Composite material gas cylinder acoustic emission early warning and grading method

Abstract

The application discloses a composite material gas cylinder acoustic emission early warning and grading method, and relates to the field of nondestructive testing, which comprises the following steps: collecting a plurality of channel acoustic emission signals of a gas cylinder surface, forming an event list after event triggering, pretreatment and merging; dividing monitoring stages according to pressure or time interval; using a sliding window quantile statistical method to perform transient stripping in each stage to obtain a transient event stream; combining channel coverage weight and event average energy weight factor to calculate a transient stripping joint density sequence, and extracting tail window density and stage steady-state density; taking pressure as an independent variable to perform segmented statistics and curve smoothing on the density sequence to calculate discrete curvature; triggering early warning when the curvature sequence continuously exceeds a threshold at multiple points; calculating an upper tail severity index based on a high quantile event set, and performing risk grade evaluation in combination with a damage evolution trend factor to output a final risk grade. The application realizes accurate early warning and grading of structural risks of the composite material gas cylinder.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing, specifically to a method for early warning and rating of acoustic emission from composite gas cylinders. Background Technology

[0002] Composite material gas cylinders are widely used in energy storage and transportation, aerospace, and transportation fields due to their high specific strength, excellent corrosion resistance, and high design flexibility. Ensuring their structural integrity and safety during long-term service is crucial, especially under conditions of cyclic pressurization. Timely identification of potential damage evolution and instability risks is the core task of safety monitoring.

[0003] Acoustic emission (AE) technology, as a dynamic and real-time non-destructive testing method, can effectively capture elastic waves generated by the rapid release of energy during the stress process of materials, and has become an important means of monitoring the health of composite material structures. By analyzing the frequency, intensity, and spatial distribution of AE events, the damage state inside the structure can be inferred. In existing technologies, early warning methods based on AE signals usually rely on monitoring event counts, cumulative energy values, or specific parameter thresholds. These methods can reflect the overall activity level of the structure to a certain extent, but they still face challenges in practical applications. For example, complex industrial environments contain a large amount of background noise and electromagnetic interference, which may mask the true damage signals; the nonlinear relationship between AE event characteristics and damage severity makes early warning strategies based on fixed thresholds prone to false alarms or missed alarms; in addition, traditional static parameter analysis is difficult to capture the dynamic trend of damage evolution, which may lead to delayed early warning and fail to provide sufficient lead time for risk intervention. Therefore, there is an urgent need in this field for an AE early warning and rating method that can effectively suppress noise interference, dynamically identify damage characteristics, and has forward-looking risk assessment capabilities, in order to improve the accuracy, reliability, and timeliness of monitoring the structural condition of composite gas cylinders. Summary of the Invention

[0004] Based on the shortcomings of the prior art described above, the purpose of this invention is to provide a method for early warning and rating of acoustic emissions from composite gas cylinders, so as to solve the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for early warning and rating of acoustic emission from composite gas cylinders, comprising:

[0006] S1: Collect multi-channel acoustic emission signals from the surface of composite gas cylinders, and obtain an acoustic emission event list after event triggering, waveform preprocessing and event merging.

[0007] S2: Based on the list of acoustic emission events, the monitoring process is divided into multiple stages according to pressure changes or fixed time intervals to obtain a stage set;

[0008] S3: In each stage, the event counting sequence is transiently stripped using the sliding window quantile statistics method to obtain the transient event stream sequence;

[0009] S4: Based on the transient event stream sequence, combined with channel coverage weight and event average energy weight factor, calculate the transient stripping joint density sequence, and extract the end window density and stage steady-state density;

[0010] S5: Perform piecewise statistics and curve smoothing on the transient stripping joint density sequence with pressure as the independent variable, and calculate the discrete curvature sequence;

[0011] S6: When the curvature values ​​of multiple consecutive data points in a discrete curvature sequence exceed a preset curvature threshold, an early warning signal is triggered.

