A motor rotating shaft dynamic balance control method and system

Abstract

The application relates to the technical field of motor rotating shaft dynamic balance control, in particular to a motor rotating shaft dynamic balance control method and system, the method comprises the following steps: obtaining vibration performance information of a rotor system and current running speed information of the rotor system; according to the vibration performance information and the running speed information, the vibration performance information is processed to eliminate the influence of the running speed on the vibration performance, so that a value reflecting the unbalance degree of the rotor is obtained; the change of the value with time is analyzed, the running balance state of the rotor system is judged according to the change of the value with time, and early warning information is sent; and according to the early warning information, suggestions for balance adjustment in running are provided. The application can improve the accuracy and timeliness of dynamic balance control.

Description

Technical Field

[0001] This invention relates to the technical field of dynamic balance control of motor shafts, and specifically to a method and system for dynamic balance control of motor shafts. Background Technology

[0002] In large-scale industrial production environments, such as cement plants, mines, or chemical workshops, large fans or mixing equipment driven by high-power motors are core components. The motor shaft is closely connected to the fan impeller or mixing blades, forming a high-speed rotating rotor system. To ensure the safe and stable operation of the equipment, the rotor system requires precise dynamic balancing calibration after leaving the factory and after installation. This calibration is usually performed under ideal conditions, targeting a preset rated speed, and the residual imbalance is controlled within an allowable range by adding counterweights. However, the actual operating conditions of the equipment differ significantly from the ideal conditions during calibration, leading to dynamic imbalance of the motor shaft. Summary of the Invention

[0003] The purpose of this invention is to address the aforementioned shortcomings by proposing a dynamic balance control method and system for motor shafts.

[0004] The present invention adopts the following technical solution: A method for dynamic balance control of a motor shaft, comprising the following steps: To obtain vibration performance information and current operating speed information of the rotor system; Based on the vibration performance information and the operating speed information, the vibration performance information is processed to eliminate the influence of the operating speed on the vibration performance, and a value reflecting the degree of rotor imbalance is obtained. Analyze the changes in magnitude over time, determine the operational balance of the rotor system based on these changes, and issue an early warning. Based on the early warning information, suggestions for balance adjustments during operation are provided.

[0005] This technical solution can effectively distinguish the combined effects of speed variation and mass distribution variation on vibration performance, and adaptively adjust the judgment criteria and control strategy according to real-time operating conditions, thereby improving the accuracy and timeliness of dynamic balance control.

[0006] This application also discloses a dynamic balance control system for a motor shaft, the system comprising: The acquisition module acquires vibration performance information and current operating speed information of the rotor system. The processing module processes the vibration performance information based on the vibration performance information and the operating speed information to eliminate the influence of the operating speed on the vibration performance and obtain a value reflecting the degree of rotor imbalance. The analysis module analyzes the changes in magnitude over time, determines the operational balance of the rotor system based on these changes, and issues early warning information. It is recommended to provide a module that offers suggestions for balancing adjustments during operation based on early warning information.

[0007] This technical solution provides a system for implementing the aforementioned dynamic balance control method for motor shafts, offering hardware and software support for practical applications, thereby improving equipment operational stability and maintenance efficiency.

[0008] This application can more accurately reflect the true degree of rotor imbalance. At the same time, through the analysis of the changes in the value over time and the early warning mechanism, it can promptly detect the worsening trend of rotor imbalance and provide targeted balance adjustment suggestions, thereby effectively extending the service life of equipment, reducing maintenance costs, and improving production efficiency and safety.

[0009] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0010] Figure 1 This is a flowchart of a method for dynamic balance control of a motor shaft according to the present invention; Figure 2 This is a schematic diagram of the structure of a dynamic balance control system for a motor shaft according to the present invention. Detailed Implementation

[0011] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0012] This embodiment provides a method and system for dynamic balance control of a motor shaft, combined with... Figure 1 and Figure 2 As shown.

[0013] refer to Figure 1 A method for dynamic balance control of a motor shaft, comprising the following steps: To obtain vibration performance information and current operating speed information of the rotor system; Based on the vibration performance information and the operating speed information, the vibration performance information is processed to eliminate the influence of the operating speed on the vibration performance, and a value reflecting the degree of rotor imbalance is obtained. Analyze the changes in magnitude over time, determine the operational balance of the rotor system based on these changes, and issue an early warning. Based on the early warning information, suggestions for balance adjustments during operation are provided.

