A method and system for testing the performance of high temperature resistant cables

Abstract

The application relates to the technical field of electric digital data processing, and discloses a high-temperature-resistant cable performance test method and system, which comprises the following steps: synchronously collecting thermal imaging, optical imaging and gas component data streams in a combustion test, and establishing a shared time-space domain for alignment; when the temperature reaches the glass transition temperature of the material, crack and outgassing events are detected within a constrained time window, and the events that pass error checking are paired to form a causal event chain, and then a structured pyrolysis failure fingerprint is generated; the application changes the traditional end-point evaluation mode, which no longer relies on a single static index at the end of the test, but reconstructs and quantifies isolated measurement results in different physical dimensions in the whole combustion process into a dynamic failure process model with internal time sequence and causal correlation, so that the information density and decision value of the test are improved.

Description

TECHNICAL FIELD

[0001] The application relates to a high-temperature-resistant cable performance test method and system, and belongs to the technical field of electric digital data processing. BACKGROUND

[0002] In engineering fields such as aerospace and nuclear power facilities that have strict requirements on reliability, the performance evaluation of high-temperature-resistant cables is a key link to ensure system safety. At present, a standardized combustion test is generally used in the industry. By applying a certain heat load and measuring one or several macroscopic and static end-point indicators such as flame propagation length or electrical function connection after the test, it is determined whether the cable sample is qualified. This method has become the cornerstone of quality certification and standard compliance testing in the industry due to its clear results and good repeatability.

[0003] However, with the progress of material science and the increasing demand for failure mechanism understanding in system safety engineering, the limitations of the above-mentioned evaluation method, which simplifies a complex dynamic combustion process into a single static end-point, have become increasingly apparent. While providing qualification judgment, it also hides a large amount of key information carried by the failure process itself. For example, two cables with the same final burnout length may release a large amount of corrosive gas at the initial stage of combustion, while the other may experience structural collapse at the later stage. For engineers who need to conduct safety design or accident tracing, these two completely different failure processes correspond to different risk scenarios, and the existing test data cannot provide such process information.

[0004] To obtain more comprehensive information, one seemingly direct improvement idea is to add more types of sensors to the test to collect multi-dimensional data. However, this simple physical superposition does not address the core of the problem, and the result is only to obtain several parallel data curves in time but still isolated in physical meaning. It does not solve a deeper contradiction. Specifically, the existing technology mainly has the following deficiencies: 1. Lack of unified space-time reference among multi-source heterogeneous data, making correlation analysis between different physical phenomena lack objective basis; 2. Data processing logic remains passive recording and presentation, lacking a mechanism for actively identifying and extracting causal relationship events between different data streams; 3. The final output is still a set of discrete observation results, rather than a structured process model that can completely reproduce the initial cause to the final failure with internal logical relationships. Therefore, how to design a data processing method that can go beyond simple data collection and automatically identify and construct the time sequence and causal relationship between key physical events by deeply processing the multi-modal heterogeneous data streams obtained during the combustion process, thereby converting an invisible dynamic failure process into an analyzable structured data object, has become a technical problem to be solved by the present application. SUMMARY

[0005] The application provides a high-temperature-resistant cable performance test method and system, which mainly aims to solve the problem that the existing test method can only provide static end-point results and cannot quantitatively characterize and trace the causes of the dynamic failure process of the material under thermal stress.

[0006] To achieve the above-mentioned purpose, the application provides a high-temperature-resistant cable performance test method, which comprises the following steps:

[0007] In the process of performing a continuous combustion test on the cable sample, a thermal imaging data stream, an optical imaging data stream, and a gas composition data stream are synchronously collected based on the same master clock;

[0008] Based on the optical imaging data stream, the axial physical coordinates of the measured cable are calibrated, and the thermal imaging data stream is registered to the axial physical coordinates to establish a shared space-time domain;

[0009] In the shared space-time domain, when the temperature at a certain position along the axial physical coordinates first reaches the glass transition temperature of the cable material , the first detection window and the second detection window are respectively opened based on this time point;

[0010] In the first detection window, crack events are detected from the optical imaging data stream according to the pixel gradient change rule, and in the second detection window, the concentration inflection point of the target component is detected from the gas composition data stream;

[0011] Before time sequence pairing, the synchronization and registration errors of the thermal imaging data stream, the optical imaging data stream, and the gas composition data stream are checked, and data frames with errors exceeding a given tolerance are removed;

[0012] Only the temperature rise, crack events, and concentration inflection points that pass the error closed-loop check are time sequence paired to form a causal event chain;

[0013] Based on the formed causal event chain, a pyrolysis failure fingerprint representing the dynamic failure characteristics of the cable is generated and output.

[0014] Preferably, the step of calibrating the axial physical coordinates of the measured cable based on the optical imaging data stream specifically comprises: using an edge detection and feature recognition algorithm to process consecutive image frames in the optical imaging data stream to recognize and extract the outline of the measured cable in the image; determining an axis line that passes through the geometric center of the outline of the measured cable, and defining the axis line as the axial physical coordinates; and establishing a transformation relationship for mapping the coordinates of each pixel point in the thermal imaging data stream to the axial physical coordinates to perform registration.