[0012] S7: Select a high-quantile event set based on the transient stripping joint density sequence, calculate the upper tail severity index, and combine the end window density, stage steady-state density, and damage evolution trend factor calculated based on the average upper tail severity of the current and historical stages to assess the risk level and output the final risk level.

[0013] The present invention is further configured such that S1 includes:

[0014] Multi-channel acoustic emission signals are collected by multiple acoustic emission sensors deployed on the surface of composite material gas cylinders;

[0015] Based on multi-channel acoustic emission signals, an event triggering method is adopted by combining a preset amplitude threshold and a preset energy threshold.

[0016] The triggered event waveform is sequentially subjected to bandpass filtering, baseline correction, and noise reduction.

[0017] The events detected by multiple channels are merged based on the arrival time difference of the acoustic emission signals to different sensors to obtain a list of acoustic emission events.

[0018] The present invention is further configured such that S2 includes:

[0019] Based on the pressure curve changes or fixed time intervals during the pressure holding process, the monitoring process is divided into multiple continuous stages with relatively stable operating conditions, resulting in a stage set containing the acoustic emission events corresponding to each stage.

[0020] The present invention is further configured such that S3 includes:

[0021] The time axis within a stage is divided into equally spaced time units, and the number of acoustic emission events in each unit is counted to construct an event counting sequence.

[0022] The sliding window quantile statistical method is used to calculate the stage background value of the event counting sequence based on preset quantile parameters;

[0023] The background values ​​for this stage are filtered out from the event counting sequence, and negative results are zeroed out to obtain the transient event stream sequence.

[0024] The present invention is further configured such that S4 includes:

[0025] Within a preset statistical time window, the number of effective transient events detected by a preset number of sensors is counted, and the channel coverage weight is determined based on the proportion of effective transient events that meet the collaborative detection conditions.

[0026] Within the same statistical time window, the average energy of all transient events is calculated and normalized relative to a reference energy value to obtain the event average energy weighting factor.

[0027] Multiply the channel coverage weight by the event average energy weight factor, and then multiply it by the counting rate of transient events within a unit time window to obtain the transient stripping joint density sequence.

[0028] The average value of transient stripping joint density within a preset time window before the end of the pressure holding stage is calculated as the final window density, and the average value of transient stripping joint density within a preset steady-state statistical window during the stage is calculated as the stage steady-state density.

[0029] The present invention is further configured such that S5 includes:

[0030] Using pressure as the independent variable, the transient stripping joint density sequence is segmented and statistically analyzed. The average density value of each preset pressure interval is calculated to form a discrete set of density-pressure relationship points.

[0031] Spline curve smoothing is applied to the density-pressure relationship point set to obtain a continuous density-pressure curve;

[0032] Based on the density-pressure curve, the discrete curvature values ​​at each data point are calculated using the second-order difference method to form a discrete curvature sequence.

[0033] The present invention is further configured such that S6 includes:

[0034] Using pressure as the independent variable, the discrete curvature sequence is traversed in ascending order.

[0035] When the number of consecutive data points whose curvature values ​​exceed the preset curvature threshold reaches the preset minimum number of consecutive points threshold, an early warning signal is triggered.

[0036] The present invention is further configured such that S7 includes:

[0037] The transient stripping joint density values ​​within the stage are arranged in descending order of numerical value. Based on the preset high quantile parameter, a corresponding proportion of transient stripping joint density values ​​are selected to form a high quantile event set.

[0038] The arithmetic mean of all transient stripping joint density values ​​in the high quantile event set is calculated as the upper tail severity average, and the maximum value among them is selected as the upper tail severity maximum.

[0039] The average upper tail severity of the current stage and the previous m historical stages is used to form a numerical sequence. The change slope is obtained by linear fitting of the numerical sequence. The change slope is divided by the average value of the numerical sequence for normalization to obtain the damage evolution trend factor.

[0040] Based on the current stage's average upper tail severity, maximum upper tail severity, end window density, and stage steady-state density, a preliminary risk level determination is made according to the preset level boundaries.

