A performance detection and analysis method for a guide rail glass lifting system

Abstract

The present invention belongs to the technical field of smoothness detection of glass guide rail lifting systems, and discloses a method for detecting and analyzing the performance of guide rail glass lifting systems. The present invention constructs a comprehensive evaluation matrix for smoothness by performing data analysis on the noise data, output current, and vibration time domain signals, comprehensively evaluating the system status from multiple aspects such as frequency band energy, current fluctuation, and vibration frequency, comprehensively covering operational abnormality characteristics, improving the accuracy of abnormality identification, and providing a scientific basis for performance quantitative evaluation to avoid the one-sidedness of a single parameter. The present invention can accurately identify abnormal components based on the noise spectrum mapping relationship when it is determined that there is an abnormality in the smoothness of the guide rail glass lifting system, and at the same time locate the specific abnormal position by combining the current, vibration signal, and position-time mapping, thereby achieving dual positioning of component type and physical position, avoiding manual blind investigation, and improving fault diagnosis efficiency and maintenance targeting.

Description

Technical Field

[0001] The invention belongs to the technical field of smoothness detection of glass guide rail lifting systems, and relates to a performance detection and analysis method for a guide rail glass lifting system. Background Art

[0002] In automotive and other fields, the guide rail glass lift system is a key mechanism for achieving smooth glass lifting and lowering. Its performance directly impacts user experience, safety, and system lifespan. Abnormal smoothness of the glass guide rail can cause increased vibration and noise during glass lifting, impacting the user experience. It can also lead to abnormal current fluctuations, exacerbating component wear and shortening system lifespan. In severe cases, it can cause glass jamming, lift failure, and safety hazards, even leading to accidents such as glass detachment, necessitating prompt inspection and repair.

[0003] Existing methods rely on single sensors, capturing only single parameters like noise or current. These methods fail to fully reflect the multi-dimensional operating status of the system, including vibration and energy consumption, resulting in a one-sided detection dimension. Furthermore, single data has weak anti-interference capabilities and is easily distorted by environmental factors, making it difficult to accurately identify ride comfort anomalies. The lack of multi-parameter fusion analysis makes it impossible to scientifically evaluate system performance.

[0004] Existing technical solutions lack the ability to identify the type of abnormal components and locate abnormal locations based on actual monitoring data. As a result, when an abnormality occurs, components can only be checked one by one based on manual experience. This is not only inefficient, but also prone to missing hidden faults due to subjective judgment, resulting in a lack of targeted maintenance, which in turn increases maintenance costs and system downtime, making it difficult to meet the needs of automated detection and preventive maintenance. Summary of the Invention

[0005] In view of this, in order to solve the problems raised in the above background technology, a performance detection and analysis method of a guide rail glass lifting system is proposed.

[0006] The objectives of the present invention can be achieved through the following technical solutions: A method for detecting and analyzing the performance of a guide rail glass lifting system, comprising: performing a smoothness test on the guide rail glass lifting system, and using an acoustic sensor, a current sensor, and a three-axis accelerometer to collect noise time domain signals, output current time domain signals, and vibration time domain signals.

[0007] The noise data, output current and vibration time domain signals are analyzed to construct a comprehensive evaluation matrix for smoothness, including the noise frequency band energy ratio deviation, the effective current fluctuation rate and the vibration frequency energy ratio deviation.

[0008] Compare each parameter in the smoothness comprehensive evaluation matrix with a preset safety range to determine whether there is any abnormality in the smoothness of the guide rail glass lifting system.

[0009] When it is determined that there is an abnormality in the smoothness of the guide rail glass lifting system, the abnormal component is identified based on the pre-built abnormal component-noise spectrum mapping relationship, and the abnormal position set is located based on the glass rising position when the abnormality occurs based on the output current and vibration time domain signal.