[0015] Preferably, the step of detecting the concentration inflection point comprises: determining the concentration value of the target component in the gas composition data stream; and determining the concentration inflection point based on the concentration value of the target component. The process is performed to calculate the concentration value. First time derivative With the second time derivative Specifically, when the first-order time derivative and the second-order time derivative simultaneously satisfy the following conditions within the second detection window: and When the concentration inflection point is determined to have occurred, then... is a given positive threshold number used to characterize the effective growth rate of concentration.

[0016] Preferably, the method further includes: synchronously acquiring an acoustic emission data stream, which characterizes the high-frequency stress waves emitted by the cable due to internal microscopic fractures under thermal stress; and incorporating acoustic bursting events identified from the acoustic emission data stream as internal mechanical damage events into the pairing and formation process of the causal event chain, so that the final pyrolysis failure fingerprint contains event information that distinguishes between external morphological changes and internal structural damage.

[0017] Preferably, the method further includes: storing multiple generated pyrolysis failure fingerprints into a failure mode database, and using the failure mode database to train a time-series prediction model; and in a new test process, inputting causal event chain fragments generated in real time in the early stage of the test into the time-series prediction model, and having the time-series prediction model output the prediction results of the final failure mode and failure time of the cable.

[0018] Preferably, the duration of the second detection window is set based on the physical distance between the surface of the cable under test and the gas composition data stream acquisition point, as well as the physical delay time determined by the gas transmission rate of the target component under the test environment; the synchronous acquisition frequency is not less than 10 Hz; and the given tolerances are specifically: the synchronization time jitter error is less than one sampling period, and the registration spatial positioning error is less than one pixel size.

[0019] Preferably, the pyrolysis failure fingerprint is a structured data object. Within this structured data object, each event constituting a causal event chain records a unique event type identifier, an absolute timestamp of the event occurrence, the axial physical coordinates of the event occurrence, and the local temperature value and target component concentration value corresponding to the event occurrence.

[0020] Preferably, the step of detecting crack events from the optical imaging data stream based on pixel gradient change rules is as follows: within the first detection window, the pixel gray-level gradient distribution along the radial direction of the cable under test in the optical imaging data stream is calculated; when a local peak appears in the pixel gray-level gradient distribution, and the value of the local peak exceeds the given crack judgment gradient threshold, the crack event is determined to have occurred at that location.

[0021] Preferably, all steps of the method are performed under the constraints of the standard procedure and physical conditions of the continuous combustion test without interruption, to ensure direct correlation of the pyrolysis failure fingerprint with the existing industry standard test environment.

[0022] A high-temperature-resistant cable performance test system, the system comprising:

[0023] a sensing and collecting module, the sensing and collecting module comprising a thermal imaging sensor, an optical camera and a gas analyzer, and being configured to synchronously collect a thermal imaging data stream, an optical imaging data stream and a gas composition data stream under the same master clock during the process of performing a continuous combustion test on a cable sample;

[0024] a data processing module connected with the sensing and collecting module; wherein the data processing module is configured to: based on the optical imaging data stream, calibrate an axial physical coordinate of the cable under test, and register the thermal imaging data stream to the axial physical coordinate to establish a shared space-time domain; when the temperature at a certain position along the axial physical coordinate first reaches the glass transition temperature of the cable material , take this moment as the reference, respectively open the first detection window and the second detection window; limit the crack event detection from the optical imaging data stream according to the pixel gradient change rule only within the first detection window, and limit the concentration inflection point detection of the target component from the gas composition data stream only within the second detection window; before time sequence pairing, check the synchronization and registration errors of the thermal imaging data stream, the optical imaging data stream and the gas composition data stream, and delete the data frames whose errors exceed the given tolerance; only the temperature rise, crack event and concentration inflection point that pass the error closed-loop check are time sequence paired to form a causal event chain; and based on the formed causal event chain, a pyrolysis failure fingerprint representing the dynamic failure characteristics of the cable is generated and output.

[0025] Compared with the prior art, the beneficial effects of the present application are:

[0026] 1、The method synchronously collects at least two heterogeneous data streams from different physical dimensions in the combustion test, and identifies and extracts the key features in the data streams according to the preset causal correlation rules under the unified space-time reference, so as to integrate multiple measurement results originally isolated on the time axis and physically fragmented into an internal time sequence relationship and logically associated failure event chain; this processing method makes the evaluation of cable performance no longer rely on a certain static end-point indicator at the end of the test, but turns to the objective reproduction of the dynamic failure process composed of multi-field events in the entire combustion process, and the evaluation result generated and output thereby has changed from a one-dimensional numerical value or state to a high-dimensional data object capable of depicting the failure mode evolution path.

