; the trend characteristic term and the residual characteristic term are weightedly summed to obtain the abnormal possibility coefficient of the data point.

[0022] Preferably, obtaining the early warning indicator 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 includes:

[0023] All data points except the data points in the local segment corresponding to the last data point are recorded as historical data points, and 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 the historical data points is obtained, and the difference is divided by the sum of the hyperparameter and the standard deviation of the abnormal probability coefficients of the historical data points to obtain the historical deviation degree; the amplitude of the first data point in the trend item in the first sequence is divided by the amplitude of the last data point in the trend item and the sum of the hyperparameter to obtain the trend deviation degree; the normalized value of the historical deviation degree and the average value of the normalized value of the trend deviation degree are calculated to obtain the warning indicator of the last data point.

[0024] Preferably, the early warning of the current performance of the connector is performed based on the early warning indicator, including:

[0025] When the warning indicator is greater than the judgment threshold, a warning prompt will be issued

[0026] The embodiments of the present invention have at least the following beneficial effects: the present application obtains the contact resistance data sequence and the corresponding vibration data sequence of the connector, 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; then, based on the normalized values of each contact resistance data and each vibration data in the sliding window and the corresponding contact resistance performance characteristic value and vibration performance characteristic value, the seismic performance coefficient corresponding to the sliding window is obtained, and a sequence is formed to obtain a first sequence, and the abnormality possibility coefficient of each data point is obtained by combining the adjacent data points of each data point in the first sequence and the STL decomposition result of the sequence, and then the early warning indicator of the last data point in the first sequence is obtained, and finally, based on the early warning indicator, the current performance of the connector is warned to obtain a more accurate early warning result, thereby reducing the safety hazards caused by potential accidents such as poor contact of the wire braided sheath connector during operations such as mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 A flow chart of a method for detecting the performance of a mining steel wire braided sheath connector provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method for testing the performance of a braided steel wire sheathed connector for mining use, including its specific implementation, structure, features, and effectiveness. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0030] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0031] The following describes in detail a method for detecting the performance of a mining steel wire braided sheath connector provided by the present invention with reference to the accompanying drawings.

[0032] Example:

[0033] The main application scenario of the present invention is to test the performance of a mining steel wire braided sheath connector to ensure its safe use.

[0034] See also Figure 1 , which shows a method flow chart of a method for detecting the performance of a mining steel wire braided sheath connector provided by an embodiment of the present invention, the method comprising the following steps:

[0035] Step S1: Acquire a contact resistance data sequence and a corresponding vibration data sequence of a connector; and use a sliding window to slide on the contact resistance data sequence and the vibration data sequence respectively.

[0036] When monitoring the seismic performance of mining-grade braided wire sheathed connectors, a high-precision resistance detection unit and a triaxial vibration sensor are first deployed near the connector to monitor its conduction and stress status in real time. The resistance detection unit samples at a high frequency (e.g., 100Hz) with milliohm-level resolution, recording fluctuations in the connector's electrical contact resistance under vibration interference and generating connector contact resistance data. The triaxial vibration sensor uses the same sampling frequency to capture mechanical vibration disturbances affecting the connector, generating corresponding vibration data.

[0037] This multi-source data is uploaded to an edge computing node or backend platform via an industrial interface (such as RS485, CAN, or LoRa), forming a continuous data sequence that can be used for dynamic analysis. The contact resistance data is organized into a contact resistance data sequence in chronological order, and the vibration data corresponding to the connector is organized into a vibration data sequence in chronological order.

[0038] Mining-grade braided wire sheathed connectors are key components used in underground environments like coal mines, connecting cables, electrical equipment, or control systems. They are typically covered with a braided wire sheath to enhance mechanical strength, tensile strength, and protection. These connectors are widely used in underground power supply systems, communication signal transmission, and automated control equipment, and are key components for ensuring stable electrical connections and safe operations.

