Electric spark nondestructive testing and evaluating method for integrity of waterproof layer of cable trench

Abstract

The invention discloses an electric spark nondestructive testing evaluation method for the integrity of a waterproof layer of a cable trench, and belongs to the technical field of nondestructive testing, and the method comprises the following steps: applying a multi-frequency electric signal through a preset moving electrode; the initial contact pressure of the moving electrode and the surface of the waterproof layer is obtained in real time, the pressure of the contact position of the moving electrode is adjusted, an electric spark signal generated at the defect position of the cable trench waterproof layer by a multi-frequency electric signal applied by the moving electrode is captured, and characteristic parameters of the electric spark signal are extracted to generate current electric spark characteristic data; utilizing the current electric spark characteristic data to distinguish defect types so as to generate a defect evaluation result; and outputting defect distribution information and triggering an acousto-optic alarm according to a defect evaluation result. According to the method, a detection mode of combining multi-frequency electric signal excitation and electric spark signal feature extraction and analysis is adopted, waterproof layer defect types can be distinguished, the severity of the waterproof layer defects can be evaluated, and the detection accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, and in particular to an electrical spark nondestructive testing method for evaluating the integrity of waterproof layers in cable trenches. Background Technology

[0002] As a vital carrier for urban power and communication lines, the stability of the internal environment of cable trenches is crucial for the safe operation of cables. To prevent groundwater seepage and moisture erosion, a waterproof layer is typically laid on the inner wall of cable trenches. During construction or long-term use, this waterproof layer may develop defects such as pinholes, cracks, and thinning due to material aging, compromising its integrity. Therefore, regular non-destructive testing is necessary, and spark testing is one of the commonly used techniques for assessing the integrity of such insulation coatings.

[0003] Existing spark testing technology, when applied to the inspection of waterproof layers in cable trenches, typically uses a single high-voltage DC power supply. The inspector holds a probe and scans the surface of the waterproof layer. When the probe passes over a defect, the high voltage breaks down the air or medium at the defect location, creating an electric spark. The instrument then emits an audible and visual alarm, indicating the presence of the defect. The inspector manually records the approximate location of the defect based on the alarm signal.

[0004] However, existing detection methods provide relatively simple results, only indicating the presence or absence of defects, without providing deeper information such as defect type, size, or severity, leading to a lack of targeted maintenance decisions. Secondly, the detection process is significantly affected by human and environmental factors. Manually controlled probe pressure is unstable, potentially causing poor contact leading to missed detections, or excessive pressure damaging the waterproofing layer. Fixed detection voltages also cannot adapt to changes in ambient temperature and humidity, easily resulting in false alarms or missed detections. Finally, the recording and processing of detection results rely on manual labor, which is inefficient and prone to errors, failing to create a clear defect distribution map and hindering a macroscopic assessment of the overall condition of the waterproofing layer. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an electrical spark nondestructive testing method for assessing the integrity of waterproof layers in cable trenches. This method employs a combination of multi-frequency electrical signal excitation, electrical spark signal feature extraction, and analysis, enabling the differentiation of waterproof layer defect types, assessment of their severity, and improvement of testing accuracy.

[0006] The above objectives can be achieved through the following approach: A non-destructive testing (NDT) method for assessing the integrity of a waterproof layer in a cable trench includes generating a multi-frequency electrical signal containing multiple frequency components and applying the multi-frequency electrical signal through a preset moving electrode; acquiring the initial contact pressure between the moving electrode and the surface of the waterproof layer in real time, and adjusting the pressure at the contact position of the moving electrode based on the initial contact pressure; capturing the electrical spark signal generated at a defect in the cable trench waterproof layer by the multi-frequency electrical signal applied by the moving electrode based on the adjusted pressure, extracting the characteristic parameters of the electrical spark signal to generate current electrical spark characteristic data; using the current electrical spark characteristic data to distinguish defect types to generate a defect assessment result; and outputting defect distribution information and triggering an audible and visual alarm based on the defect assessment result.

[0007] Optionally, generating a multi-frequency electrical signal containing multiple frequency components includes: acquiring a frequency range parameter for defining the frequency range of the signal, and determining multiple different frequency components of the electrical signal based on the frequency range parameter; acquiring real-time environmental parameter data, and adjusting the output power of the electrical signal based on the environmental parameter data; and generating a multi-frequency electrical signal based on the multiple different frequency components and the output power.

[0008] Optionally, acquiring real-time environmental parameter data and adjusting the output power of the electrical signal based on the environmental parameter data includes: separating the environmental humidity parameter and the surface temperature parameter from the real-time environmental parameter data; calculating the equivalent environmental impedance based on the environmental humidity parameter and the surface temperature parameter; determining the power adjustment parameter based on the equivalent environmental impedance; and adjusting the output power of the electrical signal based on the power adjustment parameter.

[0009] Optionally, the step of acquiring the initial contact pressure between the movable electrode and the surface of the waterproof layer in real time, and adjusting the pressure at the contact position of the movable electrode based on the initial contact pressure, includes: acquiring the initial contact pressure between the movable electrode and the surface of the waterproof layer in real time to generate target pressure data; determining a pressure range for maintaining stable electrical contact without damaging the waterproof layer; comparing the target pressure data with the pressure range in real time to generate a pressure adjustment command; and adjusting the pressure at the contact position of the movable electrode according to the pressure adjustment command.

