A method for detecting and controlling drum imbalance and collision in washing machines

Abstract

This invention discloses a method for detecting and controlling drum imbalance and collision in a washing machine, relating to the field of intelligent control technology. The method includes the following steps: First, acquiring environmental reference parameters, including basic friction torque and damping coefficient, before starting the spin cycle; then, applying dual-modal active excitation to the motor in the low-speed distribution zone, extracting a spin-drying risk feature vector corrected by the reference parameters; calculating the fluid-structure instability characterizing the risk of water accumulation based on this vector and historical adaptive coefficients; arbitrating this instability using a drum collision prevention risk arbitration model, generating a spin-drying safety index and operating condition label, and executing a graded control strategy accordingly; finally, updating the historical adaptive coefficients based on the feedback results after strategy execution, achieving self-evolution of the detection algorithm. This invention solves the problem that existing passive detection technologies struggle to identify the false balance exhibited by waterproof clothing at low speeds due to uniform fluid adhesion to the wall.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to a method for detecting and controlling inner drum imbalance and collision in washing machines. Background Technology

[0002] As drum washing machines evolve towards larger capacity, higher speeds, and greater intelligence, vibration and noise control and structural safety during the spin-drying stage have become core indicators for evaluating product performance. During high-speed spin-drying, uneven distribution of the washing load within the inner drum generates eccentric forces. If these eccentric forces are not effectively detected and controlled, they can lead to severe vibrations and displacements in the washing machine, potentially causing the inner drum to collide with the casing, doors and windows to break, or mechanical fatigue failure. However, the dynamic characteristics of the washing load are highly random and time-varying, influenced by factors such as clothing material, water absorption rate, and distribution pattern. This makes achieving accurate identification and anti-collision control of extreme eccentric conditions while pursuing high spin-drying efficiency a common and significant challenge in the current washing machine control field.

[0003] Existing technologies primarily assess load eccentricity by detecting motor operating parameters or introducing external sensors. Typically based on a rigid load model assumption, they analyze the motor's speed fluctuations at constant speeds or the periodic variations in the q-axis current, using observers or filters to extract the eccentricity amplitude and phase. When the detected eccentricity exceeds a preset safety threshold, the control system executes shutdown, jitter, or redistribution strategies until the detection results meet the balance requirements before allowing entry into the high-speed dehydration stage.

[0004] However, existing technologies based on rigid models and passive observation still have significant limitations and detection blind spots when dealing with complex fluid-structure interaction conditions. On the one hand, for non-rigid loads with fluid encapsulation characteristics, the internal fluid is constrained by centrifugal force and fabric tension during the low-speed distribution phase, often exhibiting extremely low eccentricity, which can easily lead to false balance misjudgments in existing algorithms. When the rotational speed increases and exceeds the critical point, the transient displacement or rupture of the internal fluid can trigger explosive dynamic imbalance impacts. On the other hand, when drainage system malfunctions and residual water accumulates at the bottom of the outer tub, the nonlinear viscous damping and dragging torque generated by the fluid on the inner tub can severely interfere with normal eccentricity signal detection. The damping effect of the accumulated water can suppress the physical vibration amplitude of the inner tub, causing signal masking and leading the algorithm to underestimate the actual risk.

[0005] Therefore, the present invention provides a method for detecting and controlling the imbalance and collision of the inner drum of a washing machine. Summary of the Invention

[0006] The purpose of this invention is to provide a method for detecting and controlling the imbalance and collision of the inner tub of a washing machine, so as to solve the existing problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting and controlling inner tub imbalance and collision in a washing machine, comprising the following steps: S1. Before the dehydration process starts, check the opening status of the drain pump and control the motor to be in a micro-movement or stationary state to obtain environmental reference parameters including the basic friction torque and the basic damping coefficient. S2. Control the motor to drive the inner drum into the low-speed distribution speed range, apply dual-mode active excitation to the motor, and extract the dehydration risk feature vector after correction by the environmental reference parameters. S3. Based on the dehydration risk feature vector and historical adaptive coefficient, calculate the fluid-structure instability, which characterizes the risk of non-rigid loads covered by water accumulation. S4. Construct a collision risk arbitration model to arbitrate the fluid-solid instability and generate a dehydration safety index and working condition label; S5. Based on the dehydration safety index and operating condition label, implement a graded control strategy; S6. Update the historical adaptive coefficients described in step S3 based on the feedback verification results after the strategy is executed.

