A Solenoid Valve Fault Diagnosis Method, System, Storage Medium and Program Product

Abstract

A solenoid valve fault diagnosis method, system, storage medium and program product. The present application relates to the field of measuring magnetic variables. In this method, an alternating current signal with a first preset frequency is applied to the solenoid valve coil, the inherent working current signal is collected, and a mixed current signal is generated; the mixed current signal is separated to obtain harmonic components; the target frequency is determined based on the harmonic components; an excitation current signal with a second preset frequency is generated according to the target frequency; the excitation current signal is applied to the solenoid valve coil, and the resonance current signal generated by the solenoid valve under the excitation current signal is collected; the resonance characteristics of the internal mechanical structure of the solenoid valve are determined 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 characteristics over time. The present application is used to improve the accuracy of identifying the evolution trend of fault characteristics, thereby preventing in advance the possible faults of the solenoid valve and improving the safety during the use of the solenoid valve.

Description

Technical Field

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

[0002] As a key actuator in a fluid control system, the reliability of a solenoid valve directly affects the safe and stable operation of the entire system. In practical applications, due to the solenoid valve being in a high-frequency working state for a long time, its internal parts are prone to mechanical wear, jamming, and air leakage and other faults. If these faults are not detected and processed in a timely manner, it may lead to abnormal system operation and 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 inspections and misjudgments.

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

[0004] However, when the solenoid valve is in a frequently starting and stopping working state, its transient action characteristics will gradually change with the increase of the continuous working time. There may be early fault signs hidden in this gradual change process. However, since the solenoid valve has not shown obvious performance degradation, the existing diagnosis method based on fixed thresholds is difficult to capture the weak fault feature evolution trend, resulting in missing the best preventive maintenance opportunity and ultimately reducing the safety of the solenoid valve during use. Summary of the Invention

[0005] This application provides a solenoid valve fault diagnosis method, system, storage medium, and program product, which are used to improve the accuracy of identifying the evolution trend of fault features, 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, this application provides a solenoid valve fault diagnosis method. When the solenoid valve is in a normal working state, an alternating current signal with a first preset frequency is applied to the solenoid valve coil, and at the same time, the inherent working current signal of the solenoid valve is collected to generate a mixed current signal;

[0007] The mixed current signal is separated to obtain the harmonic component in the inherent working current signal;

[0008] Based on the harmonic component, a target frequency that interferes with the first preset frequency is determined;

[0009] Generate an excitation current signal with 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;

[0010] Apply the excitation current signal to the solenoid valve coil and collect the resonance current signal generated by the solenoid valve under the excitation current signal;

[0011] Determine the resonance characteristics of the internal mechanical structure of the solenoid valve according to the amplitude and phase characteristics of the resonance current signal;

[0012] Identify the fault type of the solenoid valve based on the change trend of the resonance characteristics over time.

[0013] By adopting the above technical solution, by applying an alternating current signal with 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 excited. By separating the mixed current signal to obtain the harmonic components, and then determining the target frequency, the subsequent applied second preset frequency excitation signal can resonate with the internal mechanical structure of the solenoid valve. In the resonance state, the slight changes in the internal mechanical structure of the solenoid valve will be significantly amplified, reflected in the amplitude and phase characteristics of the resonance current signal. As time goes by, different types of faults will cause the resonance characteristics to show different change trends, and there is a definite corresponding relationship between this change trend and the fault type. By analyzing the change trend of the resonance characteristics, the fault type of the solenoid valve can be accurately identified, realizing the early diagnosis of the solenoid valve fault, improving the accuracy of identifying the evolution trend of the fault characteristics, and then preventing the possible faults of the solenoid valve in advance, improving the safety during the use of the solenoid valve.

[0014] Combined with some embodiments of the first aspect, in some embodiments, separating the mixed current signal to obtain the harmonic components in the inherent working current signal specifically includes:

[0015] Perform wavelet decomposition on the mixed current signal to obtain wavelet coefficients in multiple frequency bands;

[0016] Calculate the energy distribution characteristics of the wavelet coefficients and determine the frequency interval where the energy is concentrated;

[0017] Reconstruct the signal in the frequency interval to obtain the harmonic components in the inherent working current signal.

[0018] By adopting the above technical solution, processing the mixed current signal by wavelet decomposition can achieve multi-resolution analysis of the signal 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 abrupt components and transient features 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. Reconstructing the signal in this frequency interval can accurately extract the harmonic components in the inherent working current signal and remove the interference of noise and irrelevant signals.