[0010] When it is determined that there is no abnormality in the smoothness of the guide rail glass lifting system, the parameters in the smoothness comprehensive evaluation matrix are weighted and fused to construct a smoothness comprehensive evaluation index.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention constructs a comprehensive evaluation matrix for smoothness by performing data analysis on the noise data, output current and vibration time domain signals, comprehensively evaluating the system status from multiple aspects such as frequency band energy, current fluctuation, and vibration frequency, comprehensively covering the abnormal operation characteristics, improving the accuracy of abnormality identification, and providing a scientific basis for performance quantitative evaluation, thus avoiding the one-sidedness of a single parameter.

[0012] (2) When the smoothness of the guide rail glass lifting system is judged to be abnormal, the present invention can accurately identify the abnormal component based on the noise spectrum mapping relationship, and at the same time locate the specific abnormal position by combining the current, vibration signal and position-time mapping, thereby achieving dual positioning of component type and physical position, avoiding manual blind investigation, and improving fault diagnosis efficiency and maintenance targeting. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 Schematic diagram of the steps of the method of the present invention.

[0015] Figure 2 This is an implementation flow chart corresponding to an embodiment provided by the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] See also Figure 1 、 Figure 2As shown, the present invention provides a performance detection and analysis method for a guide rail glass lifting system, including: performing a smoothness test on the guide rail glass lifting system, and using an acoustic sensor, a current sensor, and a three-axis accelerometer to collect noise time domain signals, output current time domain signals, and vibration time domain signals.

[0018] It should be noted that the present invention constructs a comprehensive evaluation matrix for smoothness by performing data analysis on the noise data, output current and vibration time domain signals, comprehensively evaluating the system status from multiple aspects such as frequency band energy, current fluctuation, and vibration frequency, comprehensively covering operational abnormality characteristics, improving the accuracy of abnormality identification, and providing a scientific basis for performance quantitative evaluation, thereby avoiding the one-sidedness of a single parameter.

[0019] The noise data, output current and vibration time domain signals are analyzed to construct a comprehensive evaluation matrix for smoothness, including the noise frequency band energy ratio deviation, the effective current fluctuation rate and the vibration frequency energy ratio deviation.

[0020] In a preferred embodiment of the present invention, the specific analysis method of the noise band energy ratio deviation is as follows: the noise time domain signal is converted into a noise frequency domain signal using fast Fourier transform, the power spectrum density in each preset frequency band is obtained, and the energy value of each frequency band is further integrated.

[0021] It's important to note that in the analysis of noise band energy ratio deviation, each band refers to the multiple intervals into which the noise frequency domain signal is divided according to its frequency range, with each interval corresponding to a specific frequency segment. A frequency band is a discrete division of a continuous frequency range, essentially dividing the entire frequency range of the noise frequency domain signal into several non-overlapping frequency intervals.

[0022] It's important to note that in a guide-glass lift system, abnormalities in various components can generate noise at specific frequencies. For example, motor bearing wear can produce energy peaks in the 1000-1500Hz band, while poor gear meshing can produce abnormalities in the 500-800Hz band. Therefore, frequency band division must cover the characteristic frequency range of each component to accurately detect abnormalities.

[0023] The total noise energy value is obtained by integrating the power spectrum density of the entire process of the guide rail glass lifting system smoothness test.

[0024] The energy value of each frequency band is calculated as a proportion of the energy value of each frequency band to the total noise energy value.

[0025] Based on a large number of pre-tests, the standard energy value ratio of each frequency band under normal working conditions is obtained, and then the average value is calculated to obtain the reference standard energy value ratio of each frequency band.

[0026] The relative difference between the energy value proportion of each frequency band and the corresponding reference standard energy value proportion is calculated to obtain the deviation degree of the energy value proportion of each frequency band.

[0027] It should be noted that the calculation of the noise band energy ratio deviation is based on the standard energy ratio under normal operating conditions obtained through extensive pre-testing. By comparing the actual tested band energy ratio with the standard value, the degree of deviation is quantified as a deviation. This standardized comparison mechanism eliminates interfering factors such as ambient noise and test conditions, allowing the deviation to objectively reflect changes in the system's inherent smoothness.

[0028] The energy ratio deviations of the frequency bands are compared, and the maximum frequency band energy ratio deviation is selected as the noise frequency band energy ratio deviation.

