Mine cable bending tester monitoring system and method based on virtual instrument technology

Abstract

The invention provides a mining cable bending tester monitoring system and method based on a virtual instrument technology, and relates to the technical field of mining equipment testing and monitoring. The system comprises a mining cable bending tester used for carrying out a bending fatigue test on a mining cable, a data acquisition module used for acquiring cable bending parameters in real time through a cable bending multi-parameter sensor array, and a fault diagnosis module used for carrying out feature extraction and fault diagnosis on the cable bending parameters, the online monitoring module and the auxiliary management module are used for simultaneously judging whether the cable bending parameter exceeds the limit and generating an alarm instruction by combining an over-limit judgment result and a fault diagnosis result; wherein the data acquisition module, the online monitoring module and the auxiliary management module are jointly integrated into a virtual instrument platform, and the modules in the platform communicate with one another. The system is suitable for real-time acquisition, processing, visualization and alarm of multiple physical quantities in the test process, and multi-dimensional monitoring and intelligent diagnosis of the whole cable bending test process are achieved.

Description

Technical Field

[0001] This invention relates to the field of mining equipment testing and monitoring technology, and in particular to a monitoring system and method for a mining cable bending tester based on virtual instrument technology. Background Technology

[0002] Mining machine cables are frequently subjected to reciprocating bending, dragging, and loading in the underground operating environment. Under long-term cyclic bending, the cable insulation, conductors, and structural components are prone to fatigue, breakage, short circuits, or insulation degradation.

[0003] Cable bending fatigue testing is a core method for evaluating their mechanical and electrical durability. However, traditional cable bending testing machines have significant drawbacks: First, they primarily rely on mechanical structures and simple counting, lacking multi-parameter synchronous monitoring capabilities. They cannot collect key parameters such as core stress, current, temperature, drag force, speed, bending radius, and base plate inclination angle online during the test, resulting in a limited evaluation system. Second, due to the lack of data acquisition and analysis, traditional methods cannot obtain performance degradation data throughout the cable's entire lifecycle, making it difficult to provide comprehensive data support for cable design, quality control, and service life assessment. Finally, traditional systems lack intelligent early warning functions, failing to alarm and record abnormal states before failures occur, leading to insufficient safety and predictability in the testing process.

[0004] Therefore, proposing a virtual instrument system that integrates multiple sensors, uses a high-performance data acquisition card, and realizes real-time display, analysis, alarm, and historical traceability on the LabVIEW platform is a necessary technical solution to improve the reliability and intelligence level of cable testing. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, this invention proposes a monitoring system and method for a mining cable bending test machine based on virtual instrument technology. This system integrates multi-sensor acquisition, a high-performance data acquisition card, and advanced signal processing algorithms. The aim is to solve the problems of single monitoring methods, poor real-time performance, lack of multi-parameter fusion analysis, and insufficient fault early warning and data traceability capabilities in the existing technologies. It is suitable for real-time acquisition, processing, visualization, and alarm of multiple physical quantities during the test, realizing multi-dimensional monitoring and intelligent diagnosis of the entire cable bending test process.

[0006] On the one hand, the present invention proposes a monitoring system for a mining cable bending tester based on virtual instrument technology. The system includes: a mining cable bending tester, a data acquisition module, an online monitoring module, and an auxiliary management module.

[0007] The mining cable bending test machine is used to conduct bending fatigue tests on mining cables.

[0008] The data acquisition module is used to acquire cable bending parameters in real time through a multi-parameter cable bending sensor array installed at a specified position in the mining cable bending tester.

[0009] The online monitoring module is used to extract features and diagnose faults in the cable bending parameters; at the same time, it uses a single-parameter threshold and multi-parameter joint judgment method to determine whether the cable bending parameters exceed the limits, and generates an alarm command by combining the limit judgment result and the fault diagnosis result.

[0010] The auxiliary management module is used to provide user permission management, data correction, historical data management, alarm processing, equipment maintenance management and operation support for the monitoring system of the mining cable bending tester;

[0011] The data acquisition module, online monitoring module, and auxiliary management module are integrated into a virtual instrument platform, and communication connections are established between the modules within the platform.

[0012] Furthermore, the monitoring system for the mining cable bending test machine also includes a relational database for storing historical data of the bending fatigue test of mining cables; and the data acquisition module, online monitoring module and auxiliary management module are all connected to the relational database through a database access interface.

[0013] Furthermore, the cable bending parameters include: the number of cable bends, the cable bending radius, the dragging end force, the current, the temperature, the dragging speed, the base plate tilt angle, the height adjustment, and the equivalent stress of the wire core.

[0014] Furthermore, the method for feature extraction and fault diagnosis of the cable bending parameters is as follows:

[0015] The cable bending parameters are denoised using a median filter to obtain denoised cable bending parameters; then, a Butterworth low-pass filter is used to filter the denoised cable bending parameters to obtain preprocessed cable bending parameters.

[0016] For any parameter among the cable bending parameters, the preprocessed parameter is regarded as a one-dimensional vibration signal. A strong noise background weak fault feature extraction method based on SSA-GSSA feature enhancement fusion is used to extract features from the one-dimensional vibration signal, obtaining SSA feature vectors and GSSA feature vectors respectively. The specific details are as follows:

[0017] The one-dimensional vibration signal is decomposed using singular value analysis: a window length is selected and a trajectory matrix is ​​constructed through delay mapping and singular value decomposition is performed. The signal components corresponding to the first k main singular values ​​are extracted and synthesized into an SSA reconstructed signal.

[0018] The SSA reconstructed signal is used to calculate multi-domain statistical features, including peak index, kurtosis index, mean, standard deviation, variance, root mean square value, root square amplitude, average amplitude, centroid frequency, mean square frequency, and frequency variance, to form the SSA feature vector.

[0019] The GSSA algorithm is used to perform sparse representation and structural shrinkage on the SSA reconstructed signal to obtain the GSSA feature vector;

[0020] The SSA and GSSA feature vectors are fused, and the fault diagnosis result of the cable bending parameters is determined based on the fusion result. The fusion process is divided into feature-level fusion and decision-level fusion. The fault diagnosis result includes any one of the following fault categories: normal state, bending device fault, and drive device fault. For each output fault category, the confidence level output by the SVM classifier is used as the fault severity. The specific details are as follows:

[0021] The feature-level fusion is as follows: normalize the SSA feature vector and the GSSA feature vector, and concatenate the normalized SSA feature vector and the GSSA feature vector to obtain a fused feature vector; use a pre-trained SVM classifier to perform fault diagnosis on the fused feature vector to generate the fault diagnosis result of the cable bending parameters.

[0022] The decision-level fusion is as follows: the SSA feature vector and the GSSA feature vector are respectively input into two pre-trained SVM classifiers to obtain two preliminary diagnostic results; the two preliminary diagnostic results are weighted and averaged using a weighted voting method to generate the fault diagnosis result of the cable bending parameters.

[0023] Furthermore, the method of using a single-parameter threshold and multi-parameter combined judgment method to determine whether the cable bending parameter exceeds the limit, and the method of generating an alarm command by combining the judgment result and the fault diagnosis result, is as follows:

[0024] The cable bending parameters are judged to exceed the threshold of a single parameter. When any parameter of the cable bending parameters exceeds the preset parameter threshold, a general alarm signal is generated.