[0010] Optionally, the step of capturing the electric spark signal generated at the defect in the waterproof layer of the cable trench by the multi-frequency electrical signal applied by the moving electrode based on the adjusted pressure, and extracting the feature parameters of the electric spark signal to generate the current electric spark feature data includes: capturing the electric spark signal generated at the defect in the waterproof layer of the cable trench by the multi-frequency electrical signal applied by the moving electrode based on the adjusted pressure; performing feature processing on the electric spark signal to obtain the signal amplitude; determining whether the signal amplitude is greater than a preset amplitude threshold; if not, determining that there is no defect.

[0011] Optionally, the step of capturing the electric spark signal generated at the defect in the waterproof layer of the cable trench based on the adjusted pressure and the multi-frequency electrical signal applied by the moving electrode, and extracting the characteristic parameters of the electric spark signal to generate current electric spark characteristic data, further includes: if the signal amplitude is greater than the amplitude threshold, analyzing the waveform of the electric spark signal to obtain a spark intensity parameter; determining the start and end time points of the waveform of the electric spark signal to obtain a spark duration parameter; identifying the dominant frequency component when the electric spark signal occurs; and combining the spark intensity parameter, the spark duration parameter, and the dominant frequency component to generate current electric spark characteristic data.

[0012] Optionally, identifying the dominant frequency component when the electric spark signal occurs includes: locating the electrical signal segment within the time window of the electric spark signal occurrence; performing spectral analysis on the electrical signal segment to generate spectral data; obtaining the energy value of each frequency point in the spectral data and sorting the energy values ​​of each frequency point from largest to smallest; and selecting the frequency component corresponding to the frequency point with the highest energy value as the dominant frequency component.

[0013] Optionally, the step of using the current electrical discharge feature data to distinguish defect types and generate defect assessment results includes: collecting historical defect types and historical electrical discharge feature data corresponding to the historical defect types to obtain a historical dataset; using the historical electrical discharge feature data as input and the defect type labels corresponding to the historical defect types as output, establishing and training a neural network model using the historical dataset to obtain a defect classification model; inputting the current electrical discharge feature data into the defect classification model to output the current defect type label; calculating the current defect severity score based on the current electrical discharge feature data; and combining the current defect type label with the current defect severity score to generate a defect assessment result.

[0014] Optionally, the step of outputting defect distribution information and triggering an audible and visual alarm based on the defect assessment result includes: extracting defect location information based on the position of the moving electrode; generating defect distribution information based on the defect location information and the defect assessment result; and triggering a differentiated audible and visual alarm based on the defect distribution information.

[0015] Based on the same inventive concept, this invention also provides an electrical spark nondestructive testing and evaluation system for the integrity of waterproof layers in cable trenches. The system includes: a signal generation module for generating a multi-frequency electrical signal containing multiple frequency components and applying the multi-frequency electrical signal through a preset moving electrode; a pressure control module for acquiring the initial contact pressure between the moving electrode and the surface of the waterproof layer in real time, and adjusting the pressure at the contact position of the moving electrode based on the initial contact pressure; a feature extraction module for capturing electrical spark signals generated at defects in the cable trench waterproof layer by the multi-frequency electrical signal applied by the moving electrode based on the adjusted pressure, extracting feature parameters of the electrical spark signals to generate current electrical spark feature data; a defect analysis module for distinguishing defect types using the current electrical spark feature data to generate defect evaluation results; and an alarm module for outputting defect distribution information and triggering an audible and visual alarm based on the defect evaluation results.

[0016] Compared with the prior art, the present invention has the following advantages: 1. This invention, by introducing multi-frequency electrical signal excitation and combining it with the analysis of electrical spark characteristic parameters, achieves accurate differentiation of defect types and quantitative assessment of severity. It can provide users with detailed information on the nature and hazards of defects, providing a scientific basis for subsequent maintenance decisions and improving the precision of detection and the accuracy of assessment. 2. This invention ensures the stable application of detection signals on uneven surfaces and avoids physical damage to the waterproof layer by dynamically adjusting the contact pressure of the moving electrode; at the same time, it adjusts the signal output power according to the ambient temperature and humidity, effectively eliminating interference from environmental factors, thereby improving the reliability, stability and repeatability of the detection results under different working conditions. 3. This invention utilizes a neural network model to automatically classify defects, reducing reliance on operator experience. Simultaneously, it binds defect assessment results with location information to generate visualized defect distribution information and trigger differentiated audible and visual alarms, achieving full automation from data acquisition to result presentation and risk warning, thereby improving on-site inspection efficiency, safety, and user experience.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic flowchart of an electric spark non-destructive testing method for evaluating the integrity of a waterproof layer in a cable trench, according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the time-domain waveform of the multi-frequency electrical signal according to an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the multi-frequency electrical signal spectrum according to an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram of the time domain segment of the electrical spark signal according to an embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram of the dominant frequency components of the electrical spark signal in an embodiment of the present invention.

