Washing machine reliability evaluation method, control device, electronic device, and storage medium

Abstract

This application provides a method, control device, electronic device, and storage medium for assessing the reliability of a washing machine, belonging to the field of washing machine technology. The method includes: based on the Monte Carlo method, extracting multiple sets of load data from a random load database and inputting them into a finite element model of the load-bearing components of the washing machine to obtain multiple maximum equivalent stress values, multiple maximum displacements, and multiple stress time histories; analyzing the multiple maximum equivalent stress values ​​and the multiple maximum displacements to obtain failure probabilities; and analyzing the multiple stress time histories to obtain fatigue life probabilities. The method provided by this application uses a random load database to provide input data for finite element simulation that is closer to actual working conditions, thereby improving the reliability of the assessment results; the assessment system is more complete, broadening the evaluation dimensions and improving the evaluation accuracy.

Description

Technical Field

[0001] This application belongs to the field of washing machine technology, and particularly relates to a washing machine reliability assessment method, control device, electronic device and storage medium. Background Technology

[0002] As an essential home appliance in modern households, the reliability of washing machines directly affects the user experience and product lifespan. Traditional washing machine reliability assessments are usually based on empirical design and safety factor methods, focusing only on static strength and employing deterministic analysis. This ignores the complex and variable nature of loads during actual operation and the wide distribution of safety factors, leading to assessment results that may be overly optimistic or conservative, which is not conducive to effectively guiding structural design and optimization. Summary of the Invention

[0003] This application provides a washing machine reliability assessment method, control device, electronic device, and storage medium to address the problem that existing washing machine reliability assessment methods neglect the complex and variable nature of loads and the wide distribution of safety factors during actual operation, which may lead to overly optimistic or conservative assessment results.

[0004] This application provides a method for evaluating the reliability of a washing machine, the method comprising: Based on the Monte Carlo method, multiple sets of load data were extracted from the random load database and input into the finite element model of the load-bearing components of the washing machine to obtain multiple maximum equivalent stress values, multiple maximum displacements and multiple stress time histories. The failure probability is obtained by analyzing the multiple maximum equivalent stress values ​​and the multiple maximum displacements; The fatigue life probability is obtained by analyzing the multiple stress time histories.

[0005] Optionally, the steps for constructing the random payload database include: The dynamic load parameters of the washing machine under various operating conditions are obtained, including load mass, centroid eccentricity, and phase angle. The random load database is established based on the dynamic load parameters.

[0006] Optionally, establishing the random load database based on the dynamic load parameters includes: The dynamic load parameters are classified according to the type of operating condition; Determine the probability distribution model and distribution parameters of the dynamic load parameters for each of the aforementioned operating conditions; The random load database is established based on the dynamic load parameters and the corresponding probability distribution model and distribution parameters.

[0007] Optionally, the steps for constructing the finite element model of the load-bearing component of the washing machine include: A parametric model is established based on the material properties, geometric dimensions, and assembly relationships of the load-bearing components; The stress concentration region of the parametric model is locally refined to obtain a refined parametric model; The refined parameter model is subjected to boundary conditions and connection constraints that correspond to the actual operating state of the washing machine to obtain the finite element model.

[0008] Optionally, the step of analyzing the plurality of maximum equivalent stress values ​​and the plurality of maximum displacements to obtain the failure probability includes: The stress accumulation distribution function is determined based on the multiple maximum equivalent stress values; The probability of strength failure is determined based on the preset allowable stress and the stress accumulation distribution function.

[0009] Optionally, the step of analyzing the plurality of maximum equivalent stress values ​​and the plurality of maximum displacements to obtain the failure probability further includes: Determine the cumulative displacement distribution function based on the plurality of maximum displacements; The probability of interference failure is determined based on the cumulative displacement distribution function and the preset gap.

