Intelligent fault detection method and system for surface acoustic wave filters

Abstract

The present invention relates to the field of fault detection technology, and includes an intelligent fault detection method and system for surface acoustic wave filters, comprising the following steps: collecting input and output signals of the surface acoustic wave filter, extracting independent frequency bands, counting the input and output power of the frequency bands, calculating the power ratio, and obtaining a multi-band energy ratio sequence. In the present invention, by decomposing the fixed bandwidth of the input and output signals, refining the energy characteristic analysis of different frequency bands, improving the abnormal signal capture capability, introducing a dynamic threshold to optimize the judgment of the power attenuation rate, and improving the adaptability and accuracy of recognition. At the same time, through comprehensive impedance characteristic analysis and change rate monitoring, the ability to identify sudden faults is enhanced, and energy attenuation and impedance mutation points are jointly analyzed to construct a multi-dimensional fault classification system, ensuring the accuracy of fault identification and the effective judgment of complex faults, and improving the stability and applicability of the overall fault diagnosis of the surface acoustic wave filter.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and in particular to an intelligent fault detection method and system for a surface acoustic wave filter. Background Art

[0002] The field of fault detection technology involves monitoring the operating status of various types of equipment, components, or systems, and identifying anomalies. It aims to identify, classify, and predict potential faults through data collection, analysis, and processing. It primarily involves core processes such as signal acquisition, feature extraction, and anomaly determination. Signal acquisition typically relies on multiple sensors to monitor the operating status of equipment in real time. Feature extraction extracts fault-related information through mathematical transformations and pattern recognition. Anomaly determination typically uses threshold setting and statistical analysis to identify equipment operating anomalies. With the development of intelligent technology, fault detection technology has gradually integrated data analysis and modeling techniques to form a more efficient and accurate intelligent diagnostic system, which is widely used in a variety of fields, including mechanical equipment, power systems, and communications equipment.

[0003] The intelligent fault detection method for surface acoustic wave filters uses mathematical operations and data analysis methods to construct a classification and judgment mechanism based on the collected electrical signal parameters, targeting abnormal conditions that may occur during the operation of the surface acoustic wave filter, and achieves intelligent identification of fault conditions. First, the filter's input and output signals are sampled, and key characteristic parameters are extracted using time series analysis or feature comparison. Then, based on specific classification criteria, these characteristic parameters are classified and analyzed using mapping calculations or statistical modeling to determine whether any abnormal conditions exist. Trend inference is then performed based on the surface acoustic wave filter's operating data to improve the accuracy of the judgment.

[0004] Existing surface acoustic wave filter fault detection processes typically use single or low-dimensional data for signal monitoring analysis, resulting in insufficient ability to capture energy variations in detail and easily overlooking localized abnormal trends. Power attenuation analysis focuses primarily on overall trends, lacking detailed analysis of specific frequency bands, making it difficult to accurately identify some early attenuation signals. Impedance measurement primarily focuses on individual changes in input or output impedance, failing to consider the coupling relationship between them, resulting in limited ability to locate internal faults. Disruption point identification relies on static threshold settings, making it difficult to adapt to the influence of diverse environmental factors. This can lead to certain critical fault signals being overlooked due to noise interference. Fault mode classification methods lack in-depth analysis of the distribution of disruptive points, making it difficult to effectively distinguish between periodic interference and random faults, thus affecting the accuracy of classification results. Due to the lack of cross-analysis of multi-dimensional data, existing methods struggle to establish accurate diagnostic models when complex faults coexist, resulting in unstable identification of some complex faults. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an intelligent fault detection method for a surface acoustic wave filter.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an intelligent fault detection method for a surface acoustic wave filter, comprising the following steps:

[0007] S1: Collect the input and output signals of the surface acoustic wave filter, extract independent frequency bands, count the input and output power of the frequency bands, calculate the power ratio, and obtain a multi-band energy ratio sequence;

[0008] S2: Based on the multi-band energy ratio sequence, calculate the energy ratio change between adjacent frequency bands, determine the local mutation pattern, extract the power decay rate per unit time, analyze whether the decay rate exceeds the limit, and obtain the energy decay pattern recognition result;

[0009] S3: collecting the impedance of the electrode input and output ends of the surface acoustic wave filter, and synchronously calculating the coupling equivalent value of the input and output impedances to obtain coupling impedance data;

[0010] S4: Based on the coupling impedance data, respectively calculate the change rates of the input impedance, output impedance, and coupling impedance per unit time, select the time point at which the impedance change rate suddenly increases, determine the distribution characteristics of the mutation point, and obtain the impedance mutation point identification result.

[0011] As a further solution of the present invention, the multi-band energy ratio sequence includes the input power of each frequency band, the output power of each frequency band, and the power ratio of each frequency band; the energy attenuation mode recognition results include normal state, abnormal attenuation state, filter failure mode, and natural performance attenuation; the coupling impedance data includes input impedance, output impedance, and the coupled equivalent value of input and output impedance; the impedance mutation point identification results include input impedance change rate mutation point, output impedance change rate mutation point, and coupling impedance change rate mutation point.

[0012] As a further solution of the present invention, the specific steps of S1 are:

[0013] S111: collecting input and output signals of the surface acoustic wave filter, performing fixed-bandwidth decomposition on the input and output signals, calling multiple set center frequencies, performing frequency division processing on the signals, obtaining input and output signals of each independent frequency band, storing the extracted signal power data, and obtaining a data set of input power and output power of each frequency band;

[0014] S112: Based on the input power and output power data sets of each frequency band, extract the corresponding frequency band data of the input power and output power using the formula:

[0015] ;

[0016] Calculate the Power ratio of frequency band , store the power ratio sequence of each frequency band, where Representative The output power of the frequency band, Represents the average output power of all frequency bands, Representative The input power of the frequency band, Represents the average input power in all frequency bands;

[0017] S113: Perform statistical analysis based on the power ratio sequence of each frequency band, integrate the power ratio data of all frequency bands, and obtain a multi-frequency band energy ratio sequence.

