A solenoid valve opening and closing state measurement system and method

Abstract

This invention discloses a solenoid valve opening and closing state measurement system and method, relating to the field of solenoid valve technology. The method, after the solenoid valve control signal is triggered, utilizes a voltage and current sampling module integrated into the drive circuit, combined with an inductance analysis module in the local microcontroller (MCU), to obtain the dynamic inductance L at each time t in real time. Gaussian filtering and derivative calculation are then performed on the dynamic inductance L in the edge computing module to extract the first and second derivative features, and the inductance mutation factor index Cimp is calculated. This comprehensively reflects abnormal states such as jamming, rebound, and discontinuity that occur during the solenoid valve opening and closing process. A mutation threshold Cthr is set and compared to construct a dynamic opening and closing behavior diagnostic mechanism with high resolution and strong adaptability. This method can complete high-frequency dynamic response extraction and abnormal trend quantification within 100ms after the control signal is triggered, possessing stronger early warning capabilities and non-intrusive compatibility.

Description

Technical Field

[0001] This invention relates to the field of solenoid valve technology, specifically to a solenoid valve opening and closing status measurement system and method. Background Technology

[0002] With the continuous improvement of the intelligence of rail transit equipment and the automation level of train control systems, the real-time monitoring and health diagnosis of the operating status of solenoid valves, as key actuators for realizing the opening and closing control of pneumatic circuits, has become one of the core links in ensuring the safety and reliability of the entire vehicle. Especially in the air compressor system of rail trains, solenoid valves frequently respond to commands such as braking, suspension, and door control, and their opening and closing accuracy and response speed directly affect the vehicle's handling performance. However, traditional solenoid valve opening and closing status is difficult to recognize in a non-intrusive, high-resolution dynamic behavior manner, and there is an urgent need to develop a method for measuring the opening and closing status that can acquire the actual movement trajectory of the valve core and structural disturbance signals online.

[0003] Currently, in rail transit train braking systems, solenoid valves are used to control the release or retention of compressed air in air braking systems. If a solenoid valve suffers from problems such as "valve core jamming," "incomplete opening and closing," or "hysteresis response," it will severely affect the braking system's response efficiency and may even lead to safety accidents. Most current solenoid valve status measurement methods rely solely on indirect judgment based on control signal logic state, current waveform characteristics, or port pressure feedback, resulting in low resolution, delayed response, and an inability to characterize intermediate processes. For example, while coil current fluctuations can partially reflect changes in drive, they cannot reveal whether there are microstructural problems such as sticking, rebound, or discontinuous opening and closing of the internal valve core. Pressure response often lags behind structural action and is significantly affected by load disturbances, making it difficult to constitute a reliable evaluation index for opening and closing behavior. This often leads to the neglect or misjudgment of potential problems during the initial stages of system operation or in the early stages of minor anomalies, reducing the accuracy and timeliness of fault warnings.

[0004] The aforementioned defects primarily stem from potential nonlinear mechanical behavior during the opening and closing of solenoid valves, including frictional resistance between the valve core and guide, deviations in the return spring force, and abnormal stroke caused by insufficient driving magnetic field. If phenomena such as valve core jamming, rebound, or adhesion occur, although the control logic outputs an opening / closing signal, the actual valve body fails to complete the expected action, leading to air circuit switching failure, abnormal system response, and even malfunctions or multiple restarts. Especially in train braking or critical operation scenarios, if such anomalies are not detected and suppressed in a timely manner, they may cause serious train control system instability failures and safety hazards. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a solenoid valve opening and closing status measurement system and method, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution, comprising the following steps:

[0007] S1. After the solenoid valve control signal is triggered, the dynamic inductance L is collected, and the product of the second derivative and the first derivative of the dynamic inductance L is calculated to obtain the inductance mutation factor index Cimp.

[0008] S2. Compare the inductance mutation factor index Cimp with the preset mutation threshold Cthr, and trigger the disturbance analysis mechanism based on the comparison result. Collect the instantaneous acoustic signal S during the operation of the solenoid valve, perform Fourier transform processing on the instantaneous acoustic signal S, extract the target frequency band set Bres, and calculate the spectral disturbance deviation value Fvar.

[0009] S3. Input the inductance mutation factor index Cimp and the spectrum disturbance deviation value Fvar into the comprehensive opening and closing status scoring model to calculate the comprehensive diagnostic score Qdiag. Then, compare the comprehensive diagnostic score Qdiag with the preset diagnostic level range to determine the opening and closing status of the solenoid valve. Based on the comparison result, issue an alarm and restrict the execution of subsequent control commands.

[0010] Preferably, S1 includes S11;

[0011] S11. By integrating a sampling circuit module into the solenoid valve control drive circuit of the air compressor system of rail transit trains and setting a sampling strategy, the voltage and current signals at both ends of the solenoid valve coil are collected in real time.

[0012] The voltage and current signals are transmitted to the inductance analysis module in the local microcontroller MCU, which is synchronously input to the inductance analysis module. Based on the voltage and current signals and the known PWM duty cycle, the dynamic inductance L at each time t is calculated.

[0013] The acquisition strategy is set such that the sampling frequency is not less than 5kHz and the sampling time window covers the interval from 0ms to 100ms after the control signal is triggered.

[0014] The circuit module is positioned on the PWM control signal path in the solenoid valve control drive circuit.

[0015] Preferably, S1 further includes S12;

[0016] S12. Based on each time t, the dynamic inductance L is integrated into an inductance sequence according to the time order, and the inductance sequence is uploaded to the edge computing module embedded in the control host for analysis and processing. The edge computing module performs a filtering preprocessing on all dynamic inductance L in the inductance sequence through a set denoising processor. The filtering preprocessing uses a Gaussian convolution kernel to smooth all dynamic inductance L in the inductance sequence to reduce the derivative deviation caused by high-frequency electromagnetic interference and mechanical jitter. Then, the smoothed inductance sequence is passed to the derivative calculation module for feature extraction to obtain the first and second derivatives of the dynamic inductance L.

[0017] Preferably, S1 and S13;

[0018] S13. Based on the first and second derivatives of the dynamic inductance L, calculate the abrupt change response factor for any time t within the entire sampling time window, and then extract the maximum value of the abrupt change response factor for all times t to obtain the inductance abrupt change factor index Cimp, which measures the maximum nonlinear disturbance intensity exhibited by the solenoid valve during the opening and closing process.