[0010] Optionally, the step of analyzing the plurality of stress time histories to obtain the fatigue life probability includes: The amplitude and mean of the multiple stress time histories were extracted based on the rainflow counting method; The number of cycles required for failure is determined based on the amplitude, the mean, and the stress-life curve. The fatigue life probability is determined based on the number of cycles and the law of cumulative fatigue damage. This application embodiment also provides a washing machine reliability assessment and control device, the device comprising: The simulation module is configured to extract multiple sets of load data from a random load database based on the Monte Carlo method and input them into the finite element model of the load-bearing components of the washing machine to obtain multiple maximum equivalent stress values, multiple maximum displacements, and multiple stress time histories. The analysis module is configured to analyze the plurality of maximum equivalent stress values ​​and the plurality of maximum displacements to obtain the failure probability; and to analyze the plurality of stress time histories to obtain the fatigue life probability.

[0011] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the washing machine reliability assessment method described above.

[0012] This application embodiment also provides a storage medium storing control instructions, which, when executed by a processor, implement the washing machine reliability assessment method described above.

[0013] The washing machine reliability assessment method provided in this application uses a random load database containing load data sets of various random factors during washing machine operation. This database realistically reflects the uncertainty of the load in actual use of the washing machine, providing input data that is closer to actual working conditions for finite element simulation, thereby improving the reliability of the assessment results. Then, a large number of random sampling simulations replace a large number of physical tests, improving assessment efficiency and ease of operation. Finally, by obtaining multiple maximum equivalent stress values, multiple maximum displacements, and multiple stress time histories, the deterministic strength, stiffness, and fatigue verification are transformed into probabilistic assessments, which can quantify the failure probability and life reliability, providing an intuitive and scientific basis for risk assessment and decision-making. Furthermore, it simultaneously considers three main failure modes: static strength failure, dynamic interference failure, and fatigue life failure, making the assessment system more complete. It avoids using a single safety factor for evaluation, overcomes the limitation of the traditional safety factor method in being unable to quantify risk, broadens the evaluation dimensions, and improves the evaluation accuracy. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings. In the following description, the same reference numerals denote the same parts.

[0016] Figure 1 This is a schematic diagram of the first process of the washing machine reliability assessment method provided in the embodiments of this application.

[0017] Figure 2 This is a schematic diagram of the second process of the washing machine reliability assessment method provided in the embodiments of this application.

[0018] Figure 3 This is a schematic diagram of the structure of the washing machine reliability assessment and control device provided in an embodiment of this application.

[0019] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0020] Figure 5 This is a schematic diagram of the structure of a washing machine provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0022] In the description of the embodiments of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, and memory, and may also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, a microprocessor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc.

[0023] This application provides a washing machine reliability assessment method, control device, electronic device, and storage medium to address the problem that existing washing machine reliability assessment methods neglect the complex and variable nature of loads and the wide distribution of safety factors during actual operation, which may lead to overly optimistic or conservative assessment results. The following description is in conjunction with the accompanying drawings.

[0024] The washing machine reliability assessment method provided in this application is applicable to washing machines. No further limitation is made to the type of washing machine here, and its structure can be referred to... Figure 5 The method includes the following steps, please refer to [link / reference]. Figure 1 : S101: Based on the Monte Carlo method, multiple sets of load data are extracted from the random load database and sequentially input into the finite element model of the load-bearing components of the washing machine to obtain multiple maximum equivalent stress values, multiple maximum displacements, and multiple stress time histories.

[0025] Among them, the load-bearing components of a washing machine refer to the key structural components that bear and transmit mechanical loads during the operation of the washing machine. The reliability of the load-bearing components directly determines the service life and safety of the washing machine. For example, load-bearing components include, but are not limited to, suspension system components (suspenders, dampers, suspension springs, suspender brackets / supports), cabinet structure components (cabinet, front / rear panels, base / bottom plate, reinforcing ribs / beams), inner drum support components (bearing seats, main shaft, bearings, sealing rings), door system load-bearing components (door hinges, door lock mechanism, glass door rings), and motor mounting components (motor brackets, pulleys, belts, shock-absorbing pads).