[0018] As a further solution of the present invention, the specific steps of S2 are:

[0019] S211: Based on the multi-band energy ratio sequence, calculating energy ratio changes between adjacent frequency bands, setting an energy ratio change threshold, and screening frequency bands with abnormal energy ratio changes by comparing the energy ratio change threshold to obtain a frequency band set with abnormal energy ratio changes;

[0020] S212: Based on the set of frequency bands with abnormal energy ratio changes, determine the local mutation pattern. If the energy ratios of all frequency bands are stable, mark them as normal. Otherwise, determine the distribution of abnormal frequency bands, call the energy ratio change threshold, compare the energy ratios of each frequency band if they exceed the threshold, and if the energy ratios of some frequency bands exceed the threshold, mark them as abnormal attenuation states, record the positions of the abnormal frequency bands, and obtain abnormal frequency band marking data.

[0021] S213: Based on the abnormal frequency band mark data, extract the power attenuation rate per unit time using the formula:

[0022] ;

[0023] Calculate the Power attenuation rate of the frequency band , combined with the upper limit of the power attenuation rate, the frequency band that exceeds the limit is compared and screened. If the attenuation rate exceeds the limit, the filter failure mode is marked, otherwise the performance is marked as natural attenuation, and the energy attenuation pattern recognition result is established, where, Representative Frequency band in time The output power, Representative Frequency band at the previous time point The output power, Represents a time interval.

[0024] As a further solution of the present invention, the specific steps of S3 are:

[0025] S311: collecting the impedance of the electrode input end and the impedance of the electrode output end of the surface acoustic wave filter, recording the impedance data, and storing them in time series to obtain an input and output impedance sequence;

[0026] S312: Based on the input and output impedance sequence, the complex form of the input impedance and the output impedance is used, using the formula:

[0027] ;

[0028] Calculate the coupling equivalent impedance value ,in, represents the complex value of the impedance at the electrode input, represents the complex value of the impedance at the output of the electrode, Represents the phase difference between input and output impedance;

[0029] S313: Based on the coupling equivalent impedance value, a coupling equivalent impedance time series is established, and the coupling impedance data is obtained by combining the input and output impedance series.

[0030] As a further solution of the present invention, the specific steps of S4 are:

[0031] S411: performing numerical differential calculation based on the coupled impedance data, calling impedance data at adjacent time points, calculating the change rates of the input, output, and coupled impedances in the time dimension, and obtaining an impedance change rate sequence;

[0032] S412: Based on the impedance change rate sequence and a preset impedance mutation threshold, the impedance change rate at each time point is compared, the time point at which the impedance change rate suddenly increases is selected, and a mutation time point data set is established;

[0033] S413: Based on the mutation time point data set, analyze the mutation point distribution characteristics, calculate the mutation point interval, distribution density and aggregation trend in the time series, store the calculated distribution characteristic data, and obtain the impedance mutation point identification result.

[0034] As a further embodiment of the present invention, the method further comprises S5;

[0035] S5: Based on the impedance mutation point identification results and the energy attenuation pattern identification results, the frequency band corresponding to the mutation point is analyzed. If the mutation point occurs at a low frequency and the impedance change rate continues to increase, the material aging failure mode is marked. If the mutation point appears in a specific frequency band and changes periodically, the environmental interference failure mode is marked. If the mutation point is irregularly distributed in multiple frequency bands and is accompanied by power attenuation, the electrode structure damage failure mode is marked. The surface acoustic wave filter failure mode classification results are comprehensively obtained.

[0036] As a further solution of the present invention, the surface acoustic wave filter failure mode classification results include material aging failure mode, environmental interference failure mode, and electrode structure damage failure mode.

[0037] As a further solution of the present invention, the specific steps of S5 are:

[0038] S511: Based on the impedance mutation point identification result and the energy attenuation pattern identification result, extract the mutation point data and frequency band energy attenuation information, analyze the frequency band position of the mutation point, extract the frequency range where the mutation point occurs, and obtain the frequency band data corresponding to the mutation point;

[0039] S512: Based on the frequency band data corresponding to the mutation points, determine the distribution characteristics of the mutation points, combine the impedance change rate sequence, analyze the distribution pattern of the mutation points in position and time, identify the change trend of the mutation points, screen the concentration of the mutation points in the frequency band, and obtain the mutation point distribution pattern;

[0040] S513: Based on the mutation point distribution pattern and the frequency band characteristics of the mutation point, the fault mode is classified and judged. If the mutation point occurs at a low frequency and the impedance change rate continues to increase, it is marked as a material aging failure mode. If the mutation point appears in a specific frequency band and changes periodically, it is marked as an environmental interference failure mode. If the mutation point is irregularly distributed in multiple frequency bands and is accompanied by power attenuation, it is marked as an electrode structure damage failure mode. The surface acoustic wave filter failure mode classification result is obtained comprehensively.