[0014] In this context, a "rotor system" typically refers to a rotating mechanical system composed of components such as a motor shaft, impeller, or agitator blades. The motor shaft is the driving component of the rotor system, responsible for transmitting the power (torque and speed) generated by the motor to the impeller or agitator blades. The impeller or agitator blades receive power and perform their functions through their connection with the motor shaft. In industrial applications, such as large fans or mixing equipment, the rotor system is a core working component, and its balance directly affects the operating performance and lifespan of the equipment.

[0015] "Vibration performance information" refers to the vibration signals of the rotor system during operation, which are collected by sensors (such as accelerometers and displacement sensors). These signals usually include parameters such as vibration frequency, vibration amplitude, and phase, and can reflect the dynamic characteristics of the rotor system.

[0016] "Operating speed information" refers to the current rotational speed of the rotor system, which is usually obtained through a speed sensor or motor encoder.

[0017] "Value" refers to a numerical value that, after processing, can directly reflect the degree of imbalance in the rotor system. This value has eliminated the interference of changes in operating speed on vibration performance, and therefore can more accurately indicate changes in rotor mass distribution.

[0018] "Early warning information" refers to the alarm issued by the system when it determines that there is a risk of imbalance in the rotor system based on the results of quantitative analysis. It can include different warning levels and detailed risk descriptions.

[0019] "Suggestions for balance adjustment during operation" refers to specific adjustment plans provided by the system to operators or automated systems based on early warning information and current operating conditions. For example, suggestions may include adjusting the counterweight or cleaning the impeller surface to restore the balance of the rotor system.

[0020] This method is typically implemented on industrial control systems or dedicated monitoring and diagnostic platforms that integrate functional modules such as data acquisition, signal processing, data analysis, decision support, and human-computer interaction.

[0021] This application proposes a dynamic balance control method for motor shafts, the core of which lies in achieving accurate identification and adaptive control of dynamic imbalance in the rotor system through a series of steps.

[0022] First, the method includes acquiring vibration performance information of the rotor system and current operating speed information of the rotor system.

[0023] Vibration performance information can be obtained in various ways. For example, accelerometers can be installed near the rotor bearing housing to collect real-time radial and axial vibration acceleration signals of the rotor system. These sensors can be piezoelectric accelerometers, whose output signal is proportional to the vibration acceleration. Alternatively, non-contact displacement sensors, such as eddy current sensors, can be used to measure the relative displacement of the rotor journals relative to the bearings, thereby obtaining vibration displacement information. Operating speed information can be obtained by installing speed sensors or encoders on the motor shaft. These sensors can monitor the motor's speed in real time and convert it into an electrical signal output. For example, an optical encoder can be used to calculate the speed by detecting the grating signal on a rotating disk.

[0024] Secondly, based on the vibration performance information and operating speed information, the vibration performance information is processed to eliminate the influence of operating speed on vibration performance, and a value reflecting the degree of rotor imbalance is obtained.

[0025] In practice, vibration performance information is often significantly affected by operating speed. For example, the centrifugal force caused by imbalance is proportional to the square of the rotational speed. To eliminate this influence, various processing methods can be used. One method is normalization, which involves dividing the collected vibration amplitude by the square of the current operating speed to obtain a relative imbalance quantity independent of the rotational speed. For example, if the vibration amplitude is A and the operating speed is N, the processed value can be expressed as A / N². Another method is to establish a mathematical model between vibration and rotational speed. Through regression analysis or machine learning algorithms, the theoretical vibration value caused by imbalance at the current rotational speed is predicted. Then, the actual vibration value is compared with the theoretical value, and the difference or ratio can be used as a quantity reflecting the degree of imbalance. For example, the vibration response of the rotor system can be measured in advance at different rotational speeds to establish a vibration amplitude-rotational speed curve. Then, based on the real-time rotational speed, a corresponding reference vibration value can be found on this curve, and the real-time measured vibration amplitude can be compared with this reference value.

[0026] Next, analyze the changes in the magnitude over time, determine the operating balance of the rotor system based on the changes in the magnitude over time, and issue an early warning message.