[0025] The cable bending parameters are subjected to multi-parameter joint over-limit judgment. When two or more parameters that are related in the fault mechanism exceed their respective parameter thresholds simultaneously or successively within a preset time window, an advanced alarm signal for the test is generated.

[0026] In addition to the single-parameter threshold and multi-parameter joint determination method, when an abnormality in the system's own operation or a safety risk in the experimental environment is detected, an advanced alarm signal for the equipment is generated.

[0027] The general alarm signal, the advanced alarm signal for the test, and the advanced alarm signal for the equipment are used as over-limit alarm signals;

[0028] The alarm signal for the cable bending parameters exceeding the limit is fused with the fault diagnosis results to generate the final alarm command;

[0029] The fusion rules are as follows:

[0030] If the fault diagnosis results do not detect any faults and no over-limit alarm signals are triggered, the system is in a normal state.

[0031] If the fault diagnosis result detects a fault but does not trigger any over-limit alarm signal, a warning command is generated;

[0032] If the fault diagnosis result does not detect a fault, but an over-limit alarm signal has been triggered, then an alarm command is generated based on the over-limit alarm signal.

[0033] If the fault diagnosis results detect a fault and an over-limit alarm signal has been triggered, then the highest-level alarm command is generated.

[0034] Furthermore, the auxiliary management module includes: a user login unit, a compensation algorithm unit, a historical record unit, an alarm query unit, a maintenance record unit, and a help unit;

[0035] The user login unit is used to verify the legitimacy of a user's identity through a username and account password, and to assign access permissions to the user based on the user's identity.

[0036] The compensation algorithm unit is used to receive cable bending parameters from the data acquisition module, correct the cable bending parameters through the compensation algorithm selected by the user, output the corrected cable bending parameters and save them;

[0037] The historical record unit is used to write the cable bending parameters, mining cable bending fatigue test setting data and alarm status as historical data into a relational database, and supports querying and retrieving the historical data according to one or more predetermined conditions, and also supports exporting the query results as a data file in a specified format.

[0038] The alarm query unit is used to respond to the alarm command and provide an alarm prompt, while generating and storing alarm logs;

[0039] The maintenance record unit is used to input maintenance information of the mining cable bending tester and establish equipment maintenance files; based on the cumulative usage data of the mining cable bending tester, it generates a periodic maintenance plan for the mining cable bending tester and issues maintenance reminders on time; it records the operating status data of the mining cable bending tester after maintenance in order to evaluate the maintenance effect of the mining cable bending tester.

[0040] The help unit is used to provide users with an operation guide, common problems and solutions, and contact information for technical support personnel for the monitoring system of the mining cable bending tester.

[0041] Furthermore, the compensation algorithm includes: a linear compensation algorithm and a nonlinear compensation algorithm; wherein the linear compensation algorithm is: based on a predefined linear compensation model with a gain coefficient as the slope and an offset as the intercept, the cable bending parameters are input into the linear compensation model for correction, and the corrected cable bending parameters are output.

[0042] The nonlinear compensation algorithm is as follows: For any sensor in the cable bending multi-parameter sensor array, several sets of calibration points containing measured values ​​and true values ​​are collected within the full range of the sensor, and a compensation curve or lookup table is constructed using all calibration points. Then, the cable bending parameters are corrected using the compensation curve or lookup table, and the corrected cable bending parameters are output.

[0043] Furthermore, the monitoring system for the mining cable bending test machine also includes: a visual human-computer interaction unit for providing a user login portal; receiving and configuring the operating parameters of the online monitoring module; displaying in real time the cable bending parameters collected by the data acquisition module and the cable bending parameters corrected by the compensation algorithm unit, as well as the changing waveforms; publishing the alarm logs generated by the alarm query unit and providing alarm prompts; and displaying the real-time working status of the mining cable bending test machine.

[0044] Furthermore, the monitoring system for the mining cable bending test machine also has a test mode for on-site verification; the test mode is used to simulate normal and abnormal working conditions, compare the obtained simulation data with the actual operating data of the monitoring system for the mining cable bending test machine, and perform engineering verification of the monitoring system for the mining cable bending test machine.

[0045] The test modes include three working modes: importing simulation data under normal operating conditions, importing simulation data under fault conditions, and full-process online detection.

[0046] On the other hand, this invention proposes a monitoring method for a mining cable bending tester based on virtual instrument technology, which includes the following process:

[0047] The cable bending parameters are collected and corrected in real time by a multi-parameter sensor array installed on the mining cable bending tester.

[0048] The corrected cable bending parameters were preprocessed by median filtering and Butterworth low-pass filtering in sequence to obtain the preprocessed cable bending parameters.

[0049] A feature extraction method for weak faults in a strong noise background based on SSA-GSSA feature enhancement fusion is used to extract features from any preprocessed cable bending parameter, and the fault diagnosis result of the cable bending parameter is determined based on the extracted SSA feature vector and GSSA feature vector.

[0050] A method combining single-parameter threshold and multi-parameter judgment is used to determine whether the cable bending parameters exceed the limit, and an alarm command is generated by combining the judgment results and fault diagnosis results.

[0051] In response to the alarm command, the system executes an alarm prompt, generates an alarm log, and synchronously writes the cable bending parameters, test setting data, and alarm status as historical data into a relational database.

[0052] The beneficial effects of adopting the above technical solution are as follows:

[0053] This invention significantly improves the intelligence level and monitoring reliability of mining cable bending testing machines. It not only solves the problem of extracting weak fault features under strong noise conditions, achieving a leap from "threshold alarm" to "intelligent diagnosis," but also provides comprehensive technical support for cable product quality control and mine power supply safety. Specific analysis is as follows:

[0054] (1) The present invention system solves the problem that traditional testing machines only monitor a single parameter such as the number of bending times, which cannot reflect the true state of the cable under multiple stress coupling effects such as bending, dragging, heating, and force, resulting in inaccurate life assessment and failure to warn of early faults;

[0055] This invention is the first to systematically construct a multi-physical quantity synchronous monitoring sensor array for mining cables (including bending radius, dragging end force, current, temperature, speed, tilt angle, height, and core stress, etc.), and clarifies the specific layout and selection of each sensor on the mining cable bending test machine (such as the surface to which the flexible curvature sensor is attached, and the tensile force sensor is installed on the cable connecting frame, etc.).

[0056] This invention proposes a dual judgment mechanism of "single-parameter threshold + multi-parameter combination". For example, it clearly defines the specific rules for triggering a high-level alarm when "equivalent stress exceeds the limit and temperature rises simultaneously". This mimics the diagnostic logic of experts, linking multiple isolated abnormal signals to achieve early and accurate identification of complex faults.

[0057] (2) The present invention systematically solves the problem that under the background of strong vibration and strong electromagnetic interference of the testing machine, the characteristics of early cable faults (such as fine cracks and local insulation deterioration) are extremely weak, and traditional filtering and FFT analysis methods are difficult to effectively extract, resulting in the lag of early warning.