[0029] It should be noted that the reason for choosing the maximum frequency band energy value ratio deviation as the noise frequency band energy ratio deviation is that system abnormalities usually produce energy peaks in specific frequency bands. For example, bearing wear corresponds to energy abnormalities in the 1000-1500Hz frequency band. The maximum value deviation can directly locate this critical frequency band and avoid being interfered with by deviations in other non-dominant frequency bands.

[0030] It should be noted that the reason for using the noise frequency band energy ratio deviation as a parameter in the comprehensive smoothness evaluation matrix is: 1. When the guide rail glass lifting system is in operation, abnormalities such as component wear and poor lubrication will directly change the frequency distribution characteristics of the noise. By analyzing the noise frequency band energy deviation, the system smoothness can be reflected.

[0031] 2. A single time-domain noise amplitude cannot accurately locate the type of anomaly, but the frequency band energy ratio deviation can quantify the degree of deviation of the noise distribution from the normal state from a frequency domain perspective. For example, bearing faults often correspond to specific frequency band energy anomalies.

[0032] 3. A multi-parameter matrix is ​​formed with the current fluctuation rate and vibration frequency energy deviation, covering multiple dimensions of noise, energy consumption, and vibration, avoiding the one-sidedness of a single parameter and improving the comprehensiveness and accuracy of ride comfort evaluation.

[0033] In a preferred embodiment of the present invention, the effective current fluctuation rate is specifically analyzed as follows: the monitoring window is divided based on a preset window length, and the effective current value in each monitoring window is calculated according to the output current time domain signal.

[0034] It should be noted that the preset window length is a pre-set time interval for quantifying the dynamic fluctuation characteristics of the current signal when analyzing the effective current fluctuation rate. Its function is to divide the continuous output current time domain signal into multiple discrete monitoring intervals for segmented statistics and analysis. The setting of the preset window length must take into account both signal details and computational efficiency: if it is too short, it will lead to excessive data volume and computational redundancy; if it is too long, local abnormal fluctuations may be ignored. This parameter is the basis for achieving quantitative analysis of current fluctuation rate. Through standardized window division, the current fluctuation characteristics under different test scenarios can be made comparable, providing a unified quantitative standard for smoothness evaluation.

[0035] The average current effective value is obtained by averaging the effective current values ​​in each monitoring window.

[0036] The effective current values ​​in the monitoring windows are compared to identify the maximum effective current value and the minimum effective current value, and calculate the difference between the maximum effective current value and the minimum effective current value.

[0037] The effective current fluctuation rate is calculated by calculating the ratio of the difference between the maximum current effective value and the minimum current effective value to the average current effective value.

[0038] It should be noted that the reason for choosing the effective current fluctuation rate as a parameter in the comprehensive smoothness evaluation matrix is ​​that the current is closely related to the mechanical state of the system, and its fluctuation can reflect changes in the motor load. When the system smoothness is abnormal, such as when the guide rail is stuck or the components are worn, the resistance will suddenly change, resulting in increased fluctuations in the effective current value. This parameter can sensitively capture such anomalies and even be the first to reflect early failures. At the same time, it complements the noise and vibration parameters, covering multiple dimensions of energy consumption, acoustics, and mechanics. The current signal is easy to collect and has strong anti-interference capabilities, making it easy to apply in engineering. In addition, the current fluctuation rate combined with the glass position can locate the abnormal resistance position, forming a logical closed loop with the abnormality diagnosis, and providing a scientific and quantitative basis for smoothness evaluation.

[0039] In a preferred embodiment of the present invention, the specific calculation method of the vibration frequency energy ratio deviation is as follows: the vibration time domain signal is converted into a vibration frequency domain signal using fast Fourier transform, the power spectrum density in each preset frequency interval is obtained, and the vibration energy value of each frequency interval is further integrated.

[0040] The total vibration energy value is obtained by integrating the power spectrum density over the entire frequency region of the smoothness test of the guide rail glass lifting system.