[0027] The change in magnitude over time is a key indicator of the rotor system's operational balance. For example, if the magnitude remains consistently low over a period of time, it indicates that the rotor system is in good balance. If the magnitude shows a slow upward trend, it may indicate that material is gradually adhering to the rotor surface, leading to a gradual increase in imbalance. If the magnitude suddenly jumps sharply, it may mean that a large piece of material has detached or adhered, causing a rapid change in imbalance. By setting different thresholds and rates of change, the operational balance of the rotor system can be determined. For example, a "normal" threshold can be set; when the magnitude is below this threshold, the system considers the rotor to be in good balance. A "pay attention" threshold can be set; when the magnitude exceeds this threshold but is below the "warning" threshold, the system issues a "pay attention" level warning. A "warning" threshold can be set; when the magnitude exceeds this threshold, the system issues a "warning" level warning. Warning messages can be issued in various forms, such as audible and visual alarms, SMS notifications, and emails, and can include detailed information such as the current magnitude, trend, and warning level.

[0028] Finally, based on the early warning information, suggestions for balancing adjustments during operation are provided.

[0029] When the system issues a warning, it needs to provide corresponding balance adjustment suggestions based on the type and severity of the warning. For example, if the warning indicates a slow increase in imbalance, it may be due to gradual dust accumulation; in this case, regular cleaning or adjustment of operating parameters can be suggested to reduce dust buildup. If the warning indicates a sudden increase in imbalance, it may be due to foreign object adhesion or loose components; in this case, immediate shutdown and inspection may be necessary. Adjustment suggestions can take various forms. For example, for rotor systems with adjustable counterweights, adjusting the position or mass of the counterweight can be suggested; for systems with deposits on the impeller surface, online or offline cleaning can be suggested; for systems with structural deformation, repair or replacement of components can be suggested. These suggestions can be pre-stored in an adjustment strategy library. The system matches and filters these suggestions based on the warning information and current operating parameters (such as motor speed, load, and ambient temperature) to provide the most suitable adjustment scheme.

[0030] In summary, the motor shaft dynamic balance control method of this application, through its unique data processing and analysis mechanism, significantly improves the intelligence and precision of the dynamic balance control of the rotor system, providing strong technical support for the stable operation of industrial equipment.

[0031] This application further proposes that the method includes the following steps: Analyze the long-term changes in the values ​​to identify gradual changes in the overall mass distribution of the rotor system; Collect local temperature and humidity information near the impeller; Analyze the correlation between local temperature information, local humidity information, and long-term changes in their values; Analyze the fluctuation characteristics of the measured values ​​within a short time window; By combining the long-term changes in the magnitude and the fluctuation characteristics of the magnitude within a short time window, as well as the correlation between local temperature and humidity information and the long-term changes in the magnitude, we can distinguish between the increase in actual imbalance caused by the attachment of new sediments and the changes in the original sediments with operating conditions. Original sediments refer to substances that have been attached to the impeller surface for a first duration, while new sediments refer to substances that have newly attached relative to the original sediments within a second time range. The second time range is shorter than the first duration. The first duration can be set by domain experts or technical personnel according to specific application needs, and the second time range is much shorter than the first duration to highlight the suddenness and "new" nature of the new sediments. For example, viscoelastic composite sediments (such as dust, sludge, and other viscoelastic or hygroscopic substances) attached to the impeller surface for a period of time (i.e., the first duration) become part of the rotor system's mass distribution, forming the original sediments. During impeller operation, dust clumps, foreign objects, etc., suddenly attach to the impeller surface, forming newly attached new sediments. When these dust clumps or foreign objects remain attached to the impeller for the first duration without detaching, they become part of the original sediments. Therefore, the concepts of existing sediments and newly added sediments are relative. "The changes in existing sediments under operating conditions" refers to substances that have been attached to the rotor impeller surface for a period of time—the existing sediments. Their physical properties or distribution changes due to environmental conditions (such as local temperature and humidity), leading to alterations in rotor imbalance. For example, viscoelastic composite sediments may expand due to moisture absorption, soften, deform, or even partially peel off due to increased temperature; these are all dynamic responses of existing sediments under different operating conditions. "The increase in actual imbalance caused by the attachment of newly added sediments" refers to the attachment of new, previously non-existent substances (such as sudden dust clumps or foreign objects) to the rotor impeller surface, causing a relatively discrete or sudden change in rotor mass distribution, thus increasing the imbalance.

