A method and system for monitoring the life of motor bearings

Abstract

This invention relates to the field of motor bearing life monitoring technology, and provides a method and system for monitoring motor bearing life. By determining the observation window period based on the operating stage and internal state, and collecting and adjusting wear characteristic quantities within the window period, the accuracy of bearing wear monitoring under complex operating conditions is improved. It has the advantages of effectively avoiding the strong interference period under complex operating conditions of washing machines, collecting data under relatively stable operating conditions, and adjusting the collected wear characteristic quantities according to operating parameters, thereby improving the accuracy and reliability of judging the wear state of motor bearings under complex operating environments.

Description

Technical Field

[0001] This application relates to the field of motor bearing life monitoring technology, and more specifically, to a method and system for monitoring motor bearing life. Background Technology

[0002] The operating conditions of a washing machine are extremely complex and dynamically changing during different programs such as washing, rinsing, and spin-drying. A complete washing cycle includes multiple operating modes, such as low-speed agitation, high-speed rotation, frequent starts and stops, forward and reverse switching, and adjustment for imbalances in the clothes. During these different operating stages, the motor load, speed, and drum balance vary significantly, resulting in a wide range of mechanical vibrations and electrical noise backgrounds.

[0003] Although washing machine control systems can grasp real-time operating status information such as the current program stage, laundry load, motor speed, and drum imbalance, existing online assessment methods for motor bearing wear often fail to fully utilize this crucial information provided by the control system that accurately describes the current operating context. The assessment module often performs data acquisition and analysis independently, lacking deep integration with the control system. It cannot dynamically select the optimal data acquisition time based on real-time operating information, nor can it perform targeted processing or adjust the judgment logic based on current operating parameters. This makes the assessment process susceptible to interference from non-target signals, making it difficult to achieve accurate and reliable assessment of early, subtle wear conditions of motor bearings in the complex and constantly changing actual operating environment of washing machines.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for monitoring the life of motor bearings. This system can effectively avoid periods of strong interference under complex operating conditions of washing machines, collect data under relatively stable operating conditions, and adjust the collected wear characteristics based on operating parameters, thereby improving the accuracy and reliability of judging the wear status of motor bearings under complex operating environments.

[0006] This application provides a method for monitoring the life of motor bearings, the technical solution of which is as follows:

[0007] Based on the current operating phase information and real-time internal status information, multiple candidate observation windows are determined for collecting information related to motor bearing wear; the current operating phase information includes the current operating program phase of the washing machine; the real-time internal status information includes the load of clothes, motor speed, and drum imbalance.

[0008] Collect motor operating characteristic information and corresponding operating parameters within any candidate observation window; determine the initial wear characteristic of the bearing based on the motor operating characteristic information, and adjust the initial wear characteristic of the bearing based on the operating parameters to generate the adjusted wear characteristic; the motor operating characteristic information includes current waveform data for a specific duration; the operating parameters include motor speed, clothing load, and current program stage;

[0009] The adjusted wear characteristics corresponding to multiple candidate observation windows and the corresponding operating parameters are combined to form a first feature sequence; the wear state of the bearing is determined based on the first feature sequence, and the bearing life is determined.

[0010] Furthermore, this application also proposes a method for monitoring the life of motor bearings, which involves determining the wear state of the bearing based on a first characteristic sequence and then determining the bearing life, including:

[0011] Determine whether there is a preset consistency relationship between multiple adjusted wear feature quantities under the corresponding operating parameters in the first feature sequence to indicate the wear state of the motor bearing, and determine the wear state of the motor bearing.

[0012] Furthermore, this application also proposes, based on the motor bearing life monitoring method, determining whether there is a preset consistency relationship between multiple adjusted wear characteristic quantities under corresponding operating parameters in the first characteristic sequence that indicate the wear state of the motor bearing, and determining the wear state of the motor bearing, including:

[0013] Obtain the second characteristic quantity that characterizes the change in the power supply voltage of the washing machine during the candidate observation window period, and form a sequence of the second characteristic quantity.

[0014] The correlation between the changing trends of multiple adjusted wear characteristics and the changing trends of the second characteristic sequence is determined, and the correlation analysis results are obtained.

[0015] In response to the wear characteristics exhibited by the changing trends of multiple adjusted wear characteristics, and the second characteristic sequence showing a unidirectional shift in the supply voltage, the evaluation of the changing trends of multiple adjusted wear characteristics is adjusted based on the historical changing trend of the second characteristic sequence and the correlation analysis results; and / or, the judgment conditions for the consistency relationship of the indicator motor bearing wear state are adjusted.

[0016] The wear condition of the motor bearing is determined based on multiple adjusted wear characteristics, as well as the adjusted assessment and / or adjusted judgment conditions.

[0017] Furthermore, this application also proposes, based on the motor bearing life monitoring method, to determine the correlation between the changing trends of multiple adjusted wear characteristic quantities and the changing trends of a second characteristic quantity sequence, and to obtain the correlation analysis results, including:

[0018] The long-term voltage variation component that characterizes the main long-term offset trend of the supply voltage is separated from the second characteristic quantity sequence, and the interference introduced by short-term voltage fluctuations in the second characteristic quantity sequence is suppressed.

[0019] Multiple adjusted wear characteristic quantities are processed to obtain the wear characteristic duration change components of multiple adjusted wear characteristic quantities;

[0020] Based on the long-term voltage variation component and the time-dependent variation component of wear characteristics, the degree of correlation between the two over time is determined, and the correlation analysis results are obtained.

[0021] Furthermore, this application also proposes, according to the motor bearing life monitoring method, in response to the changing trends of multiple adjusted wear characteristic quantities showing wear characteristics, and the second characteristic quantity sequence showing a unidirectional shift in supply voltage, then based on the historical changing trend and correlation analysis results of the second characteristic quantity sequence, the assessment of the historical changing trend of multiple adjusted wear characteristic quantities is adjusted; and / or, the judgment conditions for the consistency relationship indicating the wear state of the motor bearing are adjusted, including:

[0022] Based on the historical change trend of the second characteristic sequence, the amplitude information and duration information of the power supply voltage offset are determined.

[0023] Determine the degree to which the current values ​​of multiple adjusted wear characteristics are close to the preset alarm threshold, as well as the historical fluctuation range information of multiple adjusted wear characteristics;

[0024] Based on the magnitude information of the power supply voltage deviation, the duration information of the power supply voltage deviation, the degree of proximity, the historical fluctuation magnitude information of multiple adjusted wear characteristics, and the correlation analysis results, an adjustment strategy is selected from the preset set of adjustment strategies.

[0025] Adjust the assessment of the time-varying trends of multiple adjusted wear characteristics based on the selected adjustment strategy; and / or adjust the judgment criteria for the consistency relationship of the indicator motor bearing wear condition.

[0026] Furthermore, this application also proposes that, according to the motor bearing life monitoring method, the preset set of adjustment strategies includes an adjustment evaluation strategy, an adjustment judgment condition strategy, and a combined adjustment strategy; wherein, the adjustment evaluation strategy is used to adjust the evaluation of the time-varying trend of multiple adjusted wear characteristic quantities; the adjustment judgment condition strategy is used to adjust the judgment conditions indicating the consistency relationship of the wear state of the motor bearing; and the combined adjustment strategy is used to combine the adjustment evaluation strategy and the adjustment judgment condition strategy.

[0027] Furthermore, this application also proposes, based on the motor bearing life monitoring method, to determine multiple candidate observation windows for collecting information related to motor bearing wear, based on current operating stage information and real-time internal state information, including:

[0028] Based on the current operating phase information and real-time internal status information, the period in which the motor runs at a constant speed, the load is stable, and the unbalance of the drum is continuously below a preset threshold for a predetermined time is determined as the candidate observation window.

[0029] Furthermore, this application also proposes, based on the motor bearing life monitoring method, to select an adjustment strategy from a preset set of adjustment strategies, according to the amplitude information of the power supply voltage deviation, the duration information of the power supply voltage deviation, the degree of proximity, the historical fluctuation amplitude information of multiple adjusted wear characteristics, and the correlation analysis results, including:

[0030] The current status information is composed of the amplitude information of the power supply voltage deviation, the duration information of the power supply voltage deviation, the degree of proximity, the historical fluctuation amplitude information of multiple adjusted wear characteristics, and the correlation analysis results.

[0031] Based on the current state information combination, a preliminary adjustment strategy is selected from the preset adjustment strategy set to obtain the preliminary selected adjustment strategy; the degree of matching between the current state information combination and the preset applicable conditions of each adjustment strategy in the preset adjustment strategy set is evaluated to obtain the matching degree information of each strategy.