[0041] The vibration energy values ​​of each frequency interval are respectively calculated relative to the total vibration energy value to obtain the vibration energy value ratio of each frequency interval.

[0042] Based on a large number of pre-tests, the standard vibration energy value ratio of each frequency range under normal working conditions is obtained, and then the average value is calculated to obtain the reference standard vibration energy value ratio of each frequency range.

[0043] The relative difference between the vibration energy value ratio of each frequency interval and the corresponding reference standard vibration energy value ratio is calculated to obtain the deviation degree of the vibration energy value ratio of each frequency interval.

[0044] The vibration energy value ratio deviations of the frequency intervals are compared, and the maximum vibration energy value ratio deviation is selected as the vibration frequency energy ratio deviation.

[0045] It should be noted that the vibration frequency energy ratio deviation was selected as a parameter in the comprehensive smoothness evaluation matrix because the vibration signal is directly related to the system's mechanical state. Abnormalities such as component wear and guide rail deformation can cause variations in vibration energy at specific frequencies. This parameter quantifies the degree of mechanical vibration anomalies by analyzing the deviation of vibration frequency domain energy from the standard value. For example, a bearing fault will produce energy peaks in a specific frequency band. It complements noise and current parameters to comprehensively reflect the system's state. Furthermore, the vibration signal can intuitively reflect the smoothness of moving components. Frequency domain analysis can accurately locate abnormal frequency characteristics. Combined with mapping relationships, abnormal components can be identified, providing a quantitative basis for mechanical vibration-level smoothness assessment and improving the accuracy of multi-dimensional diagnostics.

[0046] Compare each parameter in the smoothness comprehensive evaluation matrix with a preset safety range to determine whether there is any abnormality in the smoothness of the guide rail glass lifting system.

[0047] In a preferred embodiment of the present invention, the specific method for determining whether there is an abnormality in the smoothness of the guide rail glass lifting system is as follows: comparing the noise frequency band energy ratio deviation, the effective current fluctuation rate and the vibration frequency energy ratio deviation with the preset corresponding thresholds respectively.

[0048] If any parameter is greater than the corresponding threshold, it is determined that there is an abnormality in the smoothness of the guide rail glass lifting system; otherwise, it is determined that there is no abnormality in the smoothness of the guide rail glass lifting system.

[0049] It should be noted that the above corresponding thresholds are set in the following manner: 1. By conducting a large number of pre-tests on the guide rail glass lifting system under normal operating conditions, sample data of noise frequency band energy ratio deviation, effective current fluctuation rate, and vibration frequency energy ratio deviation are collected.

[0050] 2. Perform statistics on the normal sample data of each parameter and calculate its mean and standard deviation. The threshold is usually set to the sum of the mean and three times the standard deviation to cover the normal fluctuation range in most cases.

[0051] 3. Based on the actual operating conditions of the guide rail glass lifting system, the statistical threshold is modified by engineering to avoid misjudgment due to accidental interference, ensuring that the threshold can both identify real anomalies and reduce the false alarm rate.

[0052] When it is determined that there is an abnormality in the smoothness of the guide rail glass lifting system, the abnormal component is identified based on the pre-built abnormal component-noise spectrum mapping relationship, and the abnormal position set is located based on the glass rising position when the abnormality occurs based on the output current and vibration time domain signal.

[0053] In a preferred embodiment of the present invention, the abnormal component-noise spectrum mapping relationship is specifically constructed as follows: a large number of guide rail glass lifting system smoothness tests are performed, wherein each test has any abnormal component, and the fault degree of the abnormal component is different.

[0054] Acoustic sensors are used to collect noise time domain signals in real time, and band-pass filtering is performed on the noise time domain signals to remove environmental noise interference.

[0055] The short-time Fourier transform is used to convert the time domain signal into a two-dimensional time-frequency spectrum, the spectrum is divided into multiple frequency bands, and the proportion of the energy of each frequency band to the total energy is calculated.

[0056] The proportion of each frequency band energy to the total energy is compared to identify the frequency band corresponding to the peak ratio and the peak energy proportion.