[0032] Based on the differentiation results, early warning information is issued and maintenance recommendations are provided. Specifically, analyzing long-term changes in quantities refers to monitoring and evaluating trends in quantities over longer time scales (e.g., days, weeks, or months). The purpose is to identify gradual changes in the overall mass distribution of the rotor system, such as mass increases due to the slow accumulation of dust, dirt, or other particulate matter, or mass losses due to wear, corrosion, etc. This long-term trend analysis helps to uncover subtle, persistent imbalances.

[0033] Simultaneously, collecting local temperature and humidity information near the impeller refers to acquiring real-time environmental temperature and humidity data of the area by deploying corresponding sensors near the impeller. These environmental parameters are crucial for understanding changes in the physical properties of sediments; for example, some sediments may expand, soften, or detach under high temperature or high humidity conditions.

[0034] Furthermore, analyzing the correlation between local temperature and humidity information and their long-term changes refers to exploring the correlation between environmental temperature, humidity, and long-term trends in these values ​​using statistical methods or machine learning algorithms. For example, if the values ​​show a significant upward trend when humidity increases, it may indicate the presence of hygroscopic deposits on the impeller surface. The aim is to reveal the mechanism by which environmental factors affect rotor imbalance.

[0035] Furthermore, analyzing the fluctuation characteristics of quantities within a short time window refers to analyzing the instantaneous changes and fluctuation patterns of quantities over a relatively short time scale (e.g., minutes or hours). Its purpose is to capture the impact of sudden events or transient operating conditions on unbalanced quantities, such as sudden material impacts or localized material shedding.

[0036] The solution proposed in this application, through comprehensive analysis of the long-term changes in magnitude, the correlation between local temperature and humidity information and the long-term changes in magnitude, and the fluctuation characteristics of magnitude within a short time window, can provide a more comprehensive and in-depth understanding of the causes of rotor imbalance. 1. Long-term changes in mass values: A long-term upward trend in mass values ​​usually indicates that the imbalance of the rotor system is continuously worsening. This gradual change may be caused by two situations: first, the continuous and slow accumulation of "new deposits" leads to a gradual increase in mass; second, the physical properties of "existing deposits" (such as hygroscopic expansion, thermal deformation, etc.) change slowly under long-term operating conditions, thereby altering the mass distribution.

[0037] 2. Fluctuation characteristics of values ​​within short time windows: Drastic fluctuations or sudden jumps in values ​​within short time windows (e.g., minutes or hours) typically indicate transient events. Such transient fluctuations are more likely related to the sudden attachment of new sediment (e.g., a large foreign object suddenly impacting and adhering to the impeller) or the sudden detachment of existing sediment (e.g., a piece of sediment suddenly falling off). If the value suddenly increases significantly without a clear long-term accumulation process, it is more likely to be judged as the sudden attachment of new sediment.

[0038] 3. Correlation between local temperature and humidity information and long-term changes in mass values: This is key to distinguishing the behavior of "existing sediments." Sediments in many industrial environments are viscoelastic, hygroscopic, or thermally sensitive. Their mass, density, shape, or adhesion is directly affected by local temperature and humidity. If the long-term upward trend of the mass values ​​shows a significant and consistent correlation with local temperature or humidity information (e.g., a significant increase in mass values ​​with rising humidity, or a specific pattern of change in mass values ​​due to temperature variations), it strongly suggests that the increase in imbalance is caused by changes in the physical properties of the "existing sediments" with environmental conditions. For example, hygroscopic sediments absorb moisture in humid environments, leading to an increase in mass. If the long-term upward trend of the mass values ​​does not show a significant and consistent correlation with local temperature and humidity information, or the correlation is weak, it is more likely that the increase in imbalance is mainly due to the continuous accumulation of "newly deposited sediments," because the accumulation process of these newly deposited substances may not be directly and strongly correlated with environmental temperature and humidity, or their mechanism of influence on imbalance may be unrelated to temperature and humidity.

[0039] In general, when a long-term increase in sediment concentration is observed, and this increase is highly correlated with local temperature or humidity changes, the system will classify it as "the manifestation of changes in existing sediments due to operating conditions." This means that the sediments themselves have altered their mass distribution due to environmental changes. When a long-term increase in sediment concentration is observed, but the correlation with local temperature or humidity changes is not obvious, or there are drastic fluctuations within a short time window (especially a sudden increase), the system will classify it as "an increase in actual imbalance caused by the attachment of new sediments." This means that new material is continuously accumulating or suddenly attaching, leading to imbalance. This multi-dimensional data fusion and analysis enables the system to accurately distinguish different types of imbalance sources, thus avoiding misjudgments that may occur in traditional methods.