[0032] Based on the matching degree information of each strategy, determine whether the matching degree between the current state information combination and the preset applicable conditions of the initially selected adjustment strategy is lower than the preset matching degree threshold, and determine whether the current state information combination is in the boundary area of ​​the preset applicable conditions of multiple adjustment strategies.

[0033] If the degree of matching between the current state information combination and the preset applicable conditions of the initially selected adjustment strategy is lower than the preset matching threshold, or if the current state information combination is in the boundary region of the preset applicable conditions of multiple adjustment strategies, then the execution parameters of the initially selected adjustment strategy are adjusted according to the matching degree information of each strategy and the current state information combination to form the final selected adjustment strategy.

[0034] Furthermore, this application also proposes, based on the motor bearing life monitoring method, adjusting the execution parameters of the initially selected adjustment strategy to form the final selected adjustment strategy according to the combination of matching degree information of each strategy and current state information, including:

[0035] Based on the matching degree information of each strategy, the quantitative indicators in the combination of current status information, and the preset parameter adjustment function, determine the execution parameter adjustment amount of the initially selected adjustment strategy;

[0036] Based on the adjustment amount of the execution parameters, adjust the execution parameters of the initially selected adjustment strategy to form the final selected adjustment strategy.

[0037] Furthermore, this application also proposes a motor bearing life monitoring system for a washing machine, the system comprising:

[0038] The first acquisition and determination module is used to determine multiple candidate observation windows for acquiring information related to motor bearing wear based on the current operating stage information and the real-time internal status information; the current operating stage information includes the current operating program stage of the washing machine; the real-time internal status information includes the load of clothes, motor speed and drum imbalance.

[0039] The second acquisition and generation module is used to acquire motor operating characteristic information and corresponding operating parameters within any candidate observation window; determine the initial wear characteristic of the bearing based on the motor operating characteristic information, and adjust the initial wear characteristic of the bearing based on the operating parameters to generate the adjusted wear characteristic; the motor operating characteristic information includes current waveform data for a specific duration; the operating parameters include motor speed, clothing load, and current program stage;

[0040] The judgment module is used to combine the adjusted wear characteristics corresponding to multiple candidate observation windows and the corresponding operating parameters to form a first feature sequence; and to judge the wear state of the bearing and determine the bearing life based on the first feature sequence.

[0041] As can be seen from the above, the motor bearing life monitoring method and system provided in this application improves the accuracy of bearing wear monitoring under complex operating conditions by determining the observation window period based on the operating stage and internal state, and collecting and adjusting wear characteristic quantities within the window period. It has the advantages of effectively avoiding the strong interference period under the complex operating conditions of washing machines, collecting data under relatively stable operating conditions, and adjusting the collected wear characteristic quantities according to the operating parameters, thereby improving the accuracy and reliability of judging the wear state of motor bearings under complex operating environments. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating the steps of the motor bearing life monitoring method disclosed in an embodiment of the present invention;

[0044] Figure 2This is a schematic diagram of the structure of the motor bearing life monitoring system disclosed in an embodiment of the present invention; Detailed Implementation

[0045] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0046] This application proposes a method for monitoring the life of motor bearings, used in a washing machine, the method comprising:

[0047] S101, based on the current operating phase information and the real-time internal status information, determine multiple candidate observation windows for collecting information related to motor bearing wear; the current operating phase information includes the current operating program phase of the washing machine; the real-time internal status information includes the load of clothes, motor speed and drum imbalance.

[0048] S102: Collect motor operating characteristic information and corresponding operating parameters within any candidate observation window; determine the initial wear characteristic of the bearing based on the motor operating characteristic information, and adjust the initial wear characteristic of the bearing based on the operating parameters to generate the adjusted wear characteristic; the motor operating characteristic information includes current waveform data for a specific duration; the operating parameters include motor speed, clothing load, and current program stage;

[0049] S103, combine the adjusted wear characteristics and corresponding operating parameters corresponding to multiple candidate observation windows to form a first feature sequence; determine the wear state of the bearing based on the first feature sequence and determine the bearing life.

[0050] Among them, the current operating stage information refers to the program steps currently being executed by the washing machine, such as main washing, rinsing, and spin-drying. This can be achieved by reading the internal status register or program counter of the control system, and is mainly used to provide background information on the macroscopic working status of the washing machine. The real-time internal status information refers to the specific operating parameters of the washing machine at the current moment, including the load of clothes, motor speed, and drum imbalance. This can be achieved using load sensors, speed sensors, imbalance sensors, or by estimation through motor drive parameters, and is mainly used to provide a detailed description of the microscopic operating environment of the washing machine. The candidate observation window period refers to the time period dynamically determined based on the current operating stage information and the real-time internal status information, suitable for collecting data related to motor bearing wear. This can be achieved by setting specific operating conditions as screening criteria, and is mainly used to avoid periods of strong interference and select periods with relatively low background noise for data collection. Motor operating characteristic information refers to data collected within the candidate observation window period that reflects the motor's operating status, including current waveform data of a specific duration. This can be achieved using current sensors and data acquisition modules, and is mainly used to extract raw signal features related to bearing wear. Operating parameters are... The wear characteristics are parameters collected simultaneously with the motor's operating characteristics to describe the current operating conditions, including motor speed, load, and current program stage. These parameters can be obtained by reading control system parameters or sensor data and are primarily used as auxiliary criteria to adjust and interpret the collected wear characteristics. The preliminary bearing wear characteristics are quantities directly extracted from the motor's operating characteristics to preliminarily characterize the bearing's wear level. These can be obtained by performing time-domain, frequency-domain, or time-frequency-domain analysis on current waveform data and are primarily used to preliminarily quantify bearing wear signs from the original signal. The adjusted wear characteristics are quantities obtained after correcting the preliminary bearing wear characteristics based on operating condition parameters. These can be achieved by using calibration curves or models based on different operating conditions and are primarily used to eliminate the influence of operating condition differences on the wear characteristics, making them more accurately reflect the true wear state of the bearing. The first characteristic sequence is a sequence formed by combining the adjusted wear characteristics corresponding to multiple candidate observation windows and the corresponding operating condition parameters in chronological or acquisition order. This can be implemented using data structure storage and is primarily used to provide historical information on the bearing wear state over time, facilitating trend analysis and state judgment.

[0051] In some embodiments described above, this application proposes determining multiple candidate observation windows for collecting information related to motor bearing wear based on current operating stage information and real-time internal state information. Motor operating characteristic information and corresponding operating parameters are collected within any candidate observation window. The initial wear characteristic quantity of the bearing is determined based on the motor operating characteristic information, and the initial wear characteristic quantity is adjusted according to the operating parameters to generate an adjusted wear characteristic quantity. The adjusted wear characteristic quantities corresponding to multiple candidate observation windows and the corresponding operating parameters are combined to form a first feature sequence. The wear state of the bearing is judged based on the first feature sequence, and the bearing life is determined. Specifically, this scheme can be implemented by acquiring information such as the current program stage, laundry load, motor speed, and drum imbalance in real time during the washing machine's operation. Based on this information, a specific time period with relatively stable motor operation and minimal interference is intelligently selected as the data acquisition window, such as the uniform speed operation stage during the main wash or rinse process. Then, within these selected windows, operating characteristic information such as motor current waveform data is collected, and operating parameters such as the motor speed and laundry load are recorded. The collected current waveform data is analyzed to extract preliminary characteristic quantities reflecting bearing wear, such as the amplitude of current harmonics at a specific frequency. Considering the influence of different operating conditions on these characteristic quantities, the preliminary characteristic quantities are corrected based on the recorded operating condition parameters to obtain more accurate adjusted wear characteristic quantities. The adjusted wear characteristic quantities and their corresponding operating condition parameters collected in different window periods are organized in chronological order or according to some logical relationship to form a comprehensive first characteristic sequence. Finally, based on this first characteristic sequence, the wear degree of the bearing is evaluated and the remaining life is predicted through a preset algorithm or model. This avoids data collection and analysis during periods of drastic changes or high interference in the operation of the washing machine, improving the accuracy of the preliminary wear characteristic quantities. The characteristic quantities are adjusted in combination with operating condition parameters, making the adjusted wear characteristic quantities more realistically reflect the wear state of the bearing. However, in its implementation, since the first characteristic sequence may contain data under various operating conditions, directly judging based on this sequence may lead to inaccurate evaluation results and an inability to effectively distinguish the differences in wear characteristics under different operating conditions, thus affecting the accuracy and reliability of bearing wear state judgment.

