Electromagnetic valve fault diagnosis method and system, storage medium and program product

Abstract

The invention discloses an electromagnetic valve fault diagnosis method and system, a storage medium and a program product, and relates to the field of magnetic variable measurement, in the method, an alternating current signal of a first preset frequency is applied to an electromagnetic valve coil, an inherent working current signal is collected, and a mixed current signal is generated; separating the mixed current signal to obtain a harmonic component; determining a target frequency based on the harmonic component; generating an excitation current signal of a second preset frequency according to the target frequency; applying an excitation current signal to a solenoid valve coil, and collecting a resonance current signal generated by the solenoid valve under the excitation current signal; determining the resonance characteristic of the internal mechanical structure of the electromagnetic valve according to the amplitude and the phase characteristic of the resonance current signal; and identifying the fault type of the electromagnetic valve based on the change trend of the resonance characteristic along with time. The method is used for improving the accuracy of identifying the evolution trend of the fault features, further preventing possible faults of the electromagnetic valve in advance, and improving the safety of the electromagnetic valve in the using process.

Description

Technical Field

[0001] The present application belongs to the field of measuring magnetic variables, and in particular, relates to a solenoid valve fault diagnosis method, system, storage medium and program product. Background Art

[0002] As a key actuator in the fluid control system, the reliability of the solenoid valve directly affects the safe and stable operation of the entire system. In practical applications, since the solenoid valve is in a high-frequency working state for a long time, its internal parts are prone to mechanical wear, stagnation, leakage and other faults. If these faults are not discovered and handled in time, they may cause abnormal operation of the system or even cause major safety accidents. Traditional solenoid valve fault diagnosis methods mainly rely on manual regular inspections and experience judgments, which are not only inefficient, but also prone to missed detections and misjudgments.

[0003] In related technologies, the current signal during the operation of the solenoid valve can be collected and matched with a preset fault feature template for analysis, thereby realizing the identification and location of the solenoid valve fault type. This method can realize automatic diagnosis of solenoid valve faults to a certain extent, reduce the workload of manual detection, and improve the accuracy of fault diagnosis.

[0004] However, when the solenoid valve is in a state of frequent start-stop operation, its transient action characteristics will gradually change with the increase of continuous working time. This gradual change may hide early signs of failure. However, since the solenoid valve has not yet shown obvious performance degradation, the existing diagnostic method based on fixed thresholds is difficult to capture the subtle evolution trend of fault characteristics, resulting in missing the best preventive maintenance opportunity, and ultimately reducing the safety of the solenoid valve during use. Summary of the invention

[0005] The present application provides a solenoid valve fault diagnosis method, system, storage medium and program product for improving the accuracy of identifying the evolution trend of fault characteristics, thereby preventing possible faults of the solenoid valve in advance and improving the safety of the solenoid valve during use.

[0006] In a first aspect, the present application provides a solenoid valve fault diagnosis method, in which, when the solenoid valve is in normal working state, an alternating current signal of a first preset frequency is applied to the solenoid valve coil, and an inherent working current signal of the solenoid valve is collected at the same time to generate a mixed current signal; Separate the mixed current signal to obtain the harmonic components in the inherent working current signal; determining a target frequency that interferes with the first preset frequency based on the harmonic component; generating an excitation current signal of a second preset frequency according to the target frequency, wherein a difference between the second preset frequency and the target frequency is within a preset range; Applying an excitation current signal to the solenoid valve coil, and collecting a resonant current signal generated by the solenoid valve under the excitation current signal; Determine the resonance characteristics of the internal mechanical structure of the solenoid valve based on the amplitude and phase characteristics of the resonance current signal; The fault type of the solenoid valve is identified based on the changing trend of the resonance characteristics over time.

[0007] By adopting the above technical solution, by applying an alternating current signal of a first preset frequency to the solenoid valve coil and mixing it with the inherent working current signal, the dynamic response characteristics of the internal mechanical structure of the solenoid valve can be stimulated. The harmonic component is obtained by separating the mixed current signal, and then the target frequency is determined, so that the second preset frequency excitation signal applied subsequently can resonate with the internal mechanical structure of the solenoid valve. In the resonant state, the slight changes in the internal mechanical structure of the solenoid valve will be significantly amplified, which is reflected in the amplitude and phase characteristics of the resonant current signal. Over time, different types of faults will cause the resonance characteristics to show different changing trends, and there is a definite corresponding relationship between this changing trend and the fault type. By analyzing the changing trend of the resonance characteristics, the fault type of the solenoid valve can be accurately identified, and the early diagnosis of the solenoid valve fault can be achieved, which improves the accuracy of identifying the evolution trend of the fault characteristics, thereby preventing possible faults of the solenoid valve in advance and improving the safety of the solenoid valve during use.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the mixed current signal is separated to obtain the harmonic component in the inherent working current signal, specifically including: Perform wavelet decomposition on the mixed current signal to obtain wavelet coefficients of multiple frequency bands; Calculate the energy distribution characteristics of wavelet coefficients and determine the frequency range of energy concentration; The signal in the frequency range is reconstructed to obtain the harmonic components in the inherent working current signal.

[0009] By adopting the above technical solution and processing the mixed current signal by wavelet decomposition, multi-resolution analysis of the signal can be realized in both the time domain and the frequency domain. Since the wavelet basis function has good time-frequency localization characteristics, it can accurately capture the mutation components and transient characteristics in the signal. By calculating the energy distribution characteristics of the wavelet coefficients, the aggregation of signal energy in different frequency bands can be found, and the frequency interval containing fault characteristics can be effectively identified. By reconstructing the signal in this frequency interval, the harmonic components in the inherent working current signal can be accurately extracted, and the interference of noise and irrelevant signals can be removed.