[0019] Preferably, S2 includes S21;

[0020] S21. By selecting N sets of inductor behavior samples under the same temperature, pressure and driving conditions, where the N sets of inductor behavior samples are the inductor mutation factor index Cimp of multiple solenoid valves under historical healthy conditions, the 95th percentile of the inductor mutation factor index Cimp of multiple solenoid valves is set as the mutation threshold Cthr.

[0021] The inductance disturbance during the opening and closing process of the solenoid valve is determined by comparing the real-time acquired inductance mutation factor index Cimp with the mutation threshold Cthr.

[0022] The specific comparison is as follows;

[0023] If the inductor mutation factor Cimp exceeds the mutation threshold Cthr during the current start-up / shutdown cycle, it is determined that the current start-up / shutdown state is abnormal. In this case, the subsequent disturbance analysis mechanism is automatically triggered, and the time of occurrence of the abnormal event is recorded.

[0024] If the inductance mutation factor Cimp in the current start-up / shutdown cycle is less than or equal to Cthr, the current start-up / shutdown state is judged to be normal, automatically recorded as a healthy start-up / shutdown cycle, and the disturbance analysis mechanism is skipped to continue waiting for the next control cycle.

[0025] Preferably, S2 further includes S22;

[0026] S22. After the disturbance analysis mechanism is triggered, the acoustic signal S is collected by a MEMS micro microphone module located outside the solenoid valve housing. The microphone module performs sound pressure acquisition operation at a sampling frequency of 20kHz within the first 50ms after the solenoid valve drive signal is triggered, and obtains the acoustic signal S at each time t.

[0027] After the acquisition is completed, the local control module performs short-time Fourier transform analysis on the acoustic signal S at each time t, and combines it with the preset target frequency range judgment rules to screen out the abnormal and prone frequency bands from the complete spectrum to form the target frequency band set Bres. The target frequency range includes two sub-intervals: 800Hz–1.2kHz and 2kHz–3.5kHz, which are used to capture the acoustic spectrum disturbance characteristics caused by jamming friction and micro-impact behavior.

[0028] Preferably, S2 further includes S23;

[0029] S23. Extract the amplitude-frequency response of the acoustic signal S at each time t in the frequency domain to obtain the current frame acoustic spectrum intensity value of each target frequency point;

[0030] The standard average spectrum under the target frequency band set Bres is obtained by retrieving the reference spectrum curve that matches the current ambient temperature and start-up / shutdown conditions from the historical health sample set built into the device.

[0031] The relative deviation between the current acoustic spectral intensity value and the corresponding reference spectral value is compared point by point at each frequency, and the squares of the deviations are summed to obtain the spectral perturbation deviation value Fvar, which is used to quantitatively reflect the intensity and concentration of acoustic anomalies during the current opening and closing process; the specific calculation form is as follows: Where k represents the frequency index number, fk represents the k-th frequency point, and S(fk) represents the amplitude-frequency response of the current acoustic signal S at the k-th frequency point fk. This represents the amplitude-frequency response of the acoustic signal S of the healthy sample at the k-th frequency point fk;

[0032] The formula for the spectral perturbation deviation value Fvar originates from the frequency domain statistical anomaly detection model. It belongs to the classical signal processing field and is a relative deviation measurement method for comparing the "current frame spectrum" with the "reference spectrum". It is widely used in fault diagnosis, voiceprint analysis and modal recognition.

[0033] This formula is improved in a targeted manner based on the idea of ​​normalized spectral deviation, specifically in the following aspects:

[0034] A key target frequency band set, Bres, is introduced to selectively weight the abnormally sensitive frequency range; the sum of squared relative deviations is used instead of the absolute difference to strengthen the judgment of the concentration of deviations; and the amplitude-frequency response of the acoustic signal S of a healthy sample at the k-th frequency point fk is used. Normalization is performed to make the results more consistent across different devices or backgrounds.

[0035] Preferably, S3 further includes S31;

[0036] S31. The inductance mutation factor index Cimp and the spectral disturbance deviation value Fvar are used as input feature vectors and mapped to the standard scoring space through a nonlinear normalization function, respectively. They are then input into a preset comprehensive opening and closing status scoring model. The comprehensive opening and closing status scoring model establishes a boundary interval in the scoring space through normal state samples and abnormal state samples in the training data, and outputs the comprehensive diagnostic score Qdiag as the health index of the current opening and closing status of the solenoid valve.

[0037] Preferably, S3 further includes S32;

[0038] S32. Based on healthy samples and early fault samples, a statistical threshold is preset for the diagnostic level range. The diagnostic level range includes a first diagnostic threshold F1, a second diagnostic threshold F2, and a third diagnostic threshold F3. The comprehensive diagnostic score Qdiag is compared with the diagnostic level range to determine the opening and closing status of the solenoid valve. An alarm is issued based on the comparison result, and the execution of subsequent control commands is restricted. The specific comparison content is as follows:

[0039] When the comprehensive diagnostic score Qdiag is less than the first diagnostic threshold F1, the health level is classified as Level 1, and normal procedures are followed without any intervention.

[0040] When the first diagnostic threshold F1 ≤ comprehensive diagnostic score Qdiag ≤ second diagnostic threshold F2, the health level is classified as level two. At this time, the current status is marked. If the alarm is triggered three or more times consecutively, an alarm will be triggered.

[0041] When the second diagnostic threshold F2 < the comprehensive diagnostic score Qdiag ≤ the third diagnostic threshold F3, the health level is divided into three levels. At this time, a real-time alarm is triggered, the opening and closing speed is limited to 80%, and the health log is updated.

[0042] When the comprehensive diagnostic score Qdiag > the third diagnostic threshold F3, the health level is classified as level four. At this time, an alarm signal is issued, the solenoid valve control is locked, and maintenance is prompted.

[0043] A solenoid valve opening and closing state measurement system includes an electromagnetic disturbance analysis module, a spectrum disturbance analysis module, and a comprehensive state measurement and control module;

[0044] The electromagnetic disturbance analysis module acquires the dynamic inductance L after the solenoid valve control signal is triggered, and calculates the product of the second derivative and the first derivative of the dynamic inductance L to obtain the inductance mutation factor index Cimp.

[0045] The spectral disturbance analysis module compares the inductance mutation factor index Cimp with the preset mutation threshold Cthr, and triggers the disturbance analysis mechanism based on the comparison result. It collects the instantaneous acoustic signal S during the operation of the solenoid valve, performs Fourier transform processing on the instantaneous acoustic signal S, extracts the target frequency band set Bres, and calculates the spectral disturbance deviation value Fvar.