[0026] Finite element models are used to simulate the mechanical response of load-bearing components under actual loads in a computer environment.

[0027] The Monte Carlo method approximates the probabilistic solution to complex problems by using statistical results from a large number of random experiments. Specifically, it simulates various random load conditions encountered by washing machines in actual use through extensive random sampling, thereby obtaining the statistical distribution of stress, displacement, and stress-time history of load-bearing components. This method combines deterministic finite element analysis with probabilistic statistical methods, providing a scientific, quantitative, and efficient probabilistic analysis framework for washing machine reliability assessment, enabling probabilistic evaluation of washing machine reliability.

[0028] The maximum equivalent stress value is the maximum stress intensity of a component under load, calculated based on the equivalent stress criterion (usually Von Mises stress).

[0029] Maximum displacement refers to the maximum deformation of the load-bearing components of the washing machine under the worst working conditions.

[0030] Stress time history refers to a complete record of the stress values ​​at key points on the load-bearing components of a washing machine as they change over time.

[0031] S102: Analyze multiple maximum equivalent stress values ​​and multiple maximum displacements to obtain the failure probability.

[0032] The failure probability includes the probability of strength failure and the probability of interference failure. The probability of strength failure is the probability that the material itself will be damaged, while the probability of interference failure is the probability that the component will collide with surrounding parts.

[0033] S103: Analyze multiple stress time histories to obtain fatigue life probability.

[0034] Fatigue life probability refers to the probability distribution of load-bearing components undergoing fatigue failure under cyclic loads, quantifying how long a washing machine can be used.

[0035] The washing machine reliability assessment method provided in this application uses a random load database containing load data sets of various random factors during washing machine operation. This database realistically reflects the uncertainty of the load in actual use of the washing machine, providing input data that is closer to actual working conditions for finite element simulation, thereby improving the reliability of the assessment results. Then, a large number of random sampling simulations replace a large number of physical tests, improving assessment efficiency and ease of operation. Finally, by obtaining multiple maximum equivalent stress values, multiple maximum displacements, and multiple stress time histories, the deterministic strength, stiffness, and fatigue verification are transformed into probabilistic assessments, which can quantify the failure probability and life reliability, providing an intuitive and scientific basis for risk assessment and decision-making. Furthermore, it simultaneously considers three main failure modes: static strength failure, dynamic interference failure, and fatigue life failure, making the assessment system more complete. It avoids using a single safety factor for evaluation, overcomes the limitation of the traditional safety factor method in being unable to quantify risk, broadens the evaluation dimensions, and improves the evaluation accuracy.

[0036] Optionally, the steps for constructing the random load database include: obtaining dynamic load parameters of the washing machine under various operating conditions, including load mass, centroid eccentricity, and phase angle; and establishing a random load database based on the dynamic load parameters.

[0037] Since washing machines may encounter various random load scenarios in actual use, rather than a single maximum load or standard load, a random load database that can reflect the dispersion of actual use can be established by collecting and modeling multiple load data of washing machines, thereby improving the authenticity of the data source.

[0038] The rated capacity, load type, drum speed, and load distribution state may be at least partially different or completely different under different operating conditions. For example, the rated capacity may include no load, 25%, 50%, 75%, 100%, overload (such as 110%), etc.; the load type may include standard counterweight, mixed fabrics (cotton, chemical fiber, denim, etc.), single fabric, large items (such as bed sheets, down jackets), etc.; the speed may include washing speed (such as 60 RPM), medium speed spin-drying (such as 400 RPM), high speed spin-drying (such as 800, 1000, 1200, 1400 RPM), etc.; and the distribution state may include ideal uniform distribution or artificially set typical unbalanced state.