[0041] Intelligent fault detection system for surface acoustic wave filters, including:

[0042] The signal energy ratio calculation module collects input and output signals, performs fixed bandwidth decomposition, extracts independent frequency bands, calculates input power, output power and power ratio, and generates a multi-band energy ratio sequence;

[0043] The energy attenuation pattern recognition module calculates the energy ratio changes of adjacent frequency bands based on the multi-band energy ratio sequence, filters abnormal frequency bands, extracts the power attenuation rate, compares the upper limit value, marks faults and natural attenuation, and generates an energy attenuation pattern recognition result;

[0044] The impedance change rate calculation module collects input impedance and output impedance, calculates the coupling equivalent value of input and output impedance, and generates coupling impedance data;

[0045] The impedance mutation point screening module calculates the change rates of the input impedance, output impedance and coupling impedance based on the coupling impedance data, compares them with preset thresholds, screens mutation points, determines distribution characteristics, and generates impedance mutation point identification results;

[0046] The fault mode classification module analyzes the frequency band and distribution characteristics of the mutation points based on the impedance mutation point identification result and the energy attenuation mode identification result, marks the fault mode, and generates a surface acoustic wave filter fault mode classification result.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In this invention, fixed-bandwidth decomposition of the input and output signals is performed to extract multiple independent frequency bands with fixed center frequencies, ensuring accurate analysis of the energy characteristics of different frequency bands. Based on the calculation of the power ratios of each frequency band, a multi-band energy ratio sequence is constructed. Combined with the ratio changes of adjacent frequency bands, local energy mutation patterns are identified, improving the ability to detect abnormal signals. A dynamic threshold determination method is introduced into the calculation of power decay rate, making energy decay pattern identification more adaptable and effectively distinguishing normal fluctuations from abnormal decay. By synchronously collecting impedance data from the electrode input and output, calculating the coupled impedance, and comprehensively analyzing the impedance variation characteristics, the internal status of the device is more comprehensive. The change rates of the input impedance, output impedance, and coupled impedance are used to screen mutation points. Combined with the distribution characteristics of mutation points, the ability to identify sudden faults is improved. Based on the combined analysis of energy decay patterns and impedance mutation points, a multi-dimensional fault classification system is constructed to ensure accurate identification of different fault modes. Combined with the temporal characteristics of mutation points, the ability to identify complex faults is enhanced. Through multi-parameter collaborative analysis, misjudgments that may result from a single data source are avoided, improving the stability and applicability of fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of the main steps of the present invention;

[0050] Figure 2 This is a flow chart of step S1 of the present invention;

[0051] Figure 3 This is a flow chart of step S2 of the present invention;

[0052] Figure 4 This is a flow chart of step S3 of the present invention;

[0053] Figure 5 This is a flow chart of step S4 of the present invention;

[0054] Figure 6 This is a flow chart of step S5 of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0057] See also Figure 1 , an intelligent fault detection method for a surface acoustic wave filter, comprising the following steps:

[0058] S1: Collect the input and output signals of the surface acoustic wave filter, perform fixed bandwidth decomposition on the input and output signals, extract multiple independent frequency bands with fixed center frequencies, count and store the input and output powers of each frequency band, calculate the power ratio of each frequency band, and obtain a multi-band energy ratio sequence;

[0059] S2: Based on the multi-band energy ratio sequence, calculate the energy ratio change between adjacent frequency bands, set the energy ratio change threshold, screen the frequency bands with abnormal energy ratio changes, and judge the local mutation pattern. If the energy ratio of all frequency bands is stable, mark it as normal. If the energy ratio of some frequency bands exceeds the energy ratio change threshold, mark it as abnormal attenuation and record the position of the abnormal frequency band. Extract the power attenuation rate per unit time, set the upper limit of the power attenuation rate, and if the attenuation rate exceeds the limit, mark the filter failure mode. Otherwise, mark the performance as natural attenuation, and obtain the energy attenuation pattern recognition result.

[0060] S3: collecting the electrode input impedance and the electrode output impedance of the surface acoustic wave filter, storing the acquired impedance sequence, and synchronously calculating the coupling equivalent value of the input and output impedances to obtain coupling impedance data;

[0061] S4: Based on the coupling impedance data, the input impedance change rate, output impedance change rate, and coupling impedance change rate per unit time are calculated, and compared with the preset impedance mutation threshold, the time point at which the impedance change rate suddenly increases is screened, the distribution characteristics of the mutation point are determined, and the impedance mutation point identification result is obtained;

[0062] S5: Based on the results of impedance mutation point identification and energy attenuation pattern identification, the frequency band corresponding to the mutation point is analyzed. If the mutation point occurs at a low frequency and the impedance change rate continues to increase, the material aging failure mode is marked. If the mutation point appears in a specific frequency band and changes periodically, the environmental interference failure mode is marked. If the mutation point is irregularly distributed in multiple frequency bands and is accompanied by power attenuation, the electrode structure damage failure mode is marked. The surface acoustic wave filter failure mode classification results are obtained comprehensively.

[0063] The multi-band energy ratio sequence includes the input power of each frequency band, the output power of each frequency band, and the power ratio of each frequency band. The energy attenuation mode recognition results include normal state, abnormal attenuation state, filter failure mode, and natural performance attenuation. The coupling impedance data includes input impedance, output impedance, and the coupled equivalent value of input and output impedance. The impedance mutation point identification results include the input impedance change rate mutation point, the output impedance change rate mutation point, and the coupling impedance change rate mutation point. The surface acoustic wave filter failure mode classification results include material aging failure mode, environmental interference failure mode, and electrode structure damage failure mode.

[0064] See also Figure 2 , S1 step is:

[0065] S111: collecting input and output signals of the surface acoustic wave filter, performing fixed-bandwidth decomposition on the input and output signals, calling multiple set center frequencies, performing frequency division processing on the signals, obtaining input and output signals of each independent frequency band, storing the extracted signal power data, and obtaining a data set of input power and output power of each frequency band;

[0066] When collecting the input and output signals of the surface acoustic wave filter, it is necessary to synchronously measure the input and output ends of the filter through a signal acquisition device (such as an oscilloscope or a data acquisition card). The measured signal data should include time domain waveform and power spectrum information. In practical applications, for example, for a surface acoustic wave filter with a center frequency of 2.4GHz, the sampling rate of the collected signal usually needs to be much higher than this frequency, such as above 10GHz, to ensure accurate acquisition of signal characteristics. Afterwards, the input and output signals are decomposed with a fixed bandwidth, usually using a bandpass filter group. This filter group The center frequency interval can be set according to application requirements, such as 10MHz, 50MHz or 100MHz. The signal data of each bandwidth is stored separately, and the power spectrum density of each frequency band is calculated based on FFT (Fast Fourier Transform) to obtain the input signal and output signal of each independent frequency band. For a specific frequency band, such as the 2.45GHz±10MHz band, a bandpass filter can be used to extract the signal in this frequency band, and its time domain root mean square value (RMS) is calculated to characterize the signal power. The extracted signal power data is stored to obtain the input power and output power data sets of each frequency band.

