Composite electromagnetic coil and electromagnetic valve

Abstract

The invention discloses a composite electromagnetic coil and an electromagnetic valve, and relates to the technical field of electromagnetic valves, the composite electromagnetic coil comprises a coil framework, three layers of independent windings wound on the coil framework and a magnetic core assembly, and the three layers of independent windings comprise an inner layer driving winding, a middle layer focusing winding and an outer layer recycling winding in sequence from inside to outside; the wire diameter of the inner-layer driving winding is larger than that of the middle-layer focusing winding and that of the outer-layer recycling winding, the outer-layer recycling winding is electrically connected with the diode bridge and is reversely connected to the driving power source in parallel, through the layered design of the composite electromagnetic coil, accurate control over electromagnetic driving force and improvement of energy efficiency are achieved, recycling is conducted through the energy recycling and diagnosing module, and the energy efficiency is improved. And the energy efficiency of the system is further improved. Through the optimally designed circuit and the fault diagnosis algorithm, real-time monitoring and fault diagnosis of the state of the electromagnetic valve are achieved, the reliability and stability of the system are improved, and remarkable advantages are shown in the aspects of electromagnetic driving, energy efficiency improvement and fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of solenoid valves, and particularly to a composite electromagnetic coil and a solenoid valve. Background Art

[0002] As an important control component, solenoid valves have been widely used and popularized in many industrial fields. Its working principle is to control the opening and closing of the valve by the on-off of the current, so as to achieve the control of the fluid. The performance and efficiency of the solenoid valve largely depend on the design of its internal coil, including the number of turns of the coil and the size of the wire diameter. The selection of these parameters directly affects the suction force of the solenoid valve, that is, its ability to drive the valve core.

[0003] In traditional designs, the coil usually adopts a single wire diameter and winding method, which results in a uniform magnetic field distribution. However, this uniformly distributed magnetic field requires the magnetic force covering the entire stroke when driving the valve core, which causes a certain degree of energy waste to a certain extent. In addition, when the valve core needs to be reset, the energy released by the spring cannot be effectively recycled, further reducing the overall efficiency of the solenoid valve. Therefore, we propose a composite electromagnetic coil and a solenoid valve. Summary of the Invention

[0004] The purpose of the present invention is: to solve the problems that in traditional designs, the coil usually adopts a single wire diameter and winding method, which results in a uniform magnetic field distribution. However, this uniformly distributed magnetic field requires the magnetic force covering the entire stroke when driving the valve core, which causes a certain degree of energy waste to a certain extent. In addition, when the valve core needs to be reset, the energy released by the spring cannot be effectively recycled, further reducing the overall efficiency of the solenoid valve. The present invention provides a composite electromagnetic coil and a solenoid valve.

[0005] The present invention specifically adopts the following technical solutions to achieve the above purposes:

[0006] A composite electromagnetic coil includes a coil skeleton, three layers of independent windings wound on the coil skeleton, and a magnetic core assembly. The three layers of independent windings are, from the inside to the outside, an inner layer driving winding, a middle layer focusing winding, and an outer layer recovery winding. The wire diameter of the inner layer driving winding is larger than that of the middle layer focusing winding and the outer layer recovery winding. The outer layer recovery winding is electrically connected to a diode bridge and is reversely connected in parallel to the driving power supply. The magnetic core assembly is an iron core made of a magnetic conductive material, and the magnetic core assembly penetrates through the central hole of the coil skeleton and extends to both ends to form magnetic pole faces.

[0007] Further, the inner layer driving winding adopts a thick wire diameter multi-strand stranded litz wire, and the middle layer focusing winding is a thin wire diameter silver-plated copper wire wound in a honeycomb hexagonal high turn density.

[0008] Furthermore, the inner driving winding and the middle focusing winding are wound in the opposite direction, and each layer of winding is connected to an independent MOSFET switch tube, and electrical isolation is achieved through a hardware circuit.

[0009] Furthermore, nanocrystalline soft magnetic alloy partitions are arranged between the three layers of independent windings, and a conical protrusion is processed on the top of the iron core of the magnetic core assembly.

[0010] Furthermore, the outer recovery winding is connected in series with a fast recovery diode, which only allows the reverse electromotive force when the valve core is reset to pass through, thereby blocking the interference of the forward driving current.

