A method for analyzing failure of an audio device based on a clari diagram

Abstract

This application provides a method for fault analysis of audio equipment based on the Cranny graphic, comprising: acquiring an image of the damping coating of the speaker voice coil skeleton, extracting the initial transition time from disorder to stability of the Cranny graphic, calculating its deviation from a preset critical point to assess the probability of damping reduction and the severity of attenuation anomalies; then combining the trend of attenuation anomalies and the amplitude of speaker response speed changes within a preset time period to extract the mutual constraint strength between the two and the sensitivity assessment level of the equipment to damping attenuation; finally, correlating the equipment workload and the accelerating trend of damping material aging rate, and integrating multi-dimensional features to determine the remaining service life of the audio equipment. This invention achieves early fault identification and quantitative analysis of anti-aging capabilities, significantly improving the accuracy of remaining life assessment and providing a scientific basis for the maintenance and replacement of audio equipment.

Description

Technical Field

[0001] This invention relates to the field of audio equipment technology, and in particular to a method for audio equipment fault analysis based on Cranny graphics. Background Technology

[0002] As the core equipment for audio playback, the operational stability and lifespan of audio equipment directly affect the audio output quality. The speaker, as the core sound-generating component, is particularly susceptible to damage from the degradation of its damping characteristics and response performance. With increased usage time, the damping coating of the speaker voice coil frame is prone to aging and wear, leading to reduced damping, abnormal vibration transmission paths, and consequently, sound distortion and frequent malfunctions. Therefore, effective fault analysis methods are urgently needed to provide early warning and assess remaining lifespan.

[0003] Current audio equipment fault detection technologies largely rely on audio signal distortion detection and circuit parameter measurement, which can only identify explicit problems after a fault occurs. They struggle to detect potential faults in advance by utilizing key parameters characterizing speaker vibration. While the Cranny pattern, as a crucial representation of the modal characteristics of a vibration system, is closely related to the speaker's damping state and vibration transmission efficiency during its formation process, current technologies do not fully utilize the correlation between Cranny pattern formation speed and damping changes, failing to quantify the degree of damping attenuation through transition time. Furthermore, existing lifespan assessment methods often consider only the effects of damping material aging or workload, neglecting the interrelationship between speaker response speed and damping reduction. This failure to combine both factors to quantify the equipment's anti-aging capabilities leads to significant deviations in the assessment of the remaining service life of faulty equipment, making it difficult to meet the practical needs of preventative maintenance for audio equipment. Summary of the Invention

[0004] This invention provides a fault analysis method for audio equipment based on the Cranny pattern, aiming to solve the problem that existing technologies are unable to identify faults in advance based on the correlation characteristics between the forming speed and damping change of the Cranny pattern, and the analysis of the anti-aging ability of audio equipment in combination with the speaker response speed leads to inaccurate assessment of the remaining life of faulty audio equipment.

[0005] To achieve the above objectives, this invention provides a method for fault analysis of audio equipment based on Cranny graphics, comprising the following steps: By acquiring images of the damping coating on the speaker voice coil skeleton, the transition time of the Krani pattern from disordered powder distribution to stable nodal lines is identified, and the initial transition time is extracted. By comparing the difference between the initial transition time and the preset damping reduction critical point, the degree of deviation of the transition time is obtained. The probability of damping reduction is evaluated based on the degree of deviation, the corresponding vibration attenuation abnormal signal intensity is extracted, and the severity of attenuation abnormality is determined. Identify the changing trend of the severity of the attenuation anomaly within a preset time period, collect the change amplitude of the speaker response speed, fuse the changing trend and the change amplitude of the response speed, identify the rate of change of the response speed, and extract the mutual constraint strength between the change amplitude of the response speed and the probability of damping reduction. By analyzing the variation pattern of the transition time of the Cranny graphic, abnormal vibration transmission path of the audio equipment is identified. The significance of the fault characteristics is evaluated based on the mutual constraint strength. Combined with the response speed change rate, the sensitivity assessment level of the audio equipment to damping attenuation is extracted. The aging rate of the damping material is collected, and the acceleration trend of the aging rate is assessed in conjunction with the aforementioned sensitivity assessment level. By considering the workload of the audio equipment, integrating the correlation between the intensity of abnormal vibration attenuation signals and the transition time, and combining this with the accelerating trend of aging rate, the remaining service life of the audio equipment can be determined.

[0006] Furthermore, the extraction of the initial transition duration includes the following steps: The image of the damping coating is smoothed using a Gaussian filtering algorithm to obtain a first image sequence, thereby obtaining the disordered powder distribution characteristics. To address the disordered powder distribution characteristics, temporal feature extraction is employed to determine the powder distribution change trend from the first image sequence by comparing the pixel differences between adjacent image frames. Based on the powder distribution change trend, the degree of line aggregation in the change trend is compared with the preset nodal pattern by vibration mode analysis to determine the formation time of stable nodal lines and obtain transition process data. If the transition process data exceeds a preset threshold, the standard deviation parameter of the Gaussian filtering algorithm is adjusted and the first image sequence is reprocessed to obtain the corrected initial duration. The initial transition duration is extracted by combining the corrected initial duration with the formation time of the stable wavelet line.

[0007] Furthermore, determining the severity of the attenuation anomaly includes the following steps: To assess the degree of deviation in the transition duration, a regression algorithm is used to evaluate the probability of damping reduction. The regression algorithm maps the linear combination to the interval between 0 and 1 using the sigmoid function to obtain the probability value. Based on the probability value, the corresponding abnormal signal intensity is extracted from the vibration attenuation data to determine the intensity distribution characteristics; The severity of the attenuation anomaly is determined using the intensity distribution characteristics.