[0041] When the damage evolution trend factor is greater than the preset positive trend threshold, the initial risk level will be upgraded by one level to become the final risk level; otherwise, the initial risk level will be maintained as the final risk level.

[0042] The present invention is further configured to display the density-pressure curve and the curvature value corresponding to each data point in the curve through a graphical interface.

[0043] The present invention is further configured such that the method also includes outputting the warning signal and the final risk level in real time through an audible and visual alarm device.

[0044] This invention provides a method for early warning and rating of acoustic emission from composite gas cylinders. The method comprises: S1: acquiring multi-channel acoustic emission signals from the surface of the composite gas cylinder, followed by event triggering, waveform preprocessing, and event merging to obtain an acoustic emission event list; S2: based on the acoustic emission event list, dividing the monitoring process into multiple stages according to pressure changes or fixed time intervals to obtain a stage set; S3: within each stage, using a sliding window quantile statistical method to perform transient stripping on the event count sequence to obtain a transient event stream sequence; S4: based on the transient event stream sequence, combining channel coverage weights and event average energy weighting factors, calculating the transient stripping joint density sequence. The transient stripping joint density sequence is segmented and the end window density and stage steady-state density are extracted. S5: Using pressure as the independent variable, the transient stripping joint density sequence is segmented and statistically smoothed, and a discrete curvature sequence is calculated. S6: When the curvature values ​​of multiple consecutive data points in the discrete curvature sequence exceed a preset curvature threshold, an early warning signal is triggered. S7: Based on the transient stripping joint density sequence, a high-quantile event set is selected, the upper tail severity index is calculated, and a risk level assessment is performed by combining the end window density, stage steady-state density, and the damage evolution trend factor calculated based on the average upper tail severity of the current and historical stages. The final risk level is output, and the beneficial effects include:

[0045] 1. By acquiring and processing multi-channel acoustic emission signals and events, the accuracy and reliability of acoustic emission event detection are effectively improved, providing a high-quality data foundation for subsequent analysis. Furthermore, by dividing the monitoring process into stages based on pressure changes or time intervals, refined segmented management of the monitoring process is achieved, creating conditions for feature extraction under different working conditions.

[0046] 2. The transient stripping method using the sliding window quantile statistical method can effectively separate background noise from effective acoustic emission signals, improving the accuracy of damage-related feature extraction. Furthermore, by combining the joint density calculation of channel coverage weight and event average energy weight factor, a comprehensive consideration of the spatial distribution and intensity characteristics of acoustic emission events is achieved, enhancing the comprehensiveness of damage assessment.

[0047] 3. Based on the curvature feature extraction and early warning triggering mechanism of the density-pressure curve, early warning can be achieved. By combining the risk level assessment method of the upper tail severity index and the damage evolution trend factor, the current damage status and development trend are considered, making the risk assessment results more scientific and accurate.

[0048] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0050] Figure 1 The flowchart illustrates an acoustic emission warning and rating method for composite gas cylinders, which is an exemplary embodiment of the present invention. Detailed Implementation

[0051] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0052] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0053] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0054] A method for early warning and rating of acoustic emission from composite gas cylinders, such as Figure 1 As shown, it includes:

[0055] S1: Collect multi-channel acoustic emission signals from the surface of composite gas cylinders, and obtain an acoustic emission event list after event triggering, waveform preprocessing and event merging.

[0056] S2: Based on the list of acoustic emission events, the monitoring process is divided into multiple stages according to pressure changes or fixed time intervals to obtain a stage set;

[0057] S3: In each stage, the event counting sequence is transiently stripped using the sliding window quantile statistics method to obtain the transient event stream sequence;

[0058] S4: Based on the transient event stream sequence, combined with channel coverage weight and event average energy weight factor, calculate the transient stripping joint density sequence, and extract the end window density and stage steady-state density;

[0059] S5: Perform piecewise statistics and curve smoothing on the transient stripping joint density sequence with pressure as the independent variable, and calculate the discrete curvature sequence;

[0060] S6: When the curvature values ​​of multiple consecutive data points in a discrete curvature sequence exceed a preset curvature threshold, an early warning signal is triggered.