[0039] Because mine environments are often subject to dynamic loads such as equipment vibration, geological disturbances, and mechanical shock, connectors are prone to poor contact, short circuits, and signal loss during long-term operation due to micro-displacement, looseness, or sheath fatigue. For example, in roadway transportation systems, cable connectors continuously vibrate with the swaying of equipment. If their seismic performance is insufficient, this can easily cause the internal conductive structure to loosen, posing a major safety hazard. Therefore, conducting specialized testing of the seismic performance of mining steel wire braided sheath connectors not only helps assess their structural stability and electrical reliability, but also provides a crucial foundation for ensuring mine operation safety, extending equipment life, and enabling intelligent monitoring and maintenance.

[0040] Therefore, in order to be able to perform more accurate and timely detection of the performance of the mining wire braided protective sleeve connector. First, the contact resistance data sequence and the vibration data sequence are aligned in chronological order. Then, a sliding window is set, 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, the sliding window is used to slide on the contact resistance data sequence and the vibration data sequence respectively, and subsequent analysis is performed based on the contact resistance data and vibration data in each sliding window after each slide.

[0041] Step S2: acquiring contact resistance characteristic values and vibration characteristic values corresponding to a sliding window according to the contact resistance data and vibration data in the sliding window.

[0042] 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 factors such as temperature and humidity 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 itself more obvious. This processing method can more sensitively capture early signs of degradation or potential failures caused by changes in working conditions such as vibration and corrosion. Similarly, performing first-order differentials on the vibration data in the sliding window can also enhance the ability to express the 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 warning and structural reliability analysis.

[0043] The contact resistance data and vibration data in each sliding window are respectively subjected to first-order difference 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.

[0044] The contact resistance performance characteristic value and vibration performance characteristic value corresponding to a sliding window are obtained based on the contact resistance data, contact resistance differential sequence, vibration data, and vibration differential sequence of a sliding window. Specifically, the contact resistance performance characteristic value corresponding to the sliding window is obtained by multiplying the average value of the absolute value of the contact resistance data within the sliding window by the entropy value of the contact resistance differential sequence of the sliding window. Similarly, the vibration performance characteristic value corresponding to the sliding window is obtained by multiplying the average value of the absolute value of the vibration data within the sliding window by the entropy value of the vibration differential sequence of the sliding window.

[0045] Taking the characteristic value of contact resistance as an example, the specific calculation model is:

[0046] ,

[0047] in, Indicates the characteristic value of the contact resistance corresponding to a sliding window, represents the value of the i-th contact resistance data in the sliding window, and N represents the number of contact resistance data in the sliding window; The mean of the sum of the absolute values of each contact resistance data in the sliding window. This value represents the overall size of the data in the sliding window. The larger the value, the greater the abnormal performance characteristics in the sliding window may be. This represents the information entropy of the data within the contact resistance differential sequence of the sliding window. The method for calculating this information entropy is conventional and will not be elaborated upon here. A larger information entropy value indicates a greater complexity in the random variations in the contact resistance data within the sliding window, and thus a larger characteristic value. Similarly, using the same calculation method based on the vibration data and vibration differential sequence of a sliding window, the vibration characteristic value of the sliding window can be obtained.

[0048] Step S3 , obtaining a seismic performance coefficient corresponding to a sliding window according to normalized values of each contact resistance data and each vibration data in a sliding window and the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window.

[0049] If, within the same time period, the larger the vibration performance characteristic value of the vibration data, the smaller the corresponding contact resistance characteristic value of the connector contact resistance, and the contact resistance characteristic value does not fluctuate significantly with changes in vibration intensity, this 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 connector remains low and the fluctuation amplitude is small, it means that its internal contact structure is not affected by the vibration disturbance and can still maintain a reliable conductive state. Conversely, if the resistance fluctuates violently when the vibration increases, it may mean that the structure is loose or the contact surface is unstable.

[0050] Therefore, the vibration data and contact resistance data collected by the connector within the sliding window at the same time are further obtained for further analysis.

[0051] Normalize each contact resistance data within a sliding window to obtain a first normalized sequence, and normalize each vibration data within the sliding window to obtain a second normalized sequence. Then, use the first normalized sequence and the second normalized sequence as data input, and use the DTW algorithm to obtain the distance matching value of the two normalized sequences. If the distance matching value is smaller, it means that the changes in the vibration data and the contact resistance data at this time are more similar, which indirectly means that the change in contact resistance at this time is more closely related to the change in vibration, that is, the connector's seismic performance may be weaker at this time. 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 described in detail here).