[0024] Figure 6 This is a schematic diagram illustrating the principle of classifying defect types using a neural network model according to an embodiment of the present invention.

[0025] Figure 7 This is a schematic diagram of the structure of an electrical spark non-destructive testing and evaluation system for the integrity of waterproof layers in cable trenches, according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Reference Figure 1 One embodiment of the present invention proposes an electrical spark nondestructive testing method for evaluating the integrity of waterproof layers in cable trenches. The method employs a combination of multi-frequency electrical signal excitation, electrical spark signal feature extraction and analysis, which can distinguish the types of defects in the waterproof layer, assess their severity, and improve the accuracy of the test.

[0028] The method described in this embodiment specifically includes: A multi-frequency electrical signal containing multiple frequency components is generated, and the multi-frequency electrical signal is applied through a preset moving electrode; The initial contact pressure between the moving electrode and the surface of the waterproof layer is acquired in real time, and the pressure at the contact position of the moving electrode is adjusted based on the initial contact pressure. Based on the adjusted pressure, the electric spark signal generated at the defect of the cable trench waterproof layer by the multi-frequency electrical signal applied by the moving electrode is captured, and the characteristic parameters of the electric spark signal are extracted to generate the current electric spark characteristic data. The current electrical spark characteristic data is used to distinguish defect types in order to generate defect assessment results; Based on the defect assessment results, output defect distribution information and trigger an audible and visual alarm.

[0029] This invention first employs a composite electrical signal containing multiple frequency components as an excitation source, applied to the surface of a cable trench waterproof layer via a moving electrode. The aim is to enhance detection breadth by utilizing the different responses of different frequency signals to various types of defects. Real-time pressure feedback control dynamically adjusts the contact pressure between the electrode and the waterproof layer surface, ensuring the stability and non-destructive nature of the signal application. When the moving electrode passes a defect, the generated electrical spark signal is captured by the system, and its features are extracted to generate a set of current electrical spark characteristic data describing the nature of the discharge event. Subsequently, the system uses this set of characteristic data to classify the defect type, thereby obtaining a defect assessment result that includes a nature judgment. Finally, this assessment result is combined with the position information of the moving electrode to generate a macroscopic distribution of defects, and triggers corresponding audible and visual alarms based on the severity of the assessment, achieving a complete technical closed loop from detection and analysis to early warning.

[0030] This invention employs multi-frequency electrical signal detection, combined with in-depth analysis and classification of electrical spark characteristic parameters. This allows for detection that goes beyond simply determining the presence or absence of defects, enabling the differentiation of specific defect types and improving the precision and accuracy of the assessment. Secondly, real-time adaptive pressure control effectively overcomes measurement errors caused by surface unevenness or improper human operation, ensuring the stability of the detection process and the repeatability of the results. It also ensures non-destructive testing, avoiding secondary damage to the waterproof layer. Finally, the assessment results are fused with location information to generate intuitive defect distribution information and trigger differentiated audible and visual alarms. This provides immediate and clear guidance for on-site operators, improving detection efficiency and the scientific basis of subsequent maintenance decisions, thus enhancing the intelligence level and engineering practical value of cable trench waterproof layer integrity detection.

[0031] Optionally, generating a multi-frequency electrical signal containing multiple frequency components includes: Obtain frequency range parameters to define the frequency range of a signal, and determine multiple different frequency components of the electrical signal based on the frequency range parameters; Acquire real-time environmental parameter data, and adjust the output power of the electrical signal based on the environmental parameter data; Multi-frequency electrical signals are generated based on multiple different frequency components and output power.

[0032] Specifically, the system first pre-sets a frequency range parameter to define the signal frequency range. This parameter defines the lowest and highest frequencies of the detection signal, aiming to cover the response characteristics of various defect types that may occur in the cable trench waterproofing layer. Based on this frequency range parameter, the system internally determines one or more sets of discrete frequency components, which constitute the basis of the multi-frequency electrical signal. Next, the system acquires real-time environmental parameter data from the site through integrated environmental sensors, with key data including ambient humidity and surface temperature. Based on this environmental parameter data, especially the influence of humidity and temperature on the surface electrical properties of the waterproofing layer, the system dynamically adjusts the output power of the signal generator. This adjustment process ensures that the electric field strength applied to the surface of the waterproofing layer remains at an effective and safe level regardless of whether the environment is dry or humid, avoiding missed detections or false alarms due to environmental changes. Finally, the signal generator superimposes and synthesizes the aforementioned multiple different frequency components, and generates the final multi-frequency electrical signal for detection with an output power adjusted for environmental compensation. This signal is output to the moving electrode, providing a stable and highly sensitive excitation source for subsequent electric spark detection. Figure 2 As shown, the time-domain waveform of a composite signal composed of multiple superimposed sine waves of different frequencies is illustrated, as well as... Figure 3 As shown, the corresponding spectrum diagram contains frequency components of 50Hz, 200Hz, and 500Hz.