[0019] Combined with some embodiments of the first aspect, in some embodiments, determining the target frequency that interferes with the first preset frequency based on the harmonic components specifically includes:

[0020] Construct a time-frequency distribution diagram of the harmonic components;

[0021] Identify the frequency points in the time-frequency distribution diagram where the signal energy density is greater than the preset threshold;

[0022] Sort the frequency points according to the signal energy density, and select the preset number of frequency points with the largest energy density as candidate frequencies;

[0023] Eliminate the harmonic frequencies generated by the fundamental frequency from the candidate frequencies;

[0024] Determine the frequency that interferes with the first preset frequency among the remaining frequency points as the target frequency.

[0025] By adopting the above technical solution, by constructing a time-frequency distribution diagram of the harmonic components, the variation law of signal energy with time and frequency can be intuitively displayed. Screening out the frequency points with high energy density based on the preset threshold and sorting them according to the energy density can effectively identify the main frequency components related to the fault characteristics. 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. Determining the target frequency that interferes with the first preset frequency among the remaining frequency points ensures that the subsequent 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 result more accurate and reliable.

[0026] Combined 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 characteristics over time, the method further includes:

[0027] Apply an excitation current signal of the second preset frequency to the adjacent solenoid valves connected in series or parallel with the solenoid valve in turn;

[0028] Collect the resonance current signals of the adjacent solenoid valves;

[0029] Calculate the transfer function between the resonance current signals of the solenoid valve and each adjacent solenoid valve;

[0030] Identify the propagation path of the fault characteristics based on the transfer function.

[0031] By adopting the above technical solution, by applying an excitation signal to the adjacent solenoid valves and collecting the resonance 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 influence. Identifying the propagation path of the fault characteristics based on the transfer function can trace the location of the fault source and understand the diffusion range of the fault influence. 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, improving the accuracy and reliability of fault diagnosis.

[0032] Combined with some embodiments of the first aspect, in some embodiments, identifying the propagation path of the fault characteristics based on the transfer function specifically includes:

[0033] Extract the amplitude spectrum and phase spectrum of the transfer function;

[0034] Calculate the amplitude attenuation coefficient and phase delay between adjacent solenoid valves;

[0035] Construct a spatial transfer matrix of the fault characteristics according to the attenuation coefficient and phase delay;

[0036] Analyze the singular values of the spatial transfer matrix to determine the spatial position of the fault source.

[0037] By adopting the above technical solution, 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 a 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, it is possible to accurately track the propagation law of the fault characteristics in the solenoid valve system, improving the accuracy and reliability of fault source location.

[0038] Combined with some embodiments of the first aspect, in some embodiments, after identifying the propagation path of the fault characteristics based on the transfer function, the method further includes:

[0039] Select key nodes on the propagation path of the fault characteristics;

[0040] Adjust the second preset frequency of the solenoid valve at the key node so that the second preset frequency of the solenoid valve forms an anti-phase relationship with that of the fault source solenoid valve;

[0041] Suppress the propagation of the fault characteristics through the anti-phase relationship.

[0042] By adopting the above technical solution, after identifying the propagation path of the fault feature, by selecting key nodes on the propagation path and adjusting the second preset frequency of the solenoid valves at these nodes to form an anti-phase relationship with the frequency of the fault source solenoid valve, the propagation of the fault feature can be suppressed. 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 terms of 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 critical position of the fault feature propagation, blocking the spread of the fault feature to other parts of the system.

[0043] Combined with some embodiments of the first aspect, in some embodiments, suppressing the propagation of the fault feature through the anti-phase relationship specifically includes:

[0044] Calculating the amplitude and phase of the fault feature at the key node;

[0045] Generating a compensation signal with the same amplitude but opposite phase;

[0046] Superimposing the compensation signal on the excitation current signal of the solenoid valve at the key node.

[0047] By adopting the above technical solution, by calculating the amplitude and phase of the fault feature at the key node, generating a compensation signal with the same amplitude but opposite phase, and superimposing the compensation signal on the excitation current signal of the solenoid valve at the key node, precise suppression of the fault feature is achieved. The equal amplitude of the compensation signal and the fault feature ensures that the energy of the fault feature can be completely offset, and the opposite phase guarantees that the two can undergo complete destructive interference. 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.

[0048] In a second aspect, an embodiment of the present application provides a solenoid valve fault diagnosis system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and 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 described in the first aspect and any possible implementation manner in the first aspect.

[0049] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, when the above instructions run on the system, causing the above system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0050] Fourthly, an embodiment of the present application provides a computer program product. When the computer program product runs on a system, it causes the system to execute the method described in any possible implementation manner of the first aspect.