[0052] Furthermore, a seismic performance coefficient corresponding to a sliding window is obtained based on the distance matching value of the first normalized sequence and the second normalized sequence corresponding to the sliding window, as well as the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window. Specifically, the contact resistance performance characteristic value corresponding to the sliding window is divided by the sum of the vibration performance characteristic value corresponding to the sliding window and the hyperparameter, and the resultant value is multiplied by the distance matching value to obtain the seismic performance coefficient corresponding to the sliding window.

[0053] The specific calculation model of the seismic performance coefficient is:

[0054] ,

[0055] Among them, X represents the seismic performance coefficient corresponding to a sliding window, It represents the distance matching value between the first normalized sequence and the second normalized sequence corresponding to the sliding window. The larger the value, the better the seismic performance of the connector during this period. and Respectively represent the first normalized sequence and the second normalized sequence corresponding to the sliding window; and They represent the vibration performance eigenvalue and contact resistance performance eigenvalue corresponding to the sliding window respectively. c is a hyperparameter used to prevent the denominator from being 0, and its value is 0.001. This value represents the ratio of the vibration characteristic value to the contact resistance characteristic value corresponding to the sliding window. The larger the value, the stronger the connector's seismic performance during that period. This allows you to obtain the seismic performance coefficient for each sliding window from the beginning of the sliding window to the current moment.

[0056] Step S4: The seismic performance coefficients corresponding to the sliding windows form a first sequence; and the abnormality probability coefficient of a data point in the first sequence is obtained based on the forward adjacent data points of the data point and the STL decomposition result of the first sequence.

[0057] Although the seismic performance coefficient calculated under any sliding window can reflect the contact stability of the connector to a certain extent, a single indicator 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.

[0058] Therefore, the seismic performance coefficients corresponding to each sliding window are organized into a sequence in chronological order, and this sequence is recorded as the first sequence. Furthermore, the first sequence is subjected to STL decomposition to obtain the trend term, seasonal term, and residual term (this algorithm is a well-known calculation and will not be repeated here).

[0059] Next, when analyzing the local potential abnormal performance of any data point after decomposition, select it and the preset number of data points before it as local segments for analysis, and record the preset number of data points adjacent to a data point as the forward neighboring data points of the data point. The forward neighboring data points and the data point constitute the local segment corresponding to the data point; the preset number is 3, and the implementer can adjust it according to actual conditions. The length of the first sequence after decomposition is equal to that before decomposition. The preset number of data points adjacent to the previous sequence has the same meaning whether in the original sequence or in the decomposed sequence.

[0060] Furthermore, an exponential function with a natural constant as the base is used to perform negative correlation mapping on the slope of the data point in the trend term in the local segment corresponding to a data point, and normalized to obtain the trend characteristic term; the average value of the absolute value of the amplitude of each data point in the local segment corresponding to a data point in the residual term is calculated, and normalized to obtain the residual characteristic term; the trend characteristic term and the residual characteristic term are weightedly summed to obtain the abnormal possibility coefficient of the data point.

[0061] The specific calculation model of the abnormal probability coefficient is:

[0062] ,

[0063] Where W is the abnormality probability coefficient of a data point, indicating the potential abnormality of the data point locally. β is the weight coefficient, which is 0.6, an empirical value. The trend of the connector's seismic performance change is an important indicator of possible connection abnormalities such as looseness, so it is given a higher weight. norm represents the linear normalization function. e represents the natural constant. It indicates the slope of the data point and the preset number of data points before it in the trend item, that is, the slope of the data points in the corresponding local segment in the trend item. The slope here refers to the slope determined by these points, that is, its trend. If the value is smaller, it means that the seismic performance of the connector at this time has dropped sharply, that is, The larger the value, the higher the possibility of potential abnormality at this point; It represents the amplitude of the rth data point in the residual term among the data point and the preset number of data points before it, that is, the amplitude of the rth data point in the residual term in the local segment corresponding to the data point. = represents the mean of the absolute values of these amplitudes, that is, the degree of random variation in the seismic performance coefficient of the local connector at that point. The larger the value, the worse the stability of the connection at that point, and the larger the abnormality probability coefficient. From this, the abnormality probability coefficient of each data point in the first sequence can be obtained.