[0033] Optionally, acquiring real-time environmental parameter data and adjusting the output power of the electrical signal based on the environmental parameter data includes: The ambient humidity parameter and surface temperature parameter are separated from the real-time environmental parameter data; Calculate the equivalent environmental impedance based on the environmental humidity parameter and the surface temperature parameter; The power adjustment parameters are determined based on the equivalent environmental impedance. The output power of the electrical signal is adjusted based on the power adjustment parameters.

[0034] Specifically, the system first continuously collects environmental parameter data from the site using integrated sensors deployed on the detection equipment. From this composite data stream, the system separates the ambient humidity and surface temperature parameters. The ambient humidity parameter reflects the water vapor content in the air, directly affecting the conductivity of the waterproof layer surface; the surface temperature parameter affects the dielectric constant and resistivity of the waterproof layer material itself. Subsequently, the system uses a pre-defined empirical model to calculate a comprehensive index, namely the equivalent environmental impedance, based on the real-time acquired ambient humidity and surface temperature parameters. This equivalent environmental impedance aims to quantify the combined impact of current environmental conditions on the propagation and leakage of electrical signals on the waterproof layer surface. This calculation follows the model below: , in, This represents the calculated equivalent environmental impedance; The reference environmental impedance is measured under standard test conditions and serves as a fixed reference value. The ambient humidity parameters are obtained in real time through a humidity sensor; The surface temperature parameters are obtained in real time through a temperature sensor; This is a dimensionless function characterizing the combined effect of humidity and temperature changes on impedance. The form and coefficients of this function are typically obtained through fitting and calibration with a large amount of experimental data. After obtaining the equivalent environmental impedance, the system determines a power adjustment parameter based on this value. This parameter is used to dynamically adjust the output power of the electrical signal to compensate for the effects of environmental changes. Its core objective is to maintain a constant effective electric field strength applied to the waterproof layer. The determination of the power adjustment parameter usually follows a positive correlation with the equivalent environmental impedance; that is, when the environmental impedance increases, the output power is appropriately increased to ensure effective breakdown of the air gap at the defect; conversely, when the environmental impedance decreases, the output power is reduced to avoid false alarms caused by increased surface conductivity. Finally, the system applies this power adjustment parameter to the signal generator, adjusting the final output power of the multi-frequency electrical signal in real time to complete a closed-loop adaptive power regulation.

[0035] Optionally, the step of acquiring the initial contact pressure between the movable electrode and the surface of the waterproof layer in real time, and adjusting the contact position of the movable electrode based on the initial contact pressure, includes: The initial contact pressure between the moving electrode and the surface of the waterproof layer is acquired in real time to generate target pressure data; Determine the pressure range required to maintain stable electrical contact without damaging the waterproofing layer; The target pressure data is compared with the pressure range in real time to generate a pressure adjustment command; Adjust the pressure at the contact position of the moving electrode according to the pressure adjustment command.

[0036] Specifically, a pressure sensor is first integrated into the probe assembly of the moving electrode. This sensor monitors the normal force generated when the electrode contacts the surface of the cable trench waterproofing layer in real time, converting this force signal into an electrical signal. After processing, continuous target pressure data is generated. Simultaneously, a key parameter is preset within the system: the pressure range used to maintain stable electrical contact without damaging the waterproofing layer. This pressure range includes a minimum pressure threshold and a maximum pressure threshold, determined through prior experimental calibration or according to material standards, based on the mechanical and electrical properties of the waterproofing material being tested. The minimum pressure threshold ensures a continuous and effective electrical path between the electrode and the waterproofing layer surface, preventing signal interruption or attenuation due to poor contact. The maximum pressure threshold ensures that the applied pressure will not cause indentations, scratches, or structural damage to the waterproofing layer, guaranteeing the non-destructive nature of the test. During the test, the system continuously compares the real-time collected target pressure data with the preset pressure range. If the target pressure data is lower than the minimum pressure threshold, the control unit generates a pressure adjustment command to increase the pressure; if the target pressure data is higher than the maximum pressure threshold, a pressure adjustment command to decrease the pressure is generated; if the pressure is within the preset range, the command maintains the current state. Finally, the pressure adjustment command is sent to a servo drive or pneumatic actuator connected to the moving electrode. This actuator precisely adjusts the vertical position of the moving electrode or the applied thrust according to the command, thereby dynamically adjusting the pressure at the contact position of the moving electrode, forming a closed-loop adaptive pressure control system.

[0037] Optionally, the step of capturing the electric spark signal generated at the defect in the waterproof layer of the cable trench based on the adjusted pressure and extracting the characteristic parameters of the electric spark signal to generate the current electric spark characteristic data includes: Based on the adjusted pressure, the electric spark signal generated at the defect of the cable trench waterproofing layer by the multi-frequency electrical signal applied by the moving electrode is captured. The electrical spark signal is subjected to feature processing to obtain the signal amplitude; Determine whether the signal amplitude is greater than a preset amplitude threshold; If not, it is determined to be defect-free.