[0008] A further improvement of this invention is that the process of obtaining environmental reference parameters includes: injecting a high-frequency micro-amplitude current signal of a preset frequency into the motor, and sampling the q-axis current feedback and high-frequency response gain of the motor at a frequency greater than 1 kHz; subsequently, filtering the sampled data, calculating the average friction torque and basic damping coefficient under the current operating conditions, and marking them as environmental reference parameters. If the basic damping coefficient exceeds the preset mechanical fault threshold, a fault alarm is triggered and the process is terminated; the environmental reference parameters are stored in a temporary register and configured as the zero-point correction reference for subsequent steps.

[0009] A further improvement of this invention is that, in the collision risk arbitration model, the dehydration safety index is mapped to a fuzzy membership vector containing different semantic levels by taking the fluid-structure instability, the modified viscous damping coupling degree, and the motor speed fluctuation rate as input variables through a fuzzification interface; the fuzzy membership vector is input into a preset fuzzy inference engine, and inference is performed using a fuzzy rule base; the inference result is defuzzified to generate a normalized dehydration safety index and a working condition label representing the current risk type.

[0010] A further improvement of this invention is that the fuzzy rule base operation process includes a strongly coupled rule for the water accumulation and cover-up condition: when the input variable shows that the membership degree of the modified viscosity damping coupling degree is high, and the membership degree of the fluid-solid instability is medium or low, the output condition label is forcibly determined as a cover-up risk, and the dehydration safety index is set to be greater than the preset warning threshold.

[0011] A further improvement of this invention is that the execution of the graded control strategy includes: comparing the dehydration safety index with a first safety threshold and a second danger threshold; if the dehydration safety index is less than or equal to the first safety threshold, executing a high-speed dehydration strategy; if the dehydration safety index is greater than the second danger threshold, terminating the dehydration process; if the dehydration safety index is greater than the first safety threshold and less than or equal to the second danger threshold, then further selecting to execute a nonlinear special dehydration mode according to the working condition label.

[0012] A further improvement of this invention is that the nonlinear special dehydration mode includes a drainage priority mode, which is triggered when the operating condition label indicates a masking risk. The drainage priority mode specifically includes: controlling the motor to maintain its current low speed without acceleration, while forcibly starting the drainage pump to run for a preset drainage window time; during the drainage window time, monitoring the rate of change of the corrected viscous damping coupling in real time; if the rate of change is detected to exceed an effective decrease threshold, a reset signal is generated to allow a reassessment of the dehydration safety index.

[0013] A further improvement of the present invention is that the nonlinear special dehydration mode also includes a pulse squeezing mode, which is triggered when the working condition label indicates a water bag type risk. The pulse squeezing mode specifically includes: controlling the motor to perform multi-level stepped speed increase, and stopping at each speed plateau for a preset squeezing time; during the squeezing time, controlling the motor to apply periodic micro-amplitude braking pulses, using inertial impact force to assist the discharge of fluid inside the fabric.

[0014] A further improvement of this invention is that the dehydration risk feature vector includes: High-frequency micro-amplitude torque excitation and transient step pulse excitation are applied to the motor sequentially; excitation response data are collected, and the data are de-environmentally corrected using the environmental reference parameters to calculate the corrected viscous damping coupling degree. and fluid inertial hysteresis index And construct a dehydration risk feature vector that includes the fluid inertial hysteresis index and the modified viscous damping coupling degree; The modified viscous damping coupling degree and the fluid inertial hysteresis index The calculation formulas are as follows: in, This represents the DC component of the steady-state q-axis current. For high-frequency response gain, This is the proportionality coefficient; For the pulse window time, The torque-position phase difference can be obtained using a Luneburg observer based on the motor equations. Specifically, the rotor position is estimated by observing the back electromotive force, and the angle between the rotor position and the stator flux linkage vector is calculated. It represents the instantaneous rate of change of acceleration.