[0046] The integrated state measurement and control module inputs the inductance mutation factor index Cimp and the spectral disturbance deviation value Fvar into the integrated opening and closing state scoring model to calculate the integrated diagnostic score Qdiag. The module then compares the integrated diagnostic score Qdiag with the preset diagnostic level range to determine the opening and closing state of the solenoid valve, issues an alarm based on the comparison result, and restricts the execution of subsequent control commands.

[0047] This invention provides a system and method for measuring the opening and closing status of a solenoid valve. It has the following advantages:

[0048] (1) This method utilizes a voltage and current sampling module integrated into the drive circuit, combined with an inductance analysis module in the local microcontroller MCU, to back-calculate the dynamic inductance L at each time t after the solenoid valve control signal is triggered. Gaussian filtering and derivative calculation are then performed on the dynamic inductance L in the edge computing module to extract the first and second derivative features, thus calculating the inductance mutation factor index Cimp. This index comprehensively reflects abnormal states such as "stuck, bounce, and discontinuity" that occur during the opening and closing of the solenoid valve. By setting a mutation threshold Cthr and comparing the results, a dynamic opening and closing behavior diagnostic mechanism with high resolution and strong adaptability is constructed. Compared to traditional methods that rely on switch quantity detection or overall current envelope identification, this method can complete high-frequency dynamic response extraction and abnormal trend quantification within 100ms after the control signal is triggered, possessing stronger early warning capabilities and non-intrusive compatibility.

[0049] (2) When the inductance mutation factor index Cimp exceeds the mutation threshold Cthr, this invention automatically triggers a disturbance analysis mechanism. Using a MEMS microphone module installed outside the solenoid valve housing, acoustic signal S is acquired within the first 50ms after the start of the driving action. A short-time Fourier transform is performed on the acoustic signal S by the local control module. The target frequency band set Bres is formed by combining the preset frequency ranges 800Hz–1.2kHz and 2kHz–3.5kHz. The spectral intensity value is then extracted and compared with the standard spectral lines of healthy samples. The spectral disturbance deviation value Fvar is calculated using a normalized relative deviation sum-of-squares model. This value accurately reflects the structural oscillation characteristics caused by valve core jamming or impact, and possesses advantages such as strong robustness, wide adaptability, and good cross-device consistency. It provides a high-confidence acoustic reference signal for subsequent abnormal state diagnosis, significantly improving the accuracy and completeness of fault identification.

[0050] (3) This method inputs the inductance mutation factor index Cimp and the spectral disturbance deviation value Fvar into the comprehensive opening and closing status scoring model, and introduces an improved normalization mapping and square weighting mechanism to construct a comprehensive diagnostic score Qdiag. The scoring model is derived from the Euclidean distance paradigm of the multimodal diagnostic space. After dimensionless normalization, the two indicators are integrated into a unified evaluation system to ensure logical closure and mathematical continuity. At the same time, a diagnostic level range is established based on statistical modeling, including the first diagnostic threshold F1, the second diagnostic threshold F2, and the third diagnostic threshold F3, and a four-level classification of health status and corresponding control strategy response are realized accordingly: for example, the first level does not require intervention, the second level triggers soft alarm logic, the third level implements speed limiting and log updates, and the fourth level directly controls locking and maintenance prompts. This mechanism can realize continuous quantitative health management of the entire process of solenoid valve opening and closing, construct a closed-loop intelligent control system of "diagnosis, evaluation and response", and greatly enhance the system stability and safety. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the steps of a method for measuring the opening and closing status of an electromagnetic valve according to the present invention;

[0052] Figure 2 This is a schematic diagram of the electromagnetic valve opening and closing status measurement system of the present invention;

[0053] Figure 3 This is a curve of the dynamic inductance L. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example 1, please refer to Figure 1 This invention provides a method for measuring the opening and closing state of a solenoid valve. To achieve the above objective, this invention is implemented through the following technical solution, including the following steps:

[0056] S1. After the solenoid valve control signal is triggered, the dynamic inductance L is collected, and the product of the second derivative and the first derivative of the dynamic inductance L is calculated to obtain the inductance mutation factor index Cimp.

[0057] S2. Compare the inductance mutation factor index Cimp with the preset mutation threshold Cthr, and trigger the disturbance analysis mechanism based on the comparison result. Collect the instantaneous acoustic signal S during the operation of the solenoid valve, perform Fourier transform processing on the instantaneous acoustic signal S, extract the target frequency band set Bres, and calculate the spectral disturbance deviation value Fvar.

[0058] S3. Input the inductance mutation factor index Cimp and the spectrum disturbance deviation value Fvar into the comprehensive opening and closing status scoring model to calculate the comprehensive diagnostic score Qdiag. Then, compare the comprehensive diagnostic score Qdiag with the preset diagnostic level range to determine the opening and closing status of the solenoid valve. Based on the comparison result, issue an alarm and restrict the execution of subsequent control commands.

[0059] In this embodiment, the method acquires the dynamic inductance L after the solenoid valve control signal is triggered, and calculates the inductance mutation factor index Cimp by combining the product structure of the first and second derivatives. The purpose is to use the dynamic change trend of the inductance during the opening and closing process to reflect the stability of the valve core movement. The dynamic inductance can sensitively respond to the nonlinear displacement and mutation behavior of the valve core, especially when there is jamming, rebound, or discontinuous opening and closing, it shows a significant derivative mutation, which is far better than the signal hysteresis reflected by the simple current curve. Furthermore, the inductance mutation factor index Cimp is combined with the mutation threshold Cthr generated by the statistics of historical healthy samples. In comparison, the subsequent acoustic disturbance analysis process is only triggered when the inductance mutation factor Cimp is significantly higher than the mutation threshold Cthr. This design effectively avoids unnecessary acoustic spectrum calculations under normal conditions, which helps improve processing efficiency and reduce the probability of false alarms. After the disturbance analysis mechanism is triggered, the acoustic acquisition unit acquires the instantaneous acoustic signal S at a frequency of 20kHz within a 50ms time window before the action, and uses Fourier transform to focus and analyze two sensitive frequency bands: 800Hz–1.2kHz and 2kHz–3.5kHz. These two frequency bands are the acoustic disturbances caused by valve core impact and friction, which have been experimentally verified. The most concentrated frequency region can effectively eliminate the influence of background noise; then, the spectral perturbation deviation value Fvar is obtained by relative deviation calculation, which is used to characterize whether the structural anomaly really exists and its intensity; finally, in S3, the inductance mutation factor index Cimp and the spectral perturbation deviation value Fvar are input into the comprehensive on-off state scoring model for normalization and merging, and the comprehensive diagnostic score Qdiag is output. The higher the score, the stronger the anomaly. The scoring model adopts the L2 norm structure to ensure that any anomaly index can improve the overall score and avoid the neglect of weak correlation features. At the same time, the dimensionless scoring makes it easy to be universal across devices and environments. The comprehensive diagnostic score Qdiag is compared with the set multi-level diagnostic thresholds F1, F2, and F3 to determine the solenoid valve's opening and closing status level and trigger control strategies. When the score falls within different level ranges, marking, alarm, speed limiting, and locking controls are executed respectively. This allows for early detection, quantitative judgment, and intelligent control response of anomalies during the solenoid valve's opening and closing process without altering the hardware structure, through electromagnetic induction and acoustic sensing. This effectively improves the operational stability and safety early warning capabilities of the solenoid valve system, making it particularly suitable for environments with extremely high real-time and reliability requirements, such as rail transit and pneumatic actuators.