[0008] Furthermore, the extraction of the mutual constraint strength between the magnitude of the response speed change and the probability of damping reduction includes the following steps: Data on the severity of attenuation anomalies are collected within a preset time period, and trend indicators are obtained by dividing the time period. For the aforementioned trend indicators, the amplitude of the speaker response speed change is collected, and the amplitude distribution value is obtained by signal strength evaluation. By integrating the amplitude distribution values ​​and the trend indicators, the rate of change in response speed is identified through the correlation between the trend amplitude; Based on the rate of change of response speed, the mutual constraint strength between the magnitude of change of response speed and the probability of damping reduction is extracted, and the strength correlation coefficient is determined by Pearson correlation analysis combined with linear regression modeling. The intensity correlation coefficient is used to evaluate the distribution of constraint intensity, determine the fusion effect of the severity of attenuation anomaly and the magnitude of response speed change, and obtain the final mutual constraint intensity using the anomaly distribution characteristics.

[0009] Furthermore, the extraction of the sensitivity assessment level of the audio device to damping attenuation includes the following steps: Data on the transition duration of the Kroni graphic is collected within a preset time period. The change pattern index is obtained through duration distribution calculation. The duration distribution calculation is based on the interval difference between graphic transition durations to determine the distribution curve. Based on the aforementioned change pattern indicators, abnormal vibration transmission paths of audio equipment are identified, and abnormal distribution values ​​are obtained using a path mapping method. The path mapping method constructs a mapping table based on the correspondence between abnormal vibration paths and duration change patterns. Based on the abnormal distribution value and the change amplitude of the response speed, the mutual constraint strength between the damping reduction probability is integrated, and the significance of the fault characteristics is determined by strength correlation calculation. The strength correlation calculation adopts amplitude strength assessment to multiply the abnormal distribution value and the mutual constraint strength to obtain the correlation coefficient. Combining the significance of the fault characteristics with the rate of change of response speed, the sensitivity assessment level of the audio equipment to damping attenuation is extracted, and a sensitivity distribution map is obtained by using a level threshold judgment. The level threshold judgment is based on the rate of change and a comparison of the significance of the fault characteristics with a preset threshold. The influence of the vibration path on the graphic transition is evaluated using the sensitivity distribution map to obtain the sensitivity assessment level of the damping attenuation of the audio equipment. The influence intensity is based on the path anomaly identification data in the sensitivity distribution map fused with the attenuation sensitivity level.

[0010] Furthermore, the assessment of the accelerating trend of aging rate includes the following steps: Data on the aging rate of damping material is obtained within a preset period. A change curve is determined by data distribution calculation. The change curve is constructed based on the difference between rates. The data distribution calculation uses difference statistics to quantify the rate value intervals into a distribution sequence. Based on the change curve and sensitivity assessment level, a level fusion method is used to obtain the development trend index. The development trend index is determined by the correspondence between the curve and the level. The level fusion method matches the curve peak with the level threshold to generate the index value. Based on the development trend index, the acceleration feature of the aging rate is extracted. The acceleration feature is obtained by feature extraction to obtain the acceleration trend value. The feature extraction is obtained by calculating the feature vector through the index slope. The acceleration trend value is used to evaluate the acceleration trend of the damping material to the aging rate, and an acceleration trend distribution map is obtained. The acceleration trend distribution map is generated by mapping the distribution area based on the trend value.

[0011] Furthermore, determining the remaining service life of the audio equipment includes the following steps: Workload correlation data is obtained from the audio equipment operation record, and vibration attenuation intensity distribution is obtained through data quantization processing. The vibration attenuation intensity distribution is generated into a quantization sequence based on load correlation. The data quantization processing uses load value interval statistics to transform the correlation data into a distribution sequence. For the vibration attenuation intensity distribution fused with abnormal signal intensity, an intensity matching method is used to obtain the transition duration characteristic index. The transition duration characteristic index is determined by the correlation between signal intensity and duration. The intensity matching method matches the abnormal signal intensity value with the corresponding distribution sequence to generate the index value. Based on the transition duration characteristic index and the aging rate trend, an acceleration trend fusion feature is extracted. The acceleration trend fusion feature is obtained by trend slope quantization to obtain a fusion vector. The trend slope quantization is calculated to obtain vector elements by the slope difference between the index and the trend. By using the accelerated trend fusion feature associated with the remaining term determination process, a trend term assessment model is constructed. The trend term assessment model generates an assessment distribution based on the fusion vector mapping. The trend term assessment model takes the fusion vector as input and outputs the assessment distribution by weighted summation of vector elements. The trend duration assessment model is used to process load signal associated data, determine the combination of abnormal signal strength and characteristic trend, and if the fusion value exceeds a preset threshold, the assessment distribution is adjusted to obtain the remaining service life of the audio equipment. The adjusted assessment distribution generates the final duration value through distribution area offset correction.

[0012] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: By acquiring images of the damping coating on the speaker voice coil skeleton, the initial transition time from disorder to stability of the Clani pattern is extracted, and the deviation from a preset critical point is calculated to assess the probability of damping reduction and the severity of attenuation anomalies. Furthermore, by combining the attenuation anomaly change trend and the speaker response speed change amplitude within a preset time period, the mutual constraint strength between the two and the sensitivity level of the equipment to damping attenuation are extracted. Finally, by correlating the equipment workload with the accelerating trend of damping material aging rate and integrating multi-dimensional features, the remaining service life of the audio equipment is determined. This invention achieves early fault identification and quantitative analysis of anti-aging capabilities, significantly improving the accuracy of remaining life assessment and providing a scientific basis for the maintenance and replacement of audio equipment. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the audio equipment fault analysis method based on Cranny graphics of the present invention.

[0014] Figure 2 This is a schematic diagram of the process for evaluating the sensitivity of an audio device to damping attenuation according to the present invention.