[0064] Step S5, obtaining a warning indicator for the last data point according to the abnormality probability coefficient of the last data point in the first sequence and the STL decomposition result; and issuing a warning for the current performance of the connector based on the warning indicator.

[0065] In the first sequence, except for the last data point, all other data points can be regarded as historical data. The last data point represents the sliding window obtained after the sliding window slides for the last time, and represents the current time in terms of time.

[0066] Therefore, the abnormal possibility coefficient of the last data point and the abnormal possibility coefficients of each data point in history can be analyzed, and then combined with the trend item in the STL decomposition result to obtain the early warning indicator of the current time. The early warning indicator of the last data point is obtained based on the abnormal possibility coefficient of the last data point in the first sequence and the STL decomposition result. Specifically, all data points except the data points in the local segment corresponding to the last data point are recorded as historical data points, and the absolute value of the difference between the abnormal possibility coefficient of the last data point and the mean of the abnormal possibility coefficients of each historical data point is obtained, and the difference is divided by the hyperparameter and the sum of the standard deviations of the abnormal possibility coefficients of each historical data point to obtain the historical deviation degree; the amplitude of the first data point in the trend item in the first sequence is divided by the amplitude of the last data point in the trend item and the hyperparameter to obtain the trend deviation degree; the normalized value of the historical deviation degree and the average value of the normalized value of the trend deviation degree are calculated to obtain the early warning indicator of the last data point.

[0067] The calculation model of the early warning indicator of the last data point is as follows:

[0068] ,

[0069] in, It represents the early warning indicator of the nth data point in the first sequence, that is, the early warning indicator of the last data point, and represents the early warning indicator at the current time; Indicates the abnormal probability coefficient of the last data point, represents the mean of the abnormal probability coefficients of all data points except the last data point and its forward adjacent data points, that is, the mean of the abnormal probability coefficients of all historical data points, c represents the hyperparameter, σ represents the standard deviation of the abnormal probability coefficients of all historical data points; norm represents the linear normalization function; Represents the amplitude of the first data point in the first sequence in the trend term, Indicates the amplitude of the last data point in the trend term; The historical deviation degree indicates the absolute value of the difference between the abnormality probability coefficient of the last data point and the mean of the abnormality probability coefficients of all historical data points, divided by the standard deviation of the abnormality probability coefficients of all historical data points. The larger the value, the greater the deviation of the potential abnormality probability coefficient of the connector performance from the historical degree, and the greater the probability of an abnormality. It is the degree of trend deviation, which means the ratio of the amplitude of the first data point in the trend item to the amplitude of the last data point in the trend item, that is, the degree of deviation of the current connector's seismic performance compared with its initial seismic performance. The larger the value, the worse the seismic performance of the connector at this time, and the greater the probability that it may be abnormal.

[0070] This allows us to obtain the current warning indicator. Furthermore, we can set a judgment threshold. Here, the judgment threshold G is set to 0.2 (an empirical value, determined by experimental testing based on the connector model, etc.). When the warning indicator is greater than the judgment threshold, a warning prompt is issued, indicating that the connection performance of the connector may be abnormal and requires timely maintenance and troubleshooting.

[0071] In summary, the present application obtains the contact resistance performance characteristic value and vibration performance characteristic value corresponding to each sliding window by analyzing the contact resistance data of the connector and the vibration data it is subjected to through a 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 coefficient corresponding to each sliding window are analyzed to obtain the abnormal possibility coefficient of each data point, and finally the early warning indicator of the last data point (the early warning indicator at the current time) is obtained, so as to achieve the purpose of more accurate detection of the connector of the mining steel wire braided protective cover connector.