[0051] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0052] 1. The present application provides a solenoid valve fault diagnosis method. By applying an alternating current signal with 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 excited. By separating the mixed current signal to obtain the harmonic components, 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 resonance state, the small changes in the internal mechanical structure of the solenoid valve will be significantly amplified, reflected in the amplitude and phase characteristics of the resonance current signal. As time goes by, different types of faults will cause different change trends in the resonance characteristics, and there is a definite corresponding relationship between this change trend and the fault type. By analyzing the change trend of the resonance characteristics, the fault type of the solenoid valve can be accurately identified, realizing the early diagnosis of the solenoid valve fault, improving the accuracy of identifying the evolution trend of the fault characteristics, and then preventing the possible faults of the solenoid valve in advance, improving the safety during the use of the solenoid valve.

[0053] 2. The present application provides a solenoid valve fault diagnosis method. By applying an excitation signal to adjacent solenoid valves and collecting the resonance current signal, the transfer function calculated 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 influence. 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 influence can be understood. This propagation path analysis method enables the system to accurately locate the fault source in a complex system where multiple solenoid valves are connected in series or in parallel, improving the accuracy and reliability of the fault diagnosis.

[0054] 3. The present application provides a solenoid valve fault diagnosis method. After identifying the propagation path of the fault characteristics, by selecting key nodes on the propagation path and adjusting the second preset frequency of the solenoid valves at these nodes to form an anti-phase relationship with the frequency of the fault source solenoid valve, the propagation of the fault characteristics can be suppressed. When the fault characteristics are transmitted along the propagation path, the anti-phase signal at the key nodes will interfere with the fault characteristics in terms of phase, weakening the propagation intensity of the fault characteristics. This active suppression method based on the propagation path can form an effective suppression barrier at the key position of the fault characteristic propagation, blocking the spread of the fault characteristics to other parts of the system. Description of the Drawings

[0055] Figure 1 It is a schematic flowchart of a solenoid valve fault diagnosis method in an embodiment of the present application.

[0056] Figure 2 It is another schematic flowchart of a solenoid valve fault diagnosis method in an embodiment of the present application.

[0057] Figure 3 It is a schematic structural diagram of an entity device of a solenoid valve fault diagnosis system provided in an embodiment of the present application. Detailed implementation manners

[0058] 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 limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0059] Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0060] Next, an embodiment is used and combined with Figure 1 , to describe a solenoid valve fault diagnosis method in an embodiment of the present application:

[0061] Please refer to Figure 1 , which is a schematic flowchart of a solenoid valve fault diagnosis method in an embodiment of the present application.

[0062] S101. When the solenoid valve is in a normal working state, apply an alternating current signal with a first preset frequency to the solenoid valve coil, and at the same time collect the inherent working current signal of the solenoid valve to generate a mixed current signal;

[0063] When the solenoid valve is in normal working condition, an alternating current signal with a first preset frequency is applied to the solenoid valve coil, and at the same time, the inherent working current signal of the solenoid valve is collected to generate a mixed current signal. The purpose of this step is to obtain the current signal characteristics under the normal working condition of the solenoid valve and provide basic data for subsequent fault diagnosis. In practical applications, the system can select appropriate frequencies and amplitudes of the alternating current signal according to the model and parameters of the solenoid valve to ensure that the solenoid valve can be effectively excited 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 across the solenoid valve coil or connecting a current sensor in series in the power supply circuit of the solenoid valve, etc., to obtain a more accurate and complete current signal.

[0064] Specifically, the system can implement this step in the following way: First, according to the rated working voltage and current of the solenoid valve, the system sets the amplitude and frequency of the alternating current signal with the first preset frequency and applies this signal to the solenoid valve coil through a signal generator. At the same time, the system collects the actual current signal in the solenoid valve coil through a current sensor and superimposes it on the alternating current signal with the first preset frequency to generate a mixed current signal. To improve the quality and reliability of the signal, the system can perform filtering, amplification and other processing on the mixed current signal to eliminate the influence of interference and noise. In addition, the system can also analyze and extract the features of the mixed current signal through software algorithms to obtain more accurate and comprehensive signal features.

[0065] In practical applications, some new technical problems may be encountered in this step, such as the influence of factors such as the impedance change and temperature drift of the solenoid valve coil on the current signal, resulting in the distortion or instability of the obtained mixed current signal. To solve this problem, the system can adopt an adaptive signal acquisition and processing method, dynamically adjust the amplitude and frequency of the alternating current signal, as well as the parameters of signal acquisition and processing according to the real-time working state and environmental conditions of the solenoid valve, to ensure a stable and reliable mixed current signal. 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.

[0066] S102. Separate the mixed current signal to obtain the harmonic components in the inherent working current signal;

[0067] The system separates the mixed current signal to obtain the harmonic components in the inherent working current signal, which specifically includes: performing wavelet decomposition on the mixed current signal to obtain wavelet coefficients in multiple frequency bands; calculating the energy distribution characteristics of the wavelet coefficients to determine the frequency interval where the energy accumulates; reconstructing the signal in the frequency interval to obtain the harmonic components in the inherent working current signal.