[0010] In combination with some embodiments of the first aspect, in some embodiments, determining a target frequency that interferes with the first preset frequency based on the harmonic component specifically includes: Construct the time-frequency distribution diagram of harmonic components; Identify frequency points in the time-frequency distribution diagram where the signal energy density is greater than a preset threshold; Sort the frequency points according to the signal energy density, and select a preset number of frequency points with the largest energy density as candidate frequencies; Eliminate the harmonic frequencies generated by the fundamental frequency from the candidate frequencies; The frequencies among the remaining frequency points that interfere with the first preset frequency are determined as the target frequencies.

[0011] By adopting the above technical solution and constructing a time-frequency distribution diagram of the harmonic components, the change pattern of signal energy over time and frequency can be intuitively displayed. By screening out frequency points with high energy density based on a preset threshold and sorting them by energy density, the main frequency components related to the fault characteristics can be effectively identified. By eliminating the harmonic frequencies generated by the fundamental frequency, the interference of these inherent frequencies on the identification of fault characteristics can be reduced. The target frequency that interferes with the first preset frequency is determined among the remaining frequency points, ensuring that the subsequently applied excitation signal can effectively interact with the fault characteristic frequency. This frequency screening method improves the extraction accuracy of fault characteristics, reduces the interference of irrelevant frequency components, and makes the fault diagnosis results more accurate and reliable.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after identifying the fault type of the solenoid valve based on the change trend of the resonance characteristic over time, the method further includes: sequentially applying an excitation current signal of a second preset frequency to adjacent solenoid valves connected in series or in parallel with the solenoid valve; Collecting resonance current signals of adjacent solenoid valves; calculating a transfer function between the resonant current signals of the solenoid valve and each adjacent solenoid valve; Identify the propagation path of fault characteristics based on the transfer function.

[0013] By adopting the above technical solution, by applying excitation signals to adjacent solenoid valves and collecting resonant current signals, the calculated transfer function reflects the propagation characteristics of the fault characteristics in the solenoid valve system. The transfer function contains the amplitude attenuation and phase delay information of the fault characteristics during the propagation process, and can accurately describe the spatial distribution law of the fault impact. Based on the transfer function to identify the propagation path of the fault characteristics, the location of the fault source can be traced and the diffusion range of the fault impact can be understood. This propagation path analysis method enables the system to accurately locate the fault source in a complex system with multiple solenoid valves in series or parallel, thereby improving the accuracy and reliability of fault diagnosis.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, identifying a propagation path of a fault feature based on a transfer function specifically includes: Extract the magnitude and phase spectra of the transfer function; Calculate the amplitude attenuation coefficient and phase delay between adjacent solenoid valves; Construct the spatial transfer matrix of fault characteristics according to the attenuation coefficient and phase delay; Analyze the singular values ​​of the spatial transfer matrix to determine the spatial location of the fault source.

[0015] By adopting the above technical scheme, by extracting the amplitude spectrum and phase spectrum of the transfer function, calculating the amplitude attenuation coefficient and phase delay between adjacent solenoid valves, constructing the spatial transfer matrix of the fault characteristics, and analyzing the singular values ​​of the spatial transfer matrix to determine the spatial position of the fault source, the propagation law of the fault characteristics in the solenoid valve system can be accurately tracked, thereby improving the accuracy and reliability of fault source positioning.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after identifying the propagation path of the fault feature based on the transfer function, the method further includes: Select key nodes on the propagation path of fault characteristics; Adjusting the second preset frequency of the solenoid valve at the key node so that the solenoid valve and the second preset frequency of the fault source solenoid valve form an anti-phase relationship; The propagation of fault signatures is suppressed by anti-phase relationship.

[0017] By adopting the above technical solution, after identifying the propagation path of the fault feature, the propagation of the fault feature can be suppressed by selecting key nodes on the propagation path and adjusting the second preset frequency of the solenoid valve at these nodes so that it forms an anti-phase relationship with the frequency of the fault source solenoid valve. When the fault feature is transmitted along the propagation path, the anti-phase signal at the key node will interfere with the fault feature in phase, weakening the propagation intensity of the fault feature. This active suppression method based on the propagation path can form an effective suppression barrier at the key position of the fault feature propagation, blocking the fault feature from spreading to other parts of the system.

[0018] In combination with some embodiments of the first aspect, in some embodiments, suppressing the propagation of fault characteristics by using an anti-phase relationship specifically includes: Calculate the amplitude and phase of fault characteristics at key nodes; generating a compensation signal of equal amplitude and opposite phase; The compensation signal is superimposed on the excitation current signal of the solenoid valve at the key node.

[0019] By adopting the above technical solution, by calculating the amplitude and phase of the fault characteristics at the key node, a compensation signal with equal amplitude but opposite phase is generated, and the compensation signal is superimposed on the excitation current signal of the solenoid valve at the key node, the fault characteristics are accurately suppressed. The amplitude of the compensation signal is equal to the fault characteristic, which ensures that the energy of the fault characteristic can be completely offset, and the opposite phase ensures that the two can completely destructively interfere. Superimposing the compensation signal on the original excitation current signal enables the solenoid valve to have the ability to suppress faults while performing normal control functions, without affecting the normal operation of the system, and improving the accuracy and effectiveness of fault suppression.

[0020] In the second aspect, an embodiment of the present application provides a solenoid valve fault diagnosis system, which solenoid valve fault diagnosis system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a solenoid valve fault diagnosis method, which can stimulate the dynamic response characteristics of the internal mechanical structure of the solenoid valve by applying an alternating current signal of a first preset frequency to the solenoid valve coil and mixing it with the inherent working current signal. The harmonic component is obtained by separating the mixed current signal, and then the target frequency is determined, so that the second preset frequency excitation signal applied subsequently can resonate with the internal mechanical structure of the solenoid valve. In the resonant state, slight changes in the internal mechanical structure of the solenoid valve will be significantly amplified, which is reflected in the amplitude and phase characteristics of the resonant current signal. Over time, different types of faults will cause the resonance characteristics to show different changing trends, and there is a definite corresponding relationship between this changing trend and the fault type. By analyzing the changing trend of the resonance characteristics, the fault type of the solenoid valve can be accurately identified, and the early diagnosis of the solenoid valve fault can be achieved, which improves the accuracy of identifying the evolution trend of the fault characteristics, thereby preventing possible faults of the solenoid valve in advance and improving the safety of the solenoid valve during use.