[0060] Example 2, please refer to Figure 1 and Figure 3 Specifically: S1 includes S11;

[0061] S11. By integrating a sampling circuit module into the solenoid valve control drive circuit of the air compressor system of rail transit trains and setting a sampling strategy, the voltage and current signals at both ends of the solenoid valve coil are collected in real time.

[0062] The voltage and current signals are transmitted to the inductance analysis module in the local microcontroller MCU, which is synchronously input to the inductance analysis module. Based on the voltage and current signals and the known PWM duty cycle, the dynamic inductance L at each time t is calculated.

[0063] The acquisition strategy is set such that the sampling frequency is no less than 5kHz and the sampling time window covers the interval from 0ms to 100ms after the control signal is triggered.

[0064] The circuit module is positioned on the PWM control signal path in the solenoid valve control drive circuit.

[0065] S1 also includes S12;

[0066] S12. Based on each time t, the dynamic inductance L is integrated into an inductance sequence according to the time order, and the inductance sequence is uploaded to the edge computing module embedded in the control host for analysis and processing. The edge computing module performs a filtering preprocessing on all dynamic inductance L in the inductance sequence through a set denoising processor. The filtering preprocessing uses a Gaussian convolution kernel to smooth all dynamic inductance L in the inductance sequence to reduce the derivative deviation caused by high-frequency electromagnetic interference and mechanical vibration. Then, the smoothed inductance sequence is passed to the derivative calculation module for feature extraction to obtain the first and second derivatives of the dynamic inductance L.

[0067] S1 and S13;

[0068] S13. Based on the first and second derivatives of the dynamic inductance L, calculate the abrupt response factor for any time t within the entire sampling time window, and then extract the maximum value of the abrupt response factor for all times t to obtain the inductance abrupt response factor index Cimp, which is used to measure the maximum nonlinear disturbance intensity exhibited by the solenoid valve during the opening and closing process.

[0069] The inductance mutation factor index Cimp is calculated and output using the following algorithm formula;

[0070] ;

[0071] In this context, max represents the maximum value function, d represents the integral function, and L(t) represents the dynamic inductance L at time t. This represents the slope enhancement adjustment factor, ranging from 0.6 to 1.2;

[0072] To comprehensively consider the coupled effects of velocity changes during valve core movement, i.e. stability and sudden acceleration, i.e. disturbance;

[0073] in: It is the second derivative. It is the first derivative;

[0074] Formula structure explanation: The product structure is chosen by combining the first and second derivatives to incorporate both the degree of abrupt change and the intensity of the trend into the calculation dimension; a slope enhancement adjustment factor is introduced. Used to enhance or suppress the sensitivity of a certain dimension, especially the weight of the first derivative, to the final index; the maximum value extraction strategy is not integral averaging, but selects the maximum response time in the entire sampling interval to reflect the most extreme abrupt change position. This formula belongs to the empirical physics-inspired improved model, which is a targeted design based on the conventional second-order eigenvalue extraction strategy and takes into account the continuous change of the solenoid valve motion.

[0075] Consistency of physical dimensions in the formula: The first and second derivatives are both inductance units, so the units on both sides are consistent. At the same time, this formula is used to construct a relative anomaly factor, not for engineering unit calculation output, so there is no need to achieve absolute physical dimension uniformity, only to ensure the mathematical logic closure.

[0076] The example illustrates that a solenoid valve acquires dynamic inductance L within 0-100ms after the control signal is triggered. After filtering and derivative calculation, it is found that the derivative changes sharply at 67ms. The calculated Cimp=0.032, which is higher than the empirical threshold Cthr=0.025. It is judged that there is a slight abnormal behavior, triggering the subsequent acoustic spectrum recognition mechanism and recording this opening and closing as potential instability.

[0077] In this embodiment, by embedding a sampling circuit module in the control drive circuit of the solenoid valve in the air compressor system of rail transit trains, and setting a sampling frequency higher than 5kHz and covering a time window of 0ms to 100ms, the nonlinear response characteristics at the initial opening and closing stage can be completely captured, avoiding response distortion or feature omission caused by low-frequency sampling. Based on the real-time voltage and current reverse-driven dynamic inductance L, the electromagnetic characteristic changes caused by the physical displacement of the valve core can be reflected more intuitively, replacing the traditional indirect method based on the driving current, and avoiding distortion caused by driving waveform disturbances. Subsequently, Gaussian kernel filtering is used for preprocessing to suppress local high-frequency noise caused by system electromagnetic interference and microstructure jitter, ensuring the smoothness and differentiability of the derivative calculation process, thereby obtaining the first and second derivatives of the dynamic inductance L in the time dimension. The derivative product structure can reflect the velocity change trend and the acceleration abrupt change amplitude. In the joint inclusion index calculation, the strongest abnormal response in the entire process is extracted using the maximum value strategy, effectively avoiding the mean strategy from masking sudden abnormal problems. For example, when the valve core is blocked, the first derivative may decay rapidly, while the second derivative shows a strong reverse jump. The product of the two can amplify the diagnostic features of such key nodes, enabling accurate location of the "opening and closing mutation point" from high-density data. The proposed inductance mutation factor index Cimp not only maintains the consistency of physical dimensions, but also has cross-device generalization ability through normalization processing, thus providing a stable premise for the subsequent acoustic spectrum anomaly triggering mechanism. In summary, this process constructs a structured path from low-cost inductance acquisition to high-fidelity filtering, derivative modeling, and nonlinear mutation identification, improving the accuracy and robustness of early anomaly detection in solenoid valve opening and closing, and is especially suitable for fault precursor identification in high-speed opening and closing or micro-jamming scenarios.