[0061] S7: Select a high-quantile event set based on the transient stripping joint density sequence, calculate the upper tail severity index, and combine the end window density, stage steady-state density, and damage evolution trend factor calculated based on the average upper tail severity of the current and historical stages to assess the risk level and output the final risk level.

[0062] The present invention is further configured such that S1 includes:

[0063] Multi-channel acoustic emission signals are collected by multiple acoustic emission sensors deployed on the surface of composite material gas cylinders;

[0064] Based on multi-channel acoustic emission signals, an event triggering method is adopted by combining a preset amplitude threshold and a preset energy threshold.

[0065] The triggered event waveform is sequentially subjected to bandpass filtering, baseline correction, and noise reduction.

[0066] Based on the arrival time difference of acoustic emission signals to different sensors, events detected by multiple channels are merged to obtain an acoustic emission event list. Specifically, multiple acoustic emission sensors are deployed on the surface of the composite gas cylinder to form a monitoring network. For example, a feasible arrangement is to use six sensors: three are evenly distributed circumferentially in the middle of the cylinder to cover the main area, two are located at the end caps of the cylinder to monitor stress concentration areas, and the other can serve as a redundancy backup to enhance reliability. The sensors, based on the piezoelectric effect, convert the acoustic emission signals generated and propagated by damage to the cylinder material under pressure into continuous voltage signals. Event triggering is performed on the acquired multi-channel continuous voltage signals. The judgment criteria are a combination of preset amplitude thresholds and energy thresholds. That is, when the peak amplitude of the signal is greater than the preset amplitude threshold and its calculated energy value is greater than the preset energy threshold, it is judged as a valid acoustic emission event, and the key parameters of the event are recorded, including arrival time, peak amplitude, energy, and duration. After the event is triggered, the corresponding waveform needs to be preprocessed. To improve the signal-to-noise ratio and data quality, the preprocessing process includes bandpass filtering, baseline correction, and denoising. Bandpass filtering preserves the main frequency components related to material damage while suppressing low-frequency mechanical vibration noise and high-frequency electromagnetic interference. Baseline correction eliminates DC offset or slow drift in the signal to stabilize the waveform baseline. Denoising further attenuates random noise in the waveform to extract true acoustic emission signal features; for example, wavelet thresholding can be used. After waveform preprocessing, multi-channel detection data needs to be merged. Since acoustic emission signals generated by the same physical damage event arrive at different sensors at different times, if the arrival time difference between events recorded by different channels is less than the threshold calculated based on the sound wave propagation speed in the material and the sensor spacing, these records are determined to originate from the same physical event and are merged. During merging, the parameters recorded by the channel with the highest energy are selected as the representative feature parameters of the event. After the complete signal acquisition, triggering, preprocessing, and merging process described above, a list of acoustic emission events is finally output.

[0067] The present invention is further configured such that S2 includes:

[0068] Based on the pressure curve changes or fixed time intervals during the pressure holding process, the monitoring process is divided into multiple continuous stages with relatively stable operating conditions, resulting in a set of stages containing the corresponding acoustic emission events for each stage. Specifically, the stage division of the monitoring process can be based on the morphological characteristics of the pressure-time curve recorded during the pressure holding process, or a fixed time interval can be used as the division criterion. For example, when the pressure curve exhibits different shapes such as a plateau period, a linear pressure increase segment, or a pressure holding segment, each relatively stable interval can be divided into an independent monitoring stage. If a fixed time interval is used, the entire pressure holding process can be segmented into equal durations. Each stage should have a relatively stable load background, that is, the pressure conditions within the stage are basically constant or exhibit a smooth and monotonous change. After the stage division is completed, the output is a set of stages composed of multiple stage units, where each unit contains all the acoustic emission events recorded within the corresponding time interval.