[0052] In this embodiment, when forming the first feature sequence, in addition to arranging the data in chronological order, logical relationships can be established based on operating condition parameters. For example, adjusted wear characteristics and corresponding operating condition parameters (such as motor speed, laundry load, and program stage) can be categorized and stored. Specifically, a subsequence can be created for each typical operating condition (e.g., high-speed spin-drying, medium-speed washing, and low-speed agitation). When a set of adjusted wear characteristics and operating condition parameters are collected, the system will classify them into the corresponding operating condition subsequence according to the operating condition parameters. For example, if a set of data is collected during the high-speed spin-drying stage, the data will be added to the end of the "high-speed spin-drying operating condition subsequence". In this way, the first feature sequence is not a single, pure time sequence, but a collection of multiple subsequences categorized by operating condition. The data within each subsequence is still arranged in chronological order. When judging the bearing wear state, the judgment module can analyze a specific operating condition subsequence, for example, only analyzing the adjusted wear characteristics in the "high-speed spin-drying operating condition subsequence" to judge the wear trend and consistency of the bearing under that specific operating condition.

[0053] When assessing the wear level of bearings, rule-based expert systems or classification machine learning models can be used. For example, a series of rules can be preset: if the adjusted current harmonic amplitude exceeds threshold A for five consecutive observation windows and the adjusted vibration energy exceeds threshold B for five consecutive observation windows, it is judged as "slight wear"; if the adjusted current harmonic amplitude exceeds threshold C (C > A) for three consecutive observation windows and the adjusted vibration energy exceeds threshold D (D > B) for three consecutive observation windows, it is judged as "moderate wear"; if the adjusted current harmonic amplitude exceeds threshold E (E > C) or the adjusted vibration energy exceeds threshold F (F > D), it is judged as "severe wear". Alternatively, a support vector machine (SVM) or neural network classifier can be trained, with the input being the adjusted wear features and operating parameters in the first feature sequence, and the output being a predefined wear state category (e.g., normal, light, moderate, severe).

[0054] When predicting remaining lifespan, trend extrapolation models or survival analysis models can be used. For example, the system can track a composite wear index, which is calculated by weighting adjusted current harmonic amplitudes and adjusted vibration energy. Assuming this composite wear index increases linearly over time, when a bearing is judged to be "slightly worn," the system can predict the time required for it to reach the "severe wear" threshold based on the historical growth rate of this index. For example, if the current composite wear index is X, the severe wear threshold is Y, and the historical growth rate is Z units per day, then the remaining lifespan can be estimated as (YX) / Z days. Alternatively, a machine learning-based remaining lifespan prediction model can be built using historical failure data, such as a Long Short-Term Memory (LSTM) network. This model can learn temporal patterns in the first feature sequence and directly output the remaining usable time of the bearing.

[0055] Furthermore, in some embodiments, the preset model can be a trend analysis-based model. This model determines the bearing's wear condition by analyzing the changing trend of adjusted wear characteristics in a first characteristic sequence over time. For example, if an adjusted wear characteristic (such as the amplitude of a specific harmonic component of the motor current) shows a continuous upward trend over a period of time, this is generally considered an indication of accelerated bearing wear. The model can use methods such as linear regression, exponential smoothing, or moving averages to fit the trend line of the characteristic, and assess the severity of wear based on the slope or rate of change of the trend line.

[0056] In some embodiments, a threshold-based judgment model is employed. This model compares adjusted wear characteristics with preset alarm thresholds. These thresholds may be set based on the bearing's normal operating range, historical fault data, or empirical knowledge. When the adjusted wear characteristics exceed a preset warning threshold, the system can issue an early warning; when they further exceed the alarm threshold, it may determine that the bearing has reached a state requiring maintenance. In determining consistency, the model may further require multiple related adjusted wear characteristics to simultaneously exceed their respective thresholds, or for their trends to remain consistent within a preset range, thereby improving the reliability of the judgment. For example, if the adjusted current harmonic amplitude and the adjusted vibration signal energy simultaneously and continuously exceed their respective warning thresholds, it can be determined that the bearing is worn.

[0057] Finally, the determination of bearing life is usually based on a combination of the wear condition assessment results and wear development trends. For example, once a bearing is determined to have entered a wear state, the system can predict the time required for the bearing to reach the failure threshold using extrapolation or a degradation curve-based model, based on the current values ​​of the adjusted wear characteristics and their historical rates of change, thereby determining the remaining life.

[0058] In this embodiment, the wear condition of the motor bearing is closely related to its lifespan: the bearing's lifespan refers to the length of time it can operate normally under specific working conditions. As the washing machine motor bearing is used over time, the bearing components will gradually wear down. This wear will be reflected in changes in the motor's operating characteristics, such as changes in specific components of the current waveform data. The higher the degree of wear, the worse the bearing's health condition, and the shorter its remaining usable lifespan. When the wear reaches a certain level, exceeding a preset alarm threshold, the bearing may face the risk of failure, at which point its lifespan is nearing its end. Therefore, accurately judging the wear condition and degree of the bearing is the basis for predicting its remaining lifespan.

[0059] This application further proposes a step for determining the wear state of a bearing and its life based on a first characteristic sequence, including:

[0060] Determine whether there is a preset consistency relationship between multiple adjusted wear feature quantities under the corresponding operating parameters in the first feature sequence to indicate the wear state of the motor bearing, and determine the wear state of the motor bearing.

[0061] The proposed solution determines the wear state of a motor bearing by identifying whether there is a pre-defined consistency relationship between multiple adjusted wear characteristics under corresponding operating parameters in the first feature sequence, thereby indicating the wear state of the motor bearing. Because the solution no longer relies solely on a single data point or simple trend in the first feature sequence when judging the bearing wear state, but further examines whether multiple adjusted wear characteristics exhibit mutual corroboration and a shared indication of the wear state under the same or similar operating parameters, this solution effectively filters out non-wear fluctuations caused by changes in operating conditions, enhancing the ability to identify true wear signals. For example, under a specific motor speed and clothing load condition, if multiple different adjusted wear characteristics (such as current harmonic amplitude, vibration signal energy, etc.) simultaneously show an abnormally increasing trend, this "consistency" provides stronger evidence that the bearing may be worn. Compared to judging solely based on a single characteristic exceeding a threshold, this consistency-based judgment method significantly improves the accuracy and reliability of the judgment, effectively solving the misjudgment problem that easily occurs when directly judging based on sequences containing various operating condition data, making the assessment of bearing wear state more robust.

[0062] In some embodiments, as a specific implementation, when determining the wear state of a motor bearing, the system can first filter out multiple adjusted wear feature quantities collected under similar operating conditions (e.g., motor speed within a specific range, clothing load within a specific level) from a first feature sequence. Then, it checks whether these adjusted wear feature quantities meet a preset consistency relationship. For example, the preset consistency relationship can be a rule that stipulates that under the current operating condition, if the adjusted current harmonic amplitude exceeds a threshold A and the adjusted vibration signal energy exceeds a threshold B, then wear signs are considered to exist. The system will check whether the multiple adjusted wear feature quantities selected simultaneously meet these conditions. If they do, it determines that the motor bearing is in a wear state.

[0063] This application further proposes a method to determine whether there is a preset consistency relationship between multiple adjusted wear feature quantities under corresponding operating parameters in the first feature sequence, indicating the wear state of the motor bearing. The steps for determining the wear state of the motor bearing include:

[0064] Obtain the second characteristic quantity that characterizes the change in the power supply voltage of the washing machine during the candidate observation window period, and form a sequence of the second characteristic quantity.

[0065] The correlation between the changing trends of multiple adjusted wear characteristics and the changing trends of the second characteristic sequence is determined, and the correlation analysis results are obtained.

[0066] In response to the wear characteristics exhibited by the changing trends of multiple adjusted wear characteristics, and the second characteristic sequence showing a unidirectional shift in the supply voltage, the evaluation of the changing trends of multiple adjusted wear characteristics is adjusted based on the historical changing trend of the second characteristic sequence and the correlation analysis results; and / or, the judgment conditions for the consistency relationship of the indicator motor bearing wear state are adjusted.

[0067] The wear condition of the motor bearing is determined based on multiple adjusted wear characteristics, as well as the adjusted assessment and / or adjusted judgment conditions.