[0057] The results of the smoothness tests of each guide rail and glass lifting system were analyzed according to the type of abnormal components. The frequency bands corresponding to the same abnormal component were compared, and the frequency band with the most occurrences was selected as the characteristic frequency band corresponding to the abnormal component. The peak energy proportions corresponding to the smoothness tests of each guide rail and glass lifting system were averaged to obtain the peak energy proportion corresponding to each abnormal component.

[0058] The abnormal component is associated with the characteristic frequency band and the peak energy ratio to obtain an abnormal component-noise spectrum mapping relationship.

[0059] It's important to note that the purpose of mapping abnormal components to noise spectra is to establish a direct correlation between noise signatures and mechanical anomalies. This allows the faulty component to be located by energy peaks in specific frequency bands, such as motor bearing wear, which corresponds to increased energy in the 1000-1500Hz band, thus avoiding manual troubleshooting. This data-driven approach replaces empirical judgment, improving diagnostic efficiency and objectivity and shortening troubleshooting time. Real-time monitoring can be embedded to trigger early warnings, enabling proactive maintenance. This can also complement current and vibration parameters, encompassing system status from multiple physical fields, forming a closed loop between abnormal signals and component location, supporting intelligent diagnosis and predictive maintenance.

[0060] In a preferred embodiment of the present invention, the specific method of identifying abnormal components is as follows: Short-time Fourier transform is used to convert the noise time domain signal into a time-frequency two-dimensional spectrum, and the smoothness test process of the guide rail glass lifting system is divided into monitoring time periods based on a preset monitoring time.

[0061] Obtain the maximum energy proportion and corresponding frequency band corresponding to each monitoring period.

[0062] The maximum energy ratio corresponding to each monitoring period is compared with a preset maximum energy ratio threshold, and the monitoring period in which the maximum energy ratio exceeds the corresponding threshold is recorded as an abnormal monitoring period.

[0063] The maximum energy proportion and the corresponding frequency band corresponding to each abnormal monitoring period are matched with the abnormal component-noise spectrum mapping relationship to obtain the corresponding abnormal component.

[0064] For example, spectrum analysis is used to calculate the frequency band with the largest energy share in each time period, that is, the frequency range where the energy peak is located. For example, the energy share of the 1000-1500Hz frequency band in a certain time period reaches 35%, which is the maximum value of the time period. Then, this maximum energy share and its corresponding frequency band are matched with the established abnormal component-noise spectrum mapping relationship. If the mapping feature of motor bearing wear is matched to the energy share of the 1000-1500Hz frequency band exceeding 30%, it can be determined that the abnormal component in this time period is the motor bearing, thereby achieving accurate positioning from the noise energy feature to the fault source.

[0065] Summarize and obtain the abnormal parts corresponding to the smoothness test of the current guide rail glass lifting system.

[0066] It should be noted that this method for identifying abnormal components relies on a preset noise spectrum mapping library. It processes real-time noise data and matches it with features in the library, then cross-validates the data using multiple parameters such as current and vibration. Its advantages include precise positioning, which can directly identify faulty components; high diagnostic efficiency, which eliminates the need for component-by-component troubleshooting and significantly reduces diagnostic time; standardized data matching replaces manual experience, resulting in high objectivity and a low error rate; it can be embedded in real-time monitoring systems for intelligent early warning and predictive maintenance support; and it collaboratively verifies multiple physical field parameters to form a comprehensive detection system, improving diagnostic reliability from the electrical, mechanical, and acoustic dimensions, effectively reducing operation and maintenance costs.

[0067] In a preferred embodiment of the present invention, the specific method of locating the abnormal position set is as follows: the clock source of the output current and the vibration time domain signal are unified.

[0068] The smoothness test of the current guide rail glass lifting system is carried out based on equal intervals and the detection time is arranged.

[0069] The output current and vibration amplitude corresponding to each monitoring moment are obtained, and then compared with the pre-set output current threshold and vibration amplitude threshold respectively. The detection moment when the output current is greater than the output current threshold or the vibration amplitude is greater than the vibration amplitude threshold is located and recorded as the abnormal monitoring moment.