[0027] 2. When the acquired data stream further includes acoustic emission data stream, the data processing module will correlate the high-frequency stress wave events generated by micro fractures inside the material with hotspot events in the thermal imaging data stream and morphological change events in the optical imaging data stream in time. In this way, the evaluation dimension is extended from the external characterization of the cable to its internal mechanical integrity evolution. This makes the failure event chain not only record visible surface phenomena, but also include the invisible internal damage accumulation process. Thus, it is possible to make an objective and quantitative distinction between burst failure and progressive failure, two failure modes that are completely different in mechanism but may have similar macroscopic results.

[0028] 3. The structured evaluation results generated by this method, which characterize the dynamic failure properties of cables, can be included in a failure mode database because they record a continuous event chain from initial heating to final failure. When a deep learning model is trained using this database, it can analyze the event chain segments being generated in real time and compare them with existing patterns in the database during a new test. In the early stages of the test, the final failure mode and approximate failure time of the cable can be predicted. This shift in testing and analysis methods allows the focus of testing to shift from obtaining final conclusions to understanding early trends, providing a time advantage for the research and development iteration process of new materials. Attached Figure Description

[0029] Fig. 1 This is a flow chart of a high-temperature resistant cable performance testing method according to the present invention;

[0030] Fig. 2 This is a schematic diagram illustrating the determination of the critical temperature rise event in this invention.

[0031] Fig. 3 This is a functional architecture block diagram of a high-temperature resistant cable performance testing system according to the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0033] The application provides a high-temperature-resistant cable performance test method and system, and a data processing flow thereof comprises the following stages: firstly, in the process of performing a continuous combustion test on a cable sample, at least three data streams in different physical dimensions are synchronously collected by a sensing and collecting module through a same master clock; then, in a data processing module, a shared space-time domain is established for the data streams from different sources through a coordinate transformation and registration algorithm; finally, in the unified space-time domain, key features in the data streams are identified, paired under time sequence constraints and error checked according to a set of causal correlation rules representing physical failure mechanisms, so that discrete observation data are reconstructed into one or more failure event chains with internal logical relationships, and a pyrolysis failure fingerprint representing dynamic failure characteristics of the cable is generated and output based on the failure event chain.

[0034] In the high-temperature-resistant cable combustion test, the failure process of the cable is the result of the coupling of multiple physical fields such as heat, force and chemical in space-time, if the consistency of the data streams of various sensors in the time reference cannot be ensured at the source of data collection, the subsequent data processing aiming to explore the causal correlation will lack an objective basis, to deal with this, the application synchronously collects the thermal imaging data stream, the optical imaging data stream and the gas composition data stream through a same master clock in the process of performing a continuous combustion test on a cable sample; specifically, a central clock source generates a frequency-stable signal, the signal is distributed to the data collection units of the thermal imaging sensor, the optical camera and the gas analyzer respectively, as the only sampling trigger reference, the system is set to synchronously collect at a frequency not lower than 10 Hz, the setting of the frequency is based on the fact that it is higher than the characteristic frequency of physical events such as micro-crack expansion and instantaneous thermal decomposition of materials in the combustion process of the cable, so as to meet the basic requirement of signal reconstruction, in each sampling period, the data frame or data point collected by each sensor is attached with a unique time stamp generated by the counting value of the central clock source, in this way, the time reference uniformity and traceability required for subsequent space-time alignment and causal correlation analysis are ensured at the data level.

[0035] Furthermore, even though the data streams are synchronized in time, they still exist in independent coordinate systems defined by different sensors. For example, the thermal imaging data stream exists in the pixel coordinate system of the infrared focal plane array, while the optical imaging data stream exists in the pixel coordinate system of the visible light sensor. This difference in spatial reference makes it difficult to directly correlate the temperature rise at a certain location with the morphological change at the same physical location. In view of this, the system adopts the following procedure: based on the optical imaging data stream, the axial physical coordinates of the cable under test are calibrated, and the thermal imaging data stream is registered to these axial physical coordinates to establish a shared spatiotemporal domain. The execution of this procedure includes two steps. First, before the combustion test begins, an edge detection and feature recognition algorithm is used to process a frame of static image in the optical imaging data stream to identify and extract the outline of the cable under test in the image. Based on the geometric center of the outline, the axis passing through the outline is calculated and defined as a one-dimensional axial physical coordinate. Simultaneously, a pixel coordinate system is established that maps the optical image along this axis. Mapping to physical coordinates Transformation relationship Secondly, by using a known physical location of the cable sample... A controllable heat source is applied at a specific point, and thermal and optical imaging data are acquired simultaneously. The data processing module identifies the pixel coordinates of the hot spot in the thermal image. and pixel coordinates in the optical imaging map By establishing multiple sets of such corresponding point pairs, an affine transformation matrix is ​​fitted. This matrix defines the mapping of the coordinates of each pixel in the thermal imaging data stream to the optical imaging coordinate system, and finally transforms the coordinates through the transformation relationship. The transformation path, mapped to axial physical coordinates, is used to perform this registration procedure, and the system then maps all subsequently acquired data... All data streams are unified into a shared spatiotemporal domain based on physical time and the physical coordinates of the cable axis. This provides a unified reference system for subsequent event identification.