[0067] S112: Based on the input power and output power data sets of each frequency band, extract the corresponding frequency band data of the input power and output power using the formula:

[0068] ;

[0069] Calculate the Power ratio of frequency band , store the power ratio sequence of each frequency band, where Representative The output power of the frequency band, Represents the average output power of all frequency bands, Representative The input power of the frequency band, Represents the average input power in all frequency bands;

[0070] Based on the input power and output power data sets of each frequency band, the power ratio of each frequency band is calculated. First, the input power of each frequency band is extracted from the aforementioned stored data sets. and output power , then calculate the average input power of all frequency bands and the average output power , set up a specific example, assuming that the system tests 5 frequency bands, and its input power and output power data are as follows Table 1:

[0071] Table 1 collected input and output power data:

[0072] ;

[0073] Calculate the mean of the input power and the output power:

[0074] ;

[0075] ;

[0076] Then, calculate the power ratio of each frequency band , taking the third frequency band as an example:

[0077] ;

[0078] Similarly, calculate the The calculation results are finally stored to establish a power ratio sequence for each frequency band.

[0079] S113: performing statistical analysis based on the power ratio sequence of each frequency band, integrating the power ratio data of all frequency bands, and obtaining a multi-band energy ratio sequence;

[0080] Call the power ratio sequence of each frequency band and perform statistical analysis on the sequence. First, calculate the mean and standard deviation of the power ratio for all frequency bands to determine the stability of power transmission in each frequency band. Assume that the calculated ratio sequence is as follows:

[0081] ;

[0082] Calculate the mean:

[0083] ;

[0084] Calculate the standard deviation:

[0085] ;

[0086] ;

[0087] Through this calculation, the stability of the power ratio of different frequency bands can be evaluated. If the ratio of a certain frequency band deviates greatly from the mean (e.g. ), there may be abnormal signal loss, and the final calculated ratio sequence is stored to obtain a multi-band energy ratio sequence.

[0088] See also Figure 3 , step S2 is:

[0089] S211: Based on the multi-band energy ratio sequence, calculate the energy ratio change between adjacent frequency bands, set the energy ratio change threshold, and compare the energy ratio change threshold to screen frequency bands with abnormal energy ratio changes to obtain a frequency band set with abnormal energy ratio changes;

[0090] Based on the multi-band energy ratio sequence, the energy ratio data of adjacent frequency bands are extracted, and the energy ratio data of each frequency band is calculated. and adjacent frequency bands Compare and calculate the energy ratio change, set the energy ratio change threshold, and filter the frequency bands with abnormal energy ratio changes. In actual applications, after a device's filter has been working for a long time, the energy ratios of different frequency bands may change due to external interference or component aging. For example, if the energy ratios of the three frequency bands of 10MHz, 20MHz, and 30MHz are 0.85, 0.92, and 1.1 respectively, calculate the ratio changes of adjacent frequency bands. for as well as , if the preset energy ratio change threshold is based on the long-term monitoring data of the equipment, the standard deviation of the statistical energy ratio change is According to the setting rule of energy ratio change threshold = 3 times standard deviation, the energy ratio change threshold = 3x0.05 = 0.15. If the calculated ratio change exceeds 0.15, it is determined that the frequency band has an abnormal change. For example, the change of 0.18 in the 30MHz frequency band exceeds 0.15 and is marked as an abnormal change frequency band.

[0091] Table 2 Energy ratio and changes in adjacent frequency bands:

[0092] ;

[0093] As shown in Table 2, the energy ratio undergoes a significant mutation in the 30 MHz frequency band, necessitating further analysis of the anomaly. The selected frequency bands with abnormal energy ratio changes are then used to determine the local mutation pattern. If the energy ratio change in all frequency bands is below the threshold, the data is marked as normal. Otherwise, the distribution of abnormal frequency bands is further analyzed, and the anomaly category is determined based on the trend of change.

[0094] S212: Based on the set of frequency bands with abnormal energy ratio changes, determine the local mutation pattern. If the energy ratios of all frequency bands are stable, mark them as normal. Otherwise, determine the distribution of abnormal frequency bands, call the energy ratio change threshold, and compare the energy ratio exceeding the limit of each frequency band. If the energy ratio of some frequency bands exceeds the threshold, mark them as abnormal attenuation states, record the abnormal frequency band positions, and obtain abnormal frequency band marking data.

[0095] Based on the set of frequency bands with abnormal energy ratio changes, the spatial distribution of the abnormal frequency bands is detected, and multiple abnormal frequency bands are clustered to determine whether a local mutation pattern is formed. In an example, if the abnormal frequency bands are mainly concentrated in the high-frequency area (for example, the 30MHz, 32MHz, and 35MHz frequency bands all exceed the threshold), it may indicate that a component of the filter (such as a capacitor or resonator) is aging. In this process, the position data of each abnormal frequency band is used. Abnormal frequency bands adjacent to it The clustering coefficient is calculated by the interval between:

[0096] ;

[0097] The clustering coefficient is set based on the device bandwidth characteristics and the circuit resonant frequency distribution. Usually, the value range should be smaller than the passband width of the filter. For example, for a certain filter, its passband width is 10MHz, so the threshold is set to If the calculated result is lower than this value, it is considered as a frequency band cluster anomaly, otherwise it is considered as a random anomaly.