[0015] A further improvement of this invention is that the fluid-structure instability calculation process includes: reading historical adaptive coefficients stored in non-volatile memory. ; Calculation of fluid-structure instability using a nonlinear compensation model The model is configured to amplify the weights of the fluid inertial hysteresis exponent under high damping conditions using an exponential function; the fluid-structure instability The calculation formula is: ,in, This is the preset damping masking compensation constant.

[0016] A further improvement of this invention is that the updated adaptive correction coefficient includes, after executing the drainage priority mode or the pulse squeezing mode, re-detecting the change in the corrected viscous damping coupling degree; if the change is less than the effective judgment threshold, it is determined to be a false alarm, the deviation ratio between the change and the preset expected change is calculated, and the historical adaptive coefficient is proportionally reduced according to the deviation ratio. The data is then updated to memory; if physical collision protection is triggered during the execution of the high-speed dehydration strategy, it is determined to be a missed alarm, the impact intensity value at the moment of collision is obtained, and the adaptive coefficient is significantly increased based on the impact intensity value. And update it to memory.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention first employs a dual-modal active excitation mechanism to apply transient pulse torque to the motor during the low-speed distribution phase and calculates the fluid inertial hysteresis index, which characterizes the internal fluidity of the load. This solves the problem that existing passive detection technologies struggle to identify the false balance of waterproof clothing at low speeds due to the uniform adhesion of fluid to the wall, and the resulting risk of high-speed rupture or transient displacement. It achieves non-destructive and accurate identification of non-rigid fluid envelopes and flexible pulse compression treatment, effectively avoiding devastating drum collisions during high-speed spin-drying and significantly improving the structural safety of the washing machine under extreme and unsafe loads.

[0018] 2. By constructing a nonlinear compensation model for fluid-structure instability based on modified viscous damping coupling, and combining it with the strong coupling arbitration rule in fuzzy logic, the problem of detection blind spots and missed judgments caused by the fluid viscous damping effect of residual water at the bottom of the outer tank suppressing the physical vibration of the inner tank and masking the true eccentric signal was solved. This enabled penetrating identification of masked risks under complex fluid-structure coupling conditions. By actively avoiding motor overload and fluid resonance caused by water acceleration through the drainage priority mode, the operating life and reliability of the drive system were ensured. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for detecting and controlling inner drum imbalance collision in a washing machine according to the present invention. Figure 2 The diagram shows a comparison of the risk assessment of the traditional OOB detection algorithm for current fluctuation amplitude and the present invention under the condition of water accumulation and cover. Detailed Implementation

[0020] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0021] The term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone.