[0015] Figure 3 This is a schematic diagram illustrating the process of determining the remaining service life of an audio device according to the present invention. Detailed Implementation

[0016] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0017] like Figures 1-3 This embodiment of a fault analysis method for audio equipment based on Cranny graphics may specifically include: Step S101: By acquiring an image of the damping coating on the speaker voice coil skeleton, the transition time of the Clani pattern from disordered powder distribution to stable nodal lines is identified, and the initial transition time is extracted.

[0018] By acquiring images of the damping coating on the voice coil skeleton, a Gaussian filtering algorithm is used to smooth the acquired images to remove noise, resulting in a first image sequence and obtaining disordered powder distribution characteristics. For these disordered powder distribution characteristics, temporal feature extraction is employed to determine the powder distribution change trend from the first image sequence by comparing pixel differences between adjacent image frames. Based on this powder distribution change trend, vibration mode analysis is used to compare the degree of line convergence in the change trend with a preset node pattern to determine the formation time of stable node lines, thus obtaining transition process data. If the transition process data exceeds a preset threshold, the standard deviation parameter of the Gaussian filtering algorithm is adjusted, and the first image sequence is reprocessed to obtain a corrected initial duration. The initial transition duration is extracted by combining the corrected initial duration with the stability of the node lines.

[0019] In one specific implementation, fine powder is uniformly sprinkled onto the coating surface, and then a vibration signal of a specific frequency is applied, causing the powder to form a pattern under vibration. This pattern, called a Clani pattern, originates from the standing wave phenomenon on the vibrating surface, where powder accumulates in nodal regions where vibration is weaker, forming visible lines. Furthermore, the formation process of the Clani pattern begins with an initial disordered powder distribution, and as vibration continues, the powder gradually migrates to a stable position. The disordered powder distribution refers to a state of random powder dispersion, while the stable nodal lines represent a clear linear arrangement of powder along the vibrating nodes. This transition reflects the damping characteristics of the speaker assembly; for example, under high-frequency vibration, the transition duration can indicate the viscoelasticity of the coating.

[0020] The steps for identifying the transition duration include image preprocessing and feature analysis. First, the acquired images undergo grayscale conversion and noise filtering to highlight the edges of the powder distribution. Then, an edge detection algorithm is used to track the motion trajectory of the powder particles. Specifically, by comparing the pixel differences of consecutive frames, the dynamic change of the powder from a scattered state to linear aggregation is calculated. This identification focuses on the formation of nodal lines; that is, when the powder stabilizes along a specific curve, the transition is considered complete. In one specific implementation, the edge detection algorithm adopts a combination of Canny edge detection and dynamic contour optimization, with the core used to accurately track the motion trajectory and aggregation trend of the powder on the voice coil skeleton damping coating surface. The algorithm inputs a sequence of 30fps images of 1280×720 pixels or higher after Gaussian filtering and grayscale conversion, along with auxiliary parameters such as grayscale threshold and neighborhood window size. It calculates pixel gradients using the Sobel operator, refines edges through non-maximum suppression, filters connected edges using dual thresholds, and then performs cross-frame contour matching using the ICP algorithm, outputting a binary image of the powder edges, cross-frame motion trajectory data, and a set of high-linearity candidate nodal lines. This algorithm effectively separates powder from the background, quantifies the linear trend of powder aggregation, and determines the completion of the transition by the overlap of more than 90% of the candidate nodal lines in three consecutive frames. This provides quantitative support for identifying the moment when stable nodal lines are formed, and helps to accurately extract the transition duration to reflect the damping characteristics of the loudspeaker.

[0021] In one specific implementation, extracting the initial transition duration involves timing calculations. The moment vibration begins is recorded as zero, and monitoring continues until the nodal line sharpness reaches a preset threshold, such as line continuity exceeding 80%. The initial transition duration is defined as the time from the start of the disordered distribution to the appearance of the first stable nodal line. This extraction helps evaluate the speaker's response speed; for example, in low-frequency testing, a shorter duration indicates good coating damping, thus improving the overall performance of the audio equipment. The vibration frequency can be adjusted for different speaker models to cover various scenarios. For example, in small headphone speakers, using 100Hz vibration, the transition duration is typically observed to be within 5 to 10 seconds; while in large speakers, using 50Hz vibration, the duration may extend to 15 seconds. This diversity demonstrates the versatility of the technical solution in audio equipment testing.

[0022] Step S102: By comparing the numerical difference between the initial transition time and the preset damping reduction critical point, the degree of deviation of the transition time is identified, the probability of damping reduction is evaluated based on the degree of deviation, the intensity of the corresponding vibration attenuation abnormal signal is extracted, and the severity of the attenuation abnormality is determined.

[0023] By comparing the initial transition time with the preset damping reduction threshold, the numerical difference is calculated to obtain a deviation index. For this deviation index, a logistic regression algorithm is used to assess the probability of damping reduction. The logistic regression algorithm maps linear combinations to the 0-1 interval using a sigmoid function to obtain probability values. Based on these probability values, the corresponding abnormal signal intensity is extracted from the vibration attenuation data to determine the intensity distribution characteristics. Using these intensity distribution characteristics, the severity of the attenuation anomaly is determined. If the severity exceeds a preset threshold, the weight parameters of the logistic regression are optimized in reverse, the probability of damping reduction is recalculated, and then combined with the new probability of damping reduction and the deviation degree to weightedly determine the final severity of the attenuation anomaly until the severity does not exceed the preset threshold.

[0024] In one specific implementation, the initial transition time is the duration from the onset of vibration to the formation of a stable nodal line, while the preset damping reduction threshold is a threshold set based on historical test data. For example, in loudspeaker voice coil skeleton testing, this threshold could be set to 8 seconds, representing the expected response of the damping coating under standard vibration. Subtracting the threshold value from the initial transition time yields the difference value. If the difference is positive, the deviation is positive, indicating a slower transition, which may point to a reduction in damping. This comparison process ensures the accuracy of damping characteristic evaluation for loudspeaker components during production.