[0024] 2. The present application provides a solenoid valve fault diagnosis method, which applies excitation signals to adjacent solenoid valves and collects resonant current signals. The calculated transfer function reflects the propagation characteristics of the fault characteristics in the solenoid valve system. The transfer function contains the amplitude attenuation and phase delay information of the fault characteristics during the propagation process, and can accurately describe the spatial distribution law of the fault impact. Based on the transfer function to identify the propagation path of the fault characteristics, the location of the fault source can be traced and the diffusion range of the fault impact can be understood. This propagation path analysis method enables the system to accurately locate the fault source in a complex system with multiple solenoid valves connected in series or parallel, thereby improving the accuracy and reliability of fault diagnosis.

[0025] 3. The present application provides a solenoid valve fault diagnosis method. After identifying the propagation path of the fault feature, the propagation of the fault feature can be suppressed by selecting key nodes on the propagation path and adjusting the second preset frequency of the solenoid valve at these nodes so that it forms an anti-phase relationship with the frequency of the fault source solenoid valve. When the fault feature is transmitted along the propagation path, the anti-phase signal at the key node will interfere with the fault feature in phase, weakening the propagation intensity of the fault feature. This active suppression method based on the propagation path can form an effective suppression barrier at the key position of the fault feature propagation, blocking the fault feature from spreading to other parts of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a solenoid valve fault diagnosis method in an embodiment of the present application.

[0027] Figure 2 It is another flow chart of a solenoid valve fault diagnosis method in an embodiment of the present application.

[0028] Figure 3 It is a schematic diagram of the physical device structure of a solenoid valve fault diagnosis system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0030] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0031] The following uses an embodiment and combines Figure 1 , a solenoid valve fault diagnosis method in an embodiment of the present application is described: See also Figure 1 , which is a flow chart of a solenoid valve fault diagnosis method in an embodiment of the present application.

[0032] S101, when the solenoid valve is in normal working state, applying an alternating current signal of a first preset frequency to the solenoid valve coil, and simultaneously collecting an inherent working current signal of the solenoid valve to generate a mixed current signal; When the solenoid valve is in normal working state, the system applies an alternating current signal of a first preset frequency to the solenoid valve coil, and simultaneously collects the inherent working current signal of the solenoid valve to generate a mixed current signal. The purpose of this step is to obtain the current signal characteristics of the solenoid valve under normal working state, and provide basic data for subsequent fault diagnosis. In practical applications, the system can select the appropriate alternating current signal frequency and amplitude according to the model and parameters of the solenoid valve to ensure that the solenoid valve can be effectively stimulated and a stable current signal can be obtained. In addition, the system can also adopt different signal acquisition methods, such as directly measuring the voltage signal at both ends of the solenoid valve coil, or connecting a current sensor in series in the power supply line of the solenoid valve, etc., to obtain a more accurate and complete current signal.

[0033] Specifically, the system can implement this step in the following way: First, the system sets the amplitude and frequency of the alternating current signal of the first preset frequency according to the rated working voltage and current of the solenoid valve, and applies the signal to the solenoid valve coil through the signal generator. At the same time, the system collects the actual current signal in the solenoid valve coil through the current sensor, and superimposes it with the alternating current signal of the first preset frequency to generate a mixed current signal. In order to improve the quality and reliability of the signal, the system can filter, amplify and process the mixed current signal to eliminate the influence of interference and noise. In addition, the system can also analyze and extract features of the mixed current signal through software algorithms to obtain more accurate and comprehensive signal features.

[0034] In practical applications, this step may encounter some new technical problems, such as the influence of factors such as the impedance change of the solenoid valve coil and temperature drift on the current signal, resulting in the distortion or instability of the obtained mixed current signal. In order to solve this problem, the system can adopt an adaptive signal acquisition and processing method to dynamically adjust the amplitude and frequency of the alternating current signal and the parameters of signal acquisition and processing according to the real-time working status and environmental conditions of the solenoid valve to ensure that a stable and reliable mixed current signal is obtained. For example, the system can monitor the real-time temperature of the solenoid valve through a temperature sensor, and compensate and correct the current signal according to the temperature change to eliminate the influence of temperature drift.

[0035] S102, separating the mixed current signal to obtain harmonic components in the inherent working current signal; The system separates the mixed current signal to obtain the harmonic components in the inherent working current signal, specifically including: performing wavelet decomposition on the mixed current signal to obtain wavelet coefficients of multiple frequency bands; calculating the energy distribution characteristics of the wavelet coefficients to determine the frequency range where energy is concentrated; and reconstructing the signal within the frequency range to obtain the harmonic components in the inherent working current signal.

[0036] The harmonic component reflects the vibration and resonance characteristics of the internal mechanical structure of the solenoid valve, and is closely related to the type and degree of fault of the solenoid valve. In practical applications, since the mixed current signal contains multiple frequency components, and there may be coupling and interference between different frequency components, an effective signal separation method is required to accurately extract the harmonic components. Commonly used signal separation methods include Fourier transform, wavelet transform, empirical mode decomposition, etc. The system can select a suitable method for signal separation according to the characteristics and processing requirements of the mixed current signal.

[0037] Specifically, the system can use the wavelet decomposition method to separate the mixed current signal. The steps are as follows: First, the system performs a wavelet transform on the mixed current signal to decompose it into wavelet coefficients of multiple frequency bands. Then, the system calculates the energy distribution characteristics of the wavelet coefficients in each frequency band and determines the frequency interval where the energy is concentrated. Finally, the system reconstructs the signal within the frequency interval to obtain the harmonic components in the inherent working current signal. Wavelet decomposition is a time-frequency analysis method that can provide time domain and frequency domain information of the signal at the same time, and is particularly suitable for processing non-stationary signals and transient signals. By selecting appropriate wavelet basis functions and decomposition layers, the system can effectively extract the harmonic components in the mixed current signal and suppress the influence of noise and interference.