[0068] In this embodiment, the effective voltage value at the power input terminal of the washing machine during the candidate observation window period can be obtained as a second characteristic quantity characterizing the change in the power supply voltage, and recorded in chronological order to form a second characteristic quantity sequence. Then, the temporal change trend of this second characteristic quantity sequence is analyzed, for example, by calculating its average value, standard deviation, or performing trend line fitting, while simultaneously analyzing the temporal change trends of multiple adjusted wear characteristic quantities (e.g., vibration amplitude at a specific frequency, harmonic content of motor current, etc.). Next, the correlation coefficient between the trend of the adjusted wear characteristic quantity and the trend of the effective voltage value is calculated as the result of the correlation analysis. When it is detected that the trend of the adjusted wear characteristic quantity shows a continuous increase, and the effective voltage value sequence shows a continuous high or low value, a strategy is selected from a preset adjustment strategy library based on the magnitude and duration of the voltage deviation and the calculated correlation coefficient. For example, if the voltage is continuously high and positively correlated with the wear characteristic quantity, the threshold for judging bearing wear may be increased; if the voltage is continuously low and negatively correlated with the wear characteristic quantity, the judgment threshold may be decreased; or, based on the correlation strength, the current value or its rate of change of the adjusted wear characteristic quantity is weighted or corrected before evaluation or judgment. Finally, based on the adjusted evaluation results or judgment conditions, and combined with the current adjusted wear characteristic quantities, a conclusion is drawn regarding the wear condition of the motor bearing.

[0069] The above technical solution can effectively identify and correct interference introduced by power supply voltage fluctuations when judging the wear condition of motor bearings, thereby improving the accuracy of judging the wear condition of motor bearings and reducing the misjudgment rate.

[0070] This application further proposes a method to determine the correlation between the changing trends of multiple adjusted wear characteristics and the changing trends of a second characteristic sequence. The steps for obtaining the correlation analysis results include:

[0071] The long-term voltage variation component that characterizes the main long-term offset trend of the supply voltage is separated from the second characteristic quantity sequence, and the interference introduced by short-term voltage fluctuations in the second characteristic quantity sequence is suppressed.

[0072] Multiple adjusted wear characteristic quantities are processed to obtain the wear characteristic duration change components of multiple adjusted wear characteristic quantities;

[0073] Based on the long-term voltage variation component and the time-dependent variation component of wear characteristics, the degree of correlation between the two over time is determined, and the correlation analysis results are obtained.

[0074] This application improves the accuracy of correlation analysis results by performing targeted preprocessing on the original second characteristic quantity sequence and multiple adjusted wear characteristic quantity sequences. Specifically, since the original second characteristic quantity sequence may contain interference introduced by short-term voltage fluctuations, directly using its change trend for correlation analysis will introduce noise and affect the reliability of the results. By separating the long-term voltage change component that characterizes the main long-term offset trend of the supply voltage and suppressing the interference introduced by short-term voltage fluctuations, this application can obtain a purer signal that better reflects the overall, slow change trend of the supply voltage. At the same time, since the multiple adjusted wear characteristic quantities themselves may also contain noise or components not directly related to wear, directly using their change trends for analysis will also reduce accuracy. By processing these characteristic quantities to obtain the wear characteristic duration change component, this application can extract a signal that better represents the cumulative effect of bearing wear. It is precisely because of the effective noise reduction and feature extraction of the two original sequences that the subsequent determination of the degree of correlation between the long-term voltage change component and the wear characteristic duration change component can more accurately capture the true relationship between the long-term change of the supply voltage and the cumulative change of motor bearing wear. This combination of preprocessing and correlation analysis allows for more reliable correlation analysis results even when the original data is disturbed under complex operating conditions, thus enabling a more accurate assessment of the wear condition of the motor bearings.

[0075] In some embodiments, separating the long-term voltage variation component that characterizes the main long-term shift trend of the supply voltage from the second characteristic quantity sequence and suppressing the interference introduced by short-term voltage fluctuations in the second characteristic quantity sequence can be specifically achieved by applying a low-pass filter to the second characteristic quantity sequence. For example, a moving average filter or a Butterworth filter with an appropriate cutoff frequency can be used to filter out short-term fluctuation components above the cutoff frequency and retain long-term variation components below the cutoff frequency. Processing multiple adjusted wear characteristic quantities to obtain the wear characteristic duration variation components of multiple adjusted wear characteristic quantities can be specifically achieved by applying a smoothing algorithm to the multiple adjusted wear characteristic quantity sequences. For example, an exponential smoothing algorithm can be used to smooth the sequence, or a linear regression method can be used to fit the trend line of the sequence, and the fitted trend line is used as the wear characteristic duration variation component. Determining the degree of correlation between the long-term voltage variation component and the wear characteristic duration variation component based on the long-term voltage variation component and the wear characteristic duration variation component sequence can be specifically achieved by calculating the Pearson correlation coefficient between the long-term voltage variation component sequence and the wear characteristic duration variation component sequence. The value of this correlation coefficient can be used as the result of the correlation analysis, and its absolute value reflects the correlation strength, while the sign reflects the correlation direction.

[0076] In response to this, this application further proposes a step to adjust the assessment of the time-varying trends of multiple adjusted wear characteristics in response to the changing trends of multiple adjusted wear characteristics, and the second characteristic sequence showing a unidirectional shift in the supply voltage; and / or, to adjust the judgment conditions for adjusting the consistency relationship of the indicator motor bearing wear state, based on the historical changing trend and correlation analysis results of the second characteristic sequence.

[0077] Based on the historical change trend of the second characteristic sequence, the amplitude information and duration information of the power supply voltage offset are determined.

[0078] Determine the degree to which the current values ​​of multiple adjusted wear characteristics are close to the preset alarm threshold, as well as the historical fluctuation range information of multiple adjusted wear characteristics;

[0079] Based on the magnitude information of the power supply voltage deviation, the duration information of the power supply voltage deviation, the degree of proximity, the historical fluctuation magnitude information of multiple adjusted wear characteristics, and the correlation analysis results, an adjustment strategy is selected from the preset set of adjustment strategies.

[0080] Adjust the assessment of the time-varying trends of multiple adjusted wear characteristics based on the selected adjustment strategy; and / or adjust the judgment criteria for the consistency relationship of the indicator motor bearing wear condition.

[0081] This application's solution, when both wear characteristics and unidirectional supply voltage deviation are detected simultaneously, does not simply perform fixed adjustments. Instead, it further analyzes the specific characteristics of the supply voltage deviation (amplitude information, duration information) and the characteristics of multiple adjusted wear characteristics themselves (the closeness of current values ​​to preset alarm thresholds, historical fluctuation amplitude information). This information is then combined with existing correlation analysis results to form a comprehensive description of the current state. It is precisely because of the ability to acquire and comprehensively utilize this multi-dimensional information that the system can select the most suitable adjustment strategy from a preset set of adjustment strategies for the current operating condition. This dynamic and refined strategy selection mechanism allows the evaluation of the historical change trends of multiple adjusted wear characteristics and the judgment conditions for the consistency relationship of preset indicators of motor bearing wear status to more accurately reflect the true wear state, effectively distinguishing between pseudo-changes caused by supply voltage fluctuations and the true wear trend.

[0082] In some embodiments, when the system detects an upward trend in multiple adjusted wear characteristics, and the second characteristic sequence of the supply voltage shows a persistently low supply voltage, the system first quantifies the magnitude (e.g., deviation from normal value exceeding 5%) and duration (e.g., continuous low for more than 1 hour) of the low supply voltage based on the historical trend of the second characteristic sequence. Simultaneously, the system determines whether the values ​​of the multiple adjusted wear characteristics are close to a preset alarm threshold (e.g., reaching more than 80% of the threshold) and analyzes the fluctuations of these characteristics over a past period (e.g., historical fluctuations are small, and the trend is relatively stable). Furthermore, the system utilizes previously obtained correlation analysis results (e.g., showing a strong positive correlation between low supply voltage and the increase in adjusted wear characteristics). The system combines this information (large voltage deviation, long duration, wear characteristics close to the threshold, small historical fluctuations, strong correlation) into current status information. Based on a preset set of adjustment strategies, such as "increasing the judgment consistency threshold," "reducing the current data weight," and "delaying the alarm," the system selects the most suitable adjustment strategy according to the current status information. For example, if the voltage is significantly and persistently low, and the wear characteristics are close to the threshold, with small historical fluctuations and strong correlation, the system may choose the "increase the consistency threshold" strategy. This would increase the similarity threshold required to determine whether multiple wear characteristics consistently indicate the wear state from 0.8 to 0.9, reducing the risk of misjudgment due to low voltage. If the voltage is only slightly and briefly low, the wear characteristics are far from the threshold, with large historical fluctuations and weak correlation, the system may choose the "reduce the weight of current data" strategy. This would reduce the weight of data from the current observation window when evaluating the historical trend, thus mitigating the impact of short-term voltage fluctuations. The system adjusts the evaluation criteria for the historical trend of wear characteristics or the judgment criteria for consistency relationships based on the selected strategy.