[0078] Example 3, please refer to Figure 1 Specifically: S2 includes S21;

[0079] S21. By selecting N sets of inductor behavior samples under the same temperature, pressure and driving conditions, where the N sets of inductor behavior samples are the inductor mutation factor index Cimp of multiple solenoid valves under historical healthy conditions, the 95th percentile of the inductor mutation factor index Cimp of multiple solenoid valves is set as the mutation threshold Cthr.

[0080] The inductance mutation factor index Cimp acquired in real time is compared with the mutation threshold Cthr to determine the inductance disturbance during the opening and closing process of the solenoid valve; whether the solenoid valve core has "nonlinear mutation behavior" during the opening and closing action, such as the following abnormal states: valve core jamming, valve core rebound and discontinuous opening and closing.

[0081] Among them, the inductance curve corresponding to valve core jamming is characterized by a slowdown or sudden stop in the inductance change in a local segment, which will lead to delay in opening and closing action and risk of start-up control failure.

[0082] The inductance curve corresponding to the valve core rebound shows that the inductance first rises and then falls, forming local oscillations, which can lead to incomplete action, impact fatigue, and reduced lifespan.

[0083] The inductance curve corresponding to discontinuous start-stop is characterized by a sudden change in the second-order derivative and a tendency of the first-order derivative to zero, which will lead to failure of the drive signal response and signs of mechanical structure failure.

[0084] The specific comparison is as follows;

[0085] If the inductor mutation factor Cimp exceeds the mutation threshold Cthr during the current start-up / shutdown cycle, it is determined that the current start-up / shutdown state is abnormal. In this case, the subsequent disturbance analysis mechanism is automatically triggered, and the time of occurrence of the abnormal event is recorded.

[0086] If the inductance mutation factor Cimp in the current start-up / shutdown cycle is less than or equal to Cthr, the current start-up / shutdown state is judged to be normal, automatically recorded as a healthy start-up / shutdown cycle, and the disturbance analysis mechanism is skipped to continue waiting for the next control cycle.

[0087] S2 also includes S22;

[0088] S22. After the disturbance analysis mechanism is triggered, the acoustic signal S is collected by the MEMS micro microphone module set outside the solenoid valve housing. The microphone module performs sound pressure acquisition operation at a sampling frequency of 20kHz within the first 50ms after the solenoid valve drive signal is triggered, and obtains the acoustic signal S at each time t.

[0089] After the acquisition is completed, the local control module performs short-time Fourier transform analysis on the acoustic signal S at each time t, and combines it with the preset target frequency range judgment rules to screen out the abnormal and prone frequency bands from the complete spectrum, forming the target frequency band set Bres. The target frequency range includes two sub-intervals: 800Hz–1.2kHz and 2kHz–3.5kHz, which are used to capture the acoustic spectrum disturbance characteristics caused by jamming friction and micro-impact behavior.

[0090] S2 also includes S23;

[0091] S23. Extract the amplitude-frequency response of the acoustic signal S at each time t in the frequency domain to obtain the current frame acoustic spectrum intensity value of each target frequency point;

[0092] The standard average spectrum under the target frequency band set Bres is obtained by retrieving the reference spectrum curve that matches the current ambient temperature and start-up / shutdown conditions from the historical health sample set built into the device.

[0093] The relative deviation between the current acoustic spectral intensity value and the corresponding reference spectral value is compared point by point at each frequency, and the squares of the deviations are summed to obtain the spectral perturbation deviation value Fvar, which is used to quantitatively reflect the intensity and concentration of acoustic anomalies during the current opening and closing process; the specific calculation form is as follows: Where k represents the frequency index number, fk represents the k-th frequency point, and S(fk) represents the amplitude-frequency response of the current acoustic signal S at the k-th frequency point fk. This represents the amplitude-frequency response of the acoustic signal S of the healthy sample at the k-th frequency point fk;

[0094] The formula for the spectral perturbation deviation value Fvar originates from the frequency domain statistical anomaly detection model. It belongs to the classical signal processing field and is a relative deviation measurement method for comparing the "current frame spectrum" with the "reference spectrum". It is widely used in fault diagnosis, voiceprint analysis and modal recognition.

[0095] This formula is improved in a targeted manner based on the idea of ​​normalized spectral deviation, specifically in the following aspects:

[0096] A key target frequency band set, Bres, is introduced to selectively weight the abnormally sensitive frequency range; the sum of squared relative deviations is used instead of the absolute difference to strengthen the judgment of the concentration of deviations; and the amplitude-frequency response of the acoustic signal S of a healthy sample at the k-th frequency point fk is used. Normalization is performed to make the results more consistent across different devices or backgrounds;

[0097] Simplified calculation example:

[0098] Assumption:

[0099] After short-time Fourier transform analysis, 10 frequency points were obtained in the target frequency band set Bres band;

[0100] The corresponding health spectrum is [100, 102, 98, ...];

[0101] The current frame spectrum is [110, 115, 97, ...];

[0102] Calculate the square of the relative deviation at each point and sum them up to get Fvar = 0.064;

[0103] If the empirical threshold Fthr = 0.040, it is determined that there is an abnormal structural disturbance.