[0038] S103, determining a target frequency that interferes with the first preset frequency based on the harmonic component; The system determines the target frequency that interferes with the first preset frequency based on the harmonic component, specifically including: constructing a time-frequency distribution diagram of the harmonic component; identifying frequency points where the signal energy density is greater than a preset threshold in the time-frequency distribution diagram; sorting the frequency points according to the signal energy density, and selecting a preset number of frequency points with the largest energy density as candidate frequencies; eliminating the harmonic frequencies generated by the fundamental frequency from the candidate frequencies; and determining the frequencies that interfere with the first preset frequency from the remaining frequency points as the target frequencies.

[0039] In practical applications, the inherent working current signal of the solenoid valve may contain multiple frequency components, and there may be coupling and interference between different frequency components. In order to accurately identify the characteristic frequency associated with the solenoid valve failure, it is necessary to screen out the target frequency that interferes with the first preset frequency from the harmonic component. Common frequency screening methods include time-frequency analysis, energy density estimation, coherence analysis, etc. The system can select a suitable method for frequency screening based on the characteristics of the harmonic components and analysis requirements.

[0040] Specifically, the system can determine the target frequency through the following steps: First, the system constructs a time-frequency distribution diagram of the harmonic components to intuitively display the energy distribution of the harmonic components in the time and frequency domains. Then, the system identifies the frequency points whose signal energy density is greater than the preset threshold in the time-frequency distribution diagram and uses them as candidate frequencies. Next, the system sorts the candidate frequencies according to the signal energy density and selects a preset number of frequency points with the largest energy density. In order to eliminate the harmonic frequencies generated by the fundamental frequency, the system also needs to screen the candidate frequencies and remove the frequency points that are integer multiples of the fundamental frequency. Finally, the system determines the frequencies among the remaining frequency points that interfere with the first preset frequency as the target frequency. Through the above steps, the system can effectively identify the characteristic frequencies related to the solenoid valve fault and provide reliable frequency information for subsequent fault diagnosis.

[0041] In practical applications, this step may encounter some new technical problems, such as the presence of multiple frequency points with high energy density in the time-frequency distribution diagram of the harmonic component, which makes it difficult to determine the true target frequency. In order to solve this problem, the system can introduce prior knowledge and expert experience to predetermine the approximate range and characteristics of the target frequency according to the type and failure mode of the solenoid valve to guide the frequency screening process. For example, the system can establish a knowledge base of solenoid valve failure frequencies, record the typical frequency characteristics of different types of solenoid valves under different failure modes, and use them as a reference for frequency screening. In addition, the system can also adopt an adaptive threshold setting strategy to dynamically adjust the energy density threshold according to the energy distribution of the harmonic component to adapt to different signal environments and interference conditions.

[0042] S104, generating an excitation current signal of a second preset frequency according to the target frequency; The system generates an excitation current signal of a second preset frequency according to the target frequency, and the difference between the second preset frequency and the target frequency is within a preset range.

[0043] The purpose of this step is to generate an excitation current signal of a second preset frequency according to the target frequency, providing an excitation source for subsequent fault diagnosis. In practical applications, in order to effectively stimulate the resonance characteristics of the solenoid valve, it is necessary to select a suitable excitation current signal frequency. The second preset frequency should be close to the target frequency, but not exactly the same, to avoid resonance interference. Common frequency selection methods include frequency offset, frequency modulation, frequency scanning, etc. The system can select a suitable method to generate the second preset frequency according to the characteristics of the target frequency and the excitation requirements.

[0044] Specifically, the system can generate an excitation current signal of a second preset frequency in the following manner: First, the system determines the value range of the second preset frequency according to the size of the target frequency. In order to avoid resonance interference, the difference between the second preset frequency and the target frequency should be within a preset range, such as ±5% of the target frequency. Then, the system can use a frequency offset method to offset the target frequency by a certain percentage to obtain a second preset frequency. Next, the system sets the amplitude, phase and other parameters of the excitation current signal according to the size of the second preset frequency, and applies the excitation current signal to the solenoid valve coil through a signal generator. In order to improve the excitation effect, the system can optimize the design of the excitation current signal, such as using pulse width modulation, sinusoidal modulation and other technologies to improve the spectral characteristics and energy distribution of the signal.

[0045] S105, applying an excitation current signal to the solenoid valve coil, and collecting a resonant current signal generated by the solenoid valve under the excitation current signal; The purpose of this step is to apply an excitation current signal of a second preset frequency to the solenoid valve coil, and collect the resonant current signal generated by the solenoid valve under the excitation current signal, to provide key data for subsequent fault diagnosis. In practical applications, the resonant current signal reflects the vibration and resonance characteristics of the internal mechanical structure of the solenoid valve, and is closely related to the type and degree of fault of the solenoid valve. In order to accurately obtain the resonant current signal, it is necessary to select a suitable signal acquisition method and parameter setting. Commonly used signal acquisition methods include current transformer method, Hall sensor method, shunt resistor method, etc. The system can select a suitable method for signal acquisition according to the type of solenoid valve and the working environment.

[0046] Specifically, the system can implement this step in the following way: First, the system applies an excitation current signal of a second preset frequency to the solenoid valve coil through a signal generator, and controls the amplitude, phase and other parameters of the excitation current signal to ensure the excitation effect. Then, the system collects the actual current signal in the solenoid valve coil through a current sensor, and uses it as a resonant current signal for subsequent analysis. In order to improve the quality and reliability of the signal, the system can filter, amplify and process the resonant current signal to eliminate the influence of interference and noise. In addition, the system can also analyze and extract features of the resonant current signal through software algorithms to obtain more accurate and comprehensive signal features.