[0038] Specifically, after the contact pressure between the moving electrode and the surface of the waterproof layer is precisely adjusted and stabilized within a preset range, the moving electrode continuously applies multi-frequency electrical signals to the waterproof layer. The system monitors the electrical signals in the electrode circuit in real time with a high sampling rate. When the moving electrode sweeps across a defective area of ​​the cable trench waterproof layer, such as a pinhole or crack, the multi-frequency electrical signal breaks down the air gap at the defect due to the damage to the insulation performance, generating a transient, high-energy discharge pulse, which is the electric spark signal. This signal is completely captured by the data acquisition unit. Next, the system performs feature processing on the captured electric spark signal, first extracting its signal amplitude. The signal amplitude usually refers to the peak voltage or peak current of the electric spark signal on the time waveform, which is the most direct parameter characterizing the strength of the discharge energy. The system compares the extracted signal amplitude with a preset amplitude threshold. This amplitude threshold is determined by testing on a standard defect-free waterproof layer sample, analyzing the background noise level, and retaining a sufficient safety margin. If the measured signal amplitude is not greater than the threshold value, the system determines that the signal is background noise or meaningless weak fluctuations, rather than a discharge caused by a real defect. In this case, the system determines that the current detection location is defect-free and continues scanning and detection of subsequent areas without further analysis of the signal.

[0039] Optionally, the step of capturing the electric spark signal generated at the defect in the waterproof layer of the cable trench based on the adjusted pressure and extracting the characteristic parameters of the electric spark signal to generate the current electric spark characteristic data further includes: If the signal amplitude is greater than the amplitude threshold, the waveform of the electric spark signal is analyzed to obtain the spark intensity parameter; Determine the start and end times of the waveform of the electrical spark signal to obtain the spark duration parameter; Identify the dominant frequency components when the electrical spark signal occurs; The spark intensity parameter, the spark duration parameter, and the dominant frequency component are combined to generate the current electric spark characteristic data.

[0040] Specifically, the system first analyzes the complete waveform of the valid electric spark signal to obtain the spark intensity parameter. This parameter is typically obtained by calculating the integral of the signal waveform's envelope on the time axis. It reflects the total energy released in a single discharge event and is an important indicator of defect severity. Its calculation model can be expressed as: , in, Represents the spark intensity parameter; The voltage function represents the electrical spark signal over time; the integration interval is the entire duration of the electrical spark signal. Simultaneously, the system uses an algorithm to precisely determine the start and end times of the electrical spark signal waveform; the time difference between these two times is defined as the spark duration parameter. The start time is typically defined as the moment when the signal amplitude first stabilizes above the background noise level, and the end time is the moment when the signal amplitude last decays below the background noise level. This parameter reflects the persistence of the discharge process and is related to the geometry of the defect. Next, the system performs spectral analysis on the time segment of the electrical spark signal to identify the dominant frequency component that plays a decisive role in this discharge event. This process involves performing a Fast Fourier Transform on the signal segment to generate spectral data, and then identifying the frequency with the highest energy proportion from the spectral data; this frequency is the dominant frequency component. Finally, the system structurally combines the calculated spark intensity parameter, spark duration parameter, and the identified dominant frequency component to form a multi-dimensional feature vector. This vector represents the current electrical spark feature data and is used for subsequent defect type and severity assessment.

[0041] Optionally, identifying the dominant frequency component when the electrical spark signal occurs includes: Locate the electrical signal segment within the time window of the electrical spark signal occurrence; Perform spectral analysis on the electrical signal segment to generate spectral data; Obtain the energy value of each frequency point in the spectrum data, and sort the energy values ​​of each frequency point from largest to smallest; The frequency component corresponding to the frequency point with the highest energy value is selected as the dominant frequency component.

[0042] Specifically, the system first locates the captured raw electrical signal in time. Using the determined start and end times of the electrical spark signal, the system extracts a segment of the electrical signal containing the complete discharge process from the continuous data stream. This time window ensures that the analysis is limited to the signal portion directly related to the defect discharge, eliminating irrelevant background noise. Next, the system performs spectral analysis on this signal segment, typically by executing a Fast Fourier Transform (FFT) algorithm to convert the signal from a time-domain representation to a frequency-domain representation, thereby generating spectral data. This spectral data visually displays the various frequency components constituting the signal segment and their corresponding intensities. To quantify these intensities, the system further obtains the energy value corresponding to each discrete frequency point in the spectral data. A common calculation method is to calculate the square of the modulus of the Fourier transform result at that frequency point to obtain the power spectral density. , in, Representing frequency point The corresponding energy value; The frequency points are obtained through Fast Fourier Transform. Complex values ​​on; This represents the total number of data points in the electrical signal segment, used for normalization. After calculating the energy values ​​of all frequency points, the system sorts these energy values ​​from largest to smallest. Finally, the system selects the frequency point ranked first in the energy value sorting result; the frequency component corresponding to this frequency point is determined as the dominant frequency component of this electrical spark event and is output as one of the key features. Figure 4 As shown, this illustrates a segment of the electrical spark signal located and extracted from the original signal stream. Figure 5 As shown, the spectrum obtained after performing a Fast Fourier Transform (FFT) on the segment is displayed, where the frequency point with the highest energy is identified as the dominant frequency component, with a dominant frequency of 2500 Hz.