[0104] In this embodiment, the method first constructs multiple sets of benchmark behavior samples by collecting the inductance mutation factor index Cimp of the solenoid valve under the same temperature, pressure, and drive conditions in historical healthy states. A mutation threshold Cthr is set using a 95th percentile statistical strategy, which effectively avoids disturbances to the threshold by extreme values ​​or occasional fluctuations, improving the robustness and universality of the threshold. This provides a clear health reference baseline for subsequent comparisons between Cimp and Cthr. For example, different batches of solenoid valves may have manufacturing tolerances or microstructural differences. By learning from a large sample under the same operating conditions and setting the 95th percentile as Cthr, normal fluctuations can be fully accommodated, ensuring that anomaly detection does not result in false alarms. Once the real-time collected inductance mutation factor index Cimp exceeds the mutation threshold Cthr, it can be inferred that the valve core movement trajectory may exhibit nonlinear disturbance behavior, such as jamming, rebound, or intermittent opening and closing, which are complex mechanical anomalies. Jamming is often caused by valve cavity contamination or seal aging, while rebound is mostly caused by valve core inertial impact or elastic component reaction. Discontinuous opening and closing may be due to interruption of drive logic or disengagement of the mechanism. If such behavior is not identified in time, it will directly affect the accuracy of the solenoid valve's operation, thereby causing delays or even loss of control in the train's air compressor control. To further confirm whether the aforementioned inductance mutation is accompanied by structural physical anomalies, an acoustic disturbance analysis mechanism is automatically triggered when Cimp exceeds the limit. This mechanism uses a MEMS microphone module deployed outside the solenoid valve housing to collect acoustic signals S with a high-frequency accuracy of 20kHz within a 50ms time window before opening and closing, thereby capturing acoustic features generated by events such as friction, impact, and jamming during high-speed operation. Fourier transform is used to convert the time signal into the frequency domain, and the target frequency band set Bres is constructed by combining two anomalous sensitive frequency bands, 800Hz–1.2kHz and 2kHz–3.5kHz, which can directionally enhance the identification capability of high-probability anomalies such as structural resonance and frictional oscillation. Subsequently, the system compares the amplitude of the current frame's acoustic spectrum at the target frequency point with the baseline spectrum of historical healthy samples at the same frequency point frequency-by-frequency, and obtains the spectral disturbance deviation value Fvar by summing the squares of the relative deviations. This approach uses the squared relative deviation instead of the absolute difference. This strengthens the influence of points with large deviations, highlighting areas of concentrated anomalies, and eliminates the cancellation error between positive and negative directions, improving overall anomaly sensitivity. Especially when a frequency point is significantly higher than the reference spectrum in the current frame, its squared deviation will nonlinearly amplify the Fvar value, thus amplifying the diagnostic sensitivity to features such as stuck impacts. For example, if the actual acoustic spectrum is 15% higher than the reference spectrum at 2.8kHz, while other points have smaller deviations, this frequency point will dominate the final Fvar value, directly driving the system to identify it as a structural disturbance and triggering anomaly labeling. Compared to traditional models based on total energy or absolute difference, this strategy performs better in identifying weak but critical frequency band anomalies. In summary, through the joint diagnosis of Cimp and Fvar, cross-domain fusion monitoring from non-contact electromagnetic behavior to structural acoustic response is achieved.Its design not only significantly improves the dimensions and accuracy of solenoid valve opening and closing health status assessment at low cost and without intrusion, but also ensures good robustness under complex working conditions, making it particularly suitable for high-speed train air compressor control systems with extremely high requirements for real-time performance and safety.

[0105] Example 4, please refer to Figure 1 Specifically: S3 also includes S31;

[0106] S31. The inductance mutation factor index Cimp and the spectrum disturbance deviation value Fvar are used as input feature vectors and mapped to the standard scoring space through a nonlinear normalization function, respectively. They are then input into the preset comprehensive opening and closing state scoring model. The comprehensive opening and closing state scoring model establishes boundary intervals in the scoring space through normal state samples and abnormal state samples in the training data, and outputs a comprehensive diagnostic score Qdiag as the health index of the current opening and closing state of the solenoid valve. The lower the score, the higher the abnormal risk. The comprehensive opening and closing state scoring model constructs an opening and closing state distribution fitting network by using an improved radial basis kernel function. The inductance mutation factor index Cimp is used to characterize the nonlinear abnormal trend of the valve core mechanical dynamics, and the spectrum disturbance deviation value Fvar is used to characterize the energy shift or jamming harmonic abnormality of the structural acoustics.

[0107] The comprehensive diagnostic score Qdiag is calculated using the following comprehensive on / off status scoring model;

[0108] ;

[0109] In the formula, Fthr represents the spectral perturbation threshold, which is obtained from the statistical mean of the normal sample distribution;

[0110] The comprehensive diagnostic score Qdiag is built upon the outputs of two innovative modules: the inductance mutation factor index Cimp, which measures the discontinuity or abrupt trend of the valve core's motion trajectory; and the spectral disturbance deviation value Fvar, which identifies potential jamming or frictional oscillation disturbances in the acoustic band.

[0111] The original physical foundation formulas come from: the derivative operation principle of the relationship between the inductance derivative and time, the basic laws of electromagnetic induction; the calculation of amplitude shift and energy distribution based on Fourier transform to extract frequency domain response, the principle of signal processing; the anomaly scoring method based on normalized deviation value calculation in Euclidean space, and the multimodal fusion scoring model.

[0112] The sum-of-squares structure originates from Euclidean distance and the L2 norm model. It is often used to jointly evaluate two or more orthogonal or weakly correlated feature variables. The reasons for squaring are: to amplify deviations, as stronger anomalies have a greater impact; to eliminate negative interference and ensure that the total score is positive; and to maintain mathematical differentiability and continuity, facilitating subsequent fitting analysis. The additive structure indicates that the two indicators are parallel factors, and an anomaly in either one will increase the overall score; it avoids the problem of the multiplicative structure where the overall score fails if one indicator is zero.

[0113] Verification of dimensional consistency: Both terms in the fraction are dimensionless ratios, and the squaring operation retains the dimensionless value. Therefore, the comprehensive diagnostic score Qdiag is a dimensionless scalar, which is suitable for threshold comparison. The dimensions on both sides of the formula are consistent, and the formula structure is reasonable.

[0114] S3 also includes S32;

[0115] S32. Based on healthy samples and early fault samples, a statistical threshold is preset for the diagnostic level range. The diagnostic level range includes a first diagnostic threshold F1, a second diagnostic threshold F2, and a third diagnostic threshold F3. The comprehensive diagnostic score Qdiag is compared with the diagnostic level range to determine the opening and closing status of the solenoid valve. An alarm is issued based on the comparison result, and the execution of subsequent control commands is restricted. The specific comparison content is as follows:

[0116] When the comprehensive diagnostic score Qdiag < the first diagnostic threshold F1, the health level is classified as Level 1, which is normal execution without any intervention. The signal is disturbed, but the opening and closing state is not abnormal and the opening and closing is stable.

[0117] When the first diagnostic threshold F1 ≤ comprehensive diagnostic score Qdiag ≤ second diagnostic threshold F2, the health level is classified as level two. At this time, the current status is marked. If the alarm is triggered three or more times consecutively, an alarm is triggered. To avoid occasional misjudgment or occasional abnormal accidental triggering, soft decision logic is adopted.

[0118] When the second diagnostic threshold F2 < the comprehensive diagnostic score Qdiag ≤ the third diagnostic threshold F3, the health level is classified as level three. At this time, a real-time alarm is triggered, the opening and closing speed is limited to 80%, the health log is updated, indicating that there is structural viscosity or harmonic anomaly, and the risk has increased.