[0011] A solenoid valve, wherein the solenoid valve body is provided with the composite solenoid coil, and the composite solenoid coil is electrically connected to a microcontroller, and the microcontroller is provided with:

[0012] The layered coil drive module receives PWM control signals and drives three layers of independent windings in a time-sharing manner to achieve different electromagnetic functions. The inner layer driving winding is responsible for generating the main electromagnetic driving force. Its larger wire diameter can carry higher currents, thereby providing a stronger magnetic field strength. The middle layer focusing winding focuses and optimizes the magnetic field by precisely controlling the direction and size of the current, making the distribution of the electromagnetic force more uniform and precise. The outer layer recovery winding is responsible for recovering and utilizing part of the lost electromagnetic energy, thereby improving the energy efficiency of the entire system.

[0013] The energy recovery and diagnosis module includes a rectifier bridge, a supercapacitor and ADC sampling. The outer winding is connected to the H-bridge circuit. When the valve core is reset, the magnetic flux changes to generate an induced current, which is stored in the supercapacitor after rectification and voltage stabilization. The output end of the outer winding is connected to the differential amplifier circuit and the 16-bit ADC to extract the current waveform details, run the fault diagnosis algorithm, determine the fault type according to the recovery current characteristics, and trigger fault pre-tightening.

[0014] Furthermore, the fault diagnosis algorithm uses SVM classifier to train the fault classification model. For the fault types, including stuck, coil short circuit and power fluctuation, one-vs-many (OvR) strategy is adopted to train K binary classification SVMs (K is the number of fault types). Each classifier distinguishes one class from other classes, and the final decision selection The class with the highest confidence.

[0015] Furthermore, the optimization objective formula of the fault classification model is:

[0016]

[0017] Constraints:

[0018]

[0019] Where: w is the normal vector of the hyperplane, is the offset, is the kernel function mapping is the regularization parameter, which balances the classification margin and the misclassification penalty, are slack variables, allowing a small number of samples to be misclassified.

[0020] Further, the decision function formula of the fault classification model is:

[0021]

[0022] Where: are Lagrange multipliers, non-zero values corresponding to support vectors, with the output of +1 for the normal class and -1 for the fault class

[0023] Further, the warning trigger threshold rule is:

[0024]

[0025] If the same fault type is detected 3 times continuously, a warning will be triggered.

[0026] The beneficial effects of the present invention are as follows:

[0027] 1. Through the hierarchical design of the composite electromagnetic coil, the present invention realizes the precise control of the electromagnetic driving force and the improvement of energy efficiency. Specifically, the inner layer driving winding provides the main electromagnetic driving force to ensure fast and stable spool movement; the middle layer focusing winding focuses and optimizes the magnetic field, making the distribution of the electromagnetic force more uniform and improving the control accuracy; the outer layer recovery winding is responsible for recovering part of the dissipated electromagnetic energy, which is reused through the energy recovery and diagnosis module, further improving the energy efficiency of the system. In addition, through the optimized circuit and fault diagnosis algorithm, the real-time monitoring and fault diagnosis of the solenoid valve state are realized, and the reliability and stability of the system are improved, showing significant advantages in electromagnetic drive, energy efficiency improvement and fault diagnosis.

[0028] 2. The inner layer driving winding of the present invention uses thick wire diameter multi-strand stranded Litz wire to reduce the high-frequency resistance. The middle layer focusing winding is made of thin wire diameter silver-plated copper wire and wound in a honeycomb hexagonal high-turn density. The silver-plated copper wire reduces the surface resistance. The honeycomb winding of the middle layer focusing winding focuses the magnetic field on the spool balance position, which is spatially separated from the driving area (the top of the iron core) of the inner layer driving winding, realizing the decoupling of the driving and focusing areas, reducing the mutual electromagnetic interference, and improving the control stability and accuracy.