[0036] After establishing a shared spatiotemporal domain, the task of data processing is to identify discrete events that indicate changes in the material state from a continuous data stream and to construct causal relationships between them. To correlate changes in the material's physical properties, such as the glass transition, with their subsequent mechanical property degradation and chemical decomposition, the present invention is configured to, within this shared spatiotemporal domain, at a certain position along the axial physical coordinate... temperature The glass transition temperature of this cable material was reached for the first time. Then, at that time... Based on this, the first and second detection windows are opened respectively; the glass transition temperature here... are material intrinsic property values obtained from off-line physical performance tests of cable sample materials; the values are expressed in terms of the material's thermal response time; the algorithm's initiation is based on the physical fact that a state transition of the material has occurred; once the temperature rise trigger event is confirmed, the system enters a time-constrained search phase for its physical consequences, within the first detection window , crack events are detected from the optical imaging data stream according to pixel gradient variation rules, and within the second detection window , concentration inflection points of the target component are detected from the gas composition data stream; the length of the first detection window is determined according to the material's thermal response time, within which the algorithm calculates the pixel gray scale gradient distribution of the optical image at the position along the radial direction of the cable under test, when a local peak appears in the distribution and the value of the peak exceeds a preset crack decision gradient threshold , a crack event is determined to have occurred; the opening time of the second detection window, however, includes a physical delay time , the determination procedure of which is: wherein is the physical distance between the surface of the cable under test and the sampling probe of the gas analyzer, and is the average gas flow velocity under the combustion condition in the test chamber, both of which are system physical parameters; within the second detection window, the detection of concentration inflection points is realized by calculating the first-order time derivative and the second-order time derivative of the target component concentration value , when the following conditions are simultaneously met within the window: and , a concentration inflection point is determined to have occurred, wherein is a positive threshold value representing the effective growth rate of the concentration, which is set according to the baseline noise level of the gas analyzer in a static environment.

[0037] To ensure the objectivity of the final causal event chain, the synchronization and registration errors of the thermal imaging data stream, the optical imaging data stream, and the gas composition data stream are checked before time pairing, and data frames with errors exceeding the given tolerance are rejected; the given tolerance is set as: the synchronization time jitter error is less than one sampling period, and the registration spatial positioning error is less than one pixel size; only the temperature rise, crack event, and concentration inflection point that pass the error closed-loop check are allowed to be time-paired to form a causal event chain, for example: [Event 1: ]→[Event 2: Crack ( )]→[Event 3: Concentration Inflection Point ( )], wherein, and Finally, the system generates and outputs a pyrolysis failure fingerprint representing the dynamic failure characteristics of the cable based on the formed causal event chains, which is defined as a structured data object, in which the unique event type identifier, the absolute timestamp of the event occurrence, the axial physical coordinates of the event occurrence, and the local temperature value and target component concentration value corresponding to the event occurrence are recorded for each event constituting each causal event chain. The structured output converts a dynamic failure process into a quantifiable, analyzable and comparable digital object to support performance evaluation and failure mechanism analysis of high-temperature-resistant cables.

[0038] Example 1: The technical solution of the present application in a selection and verification scene of an aerospace component, the specific operation mode is as follows, in the scene, a high-temperature-resistant cable in the servo control system of a new type of launch vehicle needs to pass through the high-temperature region of the engine, the engineering team faces the decision of selecting the better one from two candidate cable samples, sample A and sample B, both of which show consistent performance in traditional combustion tests, specifically the same flame spread length and the same electrical function failure time, which leads to the inability to distinguish risks and make selection judgments based on this test data; to deal with this decision-making dilemma, the method described is used to perform a performance test on sample A and sample B, in the test process, the sensing and collecting module synchronously collects the thermal imaging data stream, the optical imaging data stream and the gas component data stream with the same master clock, the data processing module then calibrates the axial physical coordinates of the measured cable based on the optical imaging data stream and completes the registration of the thermal imaging data stream, thereby establishing a shared time and space domain; for sample A, the data processing module first identifies a temperature rise event at the middle of the cable whose temperature first reaches the glass transition temperature of the material , and within the detection window opened based on this, the system detects a hydrogen chloride gas concentration inflection point in the gas component data stream after a set time delay, but no crack event meeting the pixel gradient change rule is detected in the corresponding optical imaging data stream; for sample B, the data processing module identifies a temperature rise event at the near end of the cable reaching at a relatively higher temperature, and within the first detection window thereafter, multiple axial crack events are continuously detected from the optical imaging data stream, but the hydrogen chloride concentration in the gas component data stream does not show a clear concentration inflection point in the corresponding second detection window.