[0068] The harmonic components reflect the vibration and resonance characteristics of the internal mechanical structure of the solenoid valve, and are closely related to the type and degree of the solenoid valve's faults. In practical applications, since the mixed current signal contains multiple frequency components and there may be coupling and interference between different frequency components, effective signal separation methods need to be adopted 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 of the mixed current signal and the processing requirements.

[0069] Specifically, the system can use the wavelet decomposition method to separate the mixed current signal, and the steps are as follows: First, the system performs wavelet transform on the mixed current signal and decomposes it into wavelet coefficients in 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 accumulates. Finally, the system reconstructs the signal in 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 both the time-domain and frequency-domain information of the signal, and is particularly suitable for processing non-stationary signals and instantaneous signals. By selecting appropriate wavelet basis functions and decomposition levels, the system can effectively extract the harmonic components in the mixed current signal and suppress the influence of noise and interference.

[0070] S103. Determine the target frequency that interferes with the first preset frequency based on the harmonic components;

[0071] The system determines the target frequency that interferes with the first preset frequency based on the harmonic components, specifically including: constructing a time-frequency distribution map of the harmonic components; identifying the frequency points with signal energy density greater than the preset threshold in the time-frequency distribution map; sorting the frequency points according to the signal energy density size, and selecting the preset number of frequency points with the largest energy density as candidate frequencies; eliminating the harmonic frequencies generated by the fundamental frequency in the candidate frequencies; determining the frequencies that interfere with the first preset frequency among the remaining frequency points as the target frequencies.

[0072] 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. To accurately identify the characteristic frequencies related to the solenoid valve faults, it is necessary to screen out the target frequencies that interfere with the first preset frequency from the harmonic components. Commonly used frequency screening methods include time-frequency analysis, energy density estimation, coherence analysis, etc. The system can select a suitable method for frequency screening according to the characteristics of the harmonic components and the analysis requirements.

[0073] Specifically, the system can determine the target frequency through the following steps: First, the system constructs a time-frequency distribution map of harmonic components to visually display the energy distribution of harmonic components in the time and frequency domains. Then, the system identifies the frequency points in the time-frequency distribution map where the signal energy density is greater than a preset threshold and uses them as candidate frequencies. Next, the system sorts the candidate frequencies according to the signal energy density magnitude and selects a preset number of frequency points with the largest energy density. 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 that interfere with the first preset frequency among the remaining frequency points as the target frequencies. Through the above steps, the system can effectively identify the characteristic frequencies related to the solenoid valve failure and provide reliable frequency information for subsequent fault diagnosis.

[0074] In practical applications, some new technical problems may be encountered in this step. For example, there are multiple frequency points with relatively large energy density in the time-frequency distribution map of harmonic components, making it difficult to determine the true target frequency. To solve this problem, the system can introduce prior knowledge and expert experience, and pre-determine 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 in different failure modes, and use them as a reference for frequency screening. In addition, the system can also adopt an adaptive threshold setting strategy, dynamically adjust the magnitude of the energy density threshold according to the energy distribution of harmonic components to adapt to different signal environments and interference conditions.

[0075] S104. Generate an excitation current signal of a second preset frequency according to the target frequency;

[0076] 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.

[0077] The purpose of this step is to generate an excitation current signal of a second preset frequency according to the target frequency to provide an excitation source for subsequent fault diagnosis. In practical applications, to effectively excite 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 excitation requirements.

[0078] Specifically, the system can generate an excitation current signal with a second preset frequency in the following manner: First, the system determines the value range of the second preset frequency according to the magnitude of the target frequency. 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 the method of frequency offset to offset the target frequency by a certain percentage to obtain the second preset frequency. Next, the system sets parameters such as the amplitude and phase of the excitation current signal according to the magnitude of the second preset frequency, and applies the excitation current signal to the solenoid valve coil through a signal generator. To improve the excitation effect, the system can optimize the design of the excitation current signal, such as using techniques like pulse width modulation and sine modulation to improve the spectral characteristics and energy distribution of the signal.

[0079] S105. Apply an excitation current signal to the solenoid valve coil and collect the resonance current signal generated by the solenoid valve under the excitation current signal;

[0080] The purpose of this step is to apply an excitation current signal with a second preset frequency to the solenoid valve coil and collect the resonance current signal generated by the solenoid valve under the excitation current signal, providing key data for subsequent fault diagnosis. In practical applications, the resonance 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 the solenoid valve fault. To accurately obtain the resonance current signal, appropriate signal acquisition methods and parameter settings need to be selected. Commonly used signal acquisition methods include the current transformer method, Hall sensor method, shunt resistor method, etc. The system can select a suitable method for signal acquisition according to the type and working environment of the solenoid valve.