[0025] It should be noted that the degree of deviation is an indicator obtained by normalizing the above difference values. For example, the difference value is divided by the critical point value to obtain the relative deviation rate.

[0026] In one specific implementation, extracting the corresponding vibration attenuation anomaly signal intensity involves signal processing steps. First, anomalous components are separated from the signal data collected by the vibration testing equipment. This signal data includes the amplitude changes of the speaker voice coil skeleton under vibration. The vibration attenuation anomaly signal intensity is defined as the peak value where the signal amplitude deviates from the standard attenuation curve. For example, the intensity of the anomalous peak is identified by analyzing the frequency spectrum using Fourier transform. Specifically, the expected attenuation model is subtracted from the original signal; the remaining part is the anomalous signal, and its root mean square value is calculated as an intensity index. In large speaker testing, this extraction can reveal anomalous attenuation of the damping coating under low-frequency vibration. For example, an intensity value exceeding 5 dB indicates a significant anomaly, thus supporting quality screening. Further, the severity of the attenuation anomaly is determined based on a comparison of the extracted signal intensity with a preset threshold.

[0027] Preferably, the severity is divided into three levels: slight, moderate, and severe. For example, a signal strength of 0 to 3 dB is slight, 3 to 6 dB is moderate, and above 6 dB is severe. This determination process is achieved through threshold mapping, ensuring rapid classification of anomalies in a loudspeaker production environment.

[0028] Step S103: Identify the changing trend of the severity of attenuation abnormality within a preset time period, collect the change amplitude of the speaker response speed, fuse the changing trend and the change amplitude of the response speed, identify the change rate of the response speed, and extract the mutual constraint strength between the change amplitude of the response speed and the probability of damping reduction.

[0029] Data on the severity of attenuation anomalies is collected within a preset time period, and a trend indicator is obtained by dividing the time period. For this trend indicator, the amplitude of the speaker response speed change is collected, and an amplitude distribution value is obtained using signal strength assessment. The amplitude distribution value and the trend indicator are fused, and the response speed change rate is identified through trend amplitude correlation. Based on the response speed change rate, the mutual constraint strength between the response speed change amplitude and the probability of damping reduction is extracted, and a correlation coefficient is determined using Pearson correlation analysis combined with linear regression modeling. The strength correlation coefficient is used to evaluate the constraint strength distribution, determine the fusion effect of attenuation anomaly severity and response speed change amplitude, and obtain the final mutual constraint strength using anomaly distribution characteristics. Pearson correlation analysis quantifies the linear correlation strength between variables by calculating the correlation coefficient r: the closer the absolute value of r is to 1, the stronger the linear correlation; a positive correlation indicates that one variable increases or decreases synchronously with the other, a negative correlation indicates an inverse change, and 0 represents no linear correlation. When combined with linear regression, a regression model is constructed with the response speed change amplitude as the independent variable and the probability of damping reduction as the dependent variable. The regression coefficient clarifies the specific impact of each unit change in the former on the latter, and R² is used to further refine the model. 2 The value is used to judge the model's fit, R. 2 The closer the value is to 1, the stronger the explanatory power of the constraint relationship. This combination method not only intuitively reflects the strength and direction of the constraints between variables, but also quantifies the specific numerical value of the constraints.

[0030] In one specific implementation, identifying the changing trend of the severity of attenuation anomalies within a preset time period is achieved by analyzing time-series data of vibration signals. Specifically, the preset time period can be set as the initial 10 seconds of the vibration test, during which the numerical changes in the severity of attenuation anomalies are monitored, for example, a gradual process from slight to severe. Identifying the changing trend involves calculating the difference in severity between time points, for example, comparing the severity value at each time point with the previous time point to obtain an upward or downward trend indicator. In speaker voice coil skeleton testing, this identification helps capture the dynamic performance of the damping coating under continuous vibration; for example, a trend showing a gradual increase in severity indicates insufficient coating durability. In this way, real-time monitoring is ensured in audio equipment production. Furthermore, the amplitude of speaker response speed variation is acquired based on signal data recorded by the test equipment. The amplitude of response speed variation is defined as the difference in speed between the speaker receiving the input signal and outputting stable vibration; for example, in headphone speaker testing, a speed curve is acquired by a sensor, and the amplitude between the peak and trough values ​​is calculated.

[0031] This acquisition process includes a filtering step to remove noise interference, thereby obtaining accurate amplitude data. On the production line, this amplitude acquisition supports comparative analysis of different batches of loudspeakers. The rate of change of response speed is identified by fusing the trend and the amplitude of the change in response speed through a weighted averaging method. Specifically, the trend indicator is linearly combined with the amplitude data; for example, when the trend value is positive, the amplitude effect is amplified, and the rate of change is calculated as the ratio of speed variation per second.

[0032] In one specific implementation, for testing large speakers, the rate of change identified after fusion reveals the immediate impact of reduced damping on response speed; for example, a rate of change exceeding a preset threshold indicates a sluggish response. This fusion ensures the complementarity of the data, forming a comprehensive evaluation basis.

[0033] In one specific implementation, extracting the mutual constraint strength between the amplitude of response speed change and the probability of damping reduction involves correlation analysis. The mutual constraint strength is defined as the influence coefficient between the two, for example, by calculating the covariance of the amplitude and the probability of damping reduction, thus quantifying the degree of constraint. In a speaker vibration test scenario, if the amplitude increases while the probability of damping reduction intensifies, the constraint strength is high, indicating that the two mutually restrict vibration stability. The extraction process includes data alignment and strength calculation steps. For example, in mobile phone speaker component testing, the time series is first aligned, and then the strength value is calculated as a diagnostic indicator. This extraction helps optimize coating formulations and improve sound quality consistency.