[0022] Example 1 This invention provides a method for detecting and controlling inner tub imbalance and collision in washing machines. This method operates in a washing machine control system that includes a frequency converter, a three-phase motor, and a current sensor. Figure 1 This embodiment presents a flowchart of a method for detecting and controlling inner drum imbalance in a washing machine, the steps of which are as follows: S1. Before the dehydration process starts, check the opening status of the drain pump and control the motor to be in a micro-movement or stationary state to obtain environmental reference parameters including the basic friction torque and the basic damping coefficient. In existing technologies, the spin-drying control of washing machines typically relies on factory-fixed parameter models to assess load conditions, such as setting fixed friction torque thresholds or damping coefficient thresholds. However, this conventional method has the drawback of being unable to adapt to changes throughout the machine's entire lifecycle. As the machine ages, belt aging and loosening, bearing wear, or changes in lubricant viscosity due to temperature differences between winter and summer can all cause the machine's fundamental mechanical impedance to drift. Continuing to use fixed parameters can lead to misjudgments of water accumulation or load by the control system. To address this issue, this embodiment introduces a dynamic environmental reference reset mechanism. Specifically, in step S1, at the end of the drainage phase before the spin-drying program starts, the controller detects that the drain pump is on and controls the motor to be in a micro-movement or stationary state. At this time, a high-frequency micro-amplitude current signal of a preset frequency, such as a torque command with a frequency of 40Hz to 60Hz and an amplitude of 0.2Nm to 0.5Nm, is injected into the motor stator windings. The system samples the motor's q-axis current feedback and high-frequency response gain at a frequency higher than 1kHz using a current sensor. In existing technologies, resistance is usually determined directly using the current value, but electrical noise interference is ignored. This embodiment performs moving average filtering on the sampled data to calculate the average friction torque under the current operating conditions. and base damping coefficient Through the average frictional torque This reflects the current level of mechanical friction, while the basic damping coefficient... This reflects the inherent damping of the system under dynamic disturbances without water or load. The calculated basic damping coefficient... If the fault exceeds the preset mechanical fault threshold, the system identifies it as a serious mechanical fault and issues an alarm; otherwise, the basic damping coefficient will be adjusted. Stored in a temporary register. This embodiment eliminates zero-point errors caused by environmental drift and mechanical aging through the above methods, providing a clean reference standard for subsequent detection of weak signals.

[0023] S2. Control the motor to drive the inner drum into the low-speed distribution speed range, apply dual-mode active excitation to the motor, and extract the dehydration risk feature vector after correction by the environmental reference parameters. To address the shortcomings of passive imbalance detection in existing technologies, conventional OOB (Out of Balance) detection algorithms typically passively monitor speed or current fluctuations during motor acceleration. This method is effective for rigid loads, such as wet cotton cloth, but it exhibits significant hysteresis and deception for non-rigid fluid envelopes, such as water bags formed by waterproof clothing.

[0024] When the water bag rotates at a low, uniform speed, the internal fluid is relatively still, exhibiting extremely low eccentricity. This makes it easy to trick the control system into accelerating. However, once the rotation speed increases, the fluid is subjected to centrifugal force, causing instantaneous displacement or rupture, leading to an extremely dangerous collision. Therefore, this embodiment introduces a dual-modal active excitation mechanism, no longer passively waiting for signals but actively detecting load characteristics. The system controls the motor to accelerate the inner bucket to a low-speed distribution range, such as 80 RPM to 100 RPM, and maintains stability, sequentially applying high-frequency micro-amplitude torque excitation and transient step pulse excitation.

[0025] The system collects excitation response data and utilizes the aforementioned basic damping coefficient. To make corrections, two core parameters are calculated: the corrected viscous damping coupling degree. and fluid inertial hysteresis index Among them, viscous damping coupling degree The formula used to characterize the degree of residual water at the bottom of the outer tank is configured as follows: In this formula, The steady-state q-axis current DC component represents the steady-state torque required to maintain rotation; The high-frequency response gain represents the system's sensitivity to high-frequency vibrations. The sampled q-axis current is bandpass filtered, with the center frequency being the injection frequency. The amplitude of the high-frequency component is extracted and used as the high-frequency response gain. This is the preset scaling factor.

[0026] Through viscous damping coupling, when water accumulation is present, fluid damping can suppress high-frequency vibrations that lead to... Reduced, while fluid drag force will lead to Increasing the ratio of the two can significantly amplify the water accumulation signal, minus the foundation damping coefficient. Used to eliminate background noise during idle periods.

[0027] For fluid inertial hysteresis index It is used to characterize the fluidity of the fluid inside the load. Based on the characteristics of fluid dynamics, when a rigid load is subjected to a pulse torque impact, its motion closely follows the driving force with a very small phase difference; while the fluid load, due to the inertial hysteresis effect of the internal liquid, experiences a larger acceleration change. At the same time, significant phase lag will occur. Therefore, the formula for calculating the fluid inertial hysteresis index is configured as follows: In this formula, The duration window for the transient step pulse excitation (e.g., 100ms). This is the torque-position phase difference, which is the phase delay between the motor output torque vector and the rotor mechanical position fluctuation. This represents the instantaneous rate of change of acceleration. By integrating the product of these two factors, the unique soft characteristics of fluid loads can be effectively extracted, thus distinguishing water bags from ordinary clothing.