[0119] When the comprehensive diagnostic score Qdiag > the third diagnostic threshold F3, the health level is classified as level four. At this time, an alarm signal is issued, the solenoid valve control is locked, and maintenance is prompted. The valve core may have structural damage or a high probability of jamming, and intervention is necessary.

[0120] Among them: the first diagnostic threshold F1 is the health confidence boundary, the second diagnostic threshold F2 is the minor fault trigger threshold, and the third diagnostic threshold F3 is the obvious stuck abnormality indicator.

[0121] In this embodiment, the inductance mutation factor index Cimp and the spectral perturbation deviation value Fvar are input into the comprehensive on / off state scoring model. First, the two feature indices are nonlinearly normalized to eliminate scoring bias caused by differences in physical magnitude and distribution, ensuring that diagnostic factors from different sources can be fused and compared within the same standardized space. The comprehensive scoring model using a sum-of-squares structure amplifies the contribution of outliers to the overall score, making it particularly suitable for high-sensitivity identification of early, subtle faults. Simultaneously, an improved radial basis function establishes a nonlinear mapping relationship between the feature space and the on / off health state, enabling the model to possess not only good fitting accuracy but also a certain degree of anomaly generalization ability, maintaining effective discrimination even when facing unknown or boundary-type behaviors. The strategy of setting a three-level diagnostic grade range enables multi-level health state determination, avoiding the indiscriminate classification of all deviations as faults. For example, if the system's comprehensive diagnostic score Qdiag is consistently below the first diagnostic threshold F1, it indicates that the solenoid valve is in a fully healthy state and requires no intervention. When the score is between F1 and F2, edge disturbances or occasional interference may exist. By setting a soft-decision mechanism that triggers an alarm only after three consecutive triggers, misjudgments caused by instantaneous environmental noise or changes in operating conditions can be effectively avoided. Once the comprehensive diagnostic score Qdiag exceeds the second diagnostic threshold F2, it indicates that the system may have mechanical viscosity or structural harmonic anomalies. Therefore, a speed limiting and log recording mechanism is immediately activated to prevent the fault from escalating. When the score further exceeds the third diagnostic threshold F3, it is considered a serious fault, and control commands are immediately interrupted and maintenance is prompted to ensure the safety of equipment and personnel. The overall strategy aims to achieve dynamic perception and graded response throughout the entire process of the solenoid valve's opening and closing state, from normal to fault, by constructing a multimodal input and multi-level response mechanism. This significantly improves the real-time performance and accuracy of fault detection and effectively reduces the false alarm rate and the risk of missed detection, making it particularly suitable for scenarios with extremely high requirements for response speed and safety, such as rail transit.

[0122] Example 5, please refer to Figure 1 and Figure 2 A solenoid valve opening and closing state measurement system includes an electromagnetic disturbance analysis module, a spectrum disturbance analysis module, and a comprehensive state measurement and control module.

[0123] The electromagnetic disturbance analysis module acquires the dynamic inductance L after the solenoid valve control signal is triggered, and calculates the product of the second derivative and the first derivative of the dynamic inductance L to obtain the inductance mutation factor index Cimp.

[0124] The spectral disturbance analysis module compares the inductance mutation factor index Cimp with the preset mutation threshold Cthr, and triggers the disturbance analysis mechanism based on the comparison result. It collects the instantaneous acoustic signal S during the operation of the solenoid valve, performs Fourier transform processing on the instantaneous acoustic signal S, extracts the target frequency band set Bres, and calculates the spectral disturbance deviation value Fvar.

[0125] The integrated state measurement and control module inputs the inductance mutation factor index Cimp and the spectral disturbance deviation value Fvar into the integrated opening and closing state scoring model to calculate the integrated diagnostic score Qdiag. The module then compares the integrated diagnostic score Qdiag with the preset diagnostic level range to determine the opening and closing state of the solenoid valve, issues an alarm based on the comparison result, and restricts the execution of subsequent control commands.

[0126] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for measuring the open / closed state of a solenoid valve, characterized in that: Includes the following steps: S1. After the solenoid valve control signal is triggered, the dynamic inductance L is collected, and the product of the second derivative and the first derivative of the dynamic inductance L is calculated to obtain the inductance mutation factor index Cimp. S2. Compare the inductance mutation factor index Cimp with the preset mutation threshold Cthr, and trigger the disturbance analysis mechanism based on the comparison result. Collect the instantaneous acoustic signal S during the operation of the solenoid valve, perform Fourier transform processing on the instantaneous acoustic signal S, extract the target frequency band set Bres, and calculate the spectral disturbance deviation value Fvar. S3. Input the inductance mutation factor index Cimp and the spectrum disturbance deviation value Fvar into the comprehensive opening and closing status scoring model to calculate the comprehensive diagnostic score Qdiag. Then, compare the comprehensive diagnostic score Qdiag with the preset diagnostic level range to determine the opening and closing status of the solenoid valve. Based on the comparison result, issue an alarm and restrict the execution of subsequent control commands.

2. The method for measuring the opening and closing state of a solenoid valve according to claim 1, characterized in that: S1 includes S11; S11. By integrating a sampling circuit module into the solenoid valve control drive circuit of the air compressor system of rail transit trains and setting a sampling strategy, the voltage and current signals at both ends of the solenoid valve coil are collected in real time. The voltage and current signals are transmitted to the inductance analysis module in the local microcontroller MCU, which is synchronously input to the inductance analysis module. Based on the voltage and current signals and the known PWM duty cycle, the dynamic inductance L at each time t is calculated. The acquisition strategy is set such that the sampling frequency is not less than 5kHz and the sampling time window covers the interval from 0ms to 100ms after the control signal is triggered. The circuit module is positioned on the PWM control signal path in the solenoid valve control drive circuit.

3. The method for measuring the opening and closing state of a solenoid valve according to claim 2, characterized in that: S1 further includes S12; S12. Based on each time t, the dynamic inductance L is integrated into an inductance sequence according to the time order, and the inductance sequence is uploaded to the edge computing module embedded in the control host for analysis and processing. The edge computing module performs a filtering preprocessing on all dynamic inductance L in the inductance sequence through a set denoising processor. The filtering preprocessing uses a Gaussian convolution kernel to smooth all dynamic inductance L in the inductance sequence to reduce the derivative deviation caused by high-frequency electromagnetic interference and mechanical jitter. Then, the smoothed inductance sequence is passed to the derivative calculation module for feature extraction to obtain the first and second derivatives of the dynamic inductance L.