[0098] Calculation example: If the abnormal frequency bands are 30MHz, 32MHz, and 35MHz, then calculate:

[0099] ;

[0100] Since 5MHz is less than the set threshold of 10MHz, it indicates that the abnormal frequency band clustering phenomenon is obvious and there is a local mutation pattern. If it exceeds 10MHz, it is judged as a discrete anomaly and is finally marked as a local mutation pattern.

[0101] S213: Based on the abnormal frequency band mark data, extract the power attenuation rate per unit time using the formula:

[0102] ;

[0103] Calculate the Power attenuation rate of the frequency band , combined with the upper limit of the power attenuation rate, the frequency band that exceeds the limit is compared and screened. If the attenuation rate exceeds the limit, the filter failure mode is marked, otherwise the performance is marked as natural attenuation, and the energy attenuation pattern recognition result is established, where, Representative Frequency band in time The output power, Representative Frequency band at the previous time point The output power, represents a time interval;

[0104] Based on the abnormal frequency band mark data, extract the power attenuation rate per unit time, set the upper limit of the power attenuation rate, and compare and filter the frequency bands that exceed the limit. Assume that the power measurement data of a certain device is as follows:

[0105] Table 3 Power attenuation rate calculation data:

[0106]

[0107] As shown in Table 3, the formula is used for calculation, for example, for the 32MHz frequency band:

[0108] ;

[0109] Power decay rate upper limit Depends on the power stability parameters of the device and the maximum allowable decay rate, usually based on the maximum variation range of long-term monitoring and time window ,calculate If the calculated attenuation rate exceeds 0.7dB / s, the frequency band is marked as filter failure mode. The remaining frequency bands are within the normal range and are marked as natural performance degradation.

[0110] The results show that the power attenuation rate in the 32MHz band exceeds the set threshold, which may indicate internal filter loss or component damage. Although the 30MHz band exhibits some attenuation, it is still within the normal range and therefore requires no further processing. The attenuation rate in the 35MHz band is lower, indicating that the signal energy is stable. Ultimately, the energy attenuation pattern recognition result is established for subsequent equipment status monitoring.

[0111] See also Figure 4 , S3 steps are:

[0112] S311: collecting the impedance of the electrode input end and the impedance of the electrode output end of the surface acoustic wave filter, recording the impedance data, and storing them in time series to obtain an input and output impedance sequence;

[0113] The impedance of the electrode input and output ends of the surface acoustic wave filter is collected. First, a high-precision impedance analyzer is connected to the input and output ends of the filter respectively to measure the signal. During the measurement process, in order to ensure the stability and repeatability of the impedance data, a scanning frequency range of 10kHz to 500MHz is adopted, and the impedance value is gradually obtained in a step frequency of 1kHz. During the measurement process, due to the influence of slight noise in the signal, 5 repeated measurements are required and their average is calculated to reduce random errors. The measured impedance data includes amplitude and phase information. For example, at a specific measurement point (such as at a frequency of 100MHz), the impedance measured at the input end is Ω, the impedance measured at the output is Ω, to ensure the systematic nature of the data, the data of all measurement points are stored in the database and recorded in a time series manner. Each record includes the timestamp, frequency, input impedance, output impedance and the average value of the number of measurements, as shown in Table 4. Finally, the input and output impedance series are obtained.

[0114] Table 4 collected input and output impedance data:

[0115] ;

[0116] As shown in Table 4, the collected impedance data contains the input and output impedance information of multiple frequency points. Each data point is averaged after multiple measurements to ensure accuracy.

[0117] S312: Based on the input and output impedance sequence, the complex form of input impedance and output impedance is used, using the formula:

[0118] ;

[0119] Calculate the coupling equivalent impedance value ,in, represents the complex value of the impedance at the electrode input, represents the complex value of the impedance at the output of the electrode, Represents the phase difference between input and output impedance;

[0120] Based on the input and output impedance sequence, the coupling equivalent value of the input and output impedance is calculated. First, the modulus values of the input impedance and output impedance at the corresponding frequency point are extracted, that is, and , phase angle It can be calculated from the phase difference between input and output impedance. At 100MHz frequency, assuming the input impedance is Ω, its modulus is calculated as follows:

[0121] ;

[0122] Similarly, the output impedance Ω, its modulus is calculated as follows:

[0123] ;

[0124] Calculating the phase angle , that is, the phase difference between the input impedance and the output impedance:

[0125] ;

[0126] ;

[0127] Substitute into the formula to calculate the coupling equivalent impedance:

[0128] ;

[0129] ;

[0130] ;

[0131] Finally, the coupled equivalent impedance value is obtained. The calculation results show that at a frequency of 100MHz, the coupled equivalent impedance value of the input and output impedance is 7.31Ω. This value is used in subsequent impedance analysis to further study the impedance matching characteristics of the filter.

[0132] S313: Based on the coupling equivalent impedance value, establish a coupling equivalent impedance time series, combine the input and output impedance series, and obtain coupling impedance data;

[0133] Based on the coupling equivalent impedance value, an impedance time series is established and stored in combination with the input and output impedance series. First, the coupling equivalent impedance values calculated at all measurement frequency points are extracted and stored in chronological order, as shown in Table 5.

[0134] Table 5 Calculated coupling equivalent impedance data:

[0135] ;

[0136] As shown in Table 5, the calculated coupled equivalent impedance data varies at different frequency points. The impedance value increases at high frequencies. This is due to the influence of the amplitude and phase angle difference of the input and output impedances. Finally, the coupled impedance data is obtained for filter matching analysis.