[0043] Optionally, the step of using the current electrical spark feature data to distinguish defect types and generate defect assessment results includes: Collect historical defect types and corresponding historical electrical spark feature data to obtain a historical dataset; Using historical electrical spark feature data as input and the defect type labels corresponding to historical defect types as output, a neural network model is established and trained using the historical dataset to obtain a defect classification model. The current electrical spark feature data is input into the defect classification model to output the current defect type label; The current defect severity score is calculated based on the current electrical spark characteristic data. By combining the current defect type label with the current defect severity score, a defect assessment result is generated.

[0044] Specifically, a large number of historical defect samples are first collected experimentally. This includes artificially creating various known historical defect types on standard waterproofing materials, such as pinholes, cracks, material thinning, and bubbles, and assigning a clear defect type label to each type. Then, the detection system scans these samples, capturing their corresponding electrical spark signals and extracting historical electrical spark feature data. This constructs a historical dataset containing a large amount of feature data and its corresponding defect type labels. Based on this dataset, the system uses supervised learning to build and train a neural network model. In this process, historical electrical spark feature data is used as the model input, and the corresponding defect type label is used as the desired output. Algorithms such as backpropagation are used to continuously adjust the network weights until the model can accurately identify the defect type from the feature data, ultimately resulting in a trained defect classification model. During the online real-time evaluation phase, when the system captures a new defect and generates current electrical spark feature data, this data is first input into the pre-trained defect classification model. The model analyzes and calculates the current feature data based on its knowledge learned from the historical dataset, outputting the most likely defect type label, such as "pinhole" or "crack." At the same time, the system will also calculate a current defect severity score based on the current electrical spark characteristic data. This score is usually a comprehensive quantitative indicator, which can be obtained by weighting key parameters in the characteristic data, for example: , in, This represents the current severity score of the defect; It is a spark intensity parameter extracted from the current electrical spark characteristic data; It is the spark duration parameter extracted from the current electrical spark characteristic data; and These are preset weighting coefficients, calibrated based on extensive experimental data, used to reflect the contribution of different parameters to defect severity. Finally, the system organically combines the current defect type label output by the neural network model with the calculated current defect severity score to generate a detailed defect assessment result. This result not only indicates the nature of the defect but also quantifies its severity. Figure 6 As shown, a two-dimensional feature space is displayed, which consists of spark intensity and spark duration. Different types of historical defect data form different clusters in the space. When a currently detected defect data point, i.e. the current electrical spark feature data, is input, the model can accurately determine the defect type it belongs to based on its position in the space.

[0045] Optionally, the step of outputting defect distribution information and triggering an audible and visual alarm based on the defect assessment result includes: Based on the position of the moving electrode, the defect location information is extracted; Based on the defect location information and the defect assessment results, defect distribution information is generated; Differentiated audible and visual alarms are triggered based on the defect distribution information.

[0046] Specifically, the system first tracks the position of the moving electrode in the cable trench in real time using a positioning unit integrated on the moving electrode device, such as an odometer or inertial measurement unit. Whenever the system generates a defect assessment result based on the analysis of the electrical spark signal, it immediately binds this result with the position data output by the positioning unit at the same time, thereby extracting precise defect location information, typically expressed as coordinates or mileage relative to a reference point. Next, the system stores this complete data point containing defect location information, defect type labels, and defect severity scores. As inspection continues, all identified defect data points are aggregated into a database. Based on this database, the system can generate intuitive defect distribution information, typically represented as a digital map or diagram, marking the location, type, and severity of each defect on a two-dimensional or three-dimensional model of the cable trench using different colors or symbols, thus providing operators with a macroscopic view of the overall health of the waterproofing layer. Finally, the system uses the real-time defect assessment results to trigger differentiated audible and visual alarms. The system has a pre-set set of alarm rules that correspond different defect severity score levels to specific audible and visual alarm modes. For example, when a defect with a low severity score is detected, the system may trigger a flashing yellow warning light and a slow beep; while when a serious defect with a high severity score is detected, it will immediately trigger a high-frequency red flashing light and a rapid, loud alarm.

[0047] Based on the same inventive concept, such as Figure 7 As shown, the present invention also provides an electrical spark non-destructive testing and evaluation system for the integrity of waterproof layers in cable trenches, the system comprising: A signal generation module is used to generate a multi-frequency electrical signal containing multiple frequency components, and to apply the multi-frequency electrical signal through a preset moving electrode; The pressure control module is used to acquire the initial contact pressure between the movable electrode and the surface of the waterproof layer in real time, and adjust the pressure at the contact position of the movable electrode based on the initial contact pressure. The feature extraction module is used to capture the electric spark signal generated at the defect of the cable trench waterproof layer by the multi-frequency electrical signal applied by the moving electrode based on the adjusted pressure, and extract the feature parameters of the electric spark signal to generate the current electric spark feature data. The defect analysis module is used to distinguish defect types using the current electrical spark characteristic data in order to generate defect assessment results; The alarm module is used to output defect distribution information and trigger an audible and visual alarm based on the defect assessment results.