[0034] Step S104: Analyze the variation law of the transition time of the Clani graph, identify abnormal vibration transmission path of the audio equipment, evaluate the significance of fault characteristics based on the mutual constraint strength between the change amplitude of response speed and the probability of damping reduction, and extract the sensitivity assessment level of the audio equipment to damping attenuation based on the change rate of response speed.

[0035] Data on the transition duration of the Cranny graphic is collected within a preset time period. A variation pattern index is obtained through duration distribution calculation, which determines the distribution curve based on the interval differences between graphic transition durations. For this variation pattern index, abnormal vibration transmission paths of the audio equipment are identified. An abnormal distribution value is obtained using a path mapping method, which constructs a mapping table based on the correspondence between vibration path abnormalities and duration variation patterns. Based on the abnormal distribution value and the response speed change amplitude, the mutual constraint strength between the probability of damping reduction is integrated. The significance of the fault characteristics is determined through strength correlation calculation, which uses amplitude strength assessment to multiply the abnormal distribution value by the mutual constraint strength to obtain a correlation coefficient. Combining the significance of the fault characteristics with the response speed change rate, a sensitivity assessment level for damping attenuation of the audio equipment is extracted. A sensitivity distribution map is obtained using a level threshold judgment, which compares the significance of the fault characteristics with a preset threshold based on the change rate. Using the sensitivity distribution map, the influence strength of the vibration path on the graphic transition is evaluated to obtain the sensitivity assessment level of the audio equipment's damping attenuation. This influence strength is based on the path abnormality identification data in the sensitivity distribution map, integrated with the attenuation sensitivity level.

[0036] It's important to note that the Cranny graph refers to a transition graph based on vibration response curves, used to represent the duration evolution of audio equipment during vibration. For example, in speaker testing, the input signal is converted into a graphical curve to monitor the transition time from peak to steady state. Analysis of the changing patterns involves calculating the differences in duration sequences, such as comparing duration differences at consecutive time points to identify linear increases or fluctuating patterns. This analysis is applied in headphone and audio equipment vibration testing to help capture the equipment's adaptability to continuous vibration. In this way, a preliminary understanding of vibration dynamics is formed. Furthermore, identifying vibration transmission path anomalies in audio equipment is based on path mapping technology. The specific process involves collecting vibration signal path data from the voice coil to the housing. For example, in a speaker testing environment, sensors are used to track signal propagation and calculate the attenuation rate of each segment of the path. If the attenuation rate exceeds a preset threshold, it is marked as an anomaly. This identification ensures the assessment of path integrity.

[0037] In one specific implementation, the significance of fault characteristics is assessed by quantitative calculation based on the mutual constraint strength between the magnitude of response speed change and the probability of damping reduction. The mutual constraint strength is defined as the correlation coefficient between the magnitude and the probability of damping reduction. For example, the strength value is obtained by calculating the covariance between the two; a higher strength value indicates a stronger constraint.

[0038] In one specific implementation, for testing mobile phone audio equipment, the response speed change amplitude is first extracted, i.e., the difference in speed from signal input to output. Then, the probability of damping reduction is extracted, i.e., the percentage decrease in coating damping value. The evaluation process maps intensity values ​​to significance scores; for example, when the intensity exceeds a certain level, the fault feature is rated as highly significant. This evaluation involves a data fusion step, first aligning the amplitude and damping data sequences, then calculating the constraint intensity as a product, e.g., amplitude multiplied by a weighting coefficient for damping reduction, to obtain a comprehensive index used to determine the significance of faults such as vibration instability. On the audio equipment production line, this evaluation reveals the severity of potential defects; for example, when the constraint intensity leads to an increase in significance, it indicates that the equipment is susceptible to damping changes, thus supporting timely adjustments to coating parameters. This detailed evaluation process ensures the accuracy of fault diagnosis.

[0039] In one specific implementation, the sensitivity assessment level of an audio device to damping attenuation is extracted by combining the response speed change rate. The response speed change rate refers to the rate of change of velocity per unit time; for example, in large speaker vibration testing, the change rate data is obtained from a previous fusion step. This is then combined with the damping attenuation index to calculate a sensitivity score, categorized into low, medium, and high levels. This extraction method is applicable to various audio device scenarios, such as headphone testing where transient sensitivity is emphasized.

[0040] Step S105: Collect data on the changes in the aging rate of the damping material, combine the sensitivity assessment level to identify the development trend of the damping aging rate, and assess the accelerating trend of the aging rate.

[0041] Data on the aging rate variation of damping materials is acquired within a preset period. A variation curve is determined through data distribution calculation, based on the differences between rates. The data distribution calculation uses difference statistics to quantify the rate value intervals into a distribution sequence. For the variation curve, combined with sensitivity assessment levels, a level fusion method is used to obtain a development trend index. The development trend index is determined by the correspondence between the curve and the level. The level fusion method matches the curve peak with the level threshold to generate an index value. Based on the development trend index, acceleration characteristics of the aging rate are extracted. The acceleration characteristics are obtained by feature extraction to obtain an acceleration trend value. The feature extraction obtains a feature vector by calculating the index slope. Using the acceleration trend value, the acceleration trend of the damping material to the aging rate is evaluated, resulting in an acceleration trend distribution map. The acceleration trend distribution map generates a distribution area based on the trend value mapping.

[0042] In one specific implementation, the aging rate of a damping material refers to the rate at which the material's damping capacity decreases under vibration. For example, in a speaker assembly, sensors are used to collect time-series data on the material's thickness or elastic modulus. The acquisition process involves setting time intervals, such as recording data hourly, to form a sequence of changes. By calculating the differences between consecutive time points, the change in the aging rate is obtained. This method is applied in headphone vibration testing to help track the aging dynamics of materials under high-frequency vibration. Furthermore, this acquisition ensures the fundamental accuracy of the data, providing support for subsequent analysis. The process of combining sensitivity assessment levels is based on previously extracted level data. Specifically, the sensitivity assessment level is a classification derived from the device's response to damping attenuation, such as low, medium, and high levels.