[0058] This invention proposes a sequential combination of Singular Spectrum Analysis (SSA) and the Generalized Structural Shrinkage Algorithm (GSSA) to process vibration signals in cable bending tests. This fusion strategy fully leverages the advantages of different algorithms, improving the signal-to-noise ratio and classification accuracy. Specifically, median filtering and Butterworth low-pass filtering are used for signal preprocessing to suppress impulse noise and high-frequency interference. Then, SSA is used to decompose and reconstruct the vibration signal, extracting principal components and relevant time-domain features. GSSA is then used for sparse representation and soft-threshold shrinkage to enhance the saliency of fault features. Finally, the time-domain features extracted by SSA and the enhanced features from GSSA are normalized and concatenated to form a high-dimensional feature set that better characterizes the fault state. This set is then input into an SVM classifier to accurately identify the fault type (e.g., normal state, bending device fault, drive device fault) and its severity. This solution addresses the challenge of extracting subtle fault features in noisy environments using traditional methods, propelling the system from simple threshold-based alarms to model-based intelligent diagnosis, and significantly improving the accuracy of fault warnings and early detection capabilities.

[0059] (3) The present invention solves the problems of long cable bending test cycle and large data volume, traditional data acquisition system is prone to data loss and processing delay, and test data is disconnected from simulation model, alarm record and maintenance file, making it difficult to achieve full life cycle traceability;

[0060] This invention utilizes the LabVIEW platform to develop its modules. On the LabVIEW platform, an event-driven, producer-consumer architecture is employed to design the monitoring system's software modules. The producer loop is responsible for high-speed reading of data from the acquisition card and writing it to the buffer queue, while the consumer loop handles data decoding, filtering, compensation, display, storage, and alarm determination. This principle effectively avoids data blocking and loss, significantly improving system response speed and stability, and meeting the high real-time monitoring requirements of cable bending tests. This architecture ensures efficient parallelism between data acquisition (producer) and data processing, display, and storage (consumer), avoiding data blocking caused by I / O waits and meeting the real-time requirements under long-term, high-sampling-rate conditions.

[0061] The system of this invention connects to a relational database (such as SQL Server) through a LabSQL / ADO interface to achieve time-series alignment, visualization mapping, and parameterized querying of real-time monitoring data and cable simulation database.

[0062] (4) The system of this invention also has a complete alarm and traceability mechanism. Combining single-parameter threshold judgment and multi-parameter joint judgment rules, when the monitoring parameter exceeds the limit or the diagnostic model identifies a fault, the system of this invention will immediately trigger an audible and visual alarm and record structured alarm information. All data can be retrieved and exported according to multiple conditions such as time and test number. Through the database interface module, the system can realize the time sequence alignment and visualization mapping of real-time monitoring data and cable simulation database, providing data support for cable fatigue life prediction and reliability assessment.

[0063] In summary, the system of this invention realizes integrated management of the entire process, including real-time monitoring, intelligent diagnosis, structured alarm recording, database storage (time-series aligned with simulation data), historical data query / playback, and maintenance record association, forming a complete and traceable "data chain" that provides unprecedented data support for cable fatigue life prediction and reliability assessment. Attached Figure Description

[0064] Figure 1 This is a structural diagram of the monitoring system for the mining cable bending tester based on virtual instrument technology in this embodiment;

[0065] Figure 2 This is a schematic diagram of the visualization interface of the monitoring system for the mining cable bending tester under normal operating conditions in this embodiment;

[0066] Figure 3 This is a flowchart of the method for extracting weak fault features in a strong noise background based on SSA-GSSA feature enhancement fusion in this embodiment;

[0067] Figure 4 This is a schematic diagram illustrating the effect of the compensation algorithm used in this embodiment to correct the cable bending parameters;

[0068] Figure 5 This is a schematic diagram of the visualization interface of the monitoring system for the mining cable bending tester under fault conditions in this embodiment;

[0069] Figure 6 This is a flowchart of the monitoring method for the mining cable bending test machine based on virtual instrument technology in this embodiment. Detailed Implementation

[0070] To facilitate understanding of this application, specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. The following embodiments are illustrative of the invention but are not intended to limit its scope. Rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of this application.

[0071] The core of this invention lies in the construction of a complete hardware and software collaborative solution. On the hardware side, the system includes sensor modules for collecting multiple physical quantities such as bending count, bending curvature, dragging force, dragging speed, current, temperature, bending radius, tilt angle, and height adjustment, as well as a multi-channel data acquisition card electrically connected to the sensors. On the software side, the system uses a virtual instrument software module developed on the LabVIEW platform. This module adopts an event-driven, producer-consumer architecture to ensure real-time data acquisition and efficient processing. The producer loop is responsible for reading raw data from the data acquisition card and writing it to a queue, while the consumer loop is responsible for data decoding, filtering, compensation, display, storage, and alarm determination. The software module integrates functions such as user permission management, real-time data display, historical data query, alarm management, compensation algorithms, and database interfaces.

[0072] Example 1:

[0073] This embodiment provides a monitoring system for a mining cable bending tester based on virtual instrument technology, such as... Figure 1 As shown, the system includes: a mining cable bending tester, a data acquisition module, an online monitoring module, and an auxiliary management module;

[0074] The mining cable bending test machine is used to conduct bending fatigue tests on mining cables;

[0075] The data acquisition module is used to acquire cable bending parameters in real time through a multi-parameter cable bending sensor array installed at a specified position in the mining cable bending tester.

[0076] The online monitoring module collects cable bending parameters in real time by installing a multi-parameter sensor array for cable bending at a designated location in the mining cable bending tester. The parameters are then compared with preset thresholds, and alarm commands are generated based on the comparison results.

[0077] The cable bending parameters include: the number of cable bends, the cable bending radius, the dragging end force, the current, the temperature, the dragging speed, the base plate tilt angle, the height adjustment, and the equivalent stress of the wire core.

[0078] The inclination angle of the base plate is: the inclination angle of the working plane at the bottom of the drag cable of the mining cable bending tester; the height adjustment is: the height adjustment of the drag end of the mining cable bending tester.

[0079] In this embodiment, the coal mining machine cable of model MCPT-1.9 / 3.3 3185+195+4*10 is taken as the engineering object. It is mainly composed of power unit conductor, power unit insulation, control unit conductor, control unit insulation, control unit sheathing layer, ground core conductor, and outer sheath. In the supporting physical testing machine, namely the mining cable bending testing machine selected in this embodiment, the mining cable bending testing machine includes: a base, a height adjustment mechanism, an angle adjustment mechanism, a cable dragging plate, and a dragging mechanism. The base is constructed of high-strength steel truss components, and the height adjustment mechanism, angle adjustment mechanism, and cable dragging plate are sequentially fixed on the base according to the arrangement requirements. The height adjustment mechanism achieves lifting from 0.4m to 1.8m via a gear-rack and servo motor drive. The angle adjustment mechanism achieves tilt adjustment from 0° to 8° via a hydraulic cylinder or a shear-type lifting mechanism. The cable dragging plate is equipped with a cable clamping device for fixing one end of the coal mining machine cable. The dragging mechanism is arranged opposite to the cable dragging plate and is used to clamp and horizontally reciprocate the other end of the coal mining machine cable. The two work together to simulate the bending fatigue process of the coal mining machine cable, and the dragging mechanism achieves horizontal dragging via a dragging motor and gear-rack drive. All the above mechanical structures are equipped with mechanical limit switches, encoders, and overload protection to ensure safety.