[0083] This application further proposes that the preset set of adjustment strategies includes adjustment evaluation strategies, adjustment judgment condition strategies, and combined adjustment strategies;

[0084] The adjustment evaluation strategy is used to adjust the evaluation of the time-varying trend of the multiple adjusted wear characteristics.

[0085] The adjustment judgment condition strategy is used to adjust the preset judgment conditions for the consistency relationship of the indicator motor bearing wear state.

[0086] The combined adjustment strategy is used to combine the adjustment evaluation strategy with the adjustment judgment condition strategy.

[0087] The pre-defined set of adjustment strategies refers to a series of pre-set adjustment schemes for assessing motor bearing wear under different conditions. This set can include various strategy types, each designed for a specific adjustment target or scenario. The adjustment assessment strategy is a strategy specifically designed to modify or correct the interpretation of the historical trends of multiple adjusted wear characteristics. It can be implemented using techniques such as weighting, filtering, and trend line correction, aiming to more accurately reflect the true wear trend under specific operating conditions or external factors. The adjustment judgment condition strategy is a strategy specifically designed to modify or correct the pre-defined judgment criteria that indicate the consistency of motor bearing wear status. It can be implemented using techniques such as dynamic threshold adjustment and judgment logic switching, aiming to make wear status judgment more robust and reduce misjudgments or omissions. The combined adjustment strategy is a strategy that combines the adjustment assessment strategy with the adjustment judgment condition strategy. It can be implemented using serial, parallel, or concurrent execution methods, aiming to achieve coordinated adjustment of wear assessment and judgment criteria to cope with more complex operating condition changes.

[0088] This application's solution uses a pre-set set of multiple adjustment strategies. Based on current state information such as the magnitude and duration of the power supply voltage deviation, its proximity, historical fluctuations of multiple adjusted wear characteristics, and correlation analysis results, it selects an appropriate strategy from this set to adjust the assessment of the historical trends of multiple adjusted wear characteristics and / or the judgment conditions for the consistency of the wear state of the indicating motor bearing. This approach allows wear assessment adjustments to move beyond a single method, flexibly selecting the most suitable adjustment scheme based on the degree of external interference, the current state and historical performance of the wear characteristics, and their correlation with power supply voltage changes. For example, when the power supply voltage shows a slight, short-term deviation, an adjustment strategy might be chosen to smooth the short-term fluctuations of the wear characteristics; when the power supply voltage shows a significant, long-term deviation, and the wear characteristics are close to the alarm threshold, a combined adjustment strategy might be chosen, simultaneously adjusting the assessment method and the judgment threshold. This dynamic selection and application mechanism based on a set of multiple strategies enables more accurate judgment of the wear state under complex washing machine operating conditions, especially when unstable power supply voltage affects the wear characteristics. By integrating multiple adjustment strategies into a preset set and selecting strategies based on real-time status information, this solution can effectively cope with power supply voltage interference of different degrees and types, as well as the characteristic performance of different wear stages, thereby improving the accuracy and reliability of motor bearing wear assessment and solving the problem that a single adjustment strategy is difficult to adapt to changing operating conditions.

[0089] In some embodiments, a preset set of adjustment strategies can be stored in a lookup table or rule base. This set includes adjustment evaluation strategies, adjustment judgment condition strategies, and combined adjustment strategies. For example, the adjustment evaluation strategy can be specifically set as a weighted average algorithm to reduce the impact of short-term voltage fluctuations on the trend evaluation of wear characteristic quantities; the adjustment judgment condition strategy can be specifically set as a dynamic threshold adjustment rule to appropriately relax or tighten the wear judgment threshold based on the magnitude and duration of the power supply voltage deviation; the combined adjustment strategy can be specifically set to first apply a weighted average algorithm to adjust the evaluation, and then make a judgment based on the adjusted evaluation result and the dynamic threshold. In actual operation, the system constructs a combination of current state information based on the magnitude information of the power supply voltage deviation, the duration information, the proximity of the wear characteristic quantity to the alarm threshold, the historical fluctuation magnitude, and the correlation analysis results. Then, the system selects the adjustment strategy most suitable for the current state information combination from the preset set of adjustment strategies according to a preset strategy selection logic (e.g., based on rules or machine learning models). For example, if the power supply voltage deviation is large and the duration is long, and the wear characteristic quantity is close to the alarm threshold, the system may select a combined adjustment strategy. If the power supply voltage deviation is small and short-lived, and the wear characteristic quantity is far from the alarm threshold, the system may choose to adjust the evaluation strategy. After selecting a strategy, the system adjusts the evaluation of the time-varying trend of multiple adjusted wear characteristic quantities according to the specific rules or algorithm of the strategy, and / or adjusts the judgment conditions of the preset consistency relationship of the indicator motor bearing wear state.

[0090] This application further proposes steps for determining multiple candidate observation windows for collecting information related to motor bearing wear, including:

[0091] Based on the current operational phase information and real-time internal status information, the period in which the motor runs at a constant speed, the load is stable, and the drum imbalance is continuously below a preset threshold for a predetermined time is determined as the candidate observation window.

[0092] This application's solution applies screening conditions—namely, uniform motor operation, stable load, and drum imbalance remaining below a preset threshold for a continuous predetermined time—to a preliminary selection of time periods based on current operational phase information and real-time internal status information. This screening mechanism ensures that the final candidate observation window is a period of stable washing machine operation with minimal external interference. Collecting motor operating characteristic information and operating parameters during these periods improves signal quality and reduces the impact of noise on wear feature extraction. This optimized data acquisition method provides high-quality input data for subsequent steps: determining the initial bearing wear characteristics based on motor operating characteristic information; adjusting the initial bearing wear characteristics based on operating parameters to generate adjusted wear characteristics; and combining multiple adjusted wear characteristics and operating parameters to form a first feature sequence to determine the bearing wear state and lifespan. This enables the motor bearing life monitoring method to accurately identify real wear signals, avoid misinterpreting fluctuations caused by changes in operating conditions as signs of wear, and improve the accuracy and reliability of wear assessment.

[0093] In this embodiment, determining the candidate observation window based on the current operating stage information and real-time internal state information can be achieved as follows: First, the system acquires the current operating program stage information of the washing machine, such as determining whether it is in the main washing or rinsing stage. Simultaneously, it acquires real-time internal state information, including motor speed obtained through a motor encoder or Hall sensor, load information (e.g., root mean square current value) obtained by monitoring the motor drive current, and vibration signals obtained through an accelerometer installed on the washing machine's outer drum to assess drum imbalance. Then, the system determines whether the current time period meets the following conditions: the motor speed remains stable near the target speed corresponding to the current program stage, with fluctuations less than a preset value; the fluctuation of the motor drive current is less than a preset proportion, indicating load stability; and the drum imbalance index obtained by analyzing the vibration signal is below a preset threshold for a continuous predetermined time. When these conditions are simultaneously met, this time period is determined as a candidate observation window for collecting motor operating characteristic information.

[0094] The above scheme avoids periods of significant fluctuations in washing machine operation, load fluctuations, or drum imbalance when determining candidate observation windows for collecting information on motor bearing wear. This ensures reduced interference from background noise and changes in operating conditions during motor operation characteristic data collection, improving the signal-to-noise ratio and quality of the collected data. Therefore, subsequent bearing wear feature extraction and wear condition assessment based on this data are accurate and reliable, resolving the impact of complex operating conditions on the accuracy of motor bearing wear assessment.

[0095] This application further proposes a step for selecting an adjustment strategy from a preset set of adjustment strategies based on the magnitude information of the power supply voltage offset, the duration information of the power supply voltage offset, the degree of proximity, the historical fluctuation magnitude information of multiple adjusted wear characteristics, and the correlation analysis results. The steps include:

[0096] The current status information is composed of the amplitude information of the power supply voltage deviation, the duration information of the power supply voltage deviation, the degree of proximity, the historical fluctuation amplitude information of multiple adjusted wear characteristics, and the correlation analysis results.