[0029] 3. The nanocrystalline soft magnetic alloy partition of the present invention has the characteristics of high magnetic permeability and low loss. By using its high magnetic permeability characteristic to guide the magnetic field direction, it can effectively isolate the magnetic field interference between each layer of windings, and at the same time provide a stable magnetic circuit to ensure the precise control of electromagnetic force. The magnetic field of the inner layer driving winding is restricted in the conical convex area of the iron core as the main magnetic path. The magnetic field of the middle layer focusing winding acts on the valve core balance position intensively through the honeycomb winding structure. The outer layer recovery winding only captures the leakage magnetic flux far from the main magnetic path. The application of the nanocrystalline soft magnetic alloy partition not only enhances the electrical isolation between windings, but also optimizes the magnetic field path, making the distribution of electromagnetic force more reasonable and further improving the performance of the solenoid valve. Description of the Drawings

[0030] Figure 1 is a schematic diagram of the composite electromagnetic coil structure of the present invention;

[0031] Figure 2 is a block diagram of the control system structure of the present invention.

[0032] Reference Signs: 10, coil skeleton; 21, inner layer driving winding; 22, middle layer focusing winding; 23, outer layer recovery winding; 30, magnetic core assembly. Detailed Embodiments

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0034] Please refer to Figure 1 - Figure 2 , the present invention provides a composite electromagnetic coil, including a coil skeleton 10, three layers of independent windings wound on the coil skeleton 10, and a magnetic core assembly 30. The three layers of independent windings are, from the inside to the outside, an inner layer driving winding 21, a middle layer focusing winding 22, and an outer layer recovery winding 23. The wire diameter of the inner layer driving winding 21 is larger than that of the middle layer focusing winding 22 and the outer layer recovery winding 23. The outer layer recovery winding 23 is electrically connected to a diode bridge and is reversely connected in parallel to the driving power supply. The magnetic core assembly 30 is an iron core made of a magnetic conductive material, and the magnetic core assembly 30 penetrates through the central hole of the coil skeleton 10 and extends to both ends to form magnetic pole surfaces.

[0035] Working principle and usage process of the present invention: When the device is in use, the inner winding is energized, and the thick wire diameter carries transient large current (5A / 10ms), generating a strong initial magnetic field to push the spool valve. The middle winding is energized (0.5A), and the honeycomb winding structure focuses the magnetic field on the balanced position of the spool valve, reducing the maintenance power consumption. When the spool valve resets, the outer winding captures the leakage magnetic energy, rectifies it through a diode bridge and feeds it back to the power supply, and the recovery efficiency > 30%. Through the optimized design of the three-layer independent winding, precise control of the electromagnetic driving force and efficient recovery of energy are achieved. Compared with traditional solenoid valves, the present invention shows significant advantages in improving work efficiency, reducing energy consumption, and enhancing the fault diagnosis ability. The large wire diameter design of the inner driving winding 21 ensures sufficient electromagnetic driving force, which can quickly respond and push the spool valve to act; the middle focusing winding 22 optimizes the magnetic field distribution by precisely controlling the current, reducing unnecessary energy consumption; while the outer recovery winding 23 effectively recovers the dissipated electromagnetic energy, further improving the overall energy efficiency of the system.

[0036] In this embodiment, preferably, the inner driving winding 21 adopts thick wire diameter multi-strand stranded Litz wire to reduce the high-frequency resistance. The middle focusing winding 22 is made of thin wire diameter silver-plated copper wire and wound in a honeycomb hexagonal high-turn density. The silver-plated copper wire reduces the surface resistance. The honeycomb winding of the middle focusing winding 22 focuses the magnetic field on the balanced position of the spool valve, which is spatially separated from the driving area (the top of the iron core) of the inner driving winding 21, realizing the decoupling of the driving and focusing areas, reducing the mutual electromagnetic interference, and improving the stability and precision of control.

[0037] In this embodiment, preferably, the inner driving winding 21 and the middle focusing winding 22 are wound in the opposite direction, reducing the mutual inductance coefficient of the two windings to 0.02 μH. Each layer of winding is connected with an independent MOSFET switch tube, and electrical isolation is achieved through the hardware circuit. By reducing the mutual inductance interference, independent control of the driving current and the focusing current is ensured, improving the stability and response speed of the electromagnetic system. In addition, the reverse winding helps to reduce electromagnetic noise and improve the overall electromagnetic compatibility.