[0080] The specific details of installing a multi-parameter sensor array for cable bending at a specific location in the mining cable bending testing machine are as follows: An LJA18 type inductive proximity switch is used to convert each bend of the coal mining machine cable into a pulse signal and count it to obtain the number of cable bends; a flexible curvature sensor is attached to the cable surface to output the cable bending curvature or bending radius value; a DYLY-103 S type tension sensor is installed at the connection between the cable tie and the cable clamp to measure tension in real time as the dragging end force and output an analog signal; a LEM LA25-NP type Hall current sensor is installed at the power core and control core of the coal mining machine cable to determine whether the cable has a short circuit or open circuit and to measure the current; armored thermocouples are attached along the outer sheath of the core to monitor the temperature change of the core; and an EX-m0dbus is installed at the cable tie beam. The RTU displacement velocity sensor obtains the dragging speed through pulse or frequency analysis; the dual-axis tilt sensor and TLS-05C laser ranging displacement sensor measure the tilt angle and height adjustment of the base plate, respectively; and strain gauge arrays or stress sensor arrays are arranged along key points to estimate the equivalent stress distribution of the wire core.

[0081] In this embodiment, one or more multi-channel data acquisition cards are used to simultaneously or time-divisionally receive multiple sensor signals, i.e., cable bending parameters, collected by a multi-parameter sensor array for cable bending. This embodiment can use a USB interface acquisition card supporting 16 channels, 16-bit resolution, and large-capacity buffering. The sensor channels are bound according to a preset mapping: Channel 1—base plate tilt angle; Channel 2—height adjustment; Channel 3—bending radius / curvature; Channel 4—drag speed; Channel 5—drag end force; Channels 6 & 7—power / control core current; Channels 8 & 9—power / control core temperature; Channels 10-15—core equivalent stress sampling points; Channel 16—bending pulse count. The host computer reads the acquisition card data into the LabVIEW program via USB. It is recommended to synchronously acquire multi-channel sensor data from the data acquisition card and write it to the buffer queue at a configurable sampling rate of 200Hz to meet the time-domain resolution requirements of the cable bending test. The acquisition card recognition status is verified using Measurement & Automation Explorer at startup. Figure 2 As shown, the collected cable bending parameters are visualized in the monitoring system of the mining cable bending tester.

[0082] The online monitoring module is used to extract features and diagnose faults in the cable bending parameters; at the same time, it uses a single-parameter threshold and multi-parameter combined judgment method to determine whether the cable bending parameters exceed the limits, and generates an alarm command by combining the judgment result and the fault diagnosis result.

[0083] The method for feature extraction and fault diagnosis of the cable bending parameters is as follows:

[0084] The cable bending parameters are denoised using median filtering to obtain denoised cable bending parameters; then, Butterworth low-pass filters are used to filter the denoised cable bending parameters to obtain preprocessed cable bending parameters.

[0085] In this embodiment, the consumer loop, i.e., the online monitoring module, extracts data from the cache queue for preprocessing: first, a median filter with a window length of 5 is used to suppress impulse noise, and then a fourth-order Butterworth low-pass filter with a cutoff frequency of 1kHz is used to eliminate high-frequency interference.

[0086] For any parameter among the cable bending parameters, the preprocessed parameter is regarded as a one-dimensional vibration signal. The one-dimensional vibration signal is extracted using a feature extraction method for weak faults in strong noise background based on SSA-GSSA feature enhancement fusion, and SSA feature vector and GSSA feature vector are obtained respectively.

[0087] like Figure 3As shown in the figure, this embodiment proposes a signal optimization method for a coal mining machine cable bending tester that combines Singular Spectrum Analysis (SSA) with the Generalized Structure Shrinkage Algorithm (GSSA). SSA is good at extracting dominant components from background noise, while GSSA can perform sparse representation and shrinkage of the signal, further enhancing the saliency of fault features.

[0088] The details are as follows:

[0089] The one-dimensional vibration signal is decomposed using singular value analysis: a window length is selected and a trajectory matrix is ​​constructed through delay mapping and singular value decomposition is performed. The signal components corresponding to the first k main singular values ​​are extracted and synthesized into an SSA reconstructed signal.

[0090] In this embodiment, when decomposing and reconstructing vibration signals based on singular spectrum analysis (SSA), the window length is taken as 1 / 3 of the signal length, and the first 3 principal components are retained.

[0091] The SSA reconstructed signal is used to calculate multi-domain statistical features, including peak index, kurtosis index, mean, standard deviation, variance, root mean square value, root square amplitude, average amplitude, centroid frequency, mean square frequency, and frequency variance, to form the SSA feature vector.

[0092] The GSSA algorithm is used to perform sparse representation and structural shrinkage on the SSA reconstructed signal to obtain the GSSA feature vector.

[0093] In this embodiment, the processing target of GSSA is switched from the original noise signal to the SSA reconstructed signal, and its parameter combination is optimized for this specific input, effectively overcoming the defect of poor feature enhancement selectivity of GSSA under strong noise. Finally, independent GSSA feature vectors are extracted and defined from the GSSA enhanced signal. Specifically, the process of sparse representation and structure shrinkage of the SSA reconstructed signal using the GSSA algorithm includes: optimizing at least one parameter of GSSA dictionary type, sparsity constraint threshold, and iteration number for the SSA reconstructed signal; under the optimized parameter set, the GSSA algorithm uses the generalized structure shrinkage algorithm GSSA with db4 wavelet basis for sparse representation of the input SSA reconstructed signal (regularization parameter λ=0.1); in the iterative solution process to obtain sparse representation, soft threshold shrinkage processing (threshold=0.05) is used to suppress residual noise components unrelated to fault characteristics, while enhancing the sparse coefficients corresponding to fault impact components. From the output signal after GSSA enhancement processing, a set of GSSA feature vectors that can characterize its sparse domain characteristics are extracted.

[0094] The SSA feature vector and GSSA feature vector are fused, and the fault diagnosis result of the cable bending parameters is determined based on the fusion processing result. The fusion processing is divided into feature-level fusion and decision-level fusion. The fault diagnosis result includes any one of the following fault categories: normal state, bending device fault, and drive device fault. For the output fault category, the confidence level output by the SVM classifier is used as the fault degree of the fault category.

[0095] In this embodiment, the feature vector is input into a trained SVM classifier, and the fault type and severity are determined based on the output. If the model outputs a class label of 1, the coal mining machine cable bending tester is judged to be in normal operation; if the output class label is 2, a bending device fault is judged; and if the output class label is 3, a drive device fault is judged. For faults of different severity, further judgments can be made based on the confidence level of the model output or other relevant indicators. The higher the confidence level, the more accurate the judgment of the fault type and severity. By formulating reasonable fault diagnosis decision rules and utilizing fused features and a trained SVM classifier, the fault status of the coal mining machine cable bending tester can be accurately determined, providing a basis for equipment maintenance and repair.

[0096] The details are as follows:

[0097] The feature-level fusion is as follows: the SSA feature vector and the GSSA feature vector are normalized, and the normalized SSA feature vector and the GSSA feature vector are concatenated to obtain a fused feature vector; a pre-trained SVM classifier is used to diagnose faults in the fused feature vector to generate fault diagnosis results for the cable bending parameters.