[0097] Based on the current state information combination, a preliminary adjustment strategy is selected from the preset adjustment strategy set to obtain the preliminary selected adjustment strategy; the degree of matching between the current state information combination and the preset applicable conditions of each adjustment strategy in the preset adjustment strategy set is evaluated to obtain the matching degree information of each strategy.

[0098] Based on the matching degree information of each strategy, determine whether the matching degree between the current state information combination and the preset applicable conditions of the initially selected adjustment strategy is lower than the preset matching degree threshold, and determine whether the current state information combination is in the boundary area of ​​the preset applicable conditions of multiple adjustment strategies.

[0099] If the degree of matching between the current state information combination and the preset applicable conditions of the initially selected adjustment strategy is lower than the preset matching threshold, or if the current state information combination is in the boundary region of the preset applicable conditions of multiple adjustment strategies, then the execution parameters of the initially selected adjustment strategy are adjusted according to the matching degree information of each strategy and the current state information combination to form the final selected adjustment strategy.

[0100] Among them, the preset applicable conditions refer to the specific working conditions or state ranges that each adjustment strategy in the preset adjustment strategy set is designed to effectively handle, and its purpose is to define the application boundaries of different strategies; the matching degree refers to the degree of conformity or similarity between the current state information combination and the preset applicable conditions of each adjustment strategy in the preset adjustment strategy set, and its purpose is to quantify the closeness between the current state and the applicable range of each strategy; the boundary region refers to the state space range in which the current state information combination and the preset applicable conditions of multiple adjustment strategies in the preset adjustment strategy set have a certain degree of matching, and the differences in these matching degrees are not significant, and its purpose is to identify situations where there may be uncertainty in the selection of a single strategy; the execution parameters refer to the internal parameters or coefficients of the initially selected adjustment strategy that can be modified or adjusted in specific applications, and its purpose is to allow the strategy to be fine-tuned according to the current state to improve adaptability.

[0101] This application's solution comprehensively quantifies various factors affecting bearing wear condition judgment by integrating the magnitude information of power supply voltage deviation, the duration information of power supply voltage deviation, the degree of proximity, the historical fluctuation amplitude information of multiple adjusted wear characteristics, and the results of correlation analysis to form a current state information combination. Based on this current state information combination, an initial adjustment direction is determined by initially selecting from a preset set of adjustment strategies. Simultaneously, the matching degree between the current state information combination and the preset applicable conditions of each adjustment strategy in the preset set is evaluated to obtain matching degree information for each strategy, providing a basis for subsequent adjustments. Next, by determining whether the matching degree between the current state information combination and the preset applicable conditions of the initially selected adjustment strategy is lower than a preset matching degree threshold, and by determining whether the current state information combination is in the boundary region of the preset applicable conditions of multiple adjustment strategies, the applicability of the initially selected adjustment strategy or the existence of selection uncertainty in the current state is identified. If the initially selected adjustment strategy is inapplicable (low matching degree) or in the boundary region, the execution parameters of the initially selected adjustment strategy are adjusted according to the matching degree information of each strategy and the current state information combination to form the final selected adjustment strategy. This means that when the initially selected strategy does not match the current state well, or when the current state is at the boundary of multiple strategies, the strategy is not simply abandoned. Instead, its execution parameters are adjusted to better adapt to the current state, thereby achieving smoother and more accurate strategy switching and application. This parameter adjustment mechanism can effectively avoid abrupt changes in strategy selection and improve the robustness and adaptability of the system. Combined with the steps in the aforementioned method of obtaining adjusted wear characteristics, power supply voltage change information, correlation analysis results, and preliminary selection of adjustment strategies, this approach enables more refined application of adjustment strategies under complex and variable operating conditions, thereby improving the accuracy and reliability of motor bearing wear condition judgment.

[0102] In some embodiments, specifically, the magnitude of the power supply voltage deviation can be quantified as the percentage of voltage deviation from the nominal value, the duration information as the cumulative time of deviation exceeding a preset threshold, the proximity degree as the ratio of the currently adjusted wear characteristic quantity to the preset alarm threshold, the historical fluctuation magnitude information as the standard deviation of the adjusted wear characteristic quantity over a past period, and the correlation analysis result as the correlation coefficient. These quantified values ​​constitute the current state information combination. The preset set of adjustment strategies may include adjustment evaluation strategies, adjustment judgment condition strategies, and combined adjustment strategies. Each strategy has its preset applicable conditions. For example, the adjustment evaluation strategy is suitable for situations where the power supply voltage is consistently low and the wear characteristic quantity fluctuates significantly, while the adjustment judgment condition strategy is suitable for situations where the wear characteristic quantity is close to the alarm threshold and has a high correlation. The degree of matching between the current state information combination and the preset applicable conditions of each strategy can be evaluated using a rule-based or model-based matching algorithm to obtain the matching degree score of each strategy. For example, strategy A has a matching degree of 0.6, strategy B has a matching degree of 0.9, and strategy C has a matching degree of 0.7. The preset matching degree threshold can be set to 0.8. Strategy B, which has the highest matching degree, is initially selected as the initial selected adjustment strategy. The system determines whether the matching degree (0.9) between the current state information combination and the initially selected strategy B is lower than a preset matching degree threshold (0.8), and whether the current state information combination is located in the boundary region of preset applicable conditions for multiple adjustment strategies. For example, if the difference between the highest matching degree (0.9) and the second highest matching degree (0.7) is less than 0.3, it is considered to be in the boundary region. If adjustment is required (e.g., in the boundary region), the system calculates the adjustment amount of the execution parameters (e.g., judgment threshold offset) of the initially selected strategy B using a preset parameter adjustment function, based on the matching degree information (0.6, 0.9, 0.7) of each strategy and the quantitative indicators in the current state information combination. Based on this adjustment amount, the execution parameters of strategy B are adjusted to form the final selected adjustment strategy.

[0103] This application further proposes a step for adjusting the execution parameters of the initially selected adjustment strategy to form the final selected adjustment strategy based on a combination of matching degree information and current state information of each strategy.

[0104] Based on the matching degree information of each strategy, the quantitative indicators in the combination of current status information, and the preset parameter adjustment function, determine the execution parameter adjustment amount of the initially selected adjustment strategy;

[0105] Based on the adjustment amount of the execution parameters, the execution parameters of the initially selected adjustment strategy are adjusted to form the final selected adjustment strategy.

[0106] Among them, the matching degree information of each strategy refers to the quantitative representation of the matching degree between the current state information combination and the preset applicable conditions of each strategy in the preset adjustment strategy set, which can be realized by similarity calculation, distance measurement, etc.; the current state information combination refers to the set of multiple state information used for strategy selection and parameter adjustment, including the amplitude information of power supply voltage deviation, the duration information of power supply voltage deviation, the degree of proximity, the historical fluctuation amplitude information of multiple adjusted wear characteristics, and the correlation analysis results, etc.; the execution parameters of the initially selected adjustment strategy refer to the parameters that need to be set when the initially selected adjustment strategy is executed, such as the evaluation weight in the adjustment evaluation strategy, the adjustment judgment criteria, etc. The judgment threshold in the component strategy; the final selected adjustment strategy refers to the adjustment strategy used to guide subsequent wear state judgment after parameter adjustment; the quantitative indicators in the current state information combination refer to the information in the current state information combination that can be numerically quantified, such as the amplitude value, duration value, proximity value, historical fluctuation amplitude value, correlation analysis result value, etc. of the power supply voltage deviation; the preset parameter adjustment function refers to a pre-set function or model used to calculate the output based on the input, which can be implemented by means of lookup tables, mathematical formulas, machine learning models, etc.; the execution parameter adjustment amount refers to the numerical adjustment amount of the execution parameters of the initially selected adjustment strategy.

[0107] This application's solution, based on the initial selection of an adjustment strategy, further calculates the adjustment amount of the execution parameters of the initially selected strategy using a preset parameter adjustment function, based on the matching degree information between the current state information combination and each strategy, as well as the quantitative indicators in the current state information combination. This adjustment amount reflects the degree of deviation between the current operating condition and the preset applicable conditions of the initially selected strategy, as well as the specific numerical characteristics of the current state. Based on the calculated adjustment amount, the execution parameters of the initially selected strategy are corrected, thereby forming a final adjustment strategy that is more adapted to the current actual operating conditions. For example, if the initially selected strategy is to adjust the evaluation weights, and the current state does not match this strategy well, while the voltage deviation is large, the calculated adjustment amount will cause the evaluation weights to be adjusted in a more conservative or more aggressive direction to cope with the current specific interference situation. This dynamic adjustment of parameters makes the initially selected strategy no longer fixed, but can be optimized according to real-time operating condition information, improving the flexibility and targeting of the strategy.