[0038] In this embodiment, preferably, a nanocrystalline soft magnetic alloy partition 24 is provided between the three-layer independent windings, and a conical protrusion is machined on the top of the iron core of the magnetic core assembly 30 to improve the electromagnetic efficiency. The nanocrystalline soft magnetic alloy partition 24 has the characteristics of high magnetic permeability and low loss. By utilizing its high magnetic permeability characteristic to guide the magnetic field direction, it can effectively isolate the magnetic field interference between each layer of windings, and at the same time provide a stable magnetic circuit to ensure the precise control of the electromagnetic force. The magnetic field of the inner layer drive winding 21 is restricted in the conical protrusion area of the iron core and serves as the main magnetic path. The magnetic field of the middle layer focusing winding 22 acts on the valve core balance position intensively through the honeycomb winding structure. The outer layer recovery winding 23 only captures the leakage magnetic flux far from the main magnetic path. The application of the nanocrystalline soft magnetic alloy partition 24 not only enhances the electrical isolation between the windings, but also optimizes the magnetic field path, making the distribution of the electromagnetic force more reasonable and further improving the performance of the solenoid valve.

[0039] The design of the conical protrusion on the top of the iron core forms an asymmetric magnetic circuit with the layered coil, further optimizing the magnetic field distribution, enhancing the concentration and directivity of the electromagnetic force, making the magnetic circuit form a local enhancement at the valve core balance position, which is beneficial to reducing energy loss and improving the electromagnetic drive efficiency.

[0040] In this embodiment, preferably, the outer layer recovery winding 23 is connected in series with a fast recovery diode, which only allows the reverse electromotive force during the valve core reset to pass through, blocking the interference of the forward drive current, and can quickly respond to the reverse electromotive force during the valve core reset, effectively guiding the recovered electromagnetic energy to the rectifier bridge, further improving the energy recovery efficiency. The outer side of the outer layer winding is wrapped with a 1 mm thick permalloy shielding layer to guide the leakage magnetic flux to the recovery winding and reduce the interference to other layers.

[0041] A solenoid valve, on which the above-mentioned composite electromagnetic coil is provided, and the composite electromagnetic coil is electrically connected to a microcontroller. The microcontroller is provided with:

[0042] A layered coil drive module, which receives a PWM control signal and drives the three-layer independent windings in a time-sharing manner to achieve different electromagnetic functions. The inner layer drive winding 21 is responsible for generating the main electromagnetic driving force. Its larger wire diameter can carry a higher current, thereby providing a stronger magnetic field intensity. The middle layer focusing winding 22 focuses and optimizes the magnetic field by precisely controlling the direction and magnitude of the current, making the distribution of the electromagnetic force more uniform and precise. The outer layer recovery winding 23 is responsible for recovering and utilizing part of the dissipated electromagnetic energy to improve the energy efficiency of the entire system;

[0043] Time-sharing power supply: The three windings avoid magnetic field superposition through timing logic:

[0044] Stage 1 (0 - 10 ms): Only the inner layer drive winding 21 is energized (5 A) to quickly drive the valve core;

[0045] Phase 2 (10 ms - continuous): Turn off the inner layer and switch to the middle focusing winding 22 (0.5 A).

[0046] Phase 3 (spool valve reset): Turn off all drives, and the outer winding generates electricity through the change in magnetic flux.

[0047] The circuit design of the inner drive winding 21 utilizes the high - current MOSFET IRF3205. Its high breakdown voltage and high - current capacity are suitable for the high - power requirements of the drive winding. The fast - recovery diode UF4007 is used to provide a low - loss reverse - current path and reduce switching losses. The circuit parameters are set as a pulse - current peak value of 5 A, a pulse width of 10 ms, and a duty cycle of less than 5% to control the energy output. The TVS diode is used to protect the circuit, suppress the reverse peak voltage, and prevent the MOSFET from being damaged due to over - voltage.

[0048] The circuit design of the middle focusing winding 22 adopts the integrated constant - current source chip LM317, with the set current of 0.5 A to ensure a stable current output. The magnetic isolation optocoupler TLP521 is used for signal isolation transmission, improving the anti - interference ability and safety of the circuit. The current accuracy is adjusted through the I²C interface, with an accuracy of up to ±1%, ensuring precise control of the current output.

[0049] The circuit design of the outer recovery winding 23 uses the full - bridge rectifier MB6S to convert alternating current to direct current, and the supercapacitor is used to store the recovered energy. The supercapacitor has a low on - state voltage drop (<0.3 V) and can support high - frequency energy - recovery operations up to 100 kHz, making it suitable for fast energy - recovery scenarios.