[0098] The decision-level fusion is as follows: the SSA feature vector and the GSSA feature vector are respectively input into two pre-trained SVM classifiers to obtain two preliminary diagnostic results; the two preliminary diagnostic results are weighted and averaged using a weighted voting method to generate the fault diagnosis result of the cable bending parameters.

[0099] In this embodiment, feature-level fusion involves normalizing the SSA and GSSA feature vectors and then concatenating them at the feature level to form a new, higher-dimensional super feature vector. Decision-level fusion employs a weighted voting method, with weights determined based on the performance of each model on the training set. Specifically, for the SSA and GSSA feature vectors, two classifiers, SVM_SSA and SVM_GSSA, are trained separately, and validated using a reserved validation set. Different voting weights are assigned to the two classifiers based on their performance metrics (such as accuracy, F1 score, and AUC value). Finally, the classification results from these two classifiers are combined through a weighted average to generate the final diagnostic conclusion.

[0100] The method for determining whether the cable bending parameter exceeds the limit by using a combination of single-parameter threshold and multi-parameter judgment, and generating an alarm command by combining the judgment result and the fault diagnosis result, is as follows:

[0101] The cable bending parameters are judged for single-parameter threshold exceedance. When any parameter in the cable bending parameters exceeds the preset parameter threshold, a general alarm signal is generated.

[0102] In this embodiment, single-parameter threshold determination serves as the basic monitoring layer, with the core function of detecting instantaneous exceedances of a single parameter. Examples of single parameters include: dragging speed exceeding the limit (default 0.5±0.1m / s), temperature exceeding the limit (e.g., power wire core 71). The system will issue a warning and record information after responding to a general alarm signal, such as highlighting the warning in yellow on the interface and logging the information. The handling method depends on the operator's intervention. For example, if the set number of bends (such as 9000 times) is reached, it is a low-level alarm and the system will not stop.

[0103] The cable bending parameters are subjected to multi-parameter joint over-limit judgment. When two or more parameters that are related in terms of fault mechanism exceed their respective parameter thresholds simultaneously or successively within a preset time window, an advanced alarm signal for the test is generated.

[0104] In this embodiment, multi-parameter joint judgment represents the system's intelligent diagnosis, which identifies potential compound faults by analyzing the correlation between parameters. Once triggered, the system will activate an automated emergency response mechanism, including the highest level of audible and visual alarms and forced pop-ups, automatically performing protective actions such as emergency shutdown, simultaneously saving snapshots of key data before and after the fault for in-depth analysis, and providing targeted maintenance guidance. For example, a high-level alarm is triggered when the equivalent stress exceeds the limit and the temperature rises simultaneously; if "abnormal fluctuations in dragging speed" are detected first, followed by "significantly increased dragging end force," even if the end force does not exceed the absolute threshold, the system can trigger a "suspected drive system fault" alarm, which is a high-level alarm specific to the experiment.

[0105] In addition to the single-parameter threshold and multi-parameter combined determination method, when an abnormality in the system's own operation or a safety risk in the experimental environment is detected, an advanced alarm signal for the equipment is generated.

[0106] The system's own operational abnormalities include: interruption of data communication links, failure of core software modules, or internal errors reported by lower-level control devices; the experimental environment safety risks include: failure of actuators or unauthorized intrusion into the experimental protection area.

[0107] In this embodiment, the alarm mechanism of the monitoring system for the mining cable bending tester not only targets the cable status, but also covers the comprehensive monitoring of the system's own health and the safety of the experimental environment. Such alarms are directly related to the safety of equipment and personnel, and are therefore defined as high-level alarms. A high-level alarm for the equipment will be generated and triggered when the following situations occur: (1) Communication interruption of the data acquisition link (such as continuous loss of a sensor signal), abnormal operation of the core diagnostic algorithm module, or feedback of fault codes from lower-level control devices such as servo drives and PLCs; (2) The mechanism is stuck, for example, the set parameters are adjusted to increase or decrease the angle, but the mechanism does not execute; (3) Personnel accidentally enter the experimental area while the mechanism is running.

[0108] The general alarm signal, the advanced alarm signal for the test, and the advanced alarm signal for the equipment are used as over-limit alarm signals;

[0109] The alarm signal for the cable bending parameters exceeding the limit is fused with the fault diagnosis results to generate the final alarm command;

[0110] The fusion rules are as follows:

[0111] If the fault diagnosis results do not detect any faults and no over-limit alarm signals are triggered, the system is in a normal state.

[0112] If the fault diagnosis result detects a fault but does not trigger any over-limit alarm signal, a warning command is generated.

[0113] If the fault diagnosis result does not detect a fault, but an over-limit alarm signal has been triggered, then an alarm command is generated based on the over-limit alarm signal.

[0114] If the fault diagnosis results detect a fault and an over-limit alarm signal has been triggered, then the highest-level alarm command is generated.

[0115] In this embodiment, the system implements threshold configuration and online verification for key parameters in national standards and project specifications, including dragging speed (0.5±0.1m / s), height adjustment (0.4m–1.8m), tilt angle (0°–8°), number of bends (9000 times), and bending radius limit (not less than 6 times the cable outer diameter), in terms of data acquisition, storage, and display. This is then integrated with algorithmic diagnostic results for judgment. Once an over-limit or diagnostic fault is detected, a structured alarm record (including time, type, value, and test number) and suggested operation prompts are immediately triggered. Specifically, the over-limit alarm performs immediate anomaly capture based on a single parameter threshold; while the diagnostic alarm uses an intelligent algorithm model to perform in-depth analysis of multi-dimensional features to identify potential and complex faults. Both can be triggered independently or work collaboratively: when the diagnostic alarm detects an early fault trend, it provides an early warning; and when the over-limit alarm is triggered and simultaneously confirmed by the diagnostic model, the system can trigger the highest-level alarm and execute emergency operations. All data is stored in a Microsoft SQL Server database through the LabSQL toolkit, supporting historical data playback, multi-condition retrieval, and export analysis, enabling traceability and intelligent decision support throughout the entire experiment process.

[0116] The auxiliary management module is used to provide user permission management, data correction, historical data management, alarm processing, equipment maintenance management and operation support for the monitoring system of the mining cable bending tester.

[0117] The data acquisition module, online monitoring module, and auxiliary management module are integrated into a virtual instrument platform, and communication connections are established between the modules within the platform.

[0118] In this embodiment, a data acquisition module, an online monitoring module, and an auxiliary management module are developed using the LabVIEW platform. The interfaces and signal connections between each software module and the hardware are implemented using BNC or industrial bus methods. The data link uses standard interfaces such as USB or RS485. The system settings module provides communication parameter configuration (such as baud rate, IP address, sampling frequency, etc.) and user permission setting functions to meet different field access and maintenance needs.

[0119] The auxiliary management module includes: a user login unit, a compensation algorithm unit, a historical record unit, an alarm query unit, a maintenance record unit, and a help unit.

[0120] The user login unit is used to verify the legitimacy of a user's identity through a username and account password, and to assign access permissions to the user based on the user's identity.