[0039] After the error closed-loop check of the two sample test data is completed, the data processing module respectively performs time sequence pairing and generates pyrolysis failure fingerprints with structural differences. The core causal event chain of the pyrolysis failure fingerprint of sample A is recorded as being directly triggered by the critical temperature rise event to the chemical decomposition event, that is, after reaching the glass transition temperature, the main failure path of the material is the rupture of chemical bonds and the precipitation of corrosive gas, and the overall integrity of the physical structure is maintained for a long time. In comparison, the pyrolysis failure fingerprint of sample B depicts another failure path, and the causal event chain is recorded as being triggered by the critical temperature rise event to a series of chain mechanical damage events, that is, the material reaches the deterioration point of mechanical properties under the action of thermal stress, and the main failure path is the initiation and expansion of microcracks, and finally leads to the structural collapse of the cable. Such resolution capability is derived from the synchronization collection which ensures the correlation of different physical events in time, and the space-time alignment provides a unified spatial reference for the correlation, so that the focus of evaluation is shifted from the same failure results of the two samples to the differentiation of the internal failure mechanism and evolution path. The original question of which cable is more durable is transformed into the analytical question of which failure mode of the two cables is more controllable. Finally, based on the two content different pyrolysis failure fingerprints, the engineering team makes a selection decision. The failure mode of sample A releases corrosive gas, but since the time and location of its occurrence can be quantitatively recorded by the test method, its risk can be controlled by isolating and protecting the surrounding devices. The failure mode of sample B is a structural sudden collapse, which has higher unpredictability and directly risks the functional safety of the servo control system, so sample A is determined as the technical solution to be adopted.

[0040] Example 2: To objectively verify the effectiveness of the method of the present application in distinguishing different failure modes, a comparative test was designed and performed in this example, the purpose of which was to quantify the information difference in the test results obtained by using the traditional end-point evaluation method and the method of the present application for two high-temperature-resistant cable samples with different failure mechanisms but consistent macroscopic thermal performance; the test platform was built in a combustion test box in accordance with IEC 60332-1 standard, which was equipped with the aforementioned sensing and collecting modules defined in the specific embodiment, including a non-refrigerated vanadium oxide thermal imaging sensor, an optical camera with a resolution of 1920x1080 pixels, and a Fourier transform infrared spectrum gas analyzer, and the data collection of all sensors was synchronously triggered and time-stamped by a data processing module through the same master clock at a frequency of 10 Hz; two samples were selected for this test, namely the comparative sample group and the sample group of the present application, wherein the comparative sample group used polyvinyl chloride rich in chlorine elements as the insulating material, whose failure path under thermal stress tended to be chemical decomposition; the sample group of the present application used a modified cross-linked polyolefin insulating material, whose brittleness increased after reaching the glass transition temperature, and the failure path tended to be micro-crack propagation caused by thermal stress; the conductor specifications, insulating layer thickness, and rated temperature resistance level of the two samples were consistent, and before the test, the parameters related to the material properties in the data processing module, such as the glass transition temperature , were set according to the differential scanning calorimetry test results of the two materials.

[0041] The test results showed that the method of the present application could recognize and quantify the differences in failure mechanisms between the two sample groups, while the traditional end-point test could not provide such a distinction; specifically, when using the traditional end-point test procedure, the electrical functional failure time of the comparative sample group was recorded as 185 seconds, and that of the sample group of the present application was recorded as 188 seconds, which were not distinguishable within the test error range; however, when the complete test method disclosed in the present application was enabled, the pyrolysis failure fingerprint generated showed differences in structure and event sequence, for the comparative sample group, the data processing module captured the triggering event of the temperature reaching the material value at 82 seconds, and then identified the chemical decomposition event marked by the release of hydrogen chloride within the second detection window corrected by the physical delay time; in contrast, the triggering event of the sample group of the present application occurred at 85 seconds, followed by the mechanical cracking event characterized by the change in pixel gray scale gradient within the first detection window; this data comparison confirmed that the single time scalar output by the traditional test method could not reveal the differences in the failure process of the two sample groups, while the pyrolysis failure fingerprint output by the method of the present application through the causal correlation processing of multi-modal data streams based on physical mechanisms not only recorded the close The trigger time, further in the form of a structured causal event chain, distinguishes two different failure paths, one dominated by chemical decomposition and the other by mechanical cracking; the test results confirm that the data processing method of the present application can extract and construct process information about failure mode evolution from the same physical process as traditional testing, thereby providing an analysis basis for cable performance evaluation based on failure mechanism.

[0042] Embodiment 3: This embodiment combines Figs. 1 to 3 a high-temperature-resistant cable performance test method and system, as Fig. 1 shown, the figure depicts the cooperation relationship and data flow process among the logical modules in the test process in the form of signal interaction, which starts from the temperature monitoring module on the left, which sends a trigger signal to the data processing module when detecting that the local temperature of the cable sample reaches the preset glass transition temperature, the data processing module records the time and location of the temperature rise event, and then opens a first detection window and a second detection window containing a physical delay, respectively, to activate the work of the crack detection module and the gas detection module, wherein the crack detection module identifies crack events by calculating pixel gradient changes and reports the detected results to the data processing module, and the gas detection module determines the concentration inflection point by calculating the first and second derivatives of the concentration, and also reports the detected results to the data processing module, the data processing module receives these event reports from different physical dimensions, and links the temperature rise event, crack event and concentration inflection point to the causal event chain module respectively, and finally generates a structured pyrolysis failure fingerprint from the module.