[0039] Specifically, a test bench can be constructed, which includes a driveable washing machine drum, a torque sensor, a high-precision dynamic force sensor (mounted in a bearing housing), and at least two synchronously triggered high-speed cameras. The washing machine is controlled to operate sequentially under various working conditions, conducting numerous repeated spin-drying experiments. For each working condition, multiple complete spin-drying programs are executed, covering the entire process from shaking to maximum speed.

[0040] The wet clothes ball is considered as a virtual point with mass m attached to the inner wall of the cylinder. The main centrifugal force it generates under high-speed rotation is... .

[0041] By analyzing the force sensor at a stable rotation speed The dynamic force measured below , , The centrifugal force amplitude in each round of testing can be calculated. The system uses a high-speed camera to capture images of the inside of the inner drum and employs the DIC image processing algorithm to track the shape and center of mass of the clothing clumps in real time. According to the formula By combining the data *e* directly observed by the high-speed camera, *m* can be calculated. Processing all test data yields a large amount of... Data points, i.e., dynamic load parameters.

[0042] Among them, the load mass *m*, i.e., the total weight of the clothes inside the drum, determines the magnitude of the inertial force; the eccentricity *e*, i.e., the distance by which the center of mass deviates from the center of rotation due to uneven distribution of the clothes, determines the magnitude of the unbalanced torque; and the phase angle... The angle between the eccentric direction and the reference axis determines the direction of the unbalanced force.

[0043] Optionally, establishing a random load database based on dynamic load parameters includes: classifying dynamic load parameters according to working condition types; determining the probability distribution model and distribution parameters of dynamic load parameters for each working condition type; and establishing a random load database based on the dynamic load parameters and the corresponding probability distribution model and distribution parameters.

[0044] For example, operating condition types can include categories such as high-speed spin-drying - high load, high-speed spin-drying - low load, and low-speed washing - uniform load. A structured data index is established based on the operating condition type to achieve data classification and organization.

[0045] For example, statistical tests (such as the KS test and chi-square test) or physical mechanisms can be used to select the model that best describes the random characteristics of the parameter from common probability distributions (such as the normal distribution, log-normal distribution, Weibull distribution, uniform distribution, etc.). For instance, for eccentricity... Since the values ​​are always non-negative and may exhibit skewness, the log-normal distribution or the Weibull distribution is often chosen. For the phase angle... , in 0 to Within the range, it may approximately follow a uniform distribution. For a specific load mass... It may follow a discrete distribution or a piecewise normal distribution based on market research.

[0046] For example, statistical methods (such as maximum likelihood estimation and the method of moments) can be used to estimate the parameters (mean, standard deviation, correlation coefficient, etc.) of the selected distribution model. If the eccentricity e is determined to follow a log-normal distribution, its log mean and log standard deviation need to be estimated; if it follows a Weibull distribution, the shape and scale parameters need to be estimated.

[0047] By statistically analyzing each dynamic load parameter under each type of working condition, the most suitable probability distribution model and corresponding distribution parameters are determined. This quantifies the randomness of actual loads in probabilistic form, making the random load database not only a data warehouse but also a probabilistic model library, providing accurate and efficient sampling basis for subsequent Monte Carlo simulations. This method overcomes the limitations of traditional deterministic analysis that uses a single worst-case load, enabling reliability assessments to reflect the statistical variability of load parameters in the real world, and making the assessment results more scientific and engineering-guiding.

[0048] Optionally, the steps for constructing the finite element model of the load-bearing components of the washing machine include: establishing a parametric model based on the material properties, geometric dimensions, and assembly relationships of the load-bearing components; refining the mesh locally in the stress concentration region of the parametric model to obtain a refined parametric model; and applying boundary conditions and connection constraints that conform to the actual operating state of the washing machine to the refined parametric model to obtain the finite element model.

[0049] The geometric model includes material properties such as elastic modulus, Poisson's ratio, density, yield strength, and tensile strength; geometric dimensions such as mounting hole diameter and location; stiffener height, width, and thickness; sheet metal bending radius; and cross-sectional dimensions. Assembly relationships include bolt preload, equivalent stiffness of weld points / welds, riveting locations, friction coefficients of contact pairs between components, and initial clearances / interference fits. The geometric model can be created in the parametric environment of CAD software (such as SolidWorks, CATIA, Creo) or finite element software (such as Ansys Workbench, Abaqus / CAE).