[0069] The present invention is further configured such that S3 includes:

[0070] The time axis within a stage is divided into equally spaced time units, and the number of acoustic emission events in each unit is counted to construct an event counting sequence.

[0071] The sliding window quantile statistical method is used to calculate the stage background value of the event counting sequence based on preset quantile parameters;

[0072] The transient event stream sequence is obtained by filtering out background values ​​for the selected monitoring phase from the event count sequence and zeroing out negative values. Specifically, the time axis of the selected monitoring phase is first divided into equally spaced time units, and the number of acoustic emission events in each unit is counted to construct an event count sequence with time as the independent variable. This event count sequence reflects the original time distribution of acoustic emission activities. Then, the stage background value is calculated using the sliding window quantile statistical method. That is, for each time point in the event count sequence, the event count values ​​in the previous preset time window are taken to form a statistical sample, and the specified quantile of the sample is calculated. This specified quantile is defined as the stage background value at that time point. After obtaining the complete background sequence composed of stage background values, it is differentially analyzed point by point with the event count sequence to suppress continuous or slowly changing, non-damaging background acoustic emission activities. The difference results are non-negatively processed by setting all negative values ​​to zero, and a non-negative transient event stream sequence is output. After the above processing, background noise and steady-state activities are effectively suppressed, so that the transient event stream sequence mainly retains sudden acoustic emission events whose intensity or occurrence rate deviates significantly from the background level.

[0073] The present invention is further configured such that S4 includes:

[0074] Within a preset statistical time window, the number of effective transient events detected by a preset number of sensors is counted, and the channel coverage weight is determined based on the proportion of effective transient events that meet the collaborative detection conditions.

[0075] Within the same statistical time window, the average energy of all transient events is calculated and normalized relative to a reference energy value to obtain the event average energy weighting factor.

[0076] Multiply the channel coverage weight by the event average energy weight factor, and then multiply it by the counting rate of transient events within a unit time window to obtain the transient stripping joint density sequence.

[0077] The average transient peeling density within a preset time window before the end of the pressure holding stage is calculated as the final window density, and the average transient peeling density within a preset steady-state statistical window during the stage is calculated as the stage steady-state density. Specifically, within a preset statistical time window, valid transient events that meet the collaborative detection conditions are identified and counted. The criterion is that the arrival time difference of events recorded by different sensors is less than a threshold determined by the material's acoustic wave propagation characteristics and the spatial geometry of the sensors. Channel coverage weights are calculated based on the ratio of the number of sensor channels involved in valid transient events to the total number of channels. These channel coverage weights are used to quantify the reliability of the spatial distribution of events. Within the same statistical time window, the arithmetic mean of the acoustic emission signal energy values ​​corresponding to all transient events is calculated, and this arithmetic mean is normalized to a reference energy value determined based on historical data or experimental calibration to obtain the event average energy. The event average energy weighting factor is used to reflect the relative intensity level of the event. The channel coverage weight is multiplied by the event average energy weighting factor, and then the product is multiplied by the counting rate of transient events within a unit time window to obtain the transient stripping joint density sequence. This transient stripping joint density sequence comprehensively reflects the frequency, spatial reliability, and intensity characteristics of acoustic emission events. To extract feature quantities with clear physical meaning, the average joint density of two specific time intervals is further calculated: one is the average transient stripping joint density within a preset duration window before the end of the pressure holding phase, called the end window density, which is used to characterize the local enhancement characteristics of acoustic emission activity at the end of the pressure holding phase; the other is the average transient stripping joint density within a preset steady-state statistical window within the phase, called the phase steady-state density, which is used to reflect the overall average level of acoustic emission activity in that phase.

[0078] The present invention is further configured such that S5 includes:

[0079] Using pressure as the independent variable, the transient stripping joint density sequence is segmented and statistically analyzed. The average density value of each preset pressure interval is calculated to form a discrete set of density-pressure relationship points.