[0137] See also Figure 5 , step S4 is:

[0138] S411: Perform numerical differential calculation based on the coupled impedance data, call the impedance data of adjacent time points, calculate the change rate of the input, output and coupled impedance in the time dimension, and obtain the impedance change rate sequence;

[0139] Based on the coupled impedance data, the impedance data comes from the impedance data recorded by the measuring equipment on the electrode input and output of the surface acoustic wave filter. The measurement process usually uses an impedance analyzer (such as Keysight E4990A or Wayne Kerr 6500B). Its measurement frequency range is usually between 1MHz and 3GHz. The specific measurement point interval can be set to 10kHz to obtain sufficiently dense sampling point data. The obtained raw data includes input impedance and output impedance The complex value form of each measurement point stores its corresponding real part and imaginary part. The storage format is stored in a matrix as shown in the following table:

[0140] Table 6 Coupling impedance data collection table:

[0141] ;

[0142] After the data is stored, the measured input and output impedance data are numerically differentiated to calculate their rate of change per unit time, that is, the impedance change at adjacent time points is calculated and divided by the measurement time interval. For example, for adjacent measurement time intervals , the input impedance change rate is calculated as follows:

[0143] ;

[0144] Among them, for the data in the table, the input impedance change rate is calculated at the frequency point of 20MHz:

[0145] ;

[0146] Similarly, the output impedance change rate can be calculated. After the calculation is completed, the data is stored in the impedance change rate sequence.

[0147] S412: Based on the impedance change rate sequence and a preset impedance mutation threshold, the impedance change rate at each time point is compared, the time point at which the impedance change rate suddenly increases is selected, and a mutation time point data set is established;

[0148] Based on the impedance change rate sequence, a preset threshold is used to compare the change rate to screen the mutation time point. The setting method refers to the standard deviation calculation method of impedance change rate, and calculates the standard deviation of all impedance change rates. , and set the threshold to the mean plus 2 times the standard deviation:

[0149] ;

[0150] in, represents the mean impedance change rate, Represents the standard deviation of the impedance change rate. Taking the data in Table 1 as an example, assuming that its mean is , substitute the numerical value to calculate the standard deviation:

[0151] ;

[0152] Therefore, the mutation threshold is:

[0153] ;

[0154] The time points greater than this threshold are selected as impedance mutation time points. For example, if the change rate of a measurement point reaches 0.26Ω / ms, it is marked as a mutation point and stored in the mutation point data set.

[0155] S413: Based on the mutation time point data set, analyze the mutation point distribution characteristics, calculate the mutation point interval, distribution density and aggregation trend in the time series, store the calculated distribution characteristic data, and obtain the impedance mutation point identification result;

[0156] Based on the mutation time point dataset, we analyze the temporal distribution characteristics of the mutation points, calculate the average time interval between mutation points, and count the density of mutation points within the unit time window. Specifically, we use the mean of the mutation time interval to calculate:

[0157] ;

[0158] in, Representative Mutation point time, Represents the total number of mutation points. Assuming that the mutation points occur at 20ms, 35ms, and 55ms respectively, calculate the mutation time interval:

[0159] ;

[0160] For the normal working stability range of the filter, the setting of this value is based on the equivalent circuit characteristics of the filter and the dynamic impedance change range under its working state. Specifically, the rate at which the input and output impedance of the surface acoustic wave filter changes with frequency is affected by its resonant characteristics.

[0161] In actual measurements, the instrument's minimum resolvable time is typically in the microsecond range. Therefore, the impedance stability range of filters in the low-frequency range (e.g., 10MHz-100MHz) is generally determined by the system's response time and measurement resolution. Actual measurements show this value is typically around 50ms. This value is affected by factors such as temperature drift, device aging, and electrode coupling characteristics, and typically fluctuates within the 40ms-60ms range.

[0162] If the calculated mutation interval If it is much smaller than the impedance stability interval of 50ms, it can be judged as an abnormal state, and the impedance mutation point identification result is finally formed.

[0163] See also Figure 6 , step S5 is:

[0164] S511: Based on the impedance mutation point identification results and the energy attenuation pattern identification results, extract the mutation point data and frequency band energy attenuation information, analyze the frequency band position of the mutation point, extract the frequency range where the mutation point occurs, and obtain the frequency band data corresponding to the mutation point;

[0165] Based on the results of impedance mutation point identification and energy decay pattern identification, the system first extracts the timestamps and frequency band information corresponding to the impedance mutation points. This constructs an impedance change time series, recording the specific time and frequency distribution of the mutation points. To ensure data integrity, the sampling interval is set to 0.1 seconds, and the mutation points are categorized based on the frequency range in which they occur. For example, in a particular measurement, mutation points primarily occur in the 2.5MHz to 3.2MHz frequency range, with multiple consecutive mutations occurring between 1.2 and 1.5 seconds. The system then records this data for analysis in subsequent steps. For the energy decay pattern, energy ratio change data is used to compare power decay trends across different frequency bands. For example, if the power in the 2.5MHz band drops by 3dB between 1.2 and 1.5 seconds, while the power in the 3.0MHz band drops by 2.8dB, this information is recorded and combined with the mutation point information for analysis. Based on the collected data, the frequency band data corresponding to the mutation points is ultimately obtained.

[0166] S512: Based on the frequency band data corresponding to the mutation points, determine the distribution characteristics of the mutation points, combine the impedance change rate sequence, analyze the distribution pattern of the mutation points in position and time, identify the change trend of the mutation points, screen the concentration of the mutation points in the frequency band, and obtain the mutation point distribution pattern;

[0167] Based on the frequency band data corresponding to the mutation points, we first analyze the distribution pattern of the mutation points, including their time intervals, density, and trend. For example, if a certain segment of measurement data shows multiple mutation points occurring between 1.2 and 1.5 seconds, with an interval of approximately 0.05 seconds between each mutation point, we can determine that the mutation points have a certain temporal concentration. Furthermore, we categorize the distribution of the mutation points within each frequency band. For example, if the intervals between mutation points within the 2.5 MHz band are short, while those within the 3.0 MHz band are long, this indicates that the impedance variation patterns differ across frequency bands. Based on this, we calculate the impedance variation rate trend to determine whether the number of mutation points increases over time. For example, if the impedance variation rate is 0.8 Ω / s at 1.2 seconds and rises to 1.4 Ω / s at 1.5 seconds, this indicates an increasing impedance variation rate, possibly related to material aging. If the mutation points occur at periodic intervals (e.g., every 0.5 seconds), environmental interference may be present. Through this analysis, we ultimately determine the distribution pattern of the mutation points.