4. The method for measuring the opening and closing state of a solenoid valve according to claim 3, characterized in that: S1 and S13; S13. Based on the first and second derivatives of the dynamic inductance L, calculate the abrupt change response factor for any time t within the entire sampling time window, and then extract the maximum value of the abrupt change response factor for all times t to obtain the inductance abrupt change factor index Cimp, which measures the maximum nonlinear disturbance intensity exhibited by the solenoid valve during the opening and closing process.

5. The method for measuring the opening and closing state of a solenoid valve according to claim 4, characterized in that: S2 includes S21; S21. By selecting N sets of inductor behavior samples under the same temperature, pressure and driving conditions, where the N sets of inductor behavior samples are the inductor mutation factor index Cimp of multiple solenoid valves under historical healthy conditions, the 95th percentile of the inductor mutation factor index Cimp of multiple solenoid valves is set as the mutation threshold Cthr. The inductance disturbance during the opening and closing process of the solenoid valve is determined by comparing the real-time acquired inductance mutation factor index Cimp with the mutation threshold Cthr. The specific comparison is as follows; If the inductor mutation factor index Cimp in the current start-up / shutdown cycle exceeds the mutation threshold Cthr, it is determined that the current start-up / shutdown state is abnormal, and the subsequent disturbance analysis mechanism is automatically triggered, and the time of occurrence of the abnormal event is recorded. If the inductance mutation factor Cimp in the current start-up / shutdown cycle is less than or equal to Cthr, the current start-up / shutdown state is judged to be normal, automatically recorded as a healthy start-up / shutdown cycle, and the disturbance analysis mechanism is skipped to continue waiting for the next control cycle.

6. The method for measuring the opening and closing state of a solenoid valve according to claim 1, characterized in that: S2 further includes S22; S22. After the disturbance analysis mechanism is triggered, the acoustic signal S is collected by a MEMS micro microphone module located outside the solenoid valve housing. The microphone module performs sound pressure acquisition operation at a sampling frequency of 20kHz within the first 50ms after the solenoid valve drive signal is triggered, and obtains the acoustic signal S at each time t. After the acquisition is completed, the local control module performs short-time Fourier transform analysis on the acoustic signal S at each time t, and combines it with the preset target frequency range judgment rules to screen out the abnormal and prone frequency bands from the complete spectrum to form the target frequency band set Bres. The target frequency range includes two sub-intervals: 800Hz–1.2kHz and 2kHz–3.5kHz, which are used to capture the acoustic spectrum disturbance characteristics caused by jamming friction and micro-impact behavior.

7. The method for measuring the opening and closing state of a solenoid valve according to claim 6, characterized in that: S2 also includes S23; S23. Extract the amplitude-frequency response of the acoustic signal S at each time t in the frequency domain to obtain the current frame acoustic spectrum intensity value of each target frequency point; The standard average spectrum under the target frequency band set Bres is obtained by retrieving the reference spectrum curve that matches the current ambient temperature and start-up / shutdown conditions from the historical health sample set built into the device. The relative deviation between the current acoustic spectrum intensity value and the corresponding reference spectrum value is compared at each frequency point, and the squares of the deviations are summed to obtain the spectrum disturbance deviation value Fvar, which quantitatively reflects the intensity and concentration of acoustic anomalies during the current opening and closing process.

8. The method for measuring the opening and closing state of a solenoid valve according to claim 6, characterized in that: S3 also includes S31; S31. The inductance mutation factor index Cimp and the spectral disturbance deviation value Fvar are used as input feature vectors and mapped to the standard scoring space through a nonlinear normalization function, respectively. They are then input into a preset comprehensive opening and closing status scoring model. The comprehensive opening and closing status scoring model establishes a boundary interval in the scoring space through normal state samples and abnormal state samples in the training data, and outputs the comprehensive diagnostic score Qdiag as the health index of the current opening and closing status of the solenoid valve.

9. A method for measuring the opening and closing state of a solenoid valve according to claim 8, characterized in that: S3 further includes S32; S32. Based on healthy samples and early fault samples, a statistical threshold is preset for the diagnostic level range. The diagnostic level range includes a first diagnostic threshold F1, a second diagnostic threshold F2, and a third diagnostic threshold F3. The comprehensive diagnostic score Qdiag is compared with the diagnostic level range to determine the opening and closing status of the solenoid valve. An alarm is issued based on the comparison result, and the execution of subsequent control commands is restricted. The specific comparison content is as follows: When the comprehensive diagnostic score Qdiag is less than the first diagnostic threshold F1, the health level is classified as Level 1, and normal procedures are followed without any intervention. When the first diagnostic threshold F1 ≤ comprehensive diagnostic score Qdiag ≤ the second diagnostic threshold F2, the health level is classified as level two. At this time, the current status is marked. If the alarm is triggered three or more times consecutively, an alarm will be triggered. When the second diagnostic threshold F2 < the comprehensive diagnostic score Qdiag ≤ the third diagnostic threshold F3, the health level is divided into three levels. At this time, a real-time alarm is triggered, the opening and closing speed is limited to 80%, and the health log is updated. When the comprehensive diagnostic score Qdiag > the third diagnostic threshold F3, the health level is classified as level four. At this time, an alarm signal is issued, the solenoid valve control is locked, and maintenance is prompted.

10. A solenoid valve opening / closing state measurement system, applied to the solenoid valve opening / closing state measurement method according to any one of claims 1-9, characterized in that: It includes an electromagnetic disturbance analysis module, a spectrum disturbance analysis module, and a comprehensive state measurement and control module; The electromagnetic disturbance analysis module acquires the dynamic inductance L after the solenoid valve control signal is triggered, and calculates the product of the second derivative and the first derivative of the dynamic inductance L to obtain the inductance mutation factor index Cimp. The spectral disturbance analysis module compares the inductance mutation factor index Cimp with the preset mutation threshold Cthr, and triggers the disturbance analysis mechanism based on the comparison result. It collects the instantaneous acoustic signal S during the operation of the solenoid valve, performs Fourier transform processing on the instantaneous acoustic signal S, extracts the target frequency band set Bres, and calculates the spectral disturbance deviation value Fvar. The integrated state measurement and control module inputs the inductance mutation factor index Cimp and the spectral disturbance deviation value Fvar into the integrated opening and closing state scoring model to calculate the integrated diagnostic score Qdiag. The module then compares the integrated diagnostic score Qdiag with the preset diagnostic level range to determine the opening and closing state of the solenoid valve, issues an alarm based on the comparison result, and restricts the execution of subsequent control commands.

Images