[0080] Spline curve smoothing is applied to the density-pressure relationship point set to obtain a continuous density-pressure curve;

[0081] Based on the density-pressure curve, the discrete curvature values ​​at each data point are calculated using the second-order difference method, forming a discrete curvature sequence. Specifically, with pressure as the independent variable, the pressure range during the pressure holding process is divided into continuously equally spaced intervals. The arithmetic mean of the transient stripping joint density values ​​falling within each preset pressure interval is calculated, constructing a discrete density-pressure relationship point set with the interval median as the x-axis and the average density as the y-axis. This density-pressure relationship point set reflects the overall trend of density change with pressure, but may have local fluctuations. To suppress fluctuations and establish a continuously differentiable functional relationship, [further steps are taken]. Cubic spline interpolation is used to smooth the density-pressure relationship point set, generating a density-pressure curve with continuous second derivatives, which maintains the overall trend of the original data. Based on the smoothed density-pressure curve, the curvature value at each pressure data point is calculated using the discrete curvature approximation method. That is, the curvature is solved by second-order difference operation based on the function value of each point and its neighboring points and the pressure sampling interval. Finally, the discrete curvature sequence corresponding to the pressure data points is output. The extreme points of the discrete curvature sequence correspond to the inflection points of the density-pressure curve, marking the critical region where the density changes drastically with pressure.

[0082] The present invention is further configured such that S6 includes:

[0083] Using pressure as the independent variable, the discrete curvature sequence is traversed in ascending order.

[0084] When the number of consecutive data points with curvature values ​​greater than a preset curvature threshold reaches a preset minimum consecutive point threshold, an early warning signal is triggered. Specifically, the discrete curvature sequence is traversed in ascending order of pressure, with pressure as the independent variable. During this process, a curvature judgment threshold and a minimum consecutive point threshold need to be preset: the curvature judgment threshold is determined based on the statistical characteristics of curvature distribution under historical data or typical working conditions, and is used to identify the critical state of abnormal acceleration of acoustic emission activity; the minimum consecutive point threshold is used to limit the minimum number of consecutive data points exceeding the threshold, so as to eliminate misjudgments caused by random fluctuations or instantaneous interference and improve the reliability of the early warning. When multiple consecutive data points with curvature values ​​greater than the curvature judgment threshold are detected during the traversal, and the number of consecutive points exceeding the threshold is not less than the minimum consecutive point threshold, the early warning triggering condition is determined to be met, an early warning signal is generated, and a refractory period is entered to suppress repeated triggering.

[0085] The present invention is further configured such that S7 includes:

[0086] The transient stripping joint density values ​​within the stage are arranged in descending order of numerical value. Based on the preset high quantile parameter, a corresponding proportion of transient stripping joint density values ​​are selected to form a high quantile event set.

[0087] The arithmetic mean of all transient stripping joint density values ​​in the high quantile event set is calculated as the upper tail severity average, and the maximum value among them is selected as the upper tail severity maximum.

[0088] The average upper tail severity of the current stage and the previous m historical stages is used to form a numerical sequence. The change slope is obtained by linear fitting of the numerical sequence. The change slope is divided by the average value of the numerical sequence for normalization to obtain the damage evolution trend factor.

[0089] Based on the current stage's average upper tail severity, maximum upper tail severity, end window density, and stage steady-state density, a preliminary risk level determination is made according to the preset level boundaries.