[0168] S513: Based on the distribution pattern of the mutation points and the frequency band characteristics of the mutation points, the fault mode is classified and judged. If the mutation point occurs at a low frequency and the impedance change rate continues to increase, it is marked as a material aging failure mode. If the mutation point appears in a specific frequency band and changes periodically, it is marked as an environmental interference failure mode. If the mutation point is irregularly distributed in multiple frequency bands and is accompanied by power attenuation, it is marked as an electrode structure damage failure mode. The surface acoustic wave filter failure mode classification results are obtained comprehensively.

[0169] Based on the distribution pattern of the mutation points and the frequency characteristics of the mutation points, possible failure modes can be identified. First, if the mutation points are concentrated in the low-frequency band (for example, below 3 MHz) and the impedance change rate increases over time, this may be caused by material aging. For example, in a measurement, the impedance change rate in the 2.5 MHz band increased from 0.8 Ω / s to 1.5 Ω / s, which is consistent with material aging. Second, if the mutation points occur in a specific frequency band (such as 3.8 MHz to 4.2 MHz) and show periodic changes, such as an impedance mutation every 0.5 seconds, this may be the result of environmental interference. Furthermore, if the mutation points are irregularly distributed across multiple frequency bands and are accompanied by power attenuation, such as mutations occurring in the 2.5 MHz, 3.2 MHz, and 3.8 MHz bands, and the energy ratio drops by more than 2 dB, this may be caused by electrode structural damage. Based on this analysis, the SAW filter failure mode classification results are finally obtained.

[0170] Table 7: Mutation point time interval and frequency band distribution table:

[0171] ;

[0172] As shown in Table 7, the mutation points are concentrated between 1.2 seconds and 1.5 seconds, and the impedance change rate gradually increases with time, indicating that material aging may be the main factor causing the mutation.

[0173] Intelligent fault detection system for surface acoustic wave filters, including:

[0174] The signal energy ratio calculation module collects input and output signals, performs fixed bandwidth decomposition, extracts independent frequency bands, calculates input power, output power and power ratio, and generates a multi-band energy ratio sequence;

[0175] The energy decay pattern recognition module calculates the energy ratio changes of adjacent frequency bands based on the multi-band energy ratio sequence, filters out abnormal frequency bands, extracts the power decay rate, compares the upper limit value, marks faults and natural decay, and generates energy decay pattern recognition results;

[0176] The impedance change rate calculation module collects input impedance and output impedance, calculates the coupling equivalent value of input and output impedance, and generates coupling impedance data;

[0177] The impedance mutation point screening module calculates the change rates of input impedance, output impedance, and coupling impedance based on the coupled impedance data, compares them with preset thresholds, screens mutation points, determines distribution characteristics, and generates impedance mutation point identification results;

[0178] The fault mode classification module analyzes the frequency band and distribution characteristics of the mutation points based on the impedance mutation point identification results and the energy attenuation pattern identification results, marks the fault modes, and generates the surface acoustic wave filter fault mode classification results.

[0179] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent fault detection method for a surface acoustic wave filter, characterized in that: The following steps are involved: S1: Collect the input and output signals of the surface acoustic wave filter, extract independent frequency bands, count the input and output power of the frequency bands, calculate the power ratio, and obtain a multi-band energy ratio sequence; S2: Based on the multi-band energy ratio sequence, calculate the energy ratio change between adjacent frequency bands, determine the local mutation pattern, extract the power decay rate per unit time, analyze whether the decay rate exceeds the limit, and obtain the energy decay pattern recognition result; S3: collecting the impedance of the electrode input and output ends of the surface acoustic wave filter, and synchronously calculating the coupling equivalent value of the input and output impedances to obtain coupling impedance data; S4: Based on the coupling impedance data, respectively calculating the change rates of the input impedance, output impedance, and coupling impedance per unit time, screening the time point at which the impedance change rate suddenly increases, determining the distribution characteristics of the mutation point, and obtaining the impedance mutation point identification result; S5: Based on the impedance mutation point identification results and the energy attenuation pattern identification results, the frequency band corresponding to the mutation point is analyzed. If the mutation point occurs at a low frequency and the impedance change rate continues to increase, the material aging failure mode is marked. If the mutation point appears in a specific frequency band and changes periodically, the environmental interference failure mode is marked. If the mutation point is irregularly distributed in multiple frequency bands and is accompanied by power attenuation, the electrode structure damage failure mode is marked. The surface acoustic wave filter failure mode classification results are comprehensively obtained.

2. The intelligent fault detection method for surface acoustic wave filters according to claim 1, characterized in that: The multi-band energy ratio sequence includes the input power of each frequency band, the output power of each frequency band, and the power ratio of each frequency band. The energy attenuation mode recognition result includes normal state, abnormal attenuation state, filter failure mode, and natural performance attenuation. The coupled impedance data includes input impedance, output impedance, and the coupled equivalent value of input and output impedance. The impedance mutation point recognition result includes the input impedance change rate mutation point, the output impedance change rate mutation point, and the coupling impedance change rate mutation point.

3. The intelligent fault detection method for surface acoustic wave filters according to claim 1, characterized in that: The specific steps of S1 are: S111: collecting input and output signals of the surface acoustic wave filter, performing fixed-bandwidth decomposition on the input and output signals, calling multiple set center frequencies, performing frequency division processing on the signals, obtaining input and output signals of each independent frequency band, storing the extracted signal power data, and obtaining a data set of input power and output power of each frequency band; S112: Based on the input power and output power data sets of each frequency band, extract the corresponding frequency band data of the input power and output power using the formula: ； Calculate the Power ratio of frequency band , store the power ratio sequence of each frequency band, where Representative The output power of the frequency band, Represents the average output power of all frequency bands, Representative The input power of the frequency band, Represents the average input power in all frequency bands; S113: Perform statistical analysis based on the power ratio sequence of each frequency band, integrate the power ratio data of all frequency bands, and obtain a multi-frequency band energy ratio sequence.