[0028] S3. Based on the dehydration risk feature vector and historical adaptive coefficient, calculate the fluid-structure instability, which characterizes the risk of non-rigid loads covered by water accumulation. After obtaining the basic parameters, step S3 aims to address the issue of missed detection caused by multi-parameter coupling in existing technologies. Conventional methods typically determine water accumulation and eccentricity independently, i.e., by setting separate thresholds. However, this invention has discovered a highly dangerous masking effect: when water accumulates at the bottom of the outer tub, the significant fluid viscosity damping acts like a shock absorber, suppressing the swaying of the inner tub and greatly weakening the amplitude of the imbalance signal caused by the water bag. If only a linear threshold is used for judgment, under water accumulation conditions, the significant risk of the water bag may be misjudged as safe, leading to a double accident of water ring entrainment and water bag rupture after the system accelerates.

[0029] Therefore, this embodiment introduces a nonlinear compensation model to calculate fluid-structure instability. Its calculation formula is configured as follows In this formula, For historical adaptive coefficients; This is a preset damping masking compensation constant, with a value greater than 0. The exponential function is chosen because fluid damping exhibits a nonlinear decay characteristic in suppressing vibration amplitude; exponential compensation can improve the risk assessment indicators. To maintain linearized sensitivity across the entire damping range, an exponential compensation term is introduced. When the degree of water accumulation is monitored, i.e., the viscosity-damping coupling degree When the exponential term increases, it increases dramatically, thus automatically amplifying the fluid inertial hysteresis exponent. The weighting of the water bag signal. This means that the stickier the environment, the more sensitive the algorithm is to capturing the water bag signal, thus revealing the true risk hidden by the accumulated water.

[0030] S4. Construct a collision risk arbitration model to arbitrate the fluid-solid instability and generate a dehydration safety index and working condition label; This invention utilizes fuzzy logic to address the gray area problem at the decision boundary. Existing technologies often employ hard threshold cutoffs, such as stopping when the threshold value exceeds 5, but these are prone to false triggering or missed triggering in critical states. This embodiment addresses fluid-structure instability... Viscous damping coupling degree and motor speed fluctuation rate Input fuzzy inference engine.

[0031] In the aforementioned collision risk arbitration model, the dehydration safety index is obtained by taking the fluid-structure instability, modified viscous damping coupling degree, and motor speed fluctuation rate as input variables and mapping them into fuzzy membership vectors with different semantic levels through a fuzzification interface. The fuzzy membership vectors are then input into a preset fuzzy inference engine, and inference is performed using a fuzzy rule base. The inference results are then defuzzified, and a normalized dehydration safety index and a working condition label representing the current risk type are generated using the centroid method.

[0032] Specifically, this embodiment designs a strong coupling rule for the water accumulation cover condition: when the input variable displays viscous damping coupling degree It has a high membership degree and fluid-solid instability. When the membership degree is medium or low, the output operating condition label is forcibly classified as a masking risk, and the dehydration safety index is set to a high-risk value greater than the preset warning threshold. This rule directly translates the aforementioned physical findings into control logic, ensuring that even if the water bag signal is not obvious when water accumulation is severe, the system will remain highly vigilant to prevent rash acceleration.

[0033] The process of determining membership degree is essentially a process of determining a viscous damped coupling degree. It maps to a probability value between 0 and 1. Specifically: Input domain: Viscous damping coupling degree and fluid-structure instability The range of values ​​is normalized to Define three fuzzy sets: low, medium, and high.

[0034] To determine the viscous damping coupling degree Whether it falls under high or medium severity, the MCU internally executes the following segmented function: A. Determine the low membership function through the left shoulder shape function. : The smaller the value, the greater the probability of it being low.