[0090] When the damage evolution trend factor is greater than the preset positive trend threshold, the initial risk level is upgraded by one level to become the final risk level; otherwise, the initial risk level is maintained as the final risk level. Specifically, based on the transient stripping joint density sequence within the current stage, all transient stripping joint density values ​​are sorted in descending order of numerical value. According to the preset high quantile parameter, the corresponding proportion of data points ranked at the top of the transient stripping joint density sequence are selected to form a high quantile event set reflecting the most significant acoustic emission activity. The arithmetic mean of all transient stripping joint density values ​​within this high quantile event set is calculated as the upper tail severity average. At the same time, the maximum transient stripping joint density value in this high quantile event set is extracted as the upper tail severity maximum value. The upper tail severity average and the upper tail severity maximum value represent the typical intensity level and extreme intensity level of high-risk events in this stage, respectively. To assess the risk evolution trend, the upper tail severity values ​​for the current stage and the previous m consecutive historical stages are obtained. The average value of the degree constitutes a numerical sequence. Least square linear fitting is performed on this numerical sequence to obtain the slope of change. This slope is then normalized by dividing by the sum of the average value of the numerical sequence and the regularization constant, yielding a damage evolution trend factor. This trend factor is used to quantify the rate of change per unit intensity. The risk level determination process consists of two levels: First, based on four static indicators—the average upper-tail severity, the maximum upper-tail severity, the end-window density, and the stage steady-state density—a preliminary risk level is determined according to preset level boundary thresholds. Then, the damage evolution trend factor is introduced for dynamic correction. When the damage evolution trend factor exceeds a preset positive trend threshold, it indicates that the damage is accelerating, and the determined preliminary risk level is increased by one level as the final risk level output. Otherwise, the determined preliminary risk level is maintained as the final risk level output. This method, through the combination of static and dynamic indicators, achieves a comprehensive assessment and trend warning of the risk status of composite gas cylinder structures.

[0091] The present invention is further configured to display the density-pressure curve and the curvature values ​​corresponding to each data point in the curve through a graphical interface. Specifically, a graphical user interface based on dual vertical axes is constructed, where the horizontal axis represents the pressure value, the left vertical axis displays the transient stripping joint density, and the right vertical axis displays the corresponding curvature value. The density-pressure relationship is presented as a smooth curve, intuitively reflecting the trend and local characteristics of acoustic emission activity intensity with pressure. The curvature values ​​are superimposed on the graph as discrete points or auxiliary curves, clearly showing their distribution. This visualization facilitates monitoring personnel to directly observe the morphological characteristics of the density-pressure curve and accurately locate the curve inflection points corresponding to the curvature extrema, thereby timely identifying the critical state of accelerated changes in acoustic emission activity.

[0092] The invention is further configured such that the method includes real-time warning output of the warning signal and the final risk level through an audible and visual alarm device; specifically, when it is determined that a warning signal is triggered or the risk level reaches a preset threshold, the audible and visual alarm device is automatically triggered to activate the corresponding warning mode; the sound alarm component outputs a distinctive audio signal according to different risk levels, and the severity of the risk is characterized by differentiated frequency, beat and volume; the light source alarm component characterizes the risk status through different colored light signals and their flashing frequencies, for example, green constant light indicates normal, yellow slow flashing indicates low-level warning, and red fast flashing indicates high-level alarm.