[0047] S106, determining the resonance characteristics of the internal mechanical structure of the solenoid valve according to the amplitude and phase characteristics of the resonance current signal; The purpose of this step is to determine the resonance characteristics of the internal mechanical structure of the solenoid valve based on the amplitude and phase characteristics of the resonant current signal, so as to provide a basis for subsequent fault diagnosis. In practical applications, the resonance characteristics of the solenoid valve are closely related to the parameters of its internal mechanical structure, such as spring stiffness, valve core mass, damping coefficient, etc. When a solenoid valve fails, the parameters of its internal mechanical structure will change, causing the resonance characteristics to shift or distort accordingly. Therefore, by analyzing the amplitude and phase characteristics of the resonant current signal, the health status of the internal mechanical structure of the solenoid valve can be indirectly evaluated. Commonly used resonance characteristic analysis methods include frequency response method, impedance method, modal analysis method, etc. The system can select a suitable method to determine the resonance characteristics according to the characteristics of the resonant current signal and the analysis requirements.

[0048] Specifically, the system can determine the resonance characteristics of the solenoid valve through the following steps: First, the system performs a frequency domain transformation on the resonant current signal to obtain its spectrum characteristics. Then, the system identifies the frequency point with the largest amplitude in the spectrum diagram and uses it as the resonance frequency. Next, the system calculates the signal amplitude and phase angle at the resonance frequency point to obtain the amplitude and phase characteristics of the resonant current signal. Finally, the system compares the resonance frequency, amplitude, and phase characteristics with the standard values, and determines the resonance characteristics of the internal mechanical structure of the solenoid valve based on the size and direction of the deviation. For example, if the resonance frequency deviates significantly from the standard value, it may mean that the spring stiffness has changed; if the resonance amplitude is significantly reduced, it may mean that the damping coefficient has increased or the valve core mass has decreased; if the phase angle is distorted, it may mean that the internal mechanical structure has nonlinear or loose faults.

[0049] In practical applications, this step may encounter some new technical problems, such as the resonance current signal is affected by multiple factors, resulting in uncertainty in the determination of the resonance characteristics. In order to solve this problem, the system can adopt an adaptive resonance characteristic determination method, comprehensively consider multiple influencing factors, and dynamically adjust the judgment criteria and thresholds of the resonance characteristics. For example, the system can establish a mapping model between the internal mechanical structure parameters of the solenoid valve and the resonance characteristics, and train and optimize the model through a machine learning algorithm to improve the accuracy and reliability of the determination of the resonance characteristics. In addition, the system can also introduce multi-sensor information fusion technology, comprehensively utilize the signals collected by multiple sensors, such as vibration signals, acoustic emission signals, etc., to cross-validate and compensate for the resonance characteristics of the solenoid valve to reduce the uncertainty of a single signal.

[0050] S107. Identify and obtain the fault type of the solenoid valve based on the change trend of the resonance characteristic over time.

[0051] The purpose of this step is to identify the fault type of the solenoid valve based on the change trend of the resonance characteristics over time, and to realize the state monitoring and fault diagnosis of the solenoid valve. In practical applications, the resonance characteristics of the solenoid valve will change over time, reflecting the aging process of the internal mechanical structure of the solenoid valve, such as wear, fatigue, and contamination. By tracking and analyzing the change trend of the resonance characteristics, the abnormal state of the solenoid valve can be discovered in time, and the possible fault type can be predicted. Common fault identification methods include trend analysis, pattern recognition, knowledge reasoning, etc. The system can select the appropriate method for fault identification based on the changing characteristics of the resonance characteristics and the fault mechanism.

[0052] Specifically, the system can identify the fault type of the solenoid valve through the following steps: First, the system establishes a historical database of resonance characteristics to record the resonance frequency, amplitude and phase characteristics of the solenoid valve at different time points. Then, the system performs trend analysis on the historical data to extract the laws and characteristics of the resonance characteristics changing over time, such as the rate of change, mutation point, periodicity, etc. Then, the system matches the extracted change characteristics with the typical fault mode to identify the most likely fault type of the solenoid valve. For example, if the resonance frequency shows a slow downward trend, it may mean that the spring stiffness is gradually reduced, and the solenoid valve is at risk of spring fatigue or breakage; if the resonance amplitude shows a step-like decrease, it may mean that the valve core is suddenly stuck or falls off, and the solenoid valve has mechanical jamming or foreign body jamming problems; if the phase angle fluctuates randomly, it may mean that there are electrical faults such as intermittent poor contact or short circuit inside the solenoid valve.

[0053] In practical applications, this step may encounter some new technical problems, such as the complexity and diversity of the fault modes of the solenoid valve, and the change trend of the resonance characteristics is interfered by multiple factors, resulting in low accuracy and reliability of fault identification. In order to solve this problem, the system can adopt an adaptive fault identification strategy, comprehensively utilize multi-source heterogeneous information, and dynamically optimize the model and algorithm of fault identification. For example, the system can associate the resonance characteristics with other monitoring indicators (such as current, voltage, temperature, etc.), construct a multivariate fault symptom matrix, and extract more comprehensive and reliable fault features through data mining and machine learning algorithms. In addition, the system can also introduce an expert knowledge base and a fault case library to reason and verify the results of fault identification, thereby improving the accuracy and interpretability of fault identification. At the same time, the system can adopt a self-learning and self-optimization mechanism to continuously update and improve the rules and strategies of fault identification based on the feedback of the diagnostic results to adapt to the state changes and working condition disturbances of the solenoid valve.

[0054] In the above embodiment, by applying an alternating current signal of a first preset frequency to the solenoid valve coil and mixing it with the inherent working current signal, the dynamic response characteristics of the internal mechanical structure of the solenoid valve can be stimulated. By separating the mixed current signal to obtain the harmonic component, and then determining the target frequency, the second preset frequency excitation signal applied subsequently can resonate with the internal mechanical structure of the solenoid valve. In the resonant state, slight changes in the internal mechanical structure of the solenoid valve will be significantly amplified, which is reflected in the amplitude and phase characteristics of the resonant current signal. Over time, different types of faults will cause the resonance characteristics to show different changing trends, and there is a definite corresponding relationship between this changing trend and the fault type. By analyzing the changing trend of the resonance characteristics, the fault type of the solenoid valve can be accurately identified, and the early diagnosis of the solenoid valve fault can be achieved, which improves the accuracy of identifying the evolution trend of the fault characteristics, thereby preventing possible faults of the solenoid valve in advance and improving the safety of the solenoid valve during use.

[0055] The above embodiments mainly describe how to use the resonance characteristics to identify the fault type of the solenoid valve. In practical applications, solenoid valves are often connected in series or in parallel to form valve groups, and there is a mechanical and electromagnetic coupling relationship between adjacent solenoid valves. When a solenoid valve fails, the fault characteristics will propagate to the surroundings through this coupling relationship, causing the performance of adjacent solenoid valves to be affected. Therefore, after identifying the faulty solenoid valve, it is also necessary to analyze the propagation law of the fault characteristics and take effective measures to suppress the propagation of the fault characteristics to prevent the expansion of the fault impact range. Figure 2 , another solenoid valve fault diagnosis method in the embodiment of the present application is described: See also Figure 2 , is another flow chart of a solenoid valve fault diagnosis method in an embodiment of the present application.

[0056] S201, sequentially applying an excitation current signal of a second preset frequency to adjacent solenoid valves connected in series or in parallel with the solenoid valve; The purpose of this step is to apply an excitation signal to the adjacent solenoid valves that are connected in series or in parallel with the faulty solenoid valve, providing a basis for the subsequent analysis of the propagation law of the fault characteristics. In practical applications, the fault characteristics of the solenoid valve often affect the adjacent solenoid valves through mechanical and electromagnetic coupling, resulting in changes in the performance of the adjacent solenoid valves. In order to comprehensively evaluate the propagation range and impact of the fault characteristics, all adjacent solenoid valves need to be excited and collected. The system can automatically identify the solenoid valves adjacent to the faulty solenoid valve according to the topological structure and connection method of the solenoid valve, and apply excitation signals in sequence according to the preset order. At the same time, the system can also optimize the frequency, amplitude and other parameters of the excitation signal according to the type and parameters of the adjacent solenoid valves to improve the accuracy and reliability of fault feature extraction.

[0057] Specifically, the system can implement this step in the following way: First, the system identifies all adjacent solenoid valves connected in series or in parallel with the faulty solenoid valve according to the layout diagram and connection table of the solenoid valve, and establishes a logical relationship matrix of the adjacent solenoid valves. Then, the system determines the excitation order and priority of the adjacent solenoid valves according to the logical relationship matrix, for example, the solenoid valve directly connected to the faulty solenoid valve is first excited, and then the solenoid valves indirectly connected are excited in turn. Next, the system selects the most matching excitation signal from the excitation signal library according to the model and specification of the adjacent solenoid valve, and applies the excitation signal to the coils of the adjacent solenoid valves in turn through the signal generator. In the process of applying the excitation signal, the system can monitor the response of the adjacent solenoid valves in real time through the current sensor, and dynamically adjust the parameters of the excitation signal according to the quality and stability of the response signal to ensure that a high-quality resonant current signal is obtained.

[0058] S202, collecting resonance current signals of adjacent solenoid valves, and calculating a transfer function between the resonance current signals of the solenoid valve and each adjacent solenoid valve; The purpose of this step is to reveal the propagation law of fault characteristics in the solenoid valve array by calculating the transfer function between the resonant current signal of the solenoid valve and the adjacent solenoid valve. In practical applications, the transfer function is a mathematical model that describes the relationship between the input and output of the system, which can reflect the characteristics of the signal such as amplitude attenuation and phase delay during the propagation process. By analyzing the transfer function between the solenoid valve and the adjacent solenoid valve, the propagation path, attenuation law and coupling strength of the fault signal in the valve group can be inferred, providing a basis for subsequent fault isolation and suppression. The system can adopt a variety of transfer function identification methods, such as frequency response method, step response method, correlation analysis method, etc. According to the collected resonant current signal, the optimal identification algorithm and model order can be adaptively selected to improve the accuracy and efficiency of transfer function estimation.

[0059] Specifically, the system can calculate the transfer function through the following steps: First, the system pre-processes the collected resonant current signals of the solenoid valve and the adjacent solenoid valve, such as denoising, normalization, synchronization, etc., to eliminate signal interference and distortion. Then, the system uses signal processing tools to transform the resonant current signal, such as Fourier transform, wavelet transform, etc., to convert the time domain signal to the frequency domain or time-frequency domain, and extract the spectral characteristics of the signal. Then, according to the spectral characteristics, the system uses a suitable transfer function model, such as a fractional model, a state space model, etc., to construct the transfer function equation between the solenoid valve and the adjacent solenoid valve. Finally, the system uses optimization algorithms, such as the least squares method, the maximum likelihood method, etc., to identify and estimate the unknown parameters in the transfer function equation to obtain the optimal transfer function expression. The system can design performance indicators for transfer function identification, such as mean square error, signal-to-noise ratio, etc., and adaptively adjust the order and parameters of the transfer function model through iterative optimization to obtain the best identification effect.

[0060] S203, identifying the propagation path of the fault feature based on the transfer function; The system identifies the propagation path of fault characteristics based on the transfer function, specifically including: extracting the amplitude spectrum and phase spectrum of the transfer function; calculating the amplitude attenuation coefficient and phase delay between adjacent solenoid valves; constructing the spatial transfer matrix of the fault characteristics based on the attenuation coefficient and phase delay; analyzing the singular values ​​of the spatial transfer matrix to determine the spatial position of the fault source.

[0061] The propagation of fault signals between solenoid valves often shows a certain directionality and selectivity, which is closely related to the topological structure and coupling strength of the solenoid valve array. By analyzing the amplitude spectrum and phase spectrum of the transfer function, the spatial distribution characteristics of the fault signal in the valve group, such as the propagation direction, attenuation rate and phase delay, can be revealed, and then the location and impact range of the fault source can be inferred. The system can use mathematical tools such as graph theory and network topology to abstract the solenoid valve array into a directed weighted graph, and use graph traversal, partitioning and other algorithms to automatically search and identify the propagation path of the fault signal.

[0062] Specifically, the system can identify the propagation path of the fault feature through the following steps: First, the system extracts the amplitude spectrum and phase spectrum of each transfer function, and calculates the amplitude attenuation coefficient and phase delay between adjacent solenoid valves. The amplitude attenuation coefficient reflects the energy attenuation rate of the fault signal during the propagation process, and the phase delay reflects the time difference between the fault signal in different solenoid valves. Then, the system constructs the spatial transfer matrix of the fault feature based on the attenuation coefficient and phase delay, which describes the spatial topological relationship of the fault signal propagating in the solenoid valve array. Next, the system performs singular value decomposition on the spatial transfer matrix to obtain the left and right singular vectors and singular values ​​of the matrix. By analyzing the size and distribution of the singular values, the main direction and path of the fault propagation can be revealed. Finally, the system determines the spatial location of the fault source based on the characteristics of the singular vectors and the physical layout of the solenoid valve array, and identifies the impact range of the fault signal. The system can design optimization strategies for path search, such as the shortest path, minimum cut, etc., and use heuristic algorithms, such as genetic algorithms and ant colony algorithms, to accelerate the path search and identification process.

[0063] S204, selecting key nodes on the propagation path of the fault characteristics; In practical applications, not all solenoid valve nodes on the propagation path are equally important. Some nodes have a greater impact on the propagation and attenuation of fault signals due to their special positions or connection relationships in the topological structure. By selecting these key nodes on the propagation path, the spread of fault signals can be blocked or suppressed to the maximum extent, thereby controlling the impact of the fault within a local range. The system can comprehensively consider topological indicators such as node degree, betweenness, distance, as well as physical parameters of nodes, fault sensitivity and other factors, and use multi-criteria decision-making, weighted scoring and other methods to automatically evaluate and screen out the most critical nodes.

[0064] Specifically, the system can select key nodes in the following ways: First, the system performs a topological analysis on the identified fault propagation path and calculates the degree, betweenness, distance and other indicators of each node on the path. The degree reflects the number of connections of the node. The larger the degree, the more important the node is in the path. The betweenness reflects the transit role of the node between different paths. The larger the betweenness, the greater the influence of the node on the propagation of the fault signal. The distance reflects the spatial distance between the node and the fault source. The smaller the distance, the more directly the node is affected by the fault. Then, the system calculates the attenuation and filtering effect of the node on the fault signal based on the physical parameters of the node, such as impedance, inductance, capacitance, etc. The stronger the attenuation and filtering ability, the greater the contribution of the node to fault suppression. Next, the system evaluates the fault sensitivity of the node. The fault sensitivity reflects the node's response ability to the type and degree of the fault. The higher the sensitivity, the more susceptible the node is to the fault. Finally, the system comprehensively considers the above indicators and uses multi-criteria decision-making methods such as hierarchical analysis method and fuzzy comprehensive evaluation to sort and screen the importance of the nodes and select the top N% of the nodes as key nodes.

[0065] S205, adjusting the second preset frequency of the solenoid valve at the key node so that the second preset frequency of the solenoid valve and the second preset frequency of the fault source solenoid valve form an anti-phase relationship; By constructing an excitation signal with an anti-phase on the fault propagation path, the interference and superposition principles of waves can be used to make the fault signal cancel and attenuate during the propagation process, thereby isolating and suppressing the impact of the fault. In order to achieve the best interference effect, it is necessary to accurately control the excitation frequency and phase of the solenoid valve at the key node so that it forms a completely anti-phase relationship with the fault source signal. The system can use adaptive control, robust control and other methods to dynamically adjust the excitation parameters of the key nodes according to the real-time spectrum and phase information of the fault signal, and ensure the accuracy and stability of the anti-phase relationship through feedback correction.

[0066] Specifically, the system can adjust the excitation frequency of the key node through the following steps: First, the system uses spectrum analysis tools, such as Fourier transform, wavelet analysis, etc., to extract the spectrum and phase of the resonant current signal of the fault source solenoid valve, and obtain the dominant frequency and initial phase of the fault signal. Then, the system calculates the frequency and phase of the fault signal at the key node according to the transfer function model, and compares it with the target anti-phase relationship to obtain the frequency and phase adjustment amount. Next, the system uses a frequency synthesizer and a phase modulator to generate an excitation signal with the same frequency and opposite phase as the fault signal, and superimposes the excitation signal on the original control signal of the key node solenoid valve to form a composite excitation signal. Under the action of the excitation signal, the key node solenoid valve will generate vibration and current response that is anti-phase with the fault signal, thereby weakening and offsetting the propagation intensity of the fault signal. Finally, the system collects the response signal of the key node solenoid valve through the current sensor, and performs correlation analysis with the fault source signal to calculate the amplitude and phase of the residual fault signal, which is input into the frequency and phase adjustment unit as feedback to achieve adaptive correction and optimization of the excitation parameters.

[0067] S206. Suppress the propagation of fault characteristics through an anti-phase relationship.

[0068] The system suppresses the propagation of fault characteristics through anti-phase relationship, specifically including: calculating the amplitude and phase of the fault characteristics at the key node; generating a compensation signal with equal amplitude and opposite phase; superimposing the compensation signal on the excitation current signal of the solenoid valve at the key node.

[0069] The purpose of this step is to suppress the propagation of fault characteristics in the solenoid valve array by constructing an anti-phase excitation signal at the key node and utilizing the principle of wave interference. In practical applications, by precisely controlling the amplitude, phase and other parameters of the anti-phase signal, a complete destructive relationship can be formed with the fault signal at the key node. In order to obtain the best suppression performance, it is necessary to adaptively adjust the parameters of the anti-phase signal according to the real-time characteristics of the fault signal so that it can dynamically match and track the changes of the fault signal. At the same time, it is also necessary to consider the feasibility of the anti-phase signal in the actual solenoid valve system, such as the frequency range, amplitude range, modulation method, etc. of the signal, to ensure the effectiveness and reliability of the suppression measures.

[0070] Specifically, the system can achieve the suppression of fault characteristics through the following steps: First, the system determines the target position where the anti-phase signal needs to be applied based on the fault propagation path and key nodes identified in steps S203 and S204. Then, the system uses signal processing tools such as phase-locked loops and notch filters to extract the amplitude and phase information of the fault characteristics from the response signal of the fault source solenoid valve. Next, the system synthesizes an anti-phase compensation signal that matches the fault characteristic parameters based on the amplitude and phase information, and superimposes the compensation signal on the excitation current signal of the solenoid valve at the key node. Under the action of the compensation signal, the solenoid valve at the key node will generate a vibration response with an amplitude equal to that of the fault signal and an opposite phase, thereby achieving active cancellation and suppression of the fault characteristics. Finally, the system collects vibration signals of key nodes and their neighborhoods through devices such as vibration sensors, and evaluates the residual level of the fault characteristics, which is input into the compensation signal generation module as a feedback signal to achieve closed-loop optimization of the suppression effect.

[0071] In the above embodiment, by applying excitation signals to adjacent solenoid valves and collecting resonant current signals, the calculated transfer function reflects the propagation characteristics of the fault feature in the solenoid valve system. The transfer function contains the amplitude attenuation and phase delay information of the fault feature during the propagation process, and can accurately describe the spatial distribution law of the fault impact. Based on the transfer function to identify the propagation path of the fault feature, the location of the fault source can be traced and the diffusion range of the fault impact can be understood. This propagation path analysis method enables the system to accurately locate the fault source in a complex system with multiple solenoid valves connected in series or in parallel, thereby improving the accuracy and reliability of fault diagnosis.

[0072] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a solenoid valve fault diagnosis system provided in an embodiment of the present application.

[0073] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0074] like Figure 3As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0075] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0076] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0077] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0078] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0079] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0080] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0081] As used in the above embodiments, the term "when..." may be interpreted as "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted as "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0082] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0083] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A solenoid valve fault diagnosis method, characterized in that: include: When the solenoid valve is in a normal working state, an alternating current signal of a first preset frequency is applied to the solenoid valve coil, and at the same time, an inherent working current signal of the solenoid valve is collected to generate a mixed current signal; Separating the mixed current signal to obtain the harmonic component in the inherent working current signal; determining a target frequency that interferes with the first preset frequency based on the harmonic component; generating an excitation current signal of a second preset frequency according to the target frequency, wherein a difference between the second preset frequency and the target frequency is within a preset range; Applying the excitation current signal to the solenoid valve coil, and collecting a resonant current signal generated by the solenoid valve under the excitation current signal; Determining the resonance characteristics of the internal mechanical structure of the solenoid valve according to the amplitude and phase characteristics of the resonance current signal; The fault type of the solenoid valve is identified based on the change trend of the resonance characteristic over time.

2. The method according to claim 1, characterized in that The separating the mixed current signal to obtain the harmonic component in the inherent working current signal specifically includes: Performing wavelet decomposition on the mixed current signal to obtain wavelet coefficients of multiple frequency bands; Calculating the energy distribution characteristics of the wavelet coefficients to determine the frequency interval of energy concentration; The signal within the frequency range is reconstructed to obtain the harmonic component in the inherent working current signal.

3. The method according to claim 1, characterized in that The determining, based on the harmonic component, a target frequency that interferes with the first preset frequency specifically includes: Constructing a time-frequency distribution diagram of the harmonic components; Identifying frequency points in the time-frequency distribution diagram where the signal energy density is greater than a preset threshold; Sort the frequency points according to the signal energy density, and select a preset number of frequency points with the largest energy density as candidate frequencies; Eliminating harmonic frequencies generated by the fundamental frequency from the candidate frequencies; The frequencies among the remaining frequency points that interfere with the first preset frequency are determined as target frequencies.

4. The method according to claim 1, characterized in that After identifying the fault type of the solenoid valve based on the change trend of the resonance characteristic over time, the method further includes: sequentially applying an excitation current signal of the second preset frequency to adjacent solenoid valves connected in series or in parallel with the solenoid valve; collecting resonance current signals of the adjacent solenoid valves; calculating a transfer function between the resonant current signal of the solenoid valve and each of the adjacent solenoid valves; A propagation path of the fault signature is identified based on the transfer function.

5. The method according to claim 4, characterized in that The identifying the propagation path of the fault feature based on the transfer function specifically includes: extracting a magnitude spectrum and a phase spectrum of the transfer function; Calculating the amplitude attenuation coefficient and phase delay between the adjacent solenoid valves; Constructing a spatial transfer matrix of the fault feature according to the attenuation coefficient and the phase delay; The singular values ​​of the spatial transfer matrix are analyzed to determine the spatial location of the fault source.

6. The method according to claim 4 or 5, characterized in that: After identifying the propagation path of the fault feature based on the transfer function, the method further includes: Selecting key nodes on the propagation path of the fault characteristics; Adjusting the second preset frequency of the solenoid valve at the key node so that the solenoid valve and the second preset frequency of the fault source solenoid valve form an anti-phase relationship; The propagation of the fault signature is suppressed by the anti-phase relationship.

7. The method according to claim 6, characterized in that The suppressing the propagation of the fault feature by the anti-phase relationship specifically includes: Calculating the amplitude and phase of the fault feature at the key node; generating a compensation signal having an amplitude equal to the amplitude and an opposite phase to the phase; The compensation signal is superimposed on the excitation current signal of the solenoid valve at the key node.

8. A solenoid valve fault diagnosis system, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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