[0035] B. Determining the membership function using trigonometric functions : This indicates that the probability is highest when the value is in the middle.

[0036] C. Determine the membership function of the height using the right shoulder shape function. : The larger the value, the greater the probability of it being high.

[0037] In this embodiment, if a traditional threshold is used, for example... An alarm will sound, and the machine will repeatedly start and stop when the value fluctuates between 6.9 and 7.1.

[0038] Using a membership function, around 7.0, it falls into both the medium and high categories. This overlapping region allows for a smooth transition in control strategies; for example, as the membership value for high categories increases from 0.1 to 0.9, the probability of the drain pump starting or the speed limit gradually increases, rather than abruptly.

[0039] Using the above function, when the viscous damping coupling degree membership degree If the value is very high, for example, 0.9, its activation weight in the rule base will be very large, thus forcibly pulling the safety index into the danger zone in the final weighted average calculation.

[0040] To better demonstrate the beneficial effects of this embodiment, the test condition created in this invention is a hidden water accumulation + waterproof clothing water bag, with a load of a waterproof jacket containing 2kg of water to simulate the water bag, and low-speed wall-mounted balance. An interference is set: 800ml of water is injected into the bottom of the outer bucket to simulate poor drainage, creating a damping and masking effect.

[0041] During the test, the motor accelerated from a distributed speed of 90 RPM to the critical resonant speed of 400 RPM. A comparison was created: an OOB detection algorithm based on traditional current fluctuation amplitude, and the detection algorithm based on fluid-structure instability in this invention.

[0042] Figure 2 The diagram shows a comparison of the risk assessment results of the traditional OOB detection algorithm for current fluctuation amplitude and the present invention under water accumulation cover conditions. Figure 2 It can be seen that in conventional technologies, during the 0-12 second period, the risk index remains low due to the damping effect of water accumulation masking the physical vibration, exhibiting a false balance. This continues until after 12 seconds when the rotational speed increases, causing the water bag to suddenly become unstable, resulting in a sharp vertical rise in the curve and ultimately, a collision with the bucket. In contrast, this invention, during the low-speed phase at 2 seconds, uses active excitation and index compensation to instantly jump the risk reading to 0.85, exceeding the safety threshold. The system immediately intercepts and enters a drainage-priority mode. Subsequently, the risk gradually decreases with drainage, and no loss of control occurs throughout the process.

[0043] S5. Based on the dehydration safety index and operating condition label, implement a graded control strategy; The implementation of the graded control strategy includes: comparing the dehydration safety index with a first safety threshold and a second danger threshold; if the dehydration safety index is less than or equal to the first safety threshold, implementing a high-speed dehydration strategy; if the dehydration safety index is greater than the second danger threshold, terminating the dehydration process; if the dehydration safety index is greater than the first safety threshold and less than or equal to the second danger threshold, further selecting and implementing a nonlinear special dehydration mode based on the operating condition label.

[0044] In one possible specific embodiment, if the operating condition label is a masking risk, indicating severe water accumulation that may be masking a load problem, then a drainage priority mode is executed. The control motor is maintained at its current low speed without acceleration, while the drainage pump is forcibly started and runs for a preset drainage window time, such as 30 seconds, and the viscous damping coupling degree is monitored in real time. The rate of change. This avoids motor overload caused by accelerating with water.

[0045] In another possible embodiment, if the operating condition label is "water bag type risk," indicating no water accumulation but a non-rigid load, a pulse squeezing mode is executed. The control motor performs multi-stage stepped speed increases, pausing at each speed plateau for a preset time, and applying periodic micro-amplitude braking pulses. The inertial impact force generated by the pulses disrupts the surface tension inside the water bag, assisting in water drainage.

[0046] If the safety index is lower than the safety threshold, it will directly enter the high-speed dehydration process.

[0047] S6. Update the historical adaptive coefficients described in step S3 based on the feedback verification results after the strategy is executed.