[0043] In one implementation, changes in aging rates are correlated with these levels, for example, through weighted fusion, where high sensitivity levels are assigned weight coefficients corresponding to rapid aging changes. In a speaker production testing environment, the aging rate sequence is first aligned with sensitivity data, and then a fusion index is calculated. This combination reveals the interplay between aging and device sensitivity.

[0044] In one specific implementation, identifying the development trend of damped aging rate is achieved by analyzing patterns in the change sequence. The development trend of damped aging rate refers to the evolutionary trend of the rate from slow to accelerating, such as linear growth or exponential change. The identification process includes extracting the slope value of the sequence, for example, calculating the average rate of change within a time window; if the slope increases positively, it is marked as an accelerating trend. In Bluetooth speaker device testing, this identification targets low-frequency vibration paths, tracking the transition of material aging from initial stability to rapid degradation.

[0045] In one specific implementation, the acceleration trend refers to the magnitude of the increase in the aging rate, for example, by calculating the second-order change of the sequence through difference to obtain the acceleration value.

[0046] In one specific implementation, for a mobile phone speaker component, acceleration segment data is first extracted from the identified trajectory, and then the relationship between amplitude and time is calculated. For example, if the amplitude exceeds a threshold, it is assessed as a high acceleration trend. This assessment is applicable to different audio equipment scenarios; for example, large speaker testing focuses on long-term aging trends, while headphone testing emphasizes instantaneous acceleration. This detailed assessment process supports equipment maintenance decisions.

[0047] Step S106: Associate the workload of the audio equipment with the correlation characteristics between the intensity of abnormal vibration attenuation signals and the transition time, and combine this with the accelerating trend of aging rate to determine the remaining service life of the audio equipment.

[0048] Workload-related data is obtained from the audio equipment operation records. Vibration attenuation intensity distribution is obtained through data quantization processing. This distribution is generated as a quantized sequence based on load correlation. The data quantization processing uses load value interval statistics to transform the correlated data into a distribution sequence. For the vibration attenuation intensity distribution, abnormal signal intensity is fused, and a transition duration characteristic index is obtained using an intensity matching method. This index is determined by calculating the correlation between signal intensity and duration. The intensity matching method matches abnormal signal intensity values ​​with corresponding distribution sequences to generate index values. Based on the transition duration characteristic index and the aging rate trend, an acceleration trend fusion feature is extracted. This feature is obtained by trend slope quantization, which calculates vector elements by the slope difference between the index and the trend. The remaining time limit is determined by associating the acceleration trend fusion feature with the remaining time limit, and a trend time limit assessment model is constructed. This model generates an assessment distribution based on the fusion vector mapping. The model uses the fusion vector as input and outputs the assessment distribution by weighted summation of vector elements. The trend duration assessment model is used to process load signal associated data, determine the combination of abnormal signal strength and characteristic trend, and if the fusion value exceeds a preset threshold, the assessment distribution is adjusted to obtain the remaining service life of the audio equipment. The adjusted assessment distribution generates the final duration value through distribution area offset correction.

[0049] In one specific implementation, the correlation of audio equipment workload is achieved by monitoring load data during equipment operation. Specifically, workload refers to the power consumption and vibration intensity of the audio equipment when playing audio. For example, in a speaker, sensors are used to record the correspondence between input power and output amplitude. The correlation process involves linking this data with the aging indicators of damping materials, such as calculating the influence coefficient of peak load on material attenuation. This method is used in wireless headphone testing to help track load dynamics under high-volume playback. Furthermore, this correlation ensures the synchronization of load data and the aging process, providing a foundation for subsequent fusion. The process of fusing the correlation characteristics of abnormal vibration attenuation signal intensity and transition duration is based on previously acquired signal data. In one specific implementation, the correlation characteristics of abnormal vibration attenuation signal intensity and transition duration are obtained through three steps: constructing a standardized sample set, quantization mapping modeling, and verification correction. First, 30 speaker samples with normal, light / medium / heavy damping attenuation are selected. Five sets of vibration signals from 200Hz to 10kHz are applied in an environment of 25℃ and 50%RH, and data such as transition duration and abnormal signal intensity are collected simultaneously to form 150 sets of four-tuple effective samples. Then, the correlation was analyzed using the Pearson correlation coefficient. For strongly correlated samples with |r|≥0.85, the least squares method was used to fit S=a·t. 2 +b·t+c polynomial mapping function (R) 2For weakly correlated samples, a KNN mapping model is constructed using transition time and vibration frequency as inputs (optimized with 5-fold cross-validation, prediction error ≤5%). Finally, the model is validated using actual damping state labels. The model output probability for normal samples must be ≥90%, and a frequency correction factor f is introduced for deviation samples for calibration.

[0050] In one specific implementation, the vibration attenuation anomaly signal intensity refers to the magnitude by which the material's attenuation rate deviates from its normal value during vibration, for example, by extracting the signal peak value through spectral analysis. Transition duration represents the time span from the onset of the anomaly to its stabilization, such as measuring the time in seconds from initial attenuation to steady state in speaker vibration testing. Integrating these characteristics involves calculating the correlation coefficient between intensity and duration, for example, using a weighted averaging method, multiplying the intensity value by a duration factor to form a comprehensive index.