[0043] As Fig. 2 shown, in the figure, the horizontal coordinate is time, in seconds (s), and the vertical coordinate is temperature, in degrees Celsius (°C), which contains a measured temperature curve represented by a solid line and a material glass transition temperature threshold line, the value of which is set to 120°C, as time goes on, the measured temperature curve shows a monotonous rising trend, and intersects with the threshold line at about 130s, which marks the triggering of an effective critical temperature rise event, thereby starting a series of subsequent associated event detection and causal chain construction processes.

[0044] As Fig. 3As shown, the figure depicts the complete processing link from data acquisition to final output, with the processing object being the measured cable sample, the left side being the sensing and acquisition module, which, under the control of a synchronous trigger signal generated by a master clock, observes the state of the sample in the combustion test through its internally integrated thermal imaging sensor, optical camera, gas analyzer, and acoustic emission sensor as the key extension module, and outputs multi-modal data streams, which are sent to the right side core data processing module, which internally contains, in sequence, a space-time alignment unit responsible for unifying the data space-time reference, a key event detection unit responsible for identifying state change points from data streams, a causal event chain construction unit responsible for constructing event correlations according to physical rules, and a pyrolysis failure fingerprint generation unit responsible for encapsulating and outputting the final results. The pyrolysis failure fingerprint generated by the unit is a structured data object that can be directly used for performance evaluation and stored in a failure mode database, which can be called to train a time series prediction model that outputs a predictive evaluation of the final result in the early stages of new testing.

[0045] Example 4: In a specific application scenario, to ensure that the internal logic parameters of the data processing module of the method of the application match the specific test physical environment before it is put into formal testing, a systematic calibration procedure needs to be performed, which aims to establish a traceable and reproducible data processing reference for all subsequent tests. The core goal is to determine the quantitative values of the gas transmission physical delay time, the crack event judgment gradient threshold, and the effective growth rate threshold of the gas concentration inflection point in the data processing module. The procedure first calibrates the gas transmission physical delay time. Since the gas components released from the surface of the cable need a certain time to be transmitted to the sampling probe of the gas analyzer, this time delay is a prerequisite for correctly associating temperature rise events and chemical decomposition events. To determine this delay, a pulse injector capable of releasing a trace amount of tracer gas is placed at the position of the standard cable sample in the test box, and under the control of the data processing module, an argon gas pulse with a duration of 100 milliseconds is injected at the same time, the gas analyzer is configured to continuously monitor the concentration change of argon gas at a frequency of 10 Hz , the data processing module records the time when the concentration reaches the peak , and then the average flow rate of the gas in the test box is calculated by , where is the pre-measured physical distance from the injection point to the sampling probe. In this calibration, when is 0.5 meters, the measured is 4.8 seconds, The velocity was determined to be approximately 0.104 m / s. This value was stored as a system parameter, and the data processing module will use this velocity to calculate the physical delay time required for the gas released at different locations to reach the sampling probe during subsequent formal testing. .

[0046] Subsequently, the procedure establishes a gradient threshold for determining crack events. Calibration is performed, as the threshold setting is crucial to the sensitivity and reliability of the algorithm in identifying microscopic mechanical damage on the material surface. Therefore, a reference sample with the same insulation material and surface characteristics as the cable under test is used. A series of microscopic damage points with different indentation sizes are pre-fabricated on its surface using a Vickers hardness tester. These sizes are measured and recorded using a microscope, covering a range from 10 micrometers to 50 micrometers. The reference sample is placed in a test chamber, and under standard lighting conditions, its surface image is acquired by an optical camera. The data processing module calculates the pixel grayscale gradient distribution along the radial direction of each pre-fabricated damage point in the image and records the gradient peak corresponding to a predetermined damage size, i.e., 20 micrometers. Finally, the crack judgment gradient threshold is determined. The gradient peak value is set to the sum of three times the standard deviation of the image background noise gradient measured by the system in the absence of a sample. This setting ensures the ability to detect microcracks of critical dimensions while avoiding misjudgments that may be caused by the noise of the image sensor itself.