[0121] In this embodiment, the user can enter a username and corresponding password on the login interface of the mining cable bending tester monitoring system. At this time, the system uses the query function of the toolkit to search the user information table in the system database to verify the username and password. The user information table stores the username, account password, and access permissions of the users logging into the system. Only after the username and password are verified correctly can the user enter the system and access the corresponding parameter setting interface according to their permissions. Otherwise, a message will appear indicating that the username does not exist or the password is incorrect, requiring the user to re-enter the correct username or password. Clicking the "Cancel" button will directly exit the login interface and prevent access to the monitoring system.

[0122] The compensation algorithm unit is used to receive cable bending parameters from the data acquisition module, correct the cable bending parameters using the compensation algorithm selected by the user, output the corrected cable bending parameters, and save them.

[0123] The compensation algorithm includes a linear compensation algorithm and a nonlinear compensation algorithm; wherein the linear compensation algorithm is based on a predefined linear compensation model with a gain coefficient as the slope and an offset as the intercept, and the cable bending parameters are input into the linear compensation model for correction, and the corrected cable bending parameters are output.

[0124] The nonlinear compensation algorithm is as follows: For any sensor in the cable bending multi-parameter sensor array, several sets of calibration points containing measured values ​​and true values ​​are collected within the full range of the sensor, and a compensation curve or lookup table is constructed using all calibration points. Then, the cable bending parameters are corrected using the compensation curve or lookup table, and the corrected cable bending parameters are output.

[0125] In this embodiment, the compensation algorithm module provides linear / nonlinear compensation options. Users can save multiple sets of parameter configurations. Linear compensation is faster, while nonlinear compensation is slower. Specifically, the linear compensation option mainly targets proportional and fixed bias errors in sensor measurements. Its data processing uses a classic "slope-intercept" model Y=aX +b to correct the acquired raw signal; where Y is the standard value; a is the gain coefficient; x is the raw value acquired by the sensor; and b is the offset. After the user calibrates by inputting the standard value, the system automatically calculates the gain coefficient a and the offset b, thereby systematically correcting the sensor's zero-point drift and sensitivity error, ensuring the accuracy and linearity of all subsequent data. The nonlinear compensation option constructs a high-precision compensation curve or a lookup table by acquiring multiple calibration points (i.e., the correspondence between the raw signal and the true value) across the full measurement range. In actual monitoring, the system uses this curve or lookup table to perform real-time, non-linear mapping and correction on each input raw data point, effectively overcoming the inherent non-linear characteristics of the sensor and significantly improving the measurement accuracy of key parameters (such as tension and stress) across the entire range. The effect of correcting cable bending parameters using the compensation algorithm is shown below. Figure 4 As shown.

[0126] The historical record unit is used to write the cable bending parameters, mining cable bending fatigue test setting data and alarm status as historical data into a relational database, and supports querying and retrieving the historical data according to one or more predetermined conditions, and also supports exporting the query results as a data file in a specified format.

[0127] In this embodiment, the historical record unit supports retrieving historical data written to a local or remote relational database based on predetermined conditions such as time range, test number, sensor type, alarm status and data point that triggered the alarm, specific physical quantity value range (such as temperature, stress), and cable model batch. It can also export the query results to common data format files (Excel, CSV) for subsequent analysis and report generation.

[0128] The alarm query unit is used to respond to the alarm command and provide an alarm prompt, while generating and storing alarm logs.

[0129] The alarm log is generated by associating the alarm time, alarm type, alarm value, and alarm content description with the test number, operator, and system status information entered before the test, and is used for fault tracing and location.

[0130] In this embodiment, as Figure 5As shown, after responding to the alarm command, the system triggers the following series of actions: the system highlights the alarm on the front panel and reminds the user through a pop-up window; it automatically generates and stores the alarm log; the alarm log in this embodiment includes: alarm time, alarm type (e.g., overload alarm, abnormal temperature alarm), alarm instantaneous value, historical curves of the relevant channels that triggered the alarm, test number, operator, system status information, and an alarm description automatically generated by the system, which can be supplemented and entered by staff later. When an alarm is triggered, the system can optionally link with an external PLC or lower-level machine to perform emergency protection actions (e.g., stop the testing machine from running).

[0131] The maintenance record unit is used to input maintenance information of the mining cable bending tester and establish equipment maintenance files; based on the cumulative usage data of the mining cable bending tester, it generates a periodic maintenance plan for the mining cable bending tester and issues maintenance reminders on time; and it records the operating status data of the mining cable bending tester after maintenance to evaluate the maintenance effect of the mining cable bending tester.

[0132] In this embodiment, the maintenance record unit allows users to record maintenance information of the testing machine, such as maintenance time, maintenance content (e.g., replacement of parts, equipment calibration), and maintenance personnel, establishing a detailed equipment maintenance file. Based on the usage of the testing machine and the manufacturer's recommendations, a regular maintenance plan is developed and reminders are sent to the system to ensure timely and effective maintenance of the equipment. The effectiveness of each maintenance is evaluated, for example, by comparing equipment performance indicators before and after maintenance to determine whether the maintenance achieved the expected results, providing a reference for subsequent maintenance work.

[0133] The help unit is used to provide users with an operation guide, common problems and solutions, and contact information for technical support personnel for the monitoring system of the mining cable bending tester.

[0134] This embodiment provides a detailed operation guide for the system, including the usage methods, operation steps, and precautions for each functional module, to help users quickly get started and use the system correctly. Common problems encountered during system use and their corresponding solutions are compiled and listed, enabling users to quickly find solutions when encountering problems and reducing time wasted due to improper operation or system malfunctions. Contact information for technical support personnel, such as phone numbers and email addresses, is provided so that users can obtain professional technical support promptly when encountering complex problems.

[0135] The monitoring system for the mining cable bending test machine also includes a relational database for storing historical data of the bending fatigue test of mining cables; and the data acquisition module, online monitoring module and auxiliary management module are all connected to the relational database through a database access interface.

[0136] In this embodiment, the system supports establishing a bidirectional connection with relational databases, such as MySQL or SQL Server, through the LabSQL / ADO interface. This enables the visualization mapping, time-series alignment, and SQL-based parameterized queries between the cable simulation database (i.e., the relational database) and real-time monitoring data to support fatigue life prediction and historical data statistical analysis.

[0137] The monitoring system for the mining cable bending tester also includes: a visual human-computer interaction unit for providing a user login portal; receiving and configuring the operating parameters of the online monitoring module; displaying in real time the cable bending parameters collected by the data acquisition module and the cable bending parameters corrected by the compensation algorithm unit, as well as the changing waveforms; publishing the alarm logs generated by the alarm query unit and providing alarm prompts; and displaying the real-time working status of the mining cable bending tester.

[0138] In this embodiment, the monitoring system for the mining cable bending test machine supports waveform display, numerical display, and historical curve playback functions. The system's host computer interface features a user-friendly human-computer interaction design, specifically including: a parameter setting area, a real-time data display area, a waveform display area, an alarm prompt area, and an equipment status information area. It also supports online help editing and maintenance record entry to improve on-site operation convenience and maintenance management efficiency.

[0139] The monitoring system for the mining cable bending test machine also has a test mode for on-site verification. The test mode is used to simulate normal and abnormal working conditions, compare the obtained simulation data with the actual operating data of the monitoring system for the mining cable bending test machine, and perform engineering verification of the monitoring system for the mining cable bending test machine.