[0048] Step S6 addresses the lack of evolutionary capability in existing algorithms by introducing an adaptive feedback mechanism. Existing technologies have fixed parameters and cannot handle inefficiencies caused by false positives or security incidents caused by false negatives. In this embodiment, The update follows an error correction logic based on data performance. Specifically, if the viscous damping coupling degree is checked again after executing the drainage priority mode, the update will be performed accordingly. If the change is less than the effective judgment threshold, for example, if the change is close to 0, it indicates that the previous judgment of water presence was a false alarm. The system calculates the deviation ratio between the change and the preset expected change and adjusts the threshold proportionally based on this ratio. To reduce system sensitivity, first set an expected reduction target, denoted as . For example: if water is present, drain the water for 30 seconds. It should decrease by 0.5.

[0049] The actual descent is measured at this point and recorded as follows: Calculate the effectiveness ratio. . This indicates that the prediction accuracy is high. Remain unchanged. If This indicates a complete false alarm, meaning there is no water. A significant reduction is needed; the adjustment formula is as follows: ,in The learning rate is 0.1 in this example, which means that the worse the actual performance (the smaller R is), the greater the penalty.

[0050] If the physical impact protection is triggered during high-speed dehydration, it indicates that the previous risk assessment was too low. The system will then obtain the impact intensity value at the moment of impact. The more severe the impact, the more severely the previous risk assessment was underestimated, requiring a significant increase in sensitivity and a substantial upward adjustment of the value accordingly. The adjustment formula is expressed as: ,in It is the radical coefficient.

[0051] The above methods enable the control algorithm to continuously optimize itself as the washing machine is used, finding the optimal balance between safety and spin-drying efficiency.

[0052] The threshold and weight settings involved in this embodiment can be set by default according to the present invention, or can be set by those skilled in the art.

[0053] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for detecting and controlling tub imbalance and collision in a washing machine, characterized in that: Includes the following steps: S1. Before the dehydration process starts, check the opening status of the drain pump and control the motor to be in a micro-movement or stationary state to obtain environmental reference parameters including the basic friction torque and the basic damping coefficient. S2. Control the motor to drive the inner drum into the low-speed distribution speed range, apply dual-mode active excitation to the motor, and extract the dehydration risk feature vector after correction by the environmental reference parameters. S3. Based on the dehydration risk feature vector and historical adaptive coefficient, calculate the fluid-structure instability, which characterizes the risk of non-rigid loads covered by water accumulation. S4. Construct a collision risk arbitration model to arbitrate the fluid-solid instability and generate a dehydration safety index and working condition label; S5. Based on the dehydration safety index and operating condition label, implement a graded control strategy; S6. Update the historical adaptive coefficients described in step S3 based on the feedback verification results after the strategy is executed.

2. The method for detecting and controlling inner tub imbalance and collision in a washing machine according to claim 1, characterized in that: The process of obtaining environmental reference parameters includes: injecting a high-frequency micro-amplitude current signal of a preset frequency into the motor, and sampling the q-axis current feedback and high-frequency response gain of the motor at a frequency greater than 1kHz; subsequently, filtering the sampled data, calculating the average friction torque and basic damping coefficient under the current operating conditions, and marking them as environmental reference parameters. If the basic damping coefficient exceeds the preset mechanical fault threshold, a fault alarm is triggered and the process is terminated; the environmental reference parameters are stored in a temporary register and configured as the zero-point correction reference for subsequent steps.

3. The method for detecting and controlling inner tub imbalance and collision in a washing machine according to claim 2, characterized in that: In the collision risk arbitration model, the dehydration safety index is obtained by taking the fluid-structure instability, modified viscous damping coupling degree, and motor speed fluctuation rate as input variables and mapping them into fuzzy membership vectors with different semantic levels through a fuzzification interface. The fuzzy membership vectors are then input into a preset fuzzy inference engine and inference is performed using a fuzzy rule base. The inference results are then defuzzified to generate a normalized dehydration safety index and a working condition label representing the current risk type.