[0050] In stress concentration areas such as variable cross-sections, holes, and welds, independent "local mesh controls" are created in these areas, specifying smaller element sizes. Gradual meshes are typically used, smoothly transitioning from refined to coarse meshes to avoid abrupt changes in element quality. This ensures a sufficient number of elements to capture the true stress peaks in areas with large stress gradients. For shells, a sufficient number of layers in the thickness direction must also be ensured (considering solid shells or when using cross-section points).

[0051] Boundary conditions include fixed constraints (applying fixed constraints at the mounting feet (such as anchor bolt holes) between the enclosure and the ground), load location, load type, load condition, and gravity (always applying gravitational acceleration, considering the self-weight of components and loads).

[0052] Connection constraints include bolted connections (simulating bolts using "beam connection" or "MPC constraint" (such as RBE2), creating bolt entities / beam models, and defining threaded contacts), welding (creating weld entities, defining common nodes or bonded contacts, and using "node coupling" or "bonded contacts"), contact (defining surface-to-surface contact and setting contact properties for components that may separate or slip), and hangers and damping systems (simulating using spring-damped elements, whose stiffness K and damping C parameters need to be obtained from component testing or whole-machine parameter identification), etc.

[0053] After applying these boundary conditions and connection constraints that correspond to the actual operating state, a complete finite element model that can be used for solving is obtained.

[0054] Optionally, key design variables, such as plate thickness, stiffener height, and corner radius, can be parameterized for subsequent optimization analysis.

[0055] By closely integrating parametric design, refined mesh, and physically realistic boundary conditions, and linking them with a random load database, the finite element model can be upgraded from "deterministic analysis" to a "probabilistic analysis" or "virtual test" platform. This allows for the systematic evaluation of the performance and reliability of load-bearing components under various possible loads, providing strong digital support for the robustness of washing machine structural design.

[0056] Furthermore, the finite element model is validated and verified to ensure its credibility, such as modal verification: calculating the natural frequencies and mode shapes of the model and comparing them with experimental modal analysis (EMA) results; static strain verification: comparing the strain values ​​at simulated and experimental measurement points under specific static loads (such as suspended weights); dynamic response verification: comparing the vibration acceleration or displacement response measured in simulation and experiments under known unbalance.

[0057] Optionally, based on the Monte Carlo method, multiple sets of load data are extracted from a random load database and sequentially input into the finite element model to obtain multiple maximum equivalent stress values, multiple maximum displacements, and multiple stress time histories. Specifically, the number of Monte Carlo samplings N can be set first, and the platform automatically executes a loop. In each loop i, a set of random input values ​​{ is extracted from the multiple sets of load data}. i, ...}, and at the same time, manufacturing tolerances and material property fluctuations can be set as random inputs to update the finite element model, run a transient or static analysis, and automatically extract and store preset output parameters, namely the maximum equivalent stress value, the maximum displacement, and the stress time history.

[0058] Optionally, the failure probability can be obtained by analyzing multiple maximum equivalent stress values ​​and multiple maximum displacements, including: determining the stress accumulation distribution function based on multiple maximum equivalent stress values; and determining the strength failure probability based on the preset allowable stress and the stress accumulation distribution function.

[0059] It should be understood that the probability of strength failure refers to the probability of strength failure of the load-bearing components of the washing machine. The preset allowable stress is the maximum stress value allowed by the engineering design; exceeding this value is considered a potential failure. Failure occurs when the actual maximum stress exceeds the allowable stress. The maximum equivalent stress value represents the most dangerous stress level for the load-bearing component under specific load conditions.