[0081] Specifically, the system can achieve this step in the following manner: First, the system applies an excitation current signal with a second preset frequency to the solenoid valve coil through a signal generator and controls parameters such as the amplitude and phase 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 the resonance current signal for subsequent analysis. To improve the quality and reliability of the signal, the system can perform processing such as filtering and amplifying on the resonance current signal to eliminate the influence of interference and noise. In addition, the system can also analyze and extract features from the resonance current signal through software algorithms to obtain more accurate and comprehensive signal features.

[0082] S106. Determine the resonance characteristics of the internal mechanical structure of the solenoid valve according to the amplitude and phase characteristics of the resonance current signal;

[0083] 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 resonance current signal, providing 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, spool mass, damping coefficient, etc. When a fault occurs in the solenoid valve, the parameters of its internal mechanical structure will change, resulting in corresponding shifts or distortions in the resonance characteristics. Therefore, by analyzing the amplitude and phase characteristics of the resonance 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 resonance current signal and the analysis requirements.

[0084] 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 resonance current signal to obtain its spectral characteristics. Then, the system identifies the frequency point with the largest amplitude in the spectrogram and takes 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 resonance 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 according to the magnitude 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 decreases significantly, it may mean that the damping coefficient has increased or the spool mass has decreased; if the phase angle is distorted, it may mean that there are faults such as nonlinearity or looseness in the internal mechanical structure.

[0085] In practical applications, some new technical problems may be encountered in this step. For example, the resonance current signal is affected by multiple factors, resulting in uncertainty in determining the resonance characteristics. 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 parameters of the internal mechanical structure of the solenoid valve and the resonance characteristics, and train and optimize the model through machine learning algorithms to improve the accuracy and reliability of determining 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 the resonance characteristics of the solenoid valve to reduce the uncertainty of a single signal.

[0086] S107. Identify the fault type of the solenoid valve based on the change trend of the resonance characteristics over time.

[0087] The purpose of this step is to identify the fault type of the solenoid valve based on the changing trend of resonance characteristics over time, and to achieve the status 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 processes such as wear, fatigue, and contamination of the internal mechanical structure of the solenoid valve. By tracking and analyzing the changing trend of resonance characteristics, the abnormal state of the solenoid valve can be detected in a timely manner, and the possible fault types can be predicted. Commonly used fault identification methods include trend analysis method, pattern recognition method, knowledge reasoning method, etc. The system can select a suitable method for fault identification according to the changing characteristics of resonance characteristics and fault mechanisms.

[0088] 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, recording the resonance frequency, amplitude, and phase characteristics of the solenoid valve at different time points. Then, the system conducts trend analysis on the historical data, extracting the laws and characteristics of the resonance characteristics changing over time, such as the change rate, mutation points, periodicity, etc. Next, the system matches the extracted changing characteristics with typical fault patterns 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 decreasing, and there is a risk of spring fatigue or fracture in the solenoid valve; if the resonance amplitude shows a stepwise decrease, it may mean that the valve core is suddenly stuck or detached, and there are problems of mechanical jamming or foreign object jamming in the solenoid valve; if the phase angle shows random fluctuations, it may mean that there are intermittent poor contacts or short circuits and other electrical faults inside the solenoid valve.

[0089] In practical applications, some new technical problems may be encountered in this step. For example, the fault modes of the solenoid valve are complex and diverse, and the changing trend of resonance characteristics is interfered by various factors, resulting in low accuracy and reliability of fault identification. To solve this problem, the system can adopt an adaptive fault identification strategy, comprehensively utilize multi-source heterogeneous information, and dynamically optimize the fault identification model and algorithm. For example, the system can conduct correlation analysis between resonance characteristics and other monitoring indicators (such as current, voltage, temperature, etc.), construct a multivariate fault symptom matrix, and extract more comprehensive and reliable fault characteristics through data mining and machine learning algorithms. In addition, the system can introduce an expert knowledge base and a fault case base to reason and verify the results of fault identification, improving the accuracy and interpretability of fault identification. At the same time, the system can adopt a self-learning and self-optimizing mechanism, and continuously update and improve the fault identification rules and strategies according to the feedback of diagnostic results to adapt to the state changes and working condition disturbances of the solenoid valve.

[0090] In the above embodiments, by applying an alternating current signal with 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 excited. By separating the mixed current signal to obtain the harmonic components, and then determining the target frequency, the subsequent applied second preset frequency excitation signal can resonate with the internal mechanical structure of the solenoid valve. In the resonance state, the minute changes in the internal mechanical structure of the solenoid valve will be significantly amplified, which are reflected in the amplitude and phase characteristics of the resonance current signal. As time goes by, different types of faults will cause different change trends in the resonance characteristics, and there is a definite corresponding relationship between this change trend and the fault type. By analyzing the change trend of the resonance characteristics, the fault type of the solenoid valve can be accurately identified, realizing the early diagnosis of the solenoid valve fault, improving the accuracy of identifying the evolution trend of the fault characteristics, and then preventing in advance the possible faults of the solenoid valve, improving the safety during the use of the solenoid valve.