4. The method for detecting and controlling inner tub imbalance and collision in a washing machine according to claim 3, characterized in that: The fuzzy rule base operation process includes a strongly coupled rule for water accumulation cover-up conditions: when the input variable shows that the membership degree of the modified viscosity damping coupling degree is high and the membership degree of the fluid-solid instability is medium or low, the output condition label is forcibly determined as a cover-up risk, and the dehydration safety index is set to be greater than the preset warning threshold.

5. The method for detecting and controlling inner tub imbalance and collision in a washing machine according to claim 1, characterized in that: The implementation of the graded control strategy includes: comparing the dehydration safety index with a first safety threshold and a second danger threshold; if the dehydration safety index is less than or equal to the first safety threshold, implementing a high-speed dehydration strategy; if the dehydration safety index is greater than the second danger threshold, terminating the dehydration process; if the dehydration safety index is greater than the first safety threshold and less than or equal to the second danger threshold, further selecting and implementing a nonlinear special dehydration mode based on the operating condition label.

6. A method for detecting and controlling inner tub imbalance and collision in a washing machine according to claim 5, characterized in that: The nonlinear special dehydration mode includes a drainage priority mode, which is triggered when the operating condition label indicates a masking risk. The drainage priority mode specifically includes: controlling the motor to maintain the current low speed without acceleration, while forcibly starting the drainage pump to run for a preset drainage window time; during the drainage window time, monitoring the rate of change of the corrected viscous damping coupling in real time; if the rate of change is detected to exceed the effective decrease threshold, a reset signal is generated to allow reassessment of the dehydration safety index.

7. A method for detecting and controlling inner tub imbalance and collision in a washing machine according to claim 5, characterized in that: The nonlinear special dehydration mode also includes a pulse squeezing mode, which is triggered when the working condition label indicates a water bag type risk. The pulse squeezing mode specifically includes: controlling the motor to perform multi-level stepped speed increase, and stopping at each speed plateau for a preset squeezing time; during the squeezing time, controlling the motor to apply periodic micro-amplitude braking pulses, using inertial impact force to assist the discharge of fluid inside the fabric.

8. A method for detecting and controlling inner tub imbalance and collision in a washing machine according to claim 1, characterized in that: The dehydration risk feature vector includes: High-frequency micro-amplitude torque excitation and transient step pulse excitation are applied to the motor sequentially; excitation response data are collected, and the data are de-environmentally corrected using the environmental reference parameters to calculate the corrected viscous damping coupling degree. and fluid inertial hysteresis index And construct a dehydration risk feature vector that includes the fluid inertial hysteresis index and the modified viscous damping coupling degree; The modified viscous damping coupling degree and the fluid inertial hysteresis index The calculation formulas are as follows: in, This represents the DC component of the steady-state q-axis current. For high-frequency response gain, This is the proportionality coefficient; For the pulse window time, Torque-position phase difference, It represents the instantaneous rate of change of acceleration.

9. A method for detecting and controlling inner tub imbalance and collision in a washing machine according to claim 8, characterized in that: The fluid-structure instability calculation process includes: reading the historical adaptive coefficients stored in the non-volatile memory. ; Calculation of fluid-structure instability using a nonlinear compensation model The model is configured to amplify the weights of the fluid inertial hysteresis exponent under high damping conditions using an exponential function; the fluid-structure instability The calculation formula is: ,in, This is the preset damping masking compensation constant.

10. A method for detecting and controlling inner tub imbalance and collision in a washing machine according to claim 9, characterized in that: The updated adaptive correction coefficient includes detecting the change in the corrected viscous damping coupling degree again after executing the drainage priority mode or pulse squeezing mode; if the change is less than the effective judgment threshold, it is determined to be a false alarm, the deviation ratio between the change and the preset expected change is calculated, and the historical adaptive coefficient is proportionally reduced according to the deviation ratio. And update to memory; If the physical impact protection is triggered during the execution of the high-speed dehydration strategy, it is determined to be a missed alarm. The impact intensity value at the moment of impact is obtained, and the adaptive coefficient is significantly increased based on the impact intensity value. And update it to memory.

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