[0060] For example, determining the stress cumulative distribution function based on multiple maximum equivalent stress values ​​includes: data sorting and organization: arranging all maximum equivalent stress values ​​in ascending order, which clearly shows the range and distribution trend of stress variation; calculating empirical cumulative probability: assigning a cumulative probability value to each sorted maximum equivalent stress value, such as the cumulative probability of the kth minimum stress value being approximately equal to k / (N+1), where N is the total amount of data, representing the proportion of cases where the stress value does not exceed the total stress value; constructing the cumulative distribution function: connecting all points (maximum equivalent stress value, corresponding cumulative probability) with the maximum equivalent stress value on the x-axis and the cumulative probability on the y-axis to form a stepped curve, i.e., the CDF plot. This curve is the "empirical cumulative distribution function," which intuitively shows the statistical distribution law of stress values.

[0061] Based on the preset allowable stress and the stress cumulative distribution function, the strength failure probability is determined. That is, the strength failure probability is read directly from the cumulative distribution function graph according to the preset allowable stress. The strength failure probability reflects that the stress of the load-bearing component will not exceed the preset allowable stress under this ratio, thus realizing the ultimate strength reliability analysis.

[0062] Optionally, the failure probability is obtained by analyzing multiple maximum equivalent stress values ​​and multiple maximum displacements, and further includes: determining the cumulative displacement distribution function based on multiple maximum displacements; and determining the interference failure probability based on the cumulative displacement distribution function and the preset gap.

[0063] Similarly, determining the cumulative displacement distribution function based on multiple maximum displacements can also include the steps described above, such as data sorting and organization, calculating empirical cumulative probabilities, and constructing the cumulative distribution function. The cumulative displacement distribution function is then obtained, and the interference failure probability is directly read from the obtained cumulative displacement distribution function and the preset gap. The interference failure probability reflects that the load-bearing component will not interfere with other components at this ratio. This achieves ultimate stiffness reliability analysis.

[0064] Optionally, fatigue life probability can be obtained by analyzing multiple stress time histories, including: extracting the amplitude and mean of multiple stress time histories based on the rainflow counting method; determining the number of cycles required for failure based on the amplitude, mean, and stress life curve; and determining the fatigue life probability based on the number of cycles and the fatigue cumulative damage law.

[0065] Among them, the stress time history refers to the continuous data sequence of stress (usually equivalent stress, such as Von Mises stress) at key points (or dangerous points) on the load-bearing components of the washing machine as a function of time. It can be obtained by transient dynamic finite element simulation. After the simulation is completed, the finite element solver will output the stress value sequence of the key point over the entire simulation period (for example, the complete process of the washing machine from start-up acceleration to stable spin-drying and then to shutdown). The stress value sequence is a stress time history sample.

[0066] The amplitude of the stress time history is the magnitude of the stress change, and the mean of the stress time history is the average stress level of the cycle.

[0067] Determining the number of cycles required for failure based on amplitude, mean, and stress-life curves (SN curves) involves converting actual stress cycles with non-zero mean values ​​into equivalent stress cycles with zero mean values ​​using modified formulas (such as Goodman or Gerber formulas) to obtain the equivalent amplitude. Using the material's stress-life curve (SN curve), the theoretical number of cycles N required to cause material failure is found or calculated based on the equivalent amplitude. This curve describes the number of cycles a material can withstand at different stress levels.

[0068] The fatigue life probability is determined based on the number of cycles and the Miner linear cumulative damage theory, including: Calculating single-cycle damage: According to Miner's linear cumulative damage theory, the "damage" caused by each stress cycle is defined as 1 / N (where N is the number of failures at that stress level). Calculating total damage: The damage caused by all stress cycles is summed to obtain the total damage value D over a period of time. When the total damage D reaches 1 (or a critical value), fatigue failure theoretically occurs. Evaluating life and probability: If the total damage over a period of time is known to be D, the fatigue life can be predicted as 1 / D unit time. Since the load is random, the life prediction results are different for each analysis. The above process is repeated for a large number of random load conditions to obtain multiple fatigue life prediction values. Based on these life values, a life probability distribution is constructed to determine the fatigue failure probability of the component within a specified service life.