[0040] Through the above technical solution, in complex industrial environments, it is possible to effectively distinguish between the actual increase in imbalance caused by newly deposited sediment and the changes in existing sediment with operating conditions. Therefore, based on more accurate diagnostic results, the system can issue more targeted early warning information and provide more precise and effective maintenance recommendations. This not only helps avoid unnecessary downtime and maintenance costs but also extends equipment lifespan and improves production efficiency and safety.

[0041] In some preferred embodiments, an industrial fan located in a humid, dusty environment is assumed, where viscoelastic composite deposits easily accumulate on its impeller surface. The system first continuously monitors the vibration and operating speed of the rotor system and calculates a value reflecting the degree of rotor imbalance. Over a period of time, a slow upward trend in the value is observed. Simultaneously, the system collects local temperature and humidity information near the impeller. Analysis reveals a significant positive correlation between the long-term upward trend of the value and local humidity information; that is, the higher the humidity, the faster the value increases. Furthermore, the system analyzes the fluctuation characteristics of the value within a short time window, finding that the fluctuation amplitude is small and there are no drastic instantaneous jumps. Combining this information, the system determines that the current increase in imbalance is mainly due to the gradual change in the mass distribution of existing viscoelastic composite deposits on the impeller surface due to hygroscopic expansion in a humid environment, rather than the sudden adhesion of new large pieces of material. Based on this distinction, the system issues a warning and provides maintenance suggestions, such as regularly dehumidifying the impeller or using specific cleaning agents to remove hygroscopic deposits, rather than simply adjusting the counterweight.

[0042] This application further proposes steps for analyzing the changes in quantities over time, including: Continuously acquire local temperature and humidity information near the impeller; When the value shows an upward trend, analyze the correlation between local temperature information, local humidity information and long-term changes in the value; The source of the upward trend in the values ​​can be determined by the degree of correlation between local temperature information, local humidity information and long-term changes in the values.

[0043] Specifically, continuously acquiring local temperature and humidity information near the impeller refers to collecting ambient temperature and humidity data in the area in real time or periodically by deploying temperature and humidity sensors near the impeller. "Continuous acquisition" aims to ensure the continuity and integrity of the data in order to capture dynamic changes in environmental factors; "near the impeller" emphasizes the locality of data acquisition, as the state of deposits on the impeller surface is most significantly affected by its direct environment.

[0044] When the measured values ​​show an upward trend, analyzing the correlation between local temperature and humidity information and long-term changes in these values ​​means that when the measured values ​​reflecting rotor imbalance show a continuous upward trend over a period of time, the system will initiate statistical or pattern recognition analysis on the collected local temperature and humidity information and the long-term changes in these values. For example, regression analysis, time series analysis, or machine learning algorithms can be used to identify whether there is a positive, negative, or lagged correlation between temperature and humidity changes and the upward trend in these values.

[0045] Determining the source of an upward trend in sediment values ​​by analyzing the correlation between local temperature and humidity information and long-term changes in these values ​​involves assessing the contribution of environmental factors to the upward trend. For example, if a significant positive correlation is found between the increase in sediment values ​​and rising local humidity, and this correlation is consistent with the hygroscopic expansion characteristics of viscoelastic sediments in historical data, then the main source of the increase in sediment values ​​can be determined to be a change in the mass distribution of existing sediments due to hygroscopic absorption. Conversely, if the increase in sediment values ​​is not significantly correlated with environmental factors, or is correlated with an increase in particulate matter concentration under specific operating conditions, it may indicate the attachment of new material.

[0046] This application's solution addresses the problem of accurately determining the source of imbalance based solely on the trend of magnitude changes by continuously acquiring and correlating local temperature and humidity information near the impeller. Specifically, when the rotor imbalance value shows an upward trend, the system no longer focuses solely on the change in the value itself, but further explores its intrinsic relationship with environmental factors (such as temperature and humidity). Because the physical properties of viscoelastic composite sediments (such as hygroscopicity and thermal expansion) are significantly affected by ambient temperature and humidity, leading to changes in their mass distribution and adhesion state, analyzing the correlation between these environmental factors and the magnitude changes allows for effective identification of whether the increase in imbalance value is due to changes in the physical properties of the original sediments or to the adhesion of new materials. This mechanism makes the determination of the source of imbalance more refined and accurate.