[0140] The test modes include three working modes: importing simulation data under normal operating conditions, importing simulation data under fault conditions, and full-process online detection.

[0141] In this embodiment, the system has test modes for field verification, including three working modes: normal operating condition simulation data import, fault operating condition simulation data import, and full-process online detection. Normal operating condition simulation data import refers to simulating the operating data of the cable in a healthy state to verify the stability of the system's baseline monitoring and data display functions. Fault operating condition simulation data import refers to simulating the automatic triggering of local alarms and recordings when various preset faults occur (such as excessive equivalent stress of cable cores, excessive drag force at the drag end, current short circuit / open circuit, excessive temperature, and reaching the lifespan target value after bending), thereby comprehensively verifying the accuracy and sensitivity of the system's early warning and alarm logic, and verifying its function of automatically triggering local alarms and recordings. Full-process online detection refers to simulating the complete closed loop from data acquisition to control output to verify the reliability and coordination of the overall system process.

[0142] Before conducting bending fatigue tests on mining cables using a monitoring system based on virtual instrument technology: Professional personnel should install and calibrate the sensors, check mechanical connections and grounding shielding, and configure communication parameters and thresholds. During the test: Test parameters (bending radius, dragging speed, target bending times, etc.) should be set via the host computer, the test should be started, and data from each channel should be monitored in real time. If an alarm occurs, the machine should be stopped immediately, and fault data should be recorded. After the test: The test report should be exported, and maintenance information should be registered in the maintenance record module. Maintenance recommendations include regular sensor calibration (recommended every six months or after each overhaul), lubrication of the guide rails, and checking for wear on gears and racks, replacing worn parts as needed.

[0143] To ensure the safety of equipment and personnel, the system of this invention incorporates multiple protections at both the hardware and software ends, including: mechanical limit switches and overload protection on the mechanical end, and encoder feedback in the drive system to prevent loss of synchronization; and communication anomaly detection, data anomaly filtering, automatic shutdown logic for prolonged inactivity, and alarm linkage mechanisms on the software end. The system records logs at critical stages and supports auditing and traceability.

[0144] It should be noted that the sensor models, sampling rates, thresholds, etc. listed in this embodiment are preferred implementation parameters. Users can select other brands or models of devices with equivalent functions according to their actual needs. At the same time, this system supports the expansion of sensor channels and advanced analysis functions, such as online spectrum analysis, short-time Fourier transform, SSA denoising, etc. These equivalent replacements or extensions are all within the protection scope of this invention.

[0145] Example 2:

[0146] This embodiment presents a monitoring method for a mining cable bending tester based on virtual instrument technology, such as... Figure 6 As shown, the method includes the following steps:

[0147] The cable bending parameters are collected and corrected in real time by a multi-parameter sensor array installed on the mining cable bending tester.

[0148] The corrected cable bending parameters were preprocessed by median filtering and Butterworth low-pass filtering in sequence to obtain the preprocessed cable bending parameters.

[0149] A feature extraction method for weak faults in a strong noise background based on SSA-GSSA feature enhancement fusion is used to extract features from any preprocessed cable bending parameter, and the fault diagnosis result of the cable bending parameter is determined based on the extracted SSA feature vector and GSSA feature vector.

[0150] A method combining single-parameter threshold and multi-parameter judgment is used to determine whether the cable bending parameters exceed the limit, and an alarm command is generated by combining the judgment results and fault diagnosis results.

[0151] In response to the alarm command, the system executes an alarm prompt, generates an alarm log, and synchronously writes the cable bending parameters, test setting data, and alarm status as historical data into a relational database.

[0152] Example 3:

[0153] This embodiment proposes a computer-readable storage medium that stores executable instructions. When these instructions are executed, if they are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0154] The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the monitoring method for mining cable bending test machine based on virtual instrument technology described in the various embodiments of this application.

[0155] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (e.g., SD (Secure Digital Memory Card) or DX (Memory Data Register, MDR) memory, etc.), random access memory (RAM), static random-access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, disk, optical disk, server, APP (Application) application store, and other media capable of storing program verification codes. These media store computer programs, which, when executed by a processor, can implement the various steps of the monitoring method for mining cable bending testing machines based on virtual instrument technology described above.

[0156] Example 4:

[0157] This embodiment proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the monitoring method for a mining cable bending test machine based on virtual instrument technology.

[0158] Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a computer program product.

[0159] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0160] The scope of protection of this application is not limited to the embodiments described above. Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from the scope and spirit of this disclosure. If such modifications and variations fall within the scope of this disclosure and its equivalents, then the intent of this disclosure also includes these modifications and variations.

Claims

1. A monitoring system for a mining cable bending test machine based on virtual instrument technology, characterized in that, The system includes: a mining cable bending tester, a data acquisition module, an online monitoring module, and an auxiliary management module; The mining cable bending test machine is used to conduct bending fatigue tests on mining cables. The data acquisition module is used to acquire cable bending parameters in real time through a multi-parameter cable bending sensor array installed at a specified position in the mining cable bending tester. The online monitoring module is used to extract features and diagnose faults in the cable bending parameters; at the same time, it uses a single-parameter threshold and multi-parameter joint judgment method to determine whether the cable bending parameters exceed the limits, and generates an alarm command by combining the limit judgment result and the fault diagnosis result. The auxiliary management module is used to provide user permission management, data correction, historical data management, alarm processing, equipment maintenance management and operation support for the monitoring system of the mining cable bending tester; The data acquisition module, online monitoring module, and auxiliary management module are integrated into a virtual instrument platform, and communication connections are established between the modules within the platform.

2. The monitoring system for a mining cable bending test machine based on virtual instrument technology according to claim 1, characterized in that, The monitoring system for the mining cable bending test machine also includes a relational database for storing historical data of the bending fatigue test of mining cables; and the data acquisition module, online monitoring module and auxiliary management module are all connected to the relational database through a database access interface.

3. The monitoring system for a mining cable bending test machine based on virtual instrument technology according to claim 2, characterized in that, The cable bending parameters include: the number of cable bends, the cable bending radius, the dragging end force, the current, the temperature, the dragging speed, the base plate tilt angle, the height adjustment, and the equivalent stress of the wire core.