[0048] To verify the feasibility of this invention in practice, it was applied to the acceptance and testing of the integrity of the waterproof layer in a newly constructed 5-kilometer-long cable trench in a certain project. This cable trench uses a polyurethane waterproof coating, and the acceptance standards are extremely high. Traditional methods such as manual observation and single-frequency electric spark testing suffer from low efficiency, susceptibility to false alarms due to environmental humidity, inability to distinguish defect types, and potential secondary damage to the waterproof layer due to improper operation.

[0049] In this embodiment, the detection device scans along the cable trench at a preset speed. The system first generates a multi-frequency electrical signal containing multiple frequency components (1kHz-20kHz). During the detection process, the system acquires environmental parameters in real time through integrated sensors. For example, at 10:00 AM on a certain day, the ambient environment is dry, the temperature is 28°C, and the humidity is 50%. Based on this, the system calculates a higher equivalent environmental impedance and automatically adjusts the output power to 110% of the preset reference value. At 3:00 PM that day, after a rain shower, the ambient humidity rises to 90%, and the temperature drops to 24°C. The system calculates a lower equivalent environmental impedance in real time and quickly reduces the output power to 95% of the reference value to avoid false alarms caused by increased surface conductivity, ensuring the consistency of detection results throughout the day.

[0050] During the movement of the mobile electrode, the pressure control module monitors the contact pressure in real time. Uneven areas exist on the surface of the cable trench. At kilometer marker K2+350, a tiny concrete protrusion caused the initial contact pressure of the mobile electrode to momentarily reach 6.2 N, exceeding the preset safe pressure range of 2.0 N to 5.0 N. Within 50 milliseconds, the pressure control module generates a pressure reduction command and, through a servo mechanism, fine-tunes the electrode height, quickly stabilizing the pressure at 4.5 N, effectively preventing scratches on the waterproof layer.

[0051] When the detection equipment reached K1+120 meters, the system captured a transient electric spark signal with an amplitude of 5.5V, exceeding the preset amplitude threshold of 1.0V, and was therefore determined to be a valid defect signal. The feature extraction module then performed in-depth analysis on the signal, calculating the spark intensity parameter to be 3.8V·μs and the spark duration parameter to be 2.5μs. Simultaneously, through spectral analysis of the signal segment, the dominant frequency component, 15kHz, was identified as having the highest energy content. These parameters were combined to form the current electric spark characteristic data.

[0052] The feature data was input into a neural network defect classification model pre-trained using historical datasets. After analysis, the model output a defect type label of "pinhole." Simultaneously, the system calculated a current defect severity score of 85 out of 100 based on spark intensity and duration. Combining the defect type and severity score, the system generated the final defect assessment result: "Location K1+120, Type: Pinhole, Severity: High." Because the severity score was higher than 80, the alarm module immediately triggered a high-frequency red audible and visual alarm.

[0053] Subsequently, at K3+500 meters, the system detected another valid defect signal with an amplitude of 3.2V. The feature extraction module analyzed the signal and found its spark intensity to be 2.1V·μs, spark duration to be 6.8μs, and dominant frequency component to be 8kHz. After model analysis, this set of feature data was labeled as a "crack" defect type, and the calculated defect severity score was 60 points. The system generated the assessment result: "Location K3+500, Type: Crack, Severity: Medium." Since the severity score was medium (50-80 points), the system triggered a low-pitched yellow audible and visual alarm.

[0054] After the entire inspection was completed, the system automatically generated a defect distribution map of the 5-kilometer cable trench waterproofing layer based on all recorded defect location information and evaluation results. The map visually indicated the precise location, type, and severity level of each defect point, providing guidance for subsequent targeted repair work.

[0055] It should be noted that the formulas mentioned above, through the principle of dimensional consistency and mathematical standardization methods (such as normalization, dimensionless parameter conversion, or unit system unification), can translate physical quantities with different properties into unitless standard values ​​or parameters that can be superimposed in the same dimension. This eliminates the interference of different dimensions on the operational logic, allowing the formulas to retain the original data distribution characteristics while possessing mathematical rationality and adaptability to objective laws. These are conventional technical methods and will not be elaborated upon here. The electrical connections between the various units mentioned above do not necessarily represent direct or indirect connections; any connection that achieves the purpose of this invention is applicable to the embodiments of this invention. The above descriptions are merely exemplary embodiments of this invention and should not be construed as limiting the scope of this invention.

[0056] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and implementing the disclosure of this invention. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for evaluating the integrity of a waterproofing layer of a cable trench by means of electrical discharge for non-destructive testing, characterized in that, The method comprises: generating a multi-frequency electrical signal containing multiple frequency components, and applying the multi-frequency electrical signal through a preset mobile electrode; real-time acquisition of initial contact pressure of the mobile electrode and the surface of the waterproof layer, and adjustment of the pressure of the mobile electrode contact position based on the initial contact pressure; based on the adjusted pressure, capturing the electrical spark signal generated by the multi-frequency electrical signal applied by the mobile electrode at the defect of the waterproof layer of the cable trench, extracting the characteristic parameters of the electrical spark signal to generate current electrical spark characteristic data; distinguishing the defect type by using the current electrical spark characteristic data to generate a defect evaluation result; according to the defect evaluation result, outputting the defect distribution information and triggering the audible and light alarm.