[0047] Through the above technical solution, this application can more accurately determine the true source of the increase in motor shaft imbalance. Compared with simply analyzing the change in value over time, combining local temperature and humidity information can effectively distinguish between changes in the properties of existing deposits caused by environmental factors and increases in imbalance caused by the adhesion of new deposits. This refined diagnostic capability significantly improves the accuracy and reliability of early warning information, laying a more solid foundation for providing subsequent recommendations for balance adjustments during operation, avoiding the risk of taking inappropriate maintenance measures due to misjudgment of the source of imbalance, thereby improving the intelligence level and maintenance efficiency of motor shaft dynamic balance control.

[0048] In some preferred embodiments, it is assumed that a motor shaft operates in a humid industrial environment. The system continuously monitors local temperature and humidity information near the impeller. When the system detects a continuous upward trend in a value reflecting the degree of rotor imbalance, it immediately initiates an analysis of the correlation between this value and historical local temperature and humidity information. Specifically, if the analysis shows a high positive correlation between the upward trend of the value and the continuous increase in local humidity, and this correlation is consistent with the physical properties of known viscoelastic composite deposits (e.g., hygroscopic dust or condensates) absorbing water and expanding in a humid environment, the system determines that the main source of this increase in value is a change in the mass distribution of the original deposits due to hygroscopic expansion. Based on this determination, the system can issue targeted warnings, such as indicating "hygroscopic expansion of impeller surface deposits leads to increased imbalance," and suggest taking dehumidification measures or cleaning the deposits in specific areas, rather than simply performing routine balance block adjustments, thereby achieving more accurate fault diagnosis and maintenance guidance.

[0049] This application further proposes the following steps for issuing early warning information: To obtain information on the long-term upward trend of the quantity value; Obtain instantaneous fluctuation characteristics of the quantity; The basic early warning level is determined based on the long-term upward trend of the value. The basic warning level is adjusted based on the instantaneous fluctuation characteristics of the value. Warning information is generated based on the revised basic warning level.

[0050] Specifically, the long-term upward trend information of quantities refers to the trend data of continuous increase in quantities extracted through statistical analysis of data over a longer period, such as using moving averages, linear regression, or exponential smoothing. Its purpose is to identify the gradual deterioration of the imbalance in the rotor system. The instantaneous fluctuation characteristics of quantities can be understood as the rapid, non-periodic changes in quantities relative to their average value or trend line within a shorter time window, obtained, for example, by calculating standard deviation, peak factor, or short-time Fourier transform. Its purpose is to detect sudden or transient imbalance events. In practical applications, determining the basic warning level means, based on the long-term upward trend information of quantities and combined with preset thresholds or models, initially judging the severity of the rotor system imbalance. For example, when the long-term upward trend exceeds a certain safety threshold, it can be determined as a medium or high warning level. Revising the basic warning level means adjusting the warning level based on the basic warning level and incorporating the instantaneous fluctuation characteristics of quantities. For example, if instantaneous fluctuations show dramatic swings, the alert level may need to be raised even if the long-term trend is not significant; conversely, if instantaneous fluctuations are stable, the alert level may be maintained or slightly lowered. Finally, based on the revised base alert level, an alert message is generated, which may include details such as the alert level, suggested response time, and possible causes of the imbalance.

[0051] This application's solution combines long-term upward trend information of quantities with instantaneous fluctuation characteristic information, enabling a more comprehensive and accurate assessment of the rotor system's operational balance. Obtaining the long-term upward trend information allows the system to identify gradual imbalance deterioration caused by factors such as material deposition and component wear, thus providing a basis for preventative maintenance. Simultaneously, obtaining the instantaneous fluctuation characteristic information allows the system to promptly detect sudden imbalance events caused by external shocks, component loosening, etc., thus supporting emergency response. By determining the basic warning level based on the long-term upward trend information of quantities, a stable risk assessment baseline can be established; further revising the basic warning level based on the instantaneous fluctuation characteristic information of quantities enables rapid response and adjustment to emergencies, avoiding misjudgments or delays that may result from a single indicator. Therefore, this solution can effectively distinguish different types of imbalance problems and provide more targeted warnings.