[0091] The above embodiments mainly describe how to identify the fault type of the solenoid valve by using the resonance characteristics. In practical applications, solenoid valves often form a valve group in series or parallel, and there are mechanical and electromagnetic coupling relationships between adjacent solenoid valves. When a certain solenoid valve fails, the fault characteristics will spread to the surrounding through this coupling relationship, resulting in the performance of adjacent solenoid valves being 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 influence range. The following will describe another solenoid valve fault diagnosis method in the embodiments of the present application in conjunction with Figure 2 , and describe another solenoid valve fault diagnosis method in the embodiments of the present application:

[0092] Please refer to Figure 2 , which is another process schematic diagram of a solenoid valve fault diagnosis method in the embodiments of the present application.

[0093] S201. Sequentially apply an excitation current signal with a second preset frequency to the adjacent solenoid valves connected in series or parallel with the solenoid valve;

[0094] The purpose of this step is to apply an excitation signal to the adjacent solenoid valves connected in series or parallel with the faulty solenoid valve, providing a basis for 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. To comprehensively evaluate the propagation range and influence degree of the fault characteristics, it is necessary to excite and collect all adjacent solenoid valves. The system can automatically identify the solenoid valves adjacent to the faulty solenoid valve according to the topological structure and connection mode of the solenoid valves, and sequentially apply the excitation signals in a preset order. At the same time, the system can also optimize parameters such as the frequency and amplitude of the excitation signal according to the type and parameters of the adjacent solenoid valves to improve the accuracy and reliability of fault characteristic extraction.

[0095] Specifically, the system can implement this step in the following way: First, based on the layout diagram and connection table of the solenoid valves, the system identifies all adjacent solenoid valves that are in series or parallel with the faulty solenoid valve and establishes a logical relationship matrix of the adjacent solenoid valves. Then, according to the logical relationship matrix, the system determines the excitation sequence and priority of the adjacent solenoid valves. For example, it preferentially excites the solenoid valves directly connected to the faulty solenoid valve and then sequentially excites the indirectly connected solenoid valves. Next, based on the models and specifications of the adjacent solenoid valves, the system selects the most matching excitation signal from the excitation signal library and applies the excitation signals to the coils of the adjacent solenoid valves in sequence through a signal generator. During the process of applying the excitation signals, the system can monitor the response of the adjacent solenoid valves in real time through a current sensor and dynamically adjust the parameters of the excitation signals according to the quality and stability of the response signals to ensure obtaining high-quality resonance current signals.

[0096] S202. Collect the resonance current signals of the adjacent solenoid valves and calculate the transfer function between the resonance current signals of the solenoid valve and each adjacent solenoid valve;

[0097] 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 resonance current signals of the solenoid valve and the adjacent solenoid valves. In practical applications, the transfer function is a mathematical model that describes the relationship between the input and output of a system and can reflect characteristics such as amplitude attenuation and phase delay during signal propagation. By analyzing the transfer function between the solenoid valve and the adjacent solenoid valves, 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 various transfer function identification methods, such as the frequency response method, step response method, correlation analysis method, etc., and adaptively select the optimal identification algorithm and model order according to the collected resonance current signals to improve the accuracy and efficiency of transfer function estimation.

[0098] Specifically, the system can calculate the transfer function through the following steps: First, the system preprocesses the resonance current signals of the solenoid valve and the adjacent solenoid valve collected, such as denoising, normalizing, synchronizing, etc., to eliminate signal interference and distortion. Then, the system uses signal processing tools to transform the resonance current signals, such as Fourier transform, wavelet transform, etc., to convert the time-domain signals into the frequency domain or time-frequency domain and extract the spectral characteristics of the signals. Next, based on the spectral characteristics, the system adopts appropriate transfer function models, such as fractional models, state-space models, 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, 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.

[0099] S203. Identify the propagation path of the fault characteristics based on the transfer function;

[0100] The system identifies the propagation path of the 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 according to the attenuation coefficient and phase delay; analyzing the singular values of the spatial transfer matrix to determine the spatial position of the fault source.

[0101] The propagation of fault signals between solenoid valves often exhibits 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 propagation direction, attenuation rate, and phase delay of the fault signal in the valve group can be revealed, and then the location and influence 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 algorithms such as graph traversal and partitioning to automatically search for and identify the propagation path of the fault signal.