[0051] In one specific implementation, for Bluetooth speaker devices, anomalous signal sequences are first aligned, and then correlation analysis reveals patterns of prolonged transition durations as intensity increases. This fusion reveals the mechanism by which anomalous signals function during aging, ensuring comprehensive integration of characteristics. Preferably, the aging rate acceleration trend is achieved by integrating the fusion results with previously evaluated trend data. The aging rate acceleration trend refers to the evolution of the rate from stable to rapidly increasing, for example, by calculating the slope change of the rate sequence. In mobile phone speaker components, this integration involves inputting fusion metrics into a trend model, for example, by adjusting the weights of acceleration values ​​and load conditions through linear regression.

[0052] In one specific implementation, the remaining service life refers to the estimated time from the current state to material failure, such as calculating the threshold crossing point based on acceleration trends and fusion characteristics. In one specific implementation, the failure threshold of the damping material is first defined, including preset critical values ​​for vibration attenuation abnormal signal intensity and transition duration. Simultaneously, the previously verified fusion correlation law and the quantitative mapping relationship between abnormal signal intensity and transition duration are confirmed, providing the basic logical support for the calculation. Next, current state data is integrated, covering measured real-time abnormal signal intensity, real-time transition duration, recent aging rate acceleration trend value, and historical equipment workload statistics. Subsequently, a polynomial trend model is constructed, incorporating fusion characteristic indicators, aging acceleration trend values, and load weight coefficients into the model to dynamically characterize the coordinated change of these two over time. The fusion characteristic indicator is the weighted result of the abnormal signal peak value and the transition duration deviation rate. Finally, the current data is substituted into the model to calculate the threshold crossing time from the current state to when both the abnormal signal and transition duration simultaneously reach the failure threshold. This time is the remaining service life of the damping material, providing a quantitative basis for equipment aging early warning and maintenance planning.

[0053] In one specific implementation, for headphone vibration monitoring, key parameters are first extracted from the combined data, and then the remaining cycle is calculated using an accumulation model, such as accumulating acceleration amplitude and load correlation to the failure threshold. In another specific implementation, the headphone remaining cycle calculation uses the classic Miner linear damage accumulation model, the core of which is to accumulate the aging damage of the damping material according to load levels until the failure threshold is reached. First, key input parameters are extracted from the fused data, including three types of core indicators: first, vibration characteristic parameters, such as real-time vibration acceleration amplitude and vibration frequency; second, load-related parameters, such as the duration of a single high-load operation and the average number of daily load cycles; and third, material characteristic parameters, such as the damage coefficient under different loads determined by fatigue testing and the material failure damage threshold. During model construction, a "load-damage coefficient" correspondence is first established through bench tests: for example, acceleration amplitude ≥ 5 m / s. 2 This means that the damage coefficient K1 under high load is 0.002 / cycle, and the acceleration amplitude is 2-5m / s. 2 That is, under medium load, K2 = 0.0008 / cycle, which is below 2m / s. 2 This means that under low load, K3 = 0.0001 / cycle. Then, the damage value is accumulated over time using the formula D = Σ(Kᵢ × Nᵢ), where Nᵢ is the number of cycles for the corresponding load level. The model output is the real-time accumulated damage value D and the remaining cycle. When D = 0.6, combined with the statistical data of 300 daily load cycles, the remaining cycle can be calculated as (D0 - D) / (Σ(Kᵢ × daily average Nᵢ)), achieving accurate quantification of the remaining cycle.

[0054] It should be noted that this determination process is applicable to different audio scenarios. For example, small speakers focus on short-term predictions, while professional audio equipment emphasizes long-term load effects. Through these calculations, an objective assessment of the equipment's lifespan is formed.

[0055] The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the invention. Those skilled in the art will understand that implementing all or part of the above-described embodiments and making equivalent changes in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A method for fault analysis of audio equipment based on Cranny graphics, characterized in that, Includes the following steps: By acquiring images of the damping coating on the speaker voice coil skeleton, the transition time of the Krani pattern from disordered powder distribution to stable nodal lines is identified, and the initial transition time is extracted. By comparing the difference between the initial transition time and the preset damping reduction critical point, the degree of deviation of the transition time is obtained. The probability of damping reduction is evaluated based on the degree of deviation, the corresponding vibration attenuation abnormal signal intensity is extracted, and the severity of attenuation abnormality is determined. Identify the changing trend of the severity of the attenuation anomaly within a preset time period, collect the change amplitude of the speaker response speed, fuse the changing trend and the change amplitude of the response speed, identify the rate of change of the response speed, and extract the mutual constraint strength between the change amplitude of the response speed and the probability of damping reduction. By analyzing the variation pattern of the transition time of the Cranny graphic, abnormal vibration transmission path of the audio equipment is identified. The significance of the fault characteristics is evaluated based on the mutual constraint strength. Combined with the response speed change rate, the sensitivity assessment level of the audio equipment to damping attenuation is extracted. The aging rate of the damping material is collected, and the acceleration trend of the aging rate is assessed in conjunction with the aforementioned sensitivity assessment level. By considering the workload of the audio equipment, integrating the correlation between the intensity of abnormal vibration attenuation signals and the transition time, and combining this with the accelerating trend of aging rate, the remaining service life of the audio equipment can be determined.

2. The Clariion graph-based sound device failure analysis method of claim 1, wherein, The extraction of the initial transition duration includes the following steps: The image of the damping coating is smoothed using a Gaussian filtering algorithm to obtain a first image sequence, thereby obtaining the disordered powder distribution characteristics. To address the disordered powder distribution characteristics, temporal feature extraction is employed to determine the powder distribution change trend from the first image sequence by comparing the pixel differences between adjacent image frames. Based on the powder distribution change trend, the degree of line aggregation in the change trend is compared with the preset nodal pattern by vibration mode analysis to determine the formation time of stable nodal lines and obtain transition process data. If the transition process data exceeds a preset threshold, the standard deviation parameter of the Gaussian filtering algorithm is adjusted and the first image sequence is reprocessed to obtain the corrected initial duration. The initial transition duration is extracted by combining the corrected initial duration with the formation time of the stable wavelet line.