[0052] Through the above technical solution, this application provides a more refined and intelligent early warning mechanism. Compared to early warning methods that rely solely on a single indicator or simple threshold, this solution significantly improves the accuracy and reliability of early warnings by comprehensively considering both the long-term trend and instantaneous fluctuations of the values. This enables the system to not only promptly detect sudden imbalances but also effectively identify gradual deterioration trends, thereby preventing equipment damage and production interruptions caused by untimely or inaccurate early warnings. Furthermore, the introduction of a tiered early warning mechanism allows maintenance personnel to formulate more reasonable and efficient maintenance strategies based on the differences in early warning levels, thereby extending equipment lifespan and reducing operating costs.

[0053] In some preferred embodiments, it is assumed that the vibration performance of a motor shaft is continuously monitored during operation and converted into a value reflecting the degree of rotor imbalance. Over a certain period, the system detects a slow but continuous upward trend in the value, which is identified as a long-term upward trend. According to preset rules, this long-term upward trend is determined to require a "moderate" basic warning level, indicating possible gradual material deposition. However, at a certain moment, due to a sudden change in the external environment (e.g., a strong wind causing temporary uneven stress on the impeller), the value experiences a sharp, instantaneous fluctuation within a short period. After capturing this instantaneous fluctuation characteristic information, the system adjusts the "moderate" basic warning level based on its intensity and duration, upgrading it to a "high" warning level and generating a corresponding warning message. This warning message not only indicates a rotor imbalance problem but may also suggest the presence of a sudden factor, recommending immediate inspection. Conversely, if the long-term trend is stable but occasional slight instantaneous fluctuations occur, only a "low" or "moderate" warning may be maintained to avoid excessive intervention. In this way, the system can provide more accurate and timely warnings based on the different natures and urgency of imbalance problems.

[0054] This application further proposes steps for providing recommendations on operational balance adjustments based on early warning information, including: Obtain the current operating condition parameters, including motor speed, local temperature information near the impeller, and local humidity information; Based on the type of early warning information and the current operating parameters, initial adjustment suggestions are selected from the preset adjustment strategy library; Based on the physical characteristics of the deposits attached to the impeller, an adaptive assessment of the initial adjustment recommendations is conducted, and the adaptive assessment results are obtained. The adaptive assessment includes simulating the potential impact of the initial adjustment recommendations on the rotor balance state. Based on the results of the adaptability assessment, recommendations for operational balance adjustments are generated.

[0055] Specifically, operating condition parameters refer to the real-time operating conditions that affect the rotor system's balance and sediment behavior. Among these, motor speed is a key parameter influencing centrifugal force and vibration modes; local temperature and humidity information near the impeller significantly impacts the physical properties of the sediment (such as viscosity, elastic modulus, and adhesion). These parameters can be obtained using sensors installed near the motor or impeller. The pre-defined adjustment strategy library is a database containing various balance adjustment schemes for different imbalance types and operating conditions. These schemes may include, but are not limited to, counterweight adjustments, cleaning recommendations, and operating parameter optimizations. The physical properties of the sediment refer to the viscous and elastic behavior exhibited by the material adhering to the rotor surface and causing imbalance. These properties determine how the sediment deforms, peels off, or redistributes under different operating conditions. Adaptability assessment involves simulating and predicting the selected initial adjustment suggestions to determine their potential impact on the rotor's balance. This assessment process aims to ensure that the proposed adjustment suggestions are safe, effective, and do not introduce new problems. For example, finite element analysis or dynamic simulation models can be used to simulate the sediment behavior and the overall rotor response under specific adjustment actions.

[0056] This application's solution, by introducing operating condition parameters, sediment physical properties, and adaptability assessments, enables a more comprehensive and accurate understanding and prediction of the dynamic behavior of rotor imbalance. Specifically, acquiring current operating condition parameters allows the system to monitor key environmental and operational conditions affecting rotor balance in real time. Based on the type of early warning information and these operating condition parameters, initial adjustment suggestions are selected from a pre-defined adjustment strategy library, ensuring the initial relevance of the suggestions. Furthermore, the core of this solution is to conduct an adaptability assessment of the initial adjustment suggestions, taking into account the physical properties of the sediments. By simulating the potential impact of the initial adjustment suggestions on rotor balance, the deformation, stripping, or redistribution of sediments under adjustment can be predicted, thereby avoiding ineffective or harmful adjustments due to a lack of understanding of sediment dynamics. Thus, the system can generate validated, more targeted, and effective in-operation balance adjustment suggestions, effectively addressing the limitations of traditional methods in handling complex imbalance problems.