[0050] The energy recovery and diagnosis module includes a rectifier bridge, a supercapacitor, and ADC sampling. The outer winding is connected to the H - bridge circuit. When the spool valve resets, the change in magnetic flux generates an induced current, which is rectified, regulated, and stored in the supercapacitor after rectification. The output terminal of the outer winding is connected to a differential amplifier circuit and a 16 - bit ADC to extract the details of the current waveform, run the fault diagnosis algorithm, judge the fault type based on the characteristics of the recovered current, and trigger fault pre - tightening.

[0051] The current sampling circuit of the energy recovery and diagnosis module includes a high - side current sensor and a filter energy recovery and diagnosis unit. High - side current sensor: The high - side current sensor of model INA240 is selected. Its common - mode voltage can reach 80 V, and the bandwidth is as high as 500 kHz, which can meet the requirements of most application scenarios. The filter energy recovery and diagnosis unit adopts a second - order RC low - pass filtering technology, with the cut - off frequency set at 10 kHz, which can effectively filter out high - frequency noise and ensure the accuracy of the signal.

[0052] The ADC parameters of the diagnostic algorithm interface of the energy recovery and diagnostic module have a high resolution of 12 bits and a high sampling rate of 10 ksps, which can accurately capture the changes in the current signal. In terms of signal processing, using the FFT hardware acceleration technology and combining with the built-in FPU (floating-point operation unit) of the STM32F4 series, it can quickly and accurately extract the characteristics of the signal, providing accurate data support for the subsequent diagnostic algorithm.

[0053] In this embodiment, preferably, the fault diagnosis algorithm uses an SVM classifier to train a fault classification model. For fault types including jamming, coil short circuit, and power supply fluctuation, a one-versus-rest (OvR) strategy is used to train K binary SVMs (K is the number of fault types). Each classifier distinguishes one class from the others, and the final decision selects the class with the highest confidence.

[0054] Due to its powerful classification ability and good generalization performance, the SVM classifier has been widely used in the field of fault diagnosis. In the present invention, the SVM classifier is used to train a fault classification model. For fault types such as jamming, coil short circuit, and power supply fluctuation that may occur in the solenoid valve, a one-versus-rest (OvR) strategy is adopted for training. Specifically, for K fault types, we train K binary SVMs, and each classifier is responsible for distinguishing one fault type from all other fault types. In the test stage, when new current signal characteristics are input, the K classifiers will respectively give the confidence that the signal belongs to their respective fault types, and finally select the class with the highest confidence as the diagnostic result. This strategy not only improves the accuracy of fault diagnosis but also effectively reduces the risks of false alarms and missed alarms.

[0055] The method for constructing the fault classification model includes the following steps:

[0056] (1) Feature extraction:

[0057] Calculate the peak value of the current waveform, rise time, integral area, FFT spectrum energy distribution, and detect microsecond-level transient anomalies through wavelet transform, such as the current drop during the jamming moment;

[0058] - Peak current: The maximum value of the current signal reflects the load mutation during the driving stage and is used to detect whether the current abnormally increases or decreases due to mechanical jamming, coil short circuit, etc. when the solenoid valve starts.

[0059]

[0060] - Rise time: The time required for the current to rise from of the steady-state value to is used to detect the coil response speed and reflects the response ability of the solenoid valve.

[0061]

[0062] - Integral area (energy index): The integral of current over time, which represents the total energy consumption of the coil during the operation cycle and is used to evaluate whether the overall power consumption of the solenoid valve is abnormal.

[0063]

[0064] - Spectrum energy distribution: Discretize the time-domain current signal and perform FFT to obtain the frequency-domain energy distribution , convert the time-domain current signal to the frequency domain, identify abnormal frequency components caused by high-frequency noise, resonance, or coil insulation deterioration. If energy spikes appear in non-working frequency bands such as the high-frequency region, it may indicate inter-turn short circuit of the coil or external electromagnetic interference.

[0065]

[0066] - Main frequency band energy ratio: The energy ratio in a specific frequency band (such as ), which quantifies the energy concentration degree in the normal working frequency band of the solenoid valve and reflects the stability of the coil working state.