[0108] In some embodiments, it is assumed that the initially selected adjustment strategy is an adjustment evaluation strategy, and the execution parameters of this strategy are weights used to evaluate the historical change trend of the adjusted wear characteristics. The current state information combination shows that the power supply voltage has deviated to a certain extent and has lasted for a period of time. Simultaneously, the current value of the adjusted wear characteristics is close to the alarm threshold, and the historical fluctuation range is large. Correlation analysis results show that there is a certain correlation between the wear characteristics and voltage changes. The system calculates the matching degree score between the current state information combination and each strategy in the preset adjustment strategy set, for example, calculating a matching degree score of 0.6 between the current state and the preset applicable conditions of the initially selected adjustment evaluation strategy. Simultaneously, it obtains the quantitative indicators in the current state information combination, such as the voltage deviation amplitude value of X volts, the duration of Y seconds, the proximity level of Z, the historical fluctuation range of W, and the correlation analysis result of R. The system calls the preset parameter adjustment function, which can be a multi-input single-output model. The inputs include the matching degree score, X, Y, Z, W, and R, and the output is the adjustment amount ΔP of the evaluation weights. For example, the preset parameter adjustment function can be designed as: ΔP = f(matching degree score, X, Y, Z, W, R). Based on the calculated evaluation weight adjustment ΔP, the execution parameters of the initially selected adjustment evaluation strategy, i.e., the evaluation weight P, are adjusted to obtain the final evaluation weight P_final = P + ΔP. The final selected adjustment strategy is the adjustment evaluation strategy that uses P_final as the evaluation weight.

[0109] In summary, this embodiment, the method for predicting remaining lifespan based on wear state and degree, can be implemented based on the technical solution proposed in this application: First, the system intelligently determines candidate observation windows for collecting information related to motor bearing wear based on the current operating stage information and real-time internal status information of the washing machine. Within these windows, the system collects motor operating characteristic information (e.g., current waveform data for a specific duration) and corresponding operating parameters (e.g., motor speed, laundry load, and current program stage). Next, based on the collected motor operating characteristic information, the system determines the initial wear characteristic quantity of the bearing. Since operating parameters affect motor operating characteristics, the system adjusts the initial wear characteristic quantity based on these operating parameters to generate an adjusted wear characteristic quantity. This adjustment aims to eliminate or reduce the influence of different operating conditions on the wear characteristic quantity, so that the adjusted wear characteristic quantity can more accurately reflect the true wear state of the bearing. Subsequently, the system combines the adjusted wear characteristic quantities corresponding to multiple candidate observation windows and the corresponding operating parameters to form a first feature sequence. This sequence contains the wear information of the bearing at different time points and under different operating conditions. When determining the bearing wear condition, the system further determines whether there is a preset consistency relationship between multiple adjusted wear feature quantities under the corresponding operating parameters in the first feature sequence, indicating the bearing wear condition of the motor. This consistency relationship ensures the reliability of the judgment and avoids misjudgment caused by fluctuations in a single data point.

[0110] To improve the accuracy of the judgment, the system also acquires a second characteristic quantity characterizing the change in the washing machine's power supply voltage, forming a second characteristic quantity sequence. Then, it determines the correlation between the changing trends of multiple adjusted wear characteristic quantities and the changing trend of the second characteristic quantity sequence, obtaining correlation analysis results. When the changing trend of the adjusted wear characteristic quantities exhibits wear characteristics, and the power supply voltage sequence shows a unidirectional shift, the system adjusts the evaluation of the historical changing trend of the adjusted wear characteristic quantities and / or adjusts the preset judgment conditions for the consistency relationship of the indicator motor bearing wear state based on the historical changing trend of the power supply voltage sequence and the correlation analysis results. This adjustment considers the influence of the external power supply environment on motor operation and wear characteristics, making the judgment of wear state more accurate. When selecting the adjustment strategy, the system constructs a combination of current state information based on the amplitude information of the power supply voltage shift, the duration information of the power supply voltage shift, the proximity of the current value information in the adjusted wear characteristic quantities to the preset alarm threshold, the historical fluctuation amplitude information of the adjusted wear characteristic quantities, and the correlation analysis results. Then, the most suitable adjustment strategy is selected from the preset set of adjustment strategies, and the evaluation of the historical change trend of the adjusted wear characteristic quantity is adjusted according to the selected strategy, and / or the judgment conditions of the preset consistency relationship of the indicator motor bearing wear state are adjusted.

[0111] Finally, based on the adjusted assessment and / or judgment conditions, combined with multiple adjusted wear characteristics, the wear state of the motor bearing is determined. Once the wear state is accurately determined and its extent (e.g., how close the current value of the adjusted wear characteristics is to the alarm threshold, or the trend of its historical variation components) is quantified, the system can predict the remaining life of the bearing based on historical data, a preset degradation model, or a machine learning algorithm.

[0112] For example, suppose a certain adjusted wear characteristic of a washing machine motor bearing (e.g., the amplitude of current harmonic components) is identified as a key indicator. The system continuously monitors this indicator and compares it with a preset alarm threshold. If the historical trend of this indicator shows a continuous increase, and correlation analysis with power supply voltage changes confirms that this increase is not caused by voltage fluctuations, then the system will determine that the bearing is in a wear state. The degree of wear can be quantified by the distance between the current value of the indicator and the alarm threshold. For example, if the alarm threshold is 100 units, the current value is 70 units, and the trend shows an increase of 1 unit per 100 hours of operation, then without considering other complex factors, the remaining lifespan can be initially predicted as (100 - 70) / 1 * 100 = 3000 hours. If a persistently low power supply voltage is detected simultaneously, and correlation analysis indicates that this low voltage will lead to an overestimation of the indicator's measured value, the system may adjust its assessment of the indicator's upward trend, for example, assuming that the actual wear rate is slower than the measured value, thereby correcting the remaining lifespan prediction to better reflect reality. This dynamic adjustment and comprehensive judgment makes the prediction of the remaining life of the bearing more accurate and reliable.

[0113] Furthermore, this application proposes a motor bearing life monitoring system for a washing machine, such as... Figure 2 As shown, the system includes:

[0114] The first acquisition and determination module 201 is used to determine multiple candidate observation windows for acquiring information related to motor bearing wear based on the current operating stage information and the real-time internal status information; the current operating stage information includes the current operating program stage of the washing machine; the real-time internal status information includes the load of clothes, motor speed and drum imbalance.

[0115] The second acquisition and generation module 202 is used to acquire motor operating characteristic information and corresponding operating parameters within any candidate observation window; determine the initial wear characteristic quantity of the bearing based on the motor operating characteristic information, and adjust the initial wear characteristic quantity of the bearing based on the operating parameters to generate the adjusted wear characteristic quantity; the motor operating characteristic information includes current waveform data for a specific duration; the operating parameters include motor speed, clothing load, and current program stage;

[0116] The judgment module 203 is used to combine the adjusted wear characteristic quantities corresponding to multiple candidate observation windows and the corresponding operating parameters to form a first feature sequence; and to judge the wear state of the bearing and determine the bearing life based on the first feature sequence.

[0117] Through the above technical solution, this application provides a motor bearing life monitoring system. The system, through its modular design, clarifies the responsibilities of each functional unit, facilitates the design, implementation, maintenance, and upgrading of the system, and improves the efficiency and accuracy of data processing. As a result, it can effectively monitor the wear condition of motor bearings and determine the bearing life.