[0047] Ultimately, the procedure sets an effective growth rate threshold for the inflection point of gas concentration. Calibration is performed, and this threshold is crucial for distinguishing the real gas evolution signal from the analyzer's background noise. The calibration process requires starting the heating and ventilation systems of the combustion test chamber without any sample, ensuring its operation matches that of a real test. Under these conditions, the gas analyzer continuously collects background concentration data of the target gas, namely hydrogen chloride, for 300 seconds. The data processing module receives the data stream and calculates its first-order time derivative. Standard deviation This standard deviation reflects the random fluctuation amplitude of the concentration signal under this working environment, and the effective growth rate threshold. Set as This setting corresponds to a predetermined low false alarm rate statistical confidence level; after this calibration process is completed, the core algorithm parameters of the data processing module are all assigned values ​​derived from physical measurements, thus providing a repeatable basis for the output results of the entire test system.

[0048] In a specific application scenario, to enable the failure mode predictive identification function included in the method of the present application, an offline construction and filling procedure of a failure mode database needs to be performed in advance. The procedure selects multiple groups of benchmark cable samples with clear material formula and known failure modes, and performs complete combustion tests on each group using the method of the present application, and generates a corresponding pyrolysis failure fingerprint for each benchmark sample. Subsequently, the data processing module further performs feature extraction operation on each pyrolysis failure fingerprint in the library, converts the original causal event chain composed of discrete events into a standardized multi-dimensional feature vector containing the following dimensions: mechanical event density within a preset time window, average time delay between chemical events and thermal events, and cumulative energy of acoustic burst events. Finally, these feature vectors and their corresponding known failure mode labels are stored as a data pair in the failure mode database, providing structured and labeled source data for subsequent training of time series prediction models.

[0049] Furthermore, when the test system of the method of the present application is further integrated with the collection of acoustic emission data stream, to avoid the interference of background mechanical vibration in the test environment on the internal microscopic fracture signal, before performing a series of test tasks, a pre-deployment calibration procedure of the acoustic channel needs to be performed. The procedure requires that without placing any cable samples, all auxiliary systems such as ventilation and heating of the combustion test box are turned on, and the acoustic emission sensor continuously collects background noise signals for at least five minutes. The data processing module performs spectral analysis on the signal, identifies the main frequency characteristics and amplitude distribution of the background noise, and generates a dynamic noise filter accordingly. In subsequent formal tests, the filter is applied to preprocess the real-time collected acoustic emission data stream. A signal is only determined as an effective acoustic burst event and included in the pairing and formation process of the causal event chain when it meets two conditions: its energy amplitude exceeds the statistical mean of the background noise by five standard deviations, and its main frequency component has a correlation with the background noise spectral feature peak that is lower than a predetermined threshold.

[0050] In a specific application scenario, to establish a high-confidence failure mode timing prediction model that can serve as a benchmark for subsequent online prediction, a standardized offline construction procedure including data preprocessing, model training, and performance verification needs to be performed. The basis of this procedure is to ensure that the source data used for model training, i.e., the pyrolysis failure fingerprint set, has high integrity and logical consistency. Therefore, when data is called from the failure mode database, a pre-check of data integrity is first performed. The data processing module checks the timestamp continuity of the original synchronous data stream frame by frame. If any data stream has more than three consecutive data frame losses within a certain time period, the data in that time period will be marked, and any causal event chain starting from or crossing that time period will be excluded from the training data set.

[0051] Furthermore, to increase the stability of the starting trigger point of the causal event chain, the procedure limits the judgment logic of the critical temperature rise event as follows: an effective temperature rise trigger event is defined as reaching the glass transition temperature of the cable material at a certain axial physical coordinate position , and must remain continuously above this temperature for at least three sampling periods. Only when this condition is met, the data processing module takes the first time reaches the glass transition temperature as the reference point to open the subsequent first detection window and second detection window; after completing data verification and trigger logic optimization, the feature vectors extracted from the database using the aforementioned procedure are divided into an 80% training set and a 20% validation set. The timing prediction model uses a timing convolutional network (TCN) architecture, and uses the training set data to train the model parameters. The goal of training is to minimize the cross-entropy loss function between the predicted failure mode and the annotated true failure mode in the database. The final output of this procedure is a performance-verified timing prediction model with a prediction accuracy of more than 95% on the validation set. This model is deployed in the data processing module, which thus has the ability to output the prediction result of the final failure mode of the cable based on the real-time generated causal event chain fragments that have passed the data integrity check and stable logic trigger in the early stage of a new test process.

[0052] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0053] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application.

Claims

1. A method of high temperature cable performance testing, characterized by, The method comprises: In the process of performing a continuous combustion test on a cable sample, synchronously collecting a thermal imaging data stream, an optical imaging data stream and a gas composition data stream with the same master clock; Based on the optical imaging data stream, calibrating the axial physical coordinates of the measured cable, and registering the thermal imaging data stream to the axial physical coordinates to establish a shared space-time domain; In the shared space-time domain, when the temperature at a certain position along the axial physical coordinate first reaches the glass transition temperature of the cable material , the first detection window and the second detection window are respectively opened based on this time point; In a first detection window, detecting crack events from the optical imaging data stream according to a pixel gradient change rule, and in a second detection window, detecting concentration inflection points of the target component from the gas composition data stream; Before performing time sequence pairing, checking the synchronization and registration errors of the thermal imaging data stream, the optical imaging data stream and the gas composition data stream, and discarding data frames with errors exceeding a given tolerance; Only the temperature rise, crack events and concentration inflection points that pass the error closed-loop check are time sequence paired to form a causal event chain; Based on the formed causal event chain, generating and outputting a pyrolysis failure fingerprint representing the dynamic failure characteristics of the cable.