[0069] Based on the obtained fatigue life probability, a reliability index can be output, such as: "Under the current design, 99.9% of the products can guarantee that after 3000 dehydration cycles, their total damage is less than 1 (i.e., no fatigue failure)." The lifetime value corresponding to the 10th percentile of the damage distribution is calculated, meaning that 90% of the products can reach this lifetime. For example, "The product's B10 lifetime (i.e., the lifetime that 90% of the products can reach) is 3000 cycles."

[0070] This method links random and complex actual loads with material fatigue characteristics, quantifies the durability and reliability risks of components under long-term variable loads from a probabilistic perspective, and realizes fatigue life reliability analysis.

[0071] In summary, the process of the washing machine reliability assessment method provided in this application can be found in some examples. Figure 2 The method introduces load randomness: by constructing a random load database based on measured or statistical laws, it realistically reflects the uncertainty of loads in actual use of washing machines, making the assessment closer to actual working conditions. It achieves probabilistic assessment: by combining Monte Carlo simulation with statistical analysis, the deterministic strength, stiffness, and fatigue checks are transformed into probabilistic assessments, which can quantify the failure probability and life reliability, providing an intuitive and scientific basis for risk assessment and decision-making. Furthermore, the method simultaneously considers three main failure modes: static strength failure, dynamic interference failure, and fatigue life failure, making the assessment system more complete. Finally, by utilizing parametric finite element models and automated simulation processes, large-scale sample calculations can be performed quickly, improving assessment efficiency. The method is also standardized, easy to repeat, and readily applicable.

[0072] This transforms structural design from a fuzzy model relying on experience and safety factors to a scientific and quantitative decision-making model based on failure probability and lifespan distribution. By accurately locating structural weak points and over-designed areas, precise lightweight design can be implemented while ensuring extremely high reliability, significantly reducing material costs. Sensitivity analysis can identify the random variables that have the greatest impact on structural lifespan, such as maximum load or manufacturing deviations, providing clear directions for quality control and design improvement.

[0073] This application also provides a washing machine reliability assessment and control device. Please refer to [link / reference]. Figure 3 The device includes a simulation module 201 and an analysis module 202. The simulation module 201 is configured to extract multiple sets of load data from a random load database based on the Monte Carlo method and input them into the finite element model of the load-bearing components of the washing machine to obtain multiple maximum equivalent stress values, multiple maximum displacements, and multiple stress time histories. The analysis module 202 is configured to analyze the multiple maximum equivalent stress values ​​and multiple maximum displacements to obtain the failure probability; and analyze the multiple stress time histories to obtain the fatigue life probability.

[0074] This application also provides an electronic device; please refer to [link / reference]. Figure 4 The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the washing machine reliability assessment method described above. The method includes the following steps: S101: Based on the Monte Carlo method, multiple sets of load data are extracted from a random load database and input into the finite element model of the washing machine's load-bearing components to obtain multiple maximum equivalent stress values, multiple maximum displacements, and multiple stress time histories. S102: The failure probability is obtained by analyzing the multiple maximum equivalent stress values ​​and multiple maximum displacements. S103: The fatigue life probability is obtained by analyzing the multiple stress time histories.

[0075] This application embodiment also provides a storage medium storing control instructions. When the control instructions are executed by a processor, they implement the washing machine reliability assessment method described above. The method includes the following steps: S101: Based on the Monte Carlo method, multiple sets of load data are extracted from a random load database and input into the finite element model of the load-bearing components of the washing machine to obtain multiple maximum equivalent stress values, multiple maximum displacements, and multiple stress time histories. S102: The multiple maximum equivalent stress values ​​and multiple maximum displacements are analyzed to obtain the failure probability. S103: The multiple stress time histories are analyzed to obtain the fatigue life probability.