[0102] Specifically, the system can identify the propagation path of fault characteristics 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 attenuation rate of the energy of the fault signal during propagation, and the phase delay reflects the time difference of the fault signal between different solenoid valves. Then, the system constructs a spatial transfer matrix of fault characteristics based on the attenuation coefficient and phase delay, and this matrix describes the spatial topological relationship of the fault signal propagation 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 magnitude and distribution of the singular values, the main directions and paths of fault propagation can be revealed. Finally, the system determines the spatial position of the fault source based on the characteristics of the singular vectors and combines with the physical layout of the solenoid valve array, and identifies the influence 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, ant colony algorithms, etc., to accelerate the path search and identification process.

[0103] S204. Select key nodes on the propagation path of fault characteristics;

[0104] In practical applications, not all solenoid valve nodes on the propagation path have the same importance. Due to their special positions or connection relationships in the topological structure, some nodes have a greater impact on the propagation and attenuation of fault signals. By selecting these key nodes on the propagation path, the diffusion of fault signals can be blocked or suppressed to the greatest extent, thereby controlling the fault impact within a local range. The system can comprehensively consider topological indicators such as the degree, betweenness, and distance of the nodes, as well as factors such as the physical parameters and fault sensitivity of the nodes, and use methods such as multi-criteria decision-making and weighted scoring to automatically evaluate and screen out the most critical nodes.

[0105] Specifically, the system can select key nodes in the following way: First, the system performs a topological analysis on the identified fault propagation path and calculates indexes such as the degree, betweenness, and distance of each node on the path. The degree reflects the number of connections of the node. The larger the degree, the higher the importance of the node in the path. The betweenness reflects the transfer role of the node between different paths. The larger the betweenness, the greater the impact 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 effects 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 response ability of the node to the type and degree of the fault. The higher the sensitivity, the more easily the node is affected by the fault. Finally, the system comprehensively considers the above indexes, adopts multi-criteria decision-making methods such as the analytic hierarchy process and fuzzy comprehensive evaluation, ranks and screens the importance of the nodes, and selects the top N% of the nodes as key nodes.

[0106] S205. Adjust the second preset frequency of the solenoid valve at the key node so that the second preset frequency of the solenoid valve forms an anti-phase relationship with that of the fault source solenoid valve;

[0107] By constructing an anti-phase excitation signal on the fault propagation path, the interference and superposition principles of waves can be utilized to cause the fault signal to cancel and attenuate during propagation, thereby achieving the purpose of isolating and suppressing the impact of the fault. To achieve the best interference effect, it is necessary to precisely control the excitation frequency and phase of the solenoid valve at the key node so that it forms a complete anti-phase relationship with the fault source signal. The system can adopt methods such as adaptive control and robust control, dynamically adjust the excitation parameters of the key node 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.

[0108] 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 resonance current signal of the fault source solenoid valve, and obtains the dominant frequency and initial phase of the fault signal. Then, the system calculates the fault signal frequency and phase at the key node according to the transfer function model, and compares it with the target anti-phase relationship to obtain the adjustment amount of frequency and phase. Next, the system uses a frequency synthesizer and a phase modulator to generate an excitation signal with the same frequency as the fault signal and the opposite phase, 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 a vibration and current response opposite to the fault signal, thereby weakening and canceling the propagation intensity of the fault signal. Finally, the system collects the response signal of the key node solenoid valve through a current sensor, performs correlation analysis with the fault source signal, calculates the amplitude and phase of the residual fault signal, and inputs it as a feedback quantity into the frequency and phase adjustment unit to realize the adaptive correction and optimization of the excitation parameters.

[0109] S206. Suppress the propagation of fault characteristics through the anti-phase relationship.

[0110] The system suppresses the propagation of fault characteristics through the anti-phase relationship, specifically including: calculating the amplitude and phase of the fault characteristics at the key node; generating a compensation signal with the same amplitude and opposite phase; superimposing the compensation signal on the excitation current signal of the solenoid valve at the key node.

[0111] The purpose of this step is to construct an anti-phase excitation signal at the key node and use the principle of wave interference to suppress the propagation of fault characteristics in the solenoid valve array. In practical applications, by precisely controlling parameters such as the amplitude and phase of the anti-phase signal, it can form a complete cancellation relationship with the fault signal at the key node. 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, the realizability of the anti-phase signal in the actual solenoid valve system also needs to be considered, such as the signal frequency range, amplitude range, modulation method, etc., to ensure the effectiveness and reliability of the suppression measures.