[0070] The monitoring device is used to obtain the position of the glass corresponding to the guide rail glass lifting system in real time, and a position-time mapping relationship is constructed.

[0071] Each abnormal monitoring moment is matched with the position-time mapping relationship to obtain each abnormal position point.

[0072] An abnormal location set is constructed based on the abnormal location points.

[0073] It's important to note that the advantages of locating anomalies described above include: combining signal strength differences from distributed sensors to narrow the anomaly's spatial distribution, and then analyzing the time-series trajectory of continuous monitoring to identify high-frequency anomaly areas and eliminate accidental interference. Its high positioning accuracy avoids blind investigations; it utilizes spatial signals to achieve three-dimensional positioning; time-series analysis improves the reliability of results; clear location guidance reduces maintenance workload; and multi-dimensional data collaborates to build a comprehensive positioning system, providing precise physical location guidance for maintenance, significantly improving maintenance efficiency.

[0074] It should be noted that the present invention can accurately identify abnormal components based on the noise spectrum mapping relationship when it is determined that there is an abnormality in the smoothness of the guide rail glass lifting system. At the same time, it can locate the specific abnormal position by combining current, vibration signals and position-time mapping, thereby achieving dual positioning of component type and physical position, avoiding manual blind troubleshooting, and improving fault diagnosis efficiency and targeted maintenance.

[0075] When it is determined that there is no abnormality in the smoothness of the guide rail glass lifting system, the parameters in the smoothness comprehensive evaluation matrix are weighted and fused to construct a smoothness comprehensive evaluation index.

[0076] In a preferred embodiment of the present invention, the specific analysis method for constructing the smoothness comprehensive evaluation index is as follows: the noise frequency band energy ratio deviation, effective current fluctuation rate, and vibration frequency energy ratio deviation corresponding to the smoothness test of the guide rail glass lifting system are fused and calculated according to weights, and the inverse is taken to obtain the smoothness comprehensive evaluation index.

[0077] It's important to explain that the smoothness evaluation index is a key metric used to quantitatively assess the smoothness of a guide rail glass lift system. It integrates the deviations of three physical parameters: noise, current, and vibration, and uses a weighted calculation and mathematical transformation to produce a numerical result. A higher index indicates smoother system operation, while a lower index indicates poorer smoothness.

[0078] In a preferred embodiment of the present invention, the weights corresponding to the various parameters in the analysis process of the comprehensive ride comfort evaluation index are obtained by fitting and calculating a large amount of historical test data using a regression analysis method.

[0079] It should be further explained that the specific analysis method for the weights corresponding to the above-mentioned noise frequency band energy ratio deviation, effective current fluctuation rate, and vibration frequency energy ratio deviation is as follows: historical test data of the large guide rail glass lifting system under normal operating conditions is collected, including the noise frequency band energy ratio deviation, effective current fluctuation rate, vibration frequency energy ratio deviation, and a comprehensive smoothness evaluation index quantified by experts based on subjective evaluation; the Pearson correlation coefficient between each parameter and the comprehensive smoothness evaluation index is calculated, and a mapping model between parameter deviation and smoothness score is established through multivariate linear regression. The standardized regression coefficient is extracted as the initial weight of each parameter, and the initial weight is normalized to a final weight with a total of 1, which is then used as the weight corresponding to the noise frequency band energy ratio deviation, effective current fluctuation rate, and vibration frequency energy ratio deviation.

[0080] It's important to note that the weights derived through regression analysis are based on statistical analysis of historical data, avoiding biases in subjective weight setting and ensuring that the weight distribution more closely aligns with actual operating patterns. By fitting a large amount of data, we can capture the implicit relationship between parameters and ride comfort, improving the accuracy and reliability of the weights. If the historical data covers a variety of operating conditions, the weights derived through regression analysis can be adapted to the evaluation needs of different scenarios.