[0057] Through the above technical solution, this application can significantly improve the accuracy and adaptability of dynamic balancing control of motor shafts. Specifically, by considering operating parameters and the physical characteristics of deposits, the generated adjustment suggestions can more accurately target the actual causes of imbalance, avoiding blind adjustments. The introduction of adaptability assessment enables the system to predict potential impacts before implementing adjustments, thereby effectively reducing the risk of equipment damage or performance degradation due to improper adjustments. This not only optimizes the balancing process and extends equipment lifespan but also improves production efficiency and operational safety, making it particularly suitable for industrial environments where complex deposits cause imbalances.

[0058] In some preferred embodiments, it is assumed that due to long-term operation, deposits have accumulated on the surface of the rotor of a wind turbine, causing an imbalance in the rotor system and triggering a warning message from the system.

[0059] First, the system acquires current operating parameters, such as the motor speed being 1500 rpm, the local temperature near the impeller being 45°C, and the local humidity being 80%. Simultaneously, the system identifies the type of warning message; for example, a warning message indicating a gradual imbalance caused by sediment accumulation.

[0060] Next, the system selects initial adjustment suggestions from a pre-set adjustment strategy library based on the type of warning information and the current operating parameters. For example, the strategy library may contain suggestions such as "adding counterweight to a specific location on the impeller," "performing localized cleaning," or "adjusting the operating speed range." Based on the current operating conditions and the warning type, the system may initially select "adding 50 grams of counterweight to a specific location on the impeller" as the initial adjustment suggestion.

[0061] Subsequently, the system incorporates the physical properties of the sediments (e.g., viscosity, elastic modulus, and density at 45°C and 80% humidity) to conduct an adaptability assessment of the initial adjustment recommendation. This assessment simulates the dynamic response of the rotor system under current operating conditions after adding 50 grams of weight, and whether the sediments will deform or detach under the new stress distribution, thus affecting the balancing effect. For example, simulation results might show that while adding weight can temporarily improve the balance, the viscoelasticity of the sediments could lead to new imbalance points or the risk of sediment detachment during long-term operation.

[0062] Based on the adaptability assessment results, the system may revise its initial recommendations. For example, it might suggest "adding 30 grams of counterweight at a specific location on the impeller and performing localized cleaning during the next maintenance shutdown to remove some of the deposits," or "temporarily adjusting the operating speed to 1400 rpm to reduce deposit deformation without affecting power generation efficiency." Ultimately, the system will generate optimized and evaluated recommendations for in-operational balance adjustments to ensure the effectiveness and safety of the adjustments.

[0063] This application further proposes that the steps for selecting initial adjustment suggestions from a preset adjustment strategy library based on the type of early warning information and current operating parameters include: Continuously acquire information on the type of deposits on the impeller surface, which is obtained through spectral analysis or image recognition of the impeller surface material; Based on the type of early warning information, current operating parameters, and the type of deposits on the impeller surface, initial adjustment suggestions matching the type of deposits on the impeller surface are selected from a pre-set adjustment strategy library.

[0064] Specifically, continuously acquiring information on the type of deposits on the impeller surface refers to the system's ability to monitor and identify the type of substances adhering to the impeller surface in real-time or near real-time during the operation of the motor shaft dynamic balancing control method. This information can be obtained through various technical means. For example, spectral analysis can be used to identify the chemical composition and physical structure of the deposits by analyzing the absorption, reflection, or emission characteristics of the materials on the impeller surface to specific wavelengths of light, thereby determining the specific type of deposit. Alternatively, image recognition technology can be used to acquire images of the impeller surface through a high-resolution camera, and combined with image processing and pattern recognition algorithms, the morphology, color, texture, and other visual features of the deposits can be analyzed to determine their type. The purpose of these techniques is to accurately understand the nature of the deposits causing rotor imbalance, such as sticky oil, hard scale, corrosion products, or loose dust.

[0065] Furthermore, after acquiring the type of warning information, current operating parameters, and the type of deposits on the impeller surface, the system filters from a pre-set adjustment strategy library. This library stores various balance adjustment schemes for different types of imbalance causes and different deposit characteristics. By comprehensively matching the type of warning information (e.g., long-term cumulative imbalance or sudden imbalance), current operating parameters (e.g., motor speed