2. An electrical discharge non-destructive testing evaluation method for the integrity of a waterproof layer of a cable trench according to claim 1, characterized in that, The generation of the multi-frequency electrical signal containing multiple frequency components comprises: acquiring a frequency range parameter for defining the signal frequency range, and determining multiple different frequency components of the electrical signal based on the frequency range parameter; acquiring real-time environmental parameter data, and adjusting the output power of the electrical signal based on the environmental parameter data; based on multiple different frequency components and output power, generating a multi-frequency electrical signal.

3. An electrical discharge non-destructive testing evaluation method for the integrity of a waterproofing layer of a cable trench according to claim 2, characterized in that, The acquisition of real-time environmental parameter data and the adjustment of the output power of the electrical signal based on the environmental parameter data comprises: separating the environmental humidity parameter and the surface temperature parameter from the real-time environmental parameter data; calculating the equivalent environmental impedance based on the environmental humidity parameter and the surface temperature parameter; determining the power adjustment parameter according to the equivalent environmental impedance; adjusting the output power of the electrical signal based on the power adjustment parameter.

4. The method for electrical discharge NDT evaluation of the integrity of a waterproofing layer of a cable trench according to claim 1, wherein, The real-time acquisition of the initial contact pressure of the mobile electrode and the surface of the waterproof layer, and the adjustment of the pressure of the mobile electrode contact position based on the initial contact pressure comprises: real-time acquisition of the initial contact pressure of the mobile electrode and the surface of the waterproof layer to generate target pressure data; determining a pressure range for maintaining stable electrical contact without damaging the waterproof layer; real-time comparison of the target pressure data and the pressure range to generate a pressure adjustment instruction; adjusting the pressure of the mobile electrode contact position according to the pressure adjustment instruction.

5. The method for electrical discharge NDT evaluation of the integrity of a waterproofing layer of a cable trench according to claim 1, wherein, The capturing of the electrical spark signal generated by the multi-frequency electrical signal applied by the mobile electrode at the defect of the waterproof layer of the cable trench based on the adjusted pressure, and the extraction of the characteristic parameters of the electrical spark signal to generate current electrical spark characteristic data comprises: based on the adjusted pressure, capturing the electrical spark signal generated by the multi-frequency electrical signal applied by the mobile electrode at the defect of the waterproof layer of the cable trench; characteristic processing of the electrical spark signal to obtain signal amplitude; determining whether the signal amplitude is greater than a preset amplitude threshold; if not, it is determined that there is no defect.

6. An electrical discharge based non-destructive testing method for evaluating the integrity of a waterproofing layer of a cable trench according to claim 5, characterized in that, The capturing of the electrical spark signal generated by the multi-frequency electrical signal applied by the mobile electrode at the defect of the waterproof layer of the cable trench based on the adjusted pressure, and the extraction of the characteristic parameters of the electrical spark signal to generate current electrical spark characteristic data further comprises: if the signal amplitude is greater than the amplitude threshold, analyzing the waveform of the electrical spark signal to obtain a spark intensity parameter; determining the start and end time points of the waveform of the electrical spark signal to obtain a spark duration parameter; identifying the dominant frequency component when the electrical spark signal occurs; combining the spark intensity parameter, the spark duration parameter, and the dominant frequency component to generate current electric spark feature data.

7. A method for electrical discharge non-destructive testing evaluation of the integrity of a waterproofing layer of a cable trench according to claim 6, characterized in that, The identifying the dominant frequency component of the electric spark signal occurrence includes: locating an electric signal segment within the electric spark signal occurrence time window; performing spectral analysis on the electric signal segment to generate spectral data; obtaining energy values of each frequency point in the spectral data and sorting the energy values of each frequency point from large to small; selecting a frequency component corresponding to a frequency point with the largest energy value as the dominant frequency component.

8. The method for electrical discharge NDT evaluation of the integrity of a waterproofing layer of a cable trench according to claim 6, wherein, The distinguishing defect types using the current electric spark feature data to generate a defect evaluation result includes: collecting historical defect types and historical electric spark feature data corresponding to the historical defect types to obtain a historical data set; using the historical data set to establish and train a neural network model by taking the historical electric spark feature data as input and taking a defect type label corresponding to the historical defect type as output to obtain a defect classification model; inputting the current electric spark feature data into the defect classification model to output a current defect type label; calculating a current defect severity score based on the current electric spark feature data; combining the current defect type label and the current defect severity score to generate a defect evaluation result.

9. An electrical discharge based non-destructive testing method for evaluating the integrity of a waterproofing layer of a cable trench according to claim 8, characterized in that, The outputting defect distribution information and triggering an audible and light alarm according to the defect evaluation result includes: extracting defect location information based on the position of the mobile electrode; generating defect distribution information based on the defect location information and the defect evaluation result; triggering a differential audible and light alarm according to the defect distribution information.

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