[0081] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A method for detecting and analyzing the performance of a guide rail glass lifting system, characterized in that: include: Conduct a smoothness test on the guide rail glass lifting system, using acoustic sensors, current sensors, and triaxial accelerometers to collect noise time domain signals, output current time domain signals, and vibration time domain signals; Performing data analysis on the noise time domain signal, the output current time domain signal, and the vibration time domain signal, and constructing a comprehensive ride comfort evaluation matrix, which includes the noise frequency band energy ratio deviation, the effective current fluctuation rate, and the vibration frequency energy ratio deviation; Comparing each parameter in the smoothness comprehensive evaluation matrix with a preset safety range to determine whether there is any abnormality in the smoothness of the guide rail glass lifting system; When an abnormality occurs, the abnormal component is identified based on the preset abnormal component-noise spectrum mapping relationship, and the abnormal position set is located based on the glass rising position when the abnormality occurs based on the output current time domain signal and the vibration time domain signal; When there is no abnormality, the parameters in the ride comfort comprehensive evaluation matrix are weighted and fused to construct a ride comfort comprehensive evaluation index. The specific analysis method of the noise band energy ratio deviation is as follows: The noise time domain signal is converted into a noise frequency domain signal using fast Fourier transform, the power spectrum density in each preset frequency band is obtained, and the energy value of each frequency band is further obtained by integration; The total noise energy value is obtained by integrating the power spectrum density of the entire process of the guide rail glass lifting system smoothness test; Calculate the energy value of each frequency band and the total noise energy value to obtain the energy value ratio of each frequency band; Based on a large number of pre-tests, the standard energy value ratio of each frequency band under normal working conditions is obtained, and then the average value is calculated to obtain the reference standard energy value ratio of each frequency band; Calculate the relative difference between the energy value proportion of each frequency band and the corresponding reference standard energy value proportion to obtain the deviation degree of the energy value proportion of each frequency band; The energy ratio deviations of the frequency bands are compared, and the maximum frequency band energy ratio deviation is selected as the noise frequency band energy ratio deviation.

2. The method for detecting and analyzing the performance of a guide rail glass lift system according to claim 1, wherein: The specific analysis method of the effective current fluctuation rate is as follows: The monitoring window is divided based on the preset window length, and the effective value of the current in each monitoring window is calculated according to the output current time domain signal; The average current effective value is obtained by averaging the effective current value in each monitoring window; Comparing the effective current values ​​in each monitoring window, identifying the maximum effective current value and the minimum effective current value, and calculating the difference between the maximum effective current value and the minimum effective current value; The effective current fluctuation rate is calculated by calculating the ratio of the difference between the maximum current effective value and the minimum current effective value to the average current effective value.

3. The method for detecting and analyzing the performance of a guide rail glass lift system according to claim 2, wherein: The specific calculation method of the vibration frequency energy ratio deviation is as follows: The vibration time domain signal is converted into a vibration frequency domain signal using fast Fourier transform, the power spectrum density in each preset frequency interval is obtained, and the vibration energy value in each frequency interval is further integrated; The total vibration energy value is obtained by integrating the power spectrum density over the entire frequency region of the guide rail glass lift system smoothness test; Calculate the proportion of the vibration energy value of each frequency interval to the total vibration energy value to obtain the proportion of the vibration energy value of each frequency interval; Based on a large number of pre-tests, the standard vibration energy value ratio of each frequency range under normal working conditions is obtained, and then the average value is calculated to obtain the reference standard vibration energy value ratio of each frequency range; Calculate the relative difference between the vibration energy value ratio of each frequency interval and the corresponding reference standard vibration energy value ratio to obtain the vibration energy value ratio deviation of each frequency interval; The vibration energy value ratio deviations of the frequency intervals are compared, and the maximum vibration energy value ratio deviation is selected as the vibration frequency energy ratio deviation.

4. The method for detecting and analyzing the performance of a guide rail glass lift system according to claim 3, wherein: The specific method for determining whether there is an abnormality in the smoothness of the guide rail glass lifting system is as follows: Comparing the noise frequency band energy ratio deviation, the effective current fluctuation rate, and the vibration frequency energy ratio deviation with preset corresponding thresholds respectively; If any parameter is greater than the corresponding threshold, it is determined that there is an abnormality in the smoothness of the guide rail glass lifting system; otherwise, it is determined that there is no abnormality in the smoothness of the guide rail glass lifting system.