[0093] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for early warning and rating of acoustic emission from composite gas cylinders, characterized in that, include: S1: Collect multi-channel acoustic emission signals from the surface of composite gas cylinders, and obtain an acoustic emission event list after event triggering, waveform preprocessing and event merging. S2: Based on the list of acoustic emission events, the monitoring process is divided into multiple stages according to pressure changes or fixed time intervals to obtain a stage set; S3: In each stage, the event counting sequence is transiently stripped using the sliding window quantile statistics method to obtain the transient event stream sequence; S4: Based on the transient event stream sequence, combined with channel coverage weight and event average energy weight factor, calculate the transient stripping joint density sequence, and extract the end window density and stage steady-state density; S4 includes: within a preset statistical time window, count the number of effective transient events detected by a preset number of sensors, and determine the channel coverage weight according to the proportion of channels occupied by effective transient events that meet the collaborative detection conditions; within the same statistical time window, calculate the average energy of all transient events, and normalize it relative to a reference energy value to obtain the event average energy weight factor; multiply the channel coverage weight by the event average energy weight factor, and then multiply it by the counting rate of transient events within a unit time window to obtain the transient stripping joint density sequence; calculate the average transient stripping joint density within a preset time window before the end of the holding pressure stage as the end window density, and calculate the average transient stripping joint density within a preset steady-state statistical window within the stage as the stage steady-state density; S5: Perform piecewise statistics and curve smoothing on the transient stripping joint density sequence with pressure as the independent variable, and calculate the discrete curvature sequence; S5 includes: performing piecewise statistics on the transient stripping joint density sequence with pressure as the independent variable, calculating the average density value of each preset pressure interval, forming a discrete density-pressure relationship point set; performing spline curve smoothing on the density-pressure relationship point set to obtain a continuous density-pressure curve; based on the density-pressure curve, calculating the discrete curvature value at each data point using the second-order difference method to form a discrete curvature sequence; S6: When the curvature values ​​of multiple consecutive data points in the discrete curvature sequence are greater than a preset curvature threshold, an early warning signal is triggered; S6 includes: traversing the discrete curvature sequence in ascending order with pressure as the independent variable; when the number of consecutive data points whose curvature values ​​are greater than the preset curvature threshold reaches a preset minimum number of consecutive points threshold, an early warning signal is triggered; S7: Based on the transient stripping joint density sequence, select a high-quantile event set, calculate the upper-tail severity index, and combine it with the end window density, stage steady-state density, and damage evolution trend factor calculated based on the average upper-tail severity of the current and historical stages to assess the risk level and output the final risk level; S7 includes: arranging the transient stripping joint density values ​​within the stage in descending order of numerical value, selecting a corresponding proportion of transient stripping joint density values ​​according to preset high-quantile parameters to form a high-quantile event set; calculating the arithmetic mean of all transient stripping joint density values ​​in the high-quantile event set as the average upper-tail severity, and selecting the maximum value as... The maximum upper tail severity is determined by: obtaining the average upper tail severity of the current stage and the previous m historical stages to form a numerical sequence; performing linear fitting on this numerical sequence to obtain the slope of change; dividing the slope of change by the average value of the numerical sequence for normalization to obtain the damage evolution trend factor; based on the average upper tail severity, maximum upper tail severity, end window density, and stage steady-state density of the current stage, a preliminary risk level is determined according to the preset level boundary; when the damage evolution trend factor is greater than the preset positive trend threshold, the determined preliminary risk level is increased by one level as the final risk level; otherwise, the determined preliminary risk level is maintained as the final risk level.

2. The method for early warning and rating of acoustic emission from composite gas cylinders according to claim 1, characterized in that, S1 includes: Multi-channel acoustic emission signals are collected by multiple acoustic emission sensors deployed on the surface of composite material gas cylinders; Based on multi-channel acoustic emission signals, an event triggering method is adopted by combining a preset amplitude threshold and a preset energy threshold. The triggered event waveform is sequentially subjected to bandpass filtering, baseline correction, and noise reduction. The events detected by multiple channels are merged based on the arrival time difference of the acoustic emission signals to different sensors to obtain a list of acoustic emission events.

3. The method for early warning and rating of acoustic emission from composite gas cylinders according to claim 1, characterized in that, S2 includes: Based on the pressure curve changes or fixed time intervals during the pressure holding process, the monitoring process is divided into multiple continuous stages with relatively stable operating conditions, resulting in a stage set containing the acoustic emission events corresponding to each stage.

4. The method for early warning and rating of acoustic emission from composite gas cylinders according to claim 1, characterized in that, S3 includes: The time axis within a stage is divided into equally spaced time units, and the number of acoustic emission events in each unit is counted to construct an event counting sequence. The sliding window quantile statistical method is used to calculate the stage background value of the event counting sequence based on preset quantile parameters; The background values ​​for this stage are filtered out from the event counting sequence, and negative results are zeroed out to obtain the transient event stream sequence.

5. The method for early warning and rating of acoustic emission from composite gas cylinders according to claim 1, characterized in that, The density-pressure curve and the curvature values ​​corresponding to each data point in the curve are displayed through a graphical interface.

6. The method for early warning and rating of acoustic emission from composite gas cylinders according to claim 1, characterized in that, The method also includes outputting early warning signals and final risk levels in real time via an audible and visual alarm device.

Images