3. The Clancy graph-based audio equipment failure analysis method of claim 1, wherein, Determining the severity of the attenuation anomaly includes the following steps: To assess the degree of deviation in the transition duration, a regression algorithm is used to evaluate the probability of damping reduction. The regression algorithm maps the linear combination to the interval between 0 and 1 using the sigmoid function to obtain the probability value. Based on the probability value, the corresponding abnormal signal intensity is extracted from the vibration attenuation data to determine the intensity distribution characteristics; The severity of the attenuation anomaly is determined using the intensity distribution characteristics.

4. The ClariFi® graph-based audio device fault analysis method of claim 1, wherein, The extraction of the mutual constraint strength between the magnitude of the change in response speed and the probability of damping reduction includes the following steps: Data on the severity of attenuation anomalies are collected within a preset time period, and trend indicators are obtained by dividing the time period. For the aforementioned trend indicators, the amplitude of the speaker response speed change is collected, and the amplitude distribution value is obtained by signal strength evaluation. By integrating the amplitude distribution values ​​and the trend indicators, the rate of change in response speed is identified through the correlation between the trend amplitude; Based on the rate of change of response speed, the mutual constraint strength between the magnitude of change of response speed and the probability of damping reduction is extracted, and the strength correlation coefficient is determined by Pearson correlation analysis combined with linear regression modeling. The intensity correlation coefficient is used to evaluate the distribution of constraint intensity, determine the fusion effect of the severity of attenuation anomaly and the magnitude of response speed change, and obtain the final mutual constraint intensity using the anomaly distribution characteristics.

5. The ClariFi graphical-based audio equipment fault analysis method of claim 1, wherein, The process of extracting the sensitivity assessment level of the audio equipment to damping attenuation includes the following steps: Data on the transition duration of the Kroni graphic is collected within a preset time period. The change pattern index is obtained through duration distribution calculation. The duration distribution calculation is based on the interval difference between graphic transition durations to determine the distribution curve. Based on the aforementioned change pattern indicators, abnormal vibration transmission paths of audio equipment are identified, and abnormal distribution values ​​are obtained using a path mapping method. The path mapping method constructs a mapping table based on the correspondence between abnormal vibration paths and duration change patterns. Based on the abnormal distribution value and the change amplitude of the response speed, the mutual constraint strength between the damping reduction probability is integrated, and the significance of the fault characteristics is determined by strength correlation calculation. The strength correlation calculation adopts amplitude strength assessment to multiply the abnormal distribution value and the mutual constraint strength to obtain the correlation coefficient. Combining the significance of the fault characteristics with the rate of change of response speed, the sensitivity assessment level of the audio equipment to damping attenuation is extracted, and a sensitivity distribution map is obtained by using a level threshold judgment. The level threshold judgment is based on the rate of change and a comparison of the significance of the fault characteristics with a preset threshold. The influence of the vibration path on the graphic transition is evaluated using the sensitivity distribution map to obtain the sensitivity assessment level of the damping attenuation of the audio equipment. The influence intensity is based on the path anomaly identification data in the sensitivity distribution map fused with the attenuation sensitivity level.

6. The Clancy graph-based audio equipment failure analysis method of claim 1, wherein, The assessment of the accelerating trend of aging rate includes the following steps: Data on the aging rate of damping material is obtained within a preset period. A change curve is determined by data distribution calculation. The change curve is constructed based on the difference between rates. The data distribution calculation uses difference statistics to quantify the rate value intervals into a distribution sequence. Based on the change curve and sensitivity assessment level, a level fusion method is used to obtain the development trend index. The development trend index is determined by the correspondence between the curve and the level. The level fusion method matches the curve peak with the level threshold to generate the index value. Based on the development trend index, the acceleration feature of the aging rate is extracted. The acceleration feature is obtained by feature extraction to obtain the acceleration trend value. The feature extraction is obtained by calculating the feature vector through the index slope. The acceleration trend value is used to evaluate the acceleration trend of the damping material to the aging rate, and an acceleration trend distribution map is obtained. The acceleration trend distribution map is generated by mapping the distribution area based on the trend value.

7. The Clancy graph-based audio equipment failure analysis method of claim 1, wherein, Determining the remaining service life of the audio equipment includes the following steps: Workload correlation data is obtained from the audio equipment operation record, and vibration attenuation intensity distribution is obtained through data quantization processing. The vibration attenuation intensity distribution is generated into a quantization sequence based on load correlation. The data quantization processing uses load value interval statistics to transform the correlation data into a distribution sequence. For the vibration attenuation intensity distribution fused with abnormal signal intensity, an intensity matching method is used to obtain the transition duration characteristic index. The transition duration characteristic index is determined by the correlation between signal intensity and duration. The intensity matching method matches the abnormal signal intensity value with the corresponding distribution sequence to generate the index value. Based on the transition duration characteristic index and the aging rate trend, an acceleration trend fusion feature is extracted. The acceleration trend fusion feature is obtained by trend slope quantization to obtain a fusion vector. The trend slope quantization is calculated to obtain vector elements by the slope difference between the index and the trend. By using the accelerated trend fusion feature associated with the remaining term determination process, a trend term assessment model is constructed. The trend term assessment model generates an assessment distribution based on the fusion vector mapping. The trend term assessment model takes the fusion vector as input and outputs the assessment distribution by weighted summation of vector elements. The trend duration assessment model is used to process load signal associated data, determine the combination of abnormal signal strength and characteristic trend, and if the fusion value exceeds a preset threshold, the assessment distribution is adjusted to obtain the remaining service life of the audio equipment. The adjusted assessment distribution generates the final duration value through distribution area offset correction.

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