4. The monitoring system for a mining cable bending test machine based on virtual instrument technology according to claim 3, characterized in that, The method for feature extraction and fault diagnosis of the cable bending parameters is as follows: The cable bending parameters are denoised using a median filter to obtain denoised cable bending parameters; then, a Butterworth low-pass filter is used to filter the denoised cable bending parameters to obtain preprocessed cable bending parameters. For any parameter in the cable bending parameters, the preprocessed parameter is regarded as a one-dimensional vibration signal. The one-dimensional vibration signal is extracted by the feature extraction method of strong noise background weak fault based on SSA-GSSA feature enhancement fusion, and the SSA feature vector and GSSA feature vector are obtained respectively. The details are as follows: The one-dimensional vibration signal is decomposed using singular value analysis: a window length is selected and a trajectory matrix is ​​constructed through delay mapping and singular value decomposition is performed. The signal components corresponding to the first k main singular values ​​are extracted and synthesized into an SSA reconstructed signal. The SSA reconstructed signal is used to calculate multi-domain statistical features, including peak index, kurtosis index, mean, standard deviation, variance, root mean square value, root square amplitude, average amplitude, centroid frequency, mean square frequency, and frequency variance, to form the SSA feature vector. The GSSA algorithm is used to perform sparse representation and structural shrinkage on the SSA reconstructed signal to obtain the GSSA feature vector; The SSA feature vector and GSSA feature vector are fused, and the fault diagnosis result of the cable bending parameters is determined based on the fusion processing result; wherein the fusion processing is divided into feature-level fusion and decision-level fusion. The fault diagnosis results include any one of the following fault categories: normal state, bending device fault, and drive device fault. For each fault category, the confidence level output by the SVM classifier is used as the fault severity. Details are as follows: The feature-level fusion is as follows: normalize the SSA feature vector and the GSSA feature vector, and concatenate the normalized SSA feature vector and the GSSA feature vector to obtain a fused feature vector; use a pre-trained SVM classifier to perform fault diagnosis on the fused feature vector to generate the fault diagnosis result of the cable bending parameters. The decision-level fusion is as follows: the SSA feature vector and the GSSA feature vector are respectively input into two pre-trained SVM classifiers to obtain two preliminary diagnostic results; the two preliminary diagnostic results are weighted and averaged using a weighted voting method to generate the fault diagnosis result of the cable bending parameters.

5. The monitoring system for a mining cable bending test machine based on virtual instrument technology according to claim 4, characterized in that, The method for determining whether the cable bending parameter exceeds the limit by using a combination of single-parameter threshold and multi-parameter judgment, and generating an alarm command by combining the judgment result and the fault diagnosis result, is as follows: The cable bending parameters are judged to exceed the threshold of a single parameter. When any parameter of the cable bending parameters exceeds the preset parameter threshold, a general alarm signal is generated. The cable bending parameters are subjected to multi-parameter joint over-limit judgment. When two or more parameters that are related in the fault mechanism exceed their respective parameter thresholds simultaneously or successively within a preset time window, an advanced alarm signal for the test is generated. In addition to the single-parameter threshold and multi-parameter joint determination method, when an abnormality in the system's own operation or a safety risk in the experimental environment is detected, an advanced alarm signal for the equipment is generated. The general alarm signal, the advanced alarm signal for the test, and the advanced alarm signal for the equipment are used as over-limit alarm signals; The alarm signal for the cable bending parameters exceeding the limit is fused with the fault diagnosis results to generate the final alarm command; The fusion rules are as follows: If the fault diagnosis results do not detect any faults and no over-limit alarm signals are triggered, the system is in a normal state. If the fault diagnosis result detects a fault but does not trigger any over-limit alarm signal, a warning command is generated; If the fault diagnosis result does not detect a fault, but an over-limit alarm signal has been triggered, then an alarm command is generated based on the over-limit alarm signal. If the fault diagnosis results detect a fault and an over-limit alarm signal has been triggered, then the highest-level alarm command is generated.

6. The monitoring system for a mining cable bending test machine based on virtual instrument technology according to claim 5, characterized in that, The auxiliary management module includes: a user login unit, a compensation algorithm unit, a historical record unit, an alarm query unit, a maintenance record unit, and a help unit; The user login unit is used to verify the legitimacy of a user's identity through a username and account password, and to assign access permissions to the user based on the user's identity. The compensation algorithm unit is used to receive cable bending parameters from the data acquisition module, correct the cable bending parameters through the compensation algorithm selected by the user, output the corrected cable bending parameters and save them; The historical record unit is used to write the cable bending parameters, mining cable bending fatigue test setting data and alarm status as historical data into a relational database, and supports querying and retrieving the historical data according to one or more predetermined conditions, and also supports exporting the query results as a data file in a specified format. The alarm query unit is used to respond to the alarm command and provide an alarm prompt, while generating and storing alarm logs; The maintenance record unit is used to input maintenance information of the mining cable bending tester and establish equipment maintenance files; based on the cumulative usage data of the mining cable bending tester, it generates a periodic maintenance plan for the mining cable bending tester and issues maintenance reminders on time; it records the operating status data of the mining cable bending tester after maintenance in order to evaluate the maintenance effect of the mining cable bending tester. The help unit is used to provide users with an operation guide, common problems and solutions, and contact information for technical support personnel for the monitoring system of the mining cable bending tester.

7. The monitoring system for a mining cable bending test machine based on virtual instrument technology according to claim 6, characterized in that, The compensation algorithm includes a linear compensation algorithm and a nonlinear compensation algorithm; wherein the linear compensation algorithm is based on a predefined linear compensation model with a gain coefficient as the slope and an offset as the intercept, and the cable bending parameters are input into the linear compensation model for correction, and the corrected cable bending parameters are output. The nonlinear compensation algorithm is as follows: For any sensor in the cable bending multi-parameter sensor array, several sets of calibration points containing measured values ​​and true values ​​are collected within the full range of the sensor, and a compensation curve or lookup table is constructed using all calibration points. Then, the cable bending parameters are corrected using the compensation curve or lookup table, and the corrected cable bending parameters are output.

8. The monitoring system for a mining cable bending test machine based on virtual instrument technology according to claim 7, characterized in that, The monitoring system for the mining cable bending tester also includes: a visual human-computer interaction unit for providing a user login portal; receiving and configuring the operating parameters of the online monitoring module; displaying in real time the cable bending parameters collected by the data acquisition module and the cable bending parameters corrected by the compensation algorithm unit, as well as the changing waveforms; publishing the alarm logs generated by the alarm query unit and providing alarm prompts; and displaying the real-time working status of the mining cable bending tester.

9. The monitoring system for a mining cable bending test machine based on virtual instrument technology according to claim 8, characterized in that, The monitoring system for the mining cable bending test machine also has a test mode for on-site verification. The test mode is used to simulate normal and abnormal working conditions, compare the obtained simulation data with the actual operating data of the monitoring system for the mining cable bending test machine, and perform engineering verification of the monitoring system for the mining cable bending test machine. The test modes include three working modes: importing simulation data under normal operating conditions, importing simulation data under fault conditions, and full-process online detection.

10. A monitoring method for a mining cable bending tester based on virtual instrument technology, implemented using the mining cable bending tester monitoring system based on virtual instrument technology as described in any one of claims 1-9, characterized in that, This method includes the following steps: The cable bending parameters are collected and corrected in real time by a multi-parameter sensor array installed on the mining cable bending tester. The corrected cable bending parameters were preprocessed by median filtering and Butterworth low-pass filtering in sequence to obtain the preprocessed cable bending parameters. A feature extraction method for weak faults in a strong noise background based on SSA-GSSA feature enhancement fusion is used to extract features from any preprocessed cable bending parameter, and the fault diagnosis result of the cable bending parameter is determined based on the extracted SSA feature vector and GSSA feature vector. A method combining single-parameter threshold and multi-parameter judgment is used to determine whether the cable bending parameters exceed the limit, and an alarm command is generated by combining the judgment results and fault diagnosis results. In response to the alarm command, the system executes an alarm prompt, generates an alarm log, and synchronously writes the cable bending parameters, test setting data, and alarm status as historical data into a relational database.

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