2. The method of claim 1, wherein the high temperature resistant cable is a high temperature resistant power cable. The step of calibrating the axial physical coordinates of the measured cable based on the optical imaging data stream specifically comprises: using an edge detection and feature recognition algorithm to process consecutive image frames in the optical imaging data stream to recognize and extract the outline of the measured cable in the image; determining an axis line passing through the geometric center of the outline of the measured cable, and defining the axis line as the axial physical coordinates; and establishing a transformation relationship for mapping the coordinates of each pixel point in the thermal imaging data stream to the axial physical coordinates.

3. The method of claim 1, wherein the high temperature resistant cable is a high temperature resistant power cable. The steps for detecting concentration inflection points include: analyzing the concentration values ​​of the target component in the gas composition data stream. The process is performed to calculate the concentration value. First time derivative With the second time derivative Specifically, when the first-order time derivative and the second-order time derivative simultaneously satisfy the following conditions within the second detection window: and When the concentration inflection point is determined to have occurred, then... is a given positive threshold number used to characterize the effective growth rate of concentration.

4. The method of claim 1, wherein the high temperature resistant cable is a high temperature resistant power cable. The method further comprises: synchronously collecting an acoustic emission data stream representing high-frequency stress waves emitted by the cable under thermal stress due to internal micro-cracks; and including acoustic burst events identified from the acoustic emission data stream as internal mechanical damage events in the pairing and forming process of the causal event chain, so that the final formed pyrolysis failure fingerprint contains event information distinguishing between external appearance changes and internal structure damage.

5. The method of claim 1, wherein the high temperature resistant cable is a high temperature resistant power cable. The method further comprises: storing multiple generated pyrolysis failure fingerprints in a failure mode database, and using the failure mode database to train a time sequence prediction model.

6. The method of claim 1, wherein the high temperature resistant cable is a high temperature resistant power cable. The length of the second detection window is determined according to the physical distance between the surface of the measured cable and the gas composition data stream collection point, and the physical delay time determined by the gas transmission rate of the target component in the test environment; the synchronous collection frequency is not less than 10 Hz; and the given tolerance specifically includes: a synchronization time jitter error less than one sampling period, and a registration space positioning error less than one pixel size.

7. The method of claim 1, wherein the high temperature resistant cable is a high temperature resistant power cable. The pyrolysis failure fingerprint is a structured data object, in which each event constituting each causal event chain is recorded with a unique event type identifier, an absolute timestamp of event occurrence, an axial physical coordinate of event occurrence, and a local temperature value and target component concentration value corresponding to the event occurrence.

8. The method of claim 1, wherein the high temperature resistant cable is a high temperature resistant power cable. The step of detecting the crack event from the optical imaging data stream according to the pixel gradient change rule specifically comprises: in the first detection window, calculating the pixel gray gradient distribution along the radial direction of the measured cable in the optical imaging data stream; when a local peak value appears in the pixel gray gradient distribution, and the value of the local peak value exceeds a given crack judgment gradient threshold, it is determined that a crack event occurs at the position.

9. The method of claim 1, wherein the high temperature resistant cable is a high temperature resistant power cable. All steps of the method are performed under the constraints of the standard procedure and physical conditions of the continuous combustion test without interruption.

10. A high temperature resistant cable performance testing system, characterized in that, The system comprises: a sensing and acquisition module comprising a thermal imaging sensor, an optical camera and a gas analyzer, and configured to acquire, in synchronization with the same master clock, a thermal imaging data stream, an optical imaging data stream and a gas composition data stream in the course of performing a continuous combustion test on a cable sample; A data processing module, the data processing module is connected with the sensing and collecting module; wherein, the data processing module is configured to: based on the optical imaging data stream, calibrate the axial physical coordinates of the measured cable, and register the thermal imaging data stream to the axial physical coordinates to establish a shared space-time domain; when the temperature at a certain position along the axial physical coordinates first reaches the glass transition temperature of the cable material , then take this moment as the reference, respectively open the first detection window and the second detection window; limit only in the first detection window, detect the crack event from the optical imaging data stream according to the pixel gradient change rule, and limit only in the second detection window, detect the concentration inflection point of the target component from the gas composition data stream; before timing pairing, check the synchronization and registration error of the thermal imaging data stream, the optical imaging data stream and the gas composition data stream, and delete the data frame whose error exceeds the given tolerance; only the temperature rise, crack event and concentration inflection point that pass the error closed loop check are timing paired to form a causal event chain; and based on the formed causal event chain, generate and output a pyrolysis failure fingerprint representing the dynamic failure characteristics of the cable.

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