4. The intelligent fault detection method for a surface acoustic wave filter according to claim 1, characterized in that: The specific steps of S2 are: S211: Based on the multi-band energy ratio sequence, calculating energy ratio changes between adjacent frequency bands, setting an energy ratio change threshold, and screening frequency bands with abnormal energy ratio changes by comparing the energy ratio change threshold to obtain a frequency band set with abnormal energy ratio changes; S212: Based on the set of frequency bands with abnormal energy ratio changes, determine the local mutation pattern. If the energy ratios of all frequency bands are stable, mark them as normal. Otherwise, determine the distribution of abnormal frequency bands, call the energy ratio change threshold, compare the energy ratios of each frequency band if they exceed the threshold, and if the energy ratios of some frequency bands exceed the threshold, mark them as abnormal attenuation states, record the positions of the abnormal frequency bands, and obtain abnormal frequency band marking data. S213: Based on the abnormal frequency band mark data, extract the power attenuation rate per unit time using the formula: ； Calculate the Power attenuation rate of the frequency band , combined with the upper limit of the power attenuation rate, the frequency band that exceeds the limit is compared and screened. If the attenuation rate exceeds the limit, the filter failure mode is marked, otherwise the performance is marked as natural attenuation, and the energy attenuation pattern recognition result is established, where, Representative Frequency band in time The output power, Representative Frequency band at the previous time point The output power, Represents a time interval.

5. The intelligent fault detection method for surface acoustic wave filters according to claim 1, characterized in that: The specific steps of S3 are: S311: collecting the impedance of the electrode input end and the impedance of the electrode output end of the surface acoustic wave filter, recording the impedance data, and storing them in time series to obtain an input and output impedance sequence; S312: Based on the input and output impedance sequence, the complex form of the input impedance and the output impedance is used, using the formula: ； Calculate the coupling equivalent impedance value ,in, represents the complex value of the impedance at the electrode input, represents the complex value of the impedance at the output of the electrode, Represents the phase difference between input and output impedance; S313: Based on the coupling equivalent impedance value, a coupling equivalent impedance time series is established, and the coupling impedance data is obtained by combining the input and output impedance series.

6. The intelligent fault detection method for a surface acoustic wave filter according to claim 1, characterized in that: The specific steps of S4 are: S411: performing numerical differential calculation based on the coupled impedance data, calling impedance data at adjacent time points, calculating the change rates of the input, output, and coupled impedances in the time dimension, and obtaining an impedance change rate sequence; S412: Based on the impedance change rate sequence and a preset impedance mutation threshold, the impedance change rate at each time point is compared, the time point at which the impedance change rate suddenly increases is selected, and a mutation time point data set is established; S413: Based on the mutation time point data set, analyze the mutation point distribution characteristics, calculate the mutation point interval, distribution density and aggregation trend in the time series, store the calculated distribution characteristic data, and obtain the impedance mutation point identification result.

7. The intelligent fault detection method for a surface acoustic wave filter according to claim 1, characterized in that: The surface acoustic wave filter failure mode classification results include material aging failure mode, environmental interference failure mode, and electrode structure damage failure mode.

8. The intelligent fault detection method for a surface acoustic wave filter according to claim 1, characterized in that: The specific steps of S5 are: S511: Based on the impedance mutation point identification result and the energy attenuation pattern identification result, extract the mutation point data and frequency band energy attenuation information, analyze the frequency band position of the mutation point, extract the frequency range where the mutation point occurs, and obtain the frequency band data corresponding to the mutation point; S512: Based on the frequency band data corresponding to the mutation points, determine the distribution characteristics of the mutation points, combine the impedance change rate sequence, analyze the distribution pattern of the mutation points in position and time, identify the change trend of the mutation points, screen the concentration of the mutation points in the frequency band, and obtain the mutation point distribution pattern; S513: Based on the mutation point distribution pattern and the frequency band characteristics of the mutation point, the fault mode is classified and judged. If the mutation point occurs at a low frequency and the impedance change rate continues to increase, it is marked as a material aging failure mode. If the mutation point appears in a specific frequency band and changes periodically, it is marked as an environmental interference failure mode. If the mutation point is irregularly distributed in multiple frequency bands and is accompanied by power attenuation, it is marked as an electrode structure damage failure mode. The surface acoustic wave filter failure mode classification result is obtained comprehensively.

9. An intelligent fault detection system for surface acoustic wave filters, characterized in that: The system is used to perform the method according to any one of claims 1 to 8, comprising: The signal energy ratio calculation module collects input and output signals, performs fixed bandwidth decomposition, extracts independent frequency bands, calculates input power, output power and power ratio, and generates a multi-band energy ratio sequence; The energy attenuation pattern recognition module calculates the energy ratio changes of adjacent frequency bands based on the multi-band energy ratio sequence, filters abnormal frequency bands, extracts the power attenuation rate, compares the upper limit value, marks faults and natural attenuation, and generates an energy attenuation pattern recognition result; The impedance change rate calculation module collects input impedance and output impedance, calculates the coupling equivalent value of input and output impedance, and generates coupling impedance data; The impedance mutation point screening module calculates the change rates of the input impedance, output impedance and coupling impedance based on the coupling impedance data, compares them with preset thresholds, screens mutation points, determines distribution characteristics, and generates impedance mutation point identification results; The fault mode classification module analyzes the frequency band and distribution characteristics of the mutation points based on the impedance mutation point identification result and the energy attenuation mode identification result, marks the fault mode, and generates a surface acoustic wave filter fault mode classification result.

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