[0067]

[0068] - Continuous wavelet transform (CWT): Decompose the signal through the mother wavelet at different scales and translations to capture microsecond-level transient characteristics (such as current drop during jamming). If the energy suddenly increases at a certain level, the time and duration of the fault occurrence can be located, such as abnormal spring rebound during spool reset.

[0069]

[0070] - Energy of discrete wavelet transform (DWT) coefficients: The energy of the detail coefficients at the th layer, which is used to quantify the intensity of transient anomalies.

[0071]

[0072] (2) Fault classification model:

[0073] 2.1. Feature vector construction

[0074] Compose the input vector by normalizing the extracted features:

[0075]

[0076] : Mean, : Standard Deviation

[0077] A feature vector is a numerical representation that transforms text or data and can be used in machine learning models. A feature vector consists of multiple normalized features such as peak time, energy, etc. Normalization is a method of adjusting the data range so that different features have similar weights in the model.

[0078] 2.2. Optimization Objective: The optimization objective formula for the fault classification model is:

[0079]

[0080] Constraints:

[0081]

[0082] where: w is the normal vector of the hyperplane, is the offset, is the kernel function mapping The RBF kernel parameter γ: [0.1, 10], is the regularization parameter, balancing the classification margin and misclassification penalty, C: [0.1, 100], are slack variables, allowing a small number of samples to be misclassified.

[0083] The optimization objective of the fault classification model is to find the best weights and biases so that the model can accurately distinguish between normal and fault classes. This is achieved by minimizing a loss function that includes classification error and a regularization term. The regularization term is used to prevent the model from overfitting, that is, the model performs well on the training data but poorly on new data.

[0084] 2.3. Decision Function: The decision function formula for the fault classification model is:

[0085]

[0086] where: are Lagrange multipliers, non - zero values corresponding to support vectors, with the output +1 for the normal class and -1 for the fault class.

[0087] The decision function is the basis for the model to classify new data. The decision function is based on the principle of Support Vector Machine (SVM), making classification decisions by calculating the similarity between the input data and the support vectors. Support vectors are the points in the training data that are closest to the decision boundary and are crucial for the classification ability of the model.

[0088] Fault diagnosis algorithms handle a large amount of data conversion and calculations to ensure that users can accurately mark, analyze, and process the required text or data areas. These algorithms rely on efficient data structures and algorithm designs to ensure fast and accurate task completion when processing large amounts of data.

[0089] In this embodiment, preferably, the warning trigger threshold rule is as follows:

[0090]

[0091] Detecting the same type of fault three times in a row triggers a warning.

[0092] The design of the warning trigger mechanism is to ensure that the system can respond in a timely manner when a fault persists. When the fault classification model classifies the same data point as a fault three times in a row, the warning trigger condition is met. This mechanism helps reduce false alarms because only persistent faults trigger warnings, and accidental noise or errors do not interfere with the normal operation of the system. Once a warning is triggered, the system can take corresponding measures, such as sending a notification to maintenance personnel or automatically adjusting device parameters to try to correct the fault. Such a design enhances the reliability and stability of the system, enabling users to have more trust in the output results of the fault classification model.

[0093] The feature extraction formula extracts interpretable physical indicators from the original signal, converting complex signals into numerical features that can be understood by machines. The SVM formula achieves precise differentiation of multiple fault modes through non-linear mapping and high-dimensional classification. The warning logic formula ensures system reliability through threshold rules, avoiding misoperations, and combines the physical characteristics of the solenoid valve, including current, magnetic field, and mechanical motion, with data-driven methods to achieve a closed-loop intelligent decision-making from "signal" to "fault diagnosis".

[0094] Through the hierarchical design of the composite electromagnetic coil, precise control of the electromagnetic driving force and improvement of energy efficiency are achieved. Specifically, the inner layer driving winding 21 provides the main electromagnetic driving force to ensure fast and stable spool movement; the middle layer focusing winding 22 focuses and optimizes the magnetic field, making the distribution of the electromagnetic force more uniform and improving the control precision; the outer layer recovery winding 23 is responsible for recovering part of the dissipated electromagnetic energy, which is reused through the energy recovery and diagnosis module, further improving the energy efficiency of the system. In addition, through the optimized design of the circuit and fault diagnosis algorithms, real-time monitoring and fault diagnosis of the solenoid valve state are achieved, and significant advantages are shown in terms of electromagnetic drive, energy efficiency improvement, and fault diagnosis, enhancing the reliability and stability of the system.