[0118] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring the life of motor bearings, used in a washing machine, characterized in that... The method includes: Based on the current operating phase information and real-time internal status information, multiple candidate observation windows are determined for collecting information related to motor bearing wear; the current operating phase information includes the current operating program phase of the washing machine; the real-time internal status information includes the load of clothes, motor speed, and drum imbalance. Collect motor operating characteristic information and corresponding operating parameters within any candidate observation window; determine the initial wear characteristic of the bearing based on the motor operating characteristic information, and adjust the initial wear characteristic of the bearing based on the operating parameters to generate the adjusted wear characteristic; the motor operating characteristic information includes current waveform data of a specific duration; the operating parameters include motor speed, clothing load, and current program stage; The adjusted wear characteristics corresponding to multiple candidate observation windows and the corresponding operating parameters are combined to form a first feature sequence; the wear state of the bearing is determined based on the first feature sequence, and the bearing life is determined. Determining the bearing's wear condition and lifespan based on the first feature sequence includes: Determine whether there is a preset consistency relationship between multiple adjusted wear feature quantities under the corresponding operating conditions in the first feature sequence to indicate the wear state of the motor bearing, and determine the wear state of the motor bearing. Determining whether there is a preset consistency relationship between multiple adjusted wear feature quantities under the corresponding operating parameters in the first feature sequence to indicate the wear state of the motor bearing, and determining the wear state of the motor bearing, includes: Obtain the second characteristic quantity that characterizes the change in the power supply voltage of the washing machine during the candidate observation window period, and form a sequence of the second characteristic quantity. The correlation between the changing trends of multiple adjusted wear characteristics and the changing trend of the second characteristic sequence is determined, and the correlation analysis results are obtained; In response to the wear characteristics exhibited by the changing trends of the plurality of adjusted wear characteristics, and the second characteristic sequence showing a unidirectional shift in the power supply voltage, the evaluation of the changing trends of the plurality of adjusted wear characteristics is adjusted based on the historical changing trend of the second characteristic sequence and the correlation analysis results; and / or, the judgment conditions for the consistency relationship indicating the wear state of the motor bearing are adjusted. The wear condition of the motor bearing is determined based on the multiple adjusted wear characteristics, as well as the adjusted evaluation and / or adjusted judgment conditions.

2. The method for monitoring the life of motor bearings according to claim 1, characterized in that, Determine the correlation between the changing trends of multiple adjusted wear characteristics and the changing trend of the second characteristic sequence, and obtain the correlation analysis results, including: The long-term voltage variation component that characterizes the main long-term offset trend of the supply voltage is separated from the second characteristic quantity sequence, and the interference introduced by short-term voltage fluctuations in the second characteristic quantity sequence is suppressed. The multiple adjusted wear characteristic quantities are processed to obtain the wear characteristic duration change components of the multiple adjusted wear characteristic quantities; Based on the long-term voltage variation component and the time-dependent wear characteristic variation component, the degree of correlation between the two over time is determined, and the correlation analysis results are obtained.

3. The method for monitoring the life of motor bearings according to claim 1, characterized in that, In response to the wear characteristics exhibited by the changing trends of the plurality of adjusted wear characteristics, and the second characteristic sequence showing a unidirectional shift in the power supply voltage, the evaluation of the changing trends of the plurality of adjusted wear characteristics is adjusted based on the historical changing trend of the second characteristic sequence and the correlation analysis results. And / or, adjusting the preset judgment conditions for the consistency of the indicator motor bearing wear state, including: Based on the historical change trend of the second feature sequence, the amplitude information and duration information of the power supply voltage offset are determined. Determine the degree to which the current values ​​of multiple adjusted wear characteristics are close to the preset alarm threshold, as well as the historical fluctuation range information of multiple adjusted wear characteristics; Based on the magnitude information of the power supply voltage offset, the duration information of the power supply voltage offset, the degree of proximity, the historical fluctuation magnitude information of multiple adjusted wear characteristics, and the correlation analysis results, an adjustment strategy is selected from a preset set of adjustment strategies. The evaluation of the time-varying trends of the multiple adjusted wear characteristics is adjusted according to the selected adjustment strategy; and / or, the judgment conditions for the consistency relationship of the preset indicator motor bearing wear state are adjusted.

4. The method for monitoring the life of motor bearings according to claim 3, characterized in that, The preset set of adjustment strategies includes adjustment evaluation strategies, adjustment judgment condition strategies, and combined adjustment strategies; wherein, the adjustment evaluation strategy is used to adjust the evaluation of the time-varying trend of the multiple adjusted wear characteristic quantities; the adjustment judgment condition strategy is used to adjust the judgment condition of the preset consistency relationship of the indicator motor bearing wear state; and the combined adjustment strategy is used to combine the adjustment evaluation strategy and the adjustment judgment condition strategy.

5. The method for monitoring the life of motor bearings according to claim 1, characterized in that, Based on current operational phase information and real-time internal status information, multiple candidate observation windows for collecting information related to motor bearing wear are identified, including: Based on the current operating phase information and real-time internal status information, the period in which the motor runs at a constant speed, the load is stable, and the unbalance of the drum is continuously below a preset threshold for a predetermined time is determined as the candidate observation window.

6. The method for monitoring the life of motor bearings according to claim 4, characterized in that, Based on the magnitude information of the power supply voltage offset, the duration information of the power supply voltage offset, the degree of proximity, the historical fluctuation magnitude information of multiple adjusted wear characteristics, and the correlation analysis results, an adjustment strategy is selected from a preset set of adjustment strategies, including: The current state information combination is constructed based on the amplitude information of the power supply voltage deviation, the duration information of the power supply voltage deviation, the degree of proximity, the historical fluctuation amplitude information of multiple adjusted wear characteristics, and the correlation analysis results. Based on the current state information combination, a preliminary adjustment strategy is selected from the preset adjustment strategy set to obtain the preliminary selected adjustment strategy; the matching degree between the current state information combination and the preset applicable conditions of each adjustment strategy in the preset adjustment strategy set is evaluated to obtain the matching degree information of each strategy. Based on the matching degree information of each strategy, it is determined whether the matching degree between the current state information combination and the preset applicable conditions of the initially selected adjustment strategy is lower than the preset matching degree threshold, and whether the current state information combination is in the boundary region of the preset applicable conditions of multiple adjustment strategies. If the degree of matching between the current state information combination and the preset applicable conditions of the initially selected adjustment strategy is lower than the preset matching threshold, or if the current state information combination is in the boundary region of the preset applicable conditions of multiple adjustment strategies, then the execution parameters of the initially selected adjustment strategy are adjusted according to the matching degree information of each strategy and the current state information combination to form the final selected adjustment strategy.

7. The method for monitoring the life of motor bearings according to claim 6, characterized in that, Based on the combination of the matching degree information of each strategy and the current state information, the execution parameters of the initially selected adjustment strategy are adjusted to form the final selected adjustment strategy, including: Based on the matching degree information of each strategy, the quantitative indicators in the combination of current state information, and the preset parameter adjustment function, the adjustment amount of the execution parameters of the initially selected adjustment strategy is determined; Based on the adjustment amount of the execution parameters, the execution parameters of the initially selected adjustment strategy are adjusted to form the final selected adjustment strategy.

8. A motor bearing life monitoring system for a washing machine, characterized in that, The system includes: The first acquisition and determination module is used to determine multiple candidate observation windows for acquiring information related to motor bearing wear based on the current operating stage information and the real-time internal status information; the current operating stage information includes the current operating program stage of the washing machine; the real-time internal status information includes the load of clothes, motor speed and drum imbalance. The second acquisition and generation module is used to acquire motor operating characteristic information and corresponding operating parameters within any candidate observation window; determine the initial wear characteristic of the bearing based on the motor operating characteristic information, and adjust the initial wear characteristic of the bearing based on the operating parameters to generate the adjusted wear characteristic; the motor operating characteristic information includes current waveform data of a specific duration; the operating parameters include motor speed, clothing load, and current program stage; The judgment module is used to combine the adjusted wear characteristics corresponding to multiple candidate observation windows and the corresponding operating parameters to form a first feature sequence; and to judge the wear state of the bearing and determine the bearing life based on the first feature sequence. Determining the bearing's wear condition and lifespan based on the first feature sequence includes: Determine whether there is a preset consistency relationship between multiple adjusted wear feature quantities under the corresponding operating conditions in the first feature sequence to indicate the wear state of the motor bearing, and determine the wear state of the motor bearing. Determining whether there is a preset consistency relationship between multiple adjusted wear feature quantities under the corresponding operating parameters in the first feature sequence to indicate the wear state of the motor bearing, and determining the wear state of the motor bearing, includes: Obtain the second characteristic quantity that characterizes the change in the power supply voltage of the washing machine during the candidate observation window period, and form a sequence of the second characteristic quantity. The correlation between the changing trends of multiple adjusted wear characteristics and the changing trend of the second characteristic sequence is determined, and the correlation analysis results are obtained; In response to the wear characteristics exhibited by the changing trends of the plurality of adjusted wear characteristics, and the second characteristic sequence showing a unidirectional shift in the power supply voltage, the evaluation of the changing trends of the plurality of adjusted wear characteristics is adjusted based on the historical changing trend of the second characteristic sequence and the correlation analysis results; and / or, the judgment conditions for the consistency relationship indicating the wear state of the motor bearing are adjusted. The wear condition of the motor bearing is determined based on the multiple adjusted wear characteristics, as well as the adjusted evaluation and / or adjusted judgment conditions.

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