5. The method for detecting and analyzing the performance of a guide rail glass lifting system according to claim 1, wherein: The specific construction method of the abnormal component-noise spectrum mapping relationship is as follows: By conducting a large number of smoothness tests on the guide rail glass lifting system, in which each test has any abnormal component, and the degree of failure of the abnormal component is different; Use acoustic sensors to collect noise time domain signals in real time, perform bandpass filtering on the noise time domain signals, and remove environmental noise interference; Use short-time Fourier transform to convert the time domain signal into a two-dimensional time-frequency spectrum, divide the spectrum into multiple frequency bands, and calculate the proportion of the energy of each frequency band to the total energy; Compare the proportion of each frequency band’s energy to the total energy, and identify the frequency band corresponding to the peak ratio and the peak energy ratio; The results of the smoothness test of each guide rail glass lift system were analyzed according to the type of abnormal component. The frequency bands corresponding to the same abnormal component were compared, and the frequency band with the most occurrences was selected as the characteristic frequency band corresponding to the abnormal component. The peak energy proportion corresponding to the smoothness test of each guide rail glass lift system was averaged to obtain the peak energy proportion corresponding to each abnormal component. The abnormal component is associated with the characteristic frequency band and the peak energy ratio to obtain an abnormal component-noise spectrum mapping relationship.

6. The method for detecting and analyzing the performance of a guide rail glass lifting system according to claim 5, characterized in that: The specific method of identifying abnormal components is as follows: Fu used short-time Fourier transform to convert the noise time domain signal into a two-dimensional time-frequency spectrum, and divided the monitoring period of the smoothness test process of the guide rail glass lifting system into two periods based on the preset monitoring time; Obtain the maximum energy proportion and corresponding frequency band corresponding to each monitoring period; Compare the maximum energy ratio corresponding to each monitoring period with the preset maximum energy ratio threshold, and record the monitoring period in which the maximum energy ratio exceeds the corresponding threshold as an abnormal monitoring period; The maximum energy proportion and the corresponding frequency band corresponding to each abnormal monitoring period are matched with the abnormal component-noise spectrum mapping relationship to obtain the corresponding abnormal component; Summarize and obtain the abnormal parts corresponding to the smoothness test of the current guide rail glass lifting system.

7. The method for detecting and analyzing the performance of a guide rail glass lifting system according to claim 6, wherein: The specific method of locating the abnormal position set is as follows: Unify the clock source of output current and vibration time domain signals; Conduct a smoothness test on the current guide rail glass lift system based on equal intervals and arrange the test time. Obtain the output current and vibration amplitude corresponding to each monitoring moment, and then compare them with the pre-set output current threshold and vibration amplitude threshold respectively. Locate the detection moment when the output current is greater than the output current threshold or the vibration amplitude is greater than the vibration amplitude threshold, and record it as the abnormal monitoring moment; Use the monitoring device to obtain the position of the glass corresponding to the guide rail glass lifting system in real time and build a position-time mapping relationship; Matching each abnormal monitoring moment with the position-time mapping relationship to obtain each abnormal position point; An abnormal location set is constructed based on the abnormal location points.

8. The method for detecting and analyzing the performance of a guide rail glass lift system according to claim 1, wherein: The specific analysis method for constructing the comprehensive evaluation index of ride comfort is as follows: The noise frequency band energy ratio deviation, effective current fluctuation rate, and vibration frequency energy ratio deviation corresponding to the smoothness test of the guide rail glass lifting system are fused and calculated according to the weights, and the inverse is taken to obtain the comprehensive smoothness evaluation index.

9. The method for detecting and analyzing the performance of a guide rail glass lifting system according to claim 8, wherein: The weights corresponding to the various parameters in the analysis process of the comprehensive ride comfort evaluation index are obtained by fitting and calculating a large amount of historical test data using a regression analysis method.

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