[0095] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A composite electromagnetic coil, characterized in that: The invention comprises a coil frame (10), three layers of independent windings wound on the coil frame (10), and a magnetic core component (30), wherein the three layers of independent windings are, from the inside to the outside, an inner driving winding (21), a middle focusing winding (22), and an outer recovery winding (23), and the inner driving winding (21) has a larger wire diameter than the middle focusing winding (22) and the outer recovery winding (23), and the outer recovery winding (23) is electrically connected to a diode bridge and reversely connected in parallel to a driving power source, and the magnetic core component (30) is an iron core made of a magnetic conductive material, and the magnetic core component (30) passes through the center hole of the coil frame (10) and extends to both ends to form magnetic pole surfaces.

2. The composite electromagnetic coil according to claim 1, wherein: The inner layer driving winding (21) is made of thick-diameter multi-stranded Litz wire, and the middle layer focusing winding (22) is made of thin-diameter silver-plated copper wire wound in a honeycomb hexagonal shape with high turn density.

3. The composite electromagnetic coil according to claim 1, wherein: The inner layer driving winding (21) and the middle layer focusing winding (22) are wound in opposite directions, and each layer of winding is connected to an independent MOSFET switch tube, and electrical isolation is achieved through a hardware circuit.

4. A composite electromagnetic coil according to claim 1, characterized in that: A nanocrystalline soft magnetic alloy partition (24) is arranged between the three layers of independent windings, and a conical protrusion is processed on the top of the iron core of the magnetic core assembly (30).

5. A composite electromagnetic coil according to claim 1, characterized in that: The outer recovery winding (23) is connected in series with a fast recovery diode, which only allows the reverse electromotive force when the valve core is reset to pass through, thereby blocking the interference of the forward driving current.

6. A solenoid valve, characterized in that: The solenoid valve body is provided with a composite solenoid coil as claimed in any one of claims 1 to 5, and the composite solenoid coil is electrically connected to a microcontroller, and the microcontroller is provided with: The layered coil driving module receives a PWM control signal and drives the three layers of independent windings in a time-sharing manner to realize different electromagnetic functions. The inner layer driving winding (21) is responsible for generating the main electromagnetic driving force. Its larger wire diameter can carry a higher current, thereby providing a stronger magnetic field strength. The middle layer focusing winding (22) focuses and optimizes the magnetic field by precisely controlling the direction and magnitude of the current, thereby making the distribution of the electromagnetic force more uniform and accurate. The outer layer recovery winding (23) is responsible for recovering and utilizing part of the lost electromagnetic energy, thereby improving the energy efficiency of the entire system. The energy recovery and diagnosis module includes a rectifier bridge, a supercapacitor and ADC sampling. The outer winding is connected to the H-bridge circuit. When the valve core is reset, the magnetic flux changes to generate an induced current, which is stored in the supercapacitor after rectification and voltage stabilization. The output end of the outer winding is connected to the differential amplifier circuit and the 16-bit ADC to extract the current waveform details, run the fault diagnosis algorithm, determine the fault type according to the recovery current characteristics, and trigger fault pre-tightening.

7. The solenoid valve according to claim 6, wherein: The fault diagnosis algorithm uses an SVM classifier to train a fault classification model. For fault types including jamming, coil short circuit, and power supply fluctuation, a one-versus-rest (OvR) strategy is adopted to train K binary SVMs (K is the number of fault types). Each classifier distinguishes one class from the others, and the final decision selects the class with the highest confidence.

8. A solenoid valve according to claim 7, characterized in that: The optimization objective formula of the fault classification model is: Constraints: where: w is the hyperplane normal vector, is the offset, is the kernel function mapping is the regularization parameter, balancing the classification margin and the misclassification penalty, is the slack variable, allowing a small number of samples to be misclassified.

9. The solenoid valve according to claim 7, characterized in that: The decision function formula of the fault classification model is: Wherein: is the Lagrange multiplier, and the non-zero value corresponds to the support vector. The output +1 represents the normal class, and -1 represents the fault class.

10. An electromagnetic valve according to claim 6, characterized in that: The warning trigger threshold rule is: If the same fault type is detected three times in a row, an early warning is triggered.

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