[0076] For example, a computer program can be divided into one or more modules / units, which are stored in memory and executed by a processor to perform the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in an electronic device.

[0077] Electronic devices can be desktop computers, laptops, handheld computers, and cloud servers, among other electronic devices. Electronic devices may include, but are not limited to, processors and memory. For example, electronic devices may also include input / output devices, network access devices, buses, etc.

[0078] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0079] In the embodiments provided by this invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0080] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0081] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0082] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0083] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features.

[0084] The washing machine reliability assessment method, control device, electronic device, and storage medium provided in the embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for evaluating the reliability of a washing machine, characterized in that, The method includes: Based on the Monte Carlo method, multiple sets of load data were extracted from the random load database and input into the finite element model of the load-bearing components of the washing machine to obtain multiple maximum equivalent stress values, multiple maximum displacements and multiple stress time histories. The failure probability is obtained by analyzing the multiple maximum equivalent stress values ​​and the multiple maximum displacements; The fatigue life probability is obtained by analyzing the multiple stress time histories.

2. The washing machine reliability assessment method according to claim 1, characterized in that, The steps for constructing the random payload database include: The dynamic load parameters of the washing machine under various operating conditions are obtained, including load mass, centroid eccentricity, and phase angle. The random load database is established based on the dynamic load parameters.

3. The washing machine reliability assessment method according to claim 2, characterized in that, The process of establishing the random load database based on the dynamic load parameters includes: The dynamic load parameters are classified according to the type of operating condition; Determine the probability distribution model and distribution parameters of the dynamic load parameters for each of the aforementioned operating conditions; The random load database is established based on the dynamic load parameters and the corresponding probability distribution model and distribution parameters.

4. The washing machine reliability assessment method according to claim 1, characterized in that, The steps for constructing the finite element model of the load-bearing components of the washing machine include: A parametric model is established based on the material properties, geometric dimensions, and assembly relationships of the load-bearing components; The stress concentration region of the parametric model is locally refined to obtain a refined parametric model; The refined parameter model is subjected to boundary conditions and connection constraints that correspond to the actual operating state of the washing machine to obtain the finite element model.

5. The washing machine reliability assessment method according to claim 1, characterized in that, The analysis of the plurality of maximum equivalent stress values ​​and the plurality of maximum displacements to obtain the failure probability includes: The stress accumulation distribution function is determined based on the multiple maximum equivalent stress values; The probability of strength failure is determined based on the preset allowable stress and the stress accumulation distribution function.

6. The washing machine reliability assessment method according to claim 1, characterized in that, The method of analyzing the plurality of maximum equivalent stress values ​​and the plurality of maximum displacements to obtain the failure probability also includes: Determine the cumulative displacement distribution function based on the plurality of maximum displacements; The probability of interference failure is determined based on the cumulative displacement distribution function and the preset gap.

7. The washing machine reliability assessment method according to claim 1, characterized in that, The analysis of the multiple stress time histories to obtain the fatigue life probability includes: The amplitude and mean of the multiple stress time histories were extracted based on the rainflow counting method; The number of cycles required for failure is determined based on the amplitude, the mean, and the stress-life curve. The fatigue life probability is determined based on the number of cycles and the law of cumulative fatigue damage.

8. A washing machine reliability assessment and control device, characterized in that, The device includes: The simulation module is configured to extract multiple sets of load data from a random load database based on the Monte Carlo method and input them into the finite element model of the load-bearing components of the washing machine to obtain multiple maximum equivalent stress values, multiple maximum displacements, and multiple stress time histories. The analysis module is configured to analyze the plurality of maximum equivalent stress values ​​and the plurality of maximum displacements to obtain the failure probability; and to analyze the plurality of stress time histories to obtain the fatigue life probability.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the washing machine reliability assessment method as described in any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium stores control instructions, which, when executed by a processor, implement the washing machine reliability assessment method as described in any one of claims 1-7.

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