[0072] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for testing the performance of a mining wire braided sheath connector, characterized in that: The method includes: Acquire a contact resistance data sequence and a corresponding vibration data sequence of the connector; and slide the contact resistance data sequence and the vibration data sequence using a sliding window. According to the contact resistance data and vibration data in a sliding window, respectively obtain the contact resistance performance characteristic value and the vibration performance characteristic value corresponding to the sliding window; Obtaining a seismic performance coefficient corresponding to a sliding window according to normalized values of each contact resistance data and each vibration data within a sliding window and a contact resistance performance characteristic value and a vibration performance characteristic value corresponding to the sliding window; The seismic performance coefficients corresponding to each sliding window form a first sequence; the abnormal possibility coefficient of a data point in the first sequence is obtained based on the forward adjacent data points of the data point and the STL decomposition result of the first sequence; Obtaining a warning indicator for the last data point based on the abnormal probability coefficient of the last data point in the first sequence and the STL decomposition result; issuing a warning for the current performance of the connector based on the warning indicator; The step of respectively obtaining a contact resistance characteristic value and a vibration characteristic value corresponding to a sliding window based on contact resistance data and vibration data in the sliding window includes: Performing first-order differences on the contact resistance data and vibration data within a sliding window, respectively, to obtain a differential sequence corresponding to the contact resistance data within the sliding window and a differential sequence corresponding to the vibration data within the sliding window, which are respectively recorded as a contact resistance differential sequence and a vibration differential sequence; obtaining a contact resistance performance characteristic value and a vibration performance characteristic value corresponding to the sliding window based on the contact resistance data, contact resistance differential sequence, vibration data, and vibration differential sequence within the sliding window; The step of respectively obtaining a contact resistance characteristic value and a vibration characteristic value corresponding to a sliding window based on the contact resistance data, the contact resistance differential sequence, the vibration data, and the vibration differential sequence of the sliding window includes: Calculate 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 difference sequence in 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 value of the vibration data in 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; The step of obtaining a seismic performance coefficient corresponding to a sliding window according to normalized values of contact resistance data and vibration data within a sliding window and contact resistance performance characteristic values and vibration performance characteristic values corresponding to the sliding window includes: Normalizing each contact resistance data within a sliding window to obtain a first normalized sequence, and normalizing each vibration data within the sliding window to obtain a second normalized sequence; obtaining a distance matching value between the first normalized sequence and the second normalized sequence using a DTW algorithm; and obtaining a seismic performance coefficient corresponding to the sliding window based on the distance matching value and a contact resistance performance characteristic value and a vibration performance characteristic value corresponding to the sliding window; The obtaining of 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 includes: The vibration performance eigenvalue corresponding to a sliding window is divided by the sum of the contact resistance performance eigenvalue corresponding to the sliding window and the hyperparameter, and multiplied by the distance matching value to obtain the seismic performance coefficient corresponding to the sliding window; Obtaining an abnormality probability coefficient of a data point according to the forward adjacent data points of the data point in the first sequence and the STL decomposition result of the first sequence includes: The STL decomposition results of the first sequence include trend terms, seasonal terms, and residual terms; a preset number of data points adjacent to a data point are recorded as the forward neighboring data points of the data point, and the forward neighboring data points and the data point constitute the local segment corresponding to the data point; an exponential function with a natural constant as the base is used to negatively correlate the slopes of the data points in the trend term in the local segment corresponding to a data point, and normalized to obtain a trend characteristic term; the average of the absolute values of the amplitudes of the data points in the local segment corresponding to a data point in the residual term is calculated, and normalized to obtain a residual characteristic term; the trend characteristic term and the residual characteristic term are weighted and summed to obtain the abnormal probability coefficient of the data point; The step of obtaining the early warning indicator 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 includes: All data points except the data points in the local segment corresponding to the last data point are recorded as historical data points, and 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 the historical data points is obtained, and the difference is divided by the sum of the hyperparameter and the standard deviation of the abnormal probability coefficients of the historical data points to obtain the historical deviation degree; the amplitude of the first data point in the trend item in the first sequence is divided by the amplitude of the last data point in the trend item and the sum of the hyperparameter to obtain the trend deviation degree; the normalized value of the historical deviation degree and the average value of the normalized value of the trend deviation degree are calculated to obtain the warning indicator of the last data point.

2. A method for testing the performance of a mining wire braided sheath connector according to claim 1, characterized in that: The early warning of the current performance of the connector based on the early warning indicator includes: When the warning indicator is greater than the judgment threshold, a warning prompt will be issued.

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