[0112] Specifically, the system can achieve the suppression of fault characteristics through the following steps: First, based on the fault propagation paths and key nodes identified in steps S203 and S204, the system determines the target positions where anti-phase signals need to be applied. Then, the system uses signal processing tools, such as phase-locked loops, notch filters, etc., to extract the amplitude and phase information of the fault characteristics from the response signal of the fault source solenoid valve. Next, according to the amplitude and phase information, the system synthesizes an anti-phase compensation signal that matches the fault characteristic parameters 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 the same amplitude but opposite phase to the fault signal, thereby achieving the active cancellation and suppression of the fault characteristics. Finally, the system collects the vibration signals of the key node and its neighborhood 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 the closed-loop optimization of the suppression effect.

[0113] In the above embodiment, by applying an excitation signal to the adjacent solenoid valve and collecting the resonance current signal, 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 influence. 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 influence 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, improving the accuracy and reliability of fault diagnosis.

[0114] The following describes the system in the embodiments of the present invention application from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of a solenoid valve fault diagnosis system provided in an embodiment of the present application.

[0115] It should be noted that Figure 3 the structure of the system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0116] Such as Figure 3As shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303, such as executing the method in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0117] 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), 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 from it can be installed into the storage section 308 as needed.

[0118] Specifically, 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 contains 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 via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by a Central Processing Unit (CPU) 301, various functions defined in the present invention are executed.

[0119] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can 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 disc 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 can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can 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 can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0121] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or may exist alone 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 embodiments.

[0122] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

[0123] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted as "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0124] 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a dedicated computer, a computer network, or other programmable devices. 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 one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer, 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 (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state drive), etc.

[0125] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by relevant hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage media include: various media that can store program codes, such as ROM, random access memory (RAM), magnetic disks, or optical discs.

Claims

1. A method for diagnosing solenoid valve faults, characterized in that, Including: When the solenoid valve is in a normal working state, an alternating current signal with a first preset frequency is applied to the solenoid valve coil, and at the same time, the inherent working current signal of the solenoid valve is collected to generate a mixed current signal. Separate the mixed current signal to obtain the harmonic components in the inherent working current signal. Based on the harmonic components, determine the target frequency that interferes with the first preset frequency. Generate an excitation current signal with 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. Apply the excitation current signal to the solenoid valve coil and collect the resonance 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 according to the amplitude and phase characteristics of the resonance current signal. Identify the fault type of the solenoid valve based on the change trend of the resonance characteristics over time.

2. The method according to claim 1, wherein The separating the mixed current signal to obtain the harmonic components in the inherent working current signal specifically includes: Perform wavelet decomposition on the mixed current signal to obtain wavelet coefficients in multiple frequency bands. Calculate the energy distribution characteristics of the wavelet coefficients and determine the frequency interval where the energy accumulates. Reconstruct the signal in the frequency interval to obtain the harmonic components in the inherent working current signal.

3. The method according to claim 1, characterized in that The determining the target frequency that interferes with the first preset frequency based on the harmonic components specifically includes: Construct a time-frequency distribution diagram of the harmonic components. Identify the frequency points with signal energy density greater than a preset threshold in the time-frequency distribution diagram. Sort the frequency points according to the signal energy density magnitude, 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 among the candidate frequencies. Determine the frequency that interferes with the first preset frequency among the remaining frequency points as the target frequency.

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 characteristics over time, the method further includes: Sequentially apply the excitation current signal with the second preset frequency to the adjacent solenoid valves connected in series or parallel with the solenoid valve. Collect the resonance current signals of the adjacent solenoid valves. Calculate the transfer function between the resonance current signals of the solenoid valve and each of the adjacent solenoid valves. Identify the propagation path of the fault characteristics based on the transfer function.

5. The method according to claim 4, wherein The identifying the propagation path of the fault characteristics based on the transfer function specifically includes: Extract the amplitude spectrum and phase spectrum of the transfer function. Calculate the amplitude attenuation coefficient and phase delay between the adjacent solenoid valves. Construct a spatial transfer matrix of the fault characteristics according to the attenuation coefficient and the phase delay. Analyze the singular values of the spatial transfer matrix to determine the spatial position of the fault source.

6. The method according to claim 4 or 5, characterized in that, After identifying the propagation path of the fault characteristics based on the transfer function, the method further includes: Select key nodes on the propagation path of the fault characteristics. Adjust the second preset frequency of the solenoid valve at the key node so that the second preset frequency of the solenoid valve forms an anti-phase relationship with that of the fault source solenoid valve. Suppress the propagation of the fault characteristics through the anti-phase relationship.

7. The method according to claim 6, wherein Suppressing the propagation of the fault feature through the anti-phase relationship specifically includes: Calculating the amplitude and phase of the fault feature at the key node; Generating a compensation signal with an equal amplitude and an opposite phase; Superimposing the compensation signal 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 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 the one or more processors call the computer instructions to cause the system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the system, it causes the system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the system, it causes the system to execute the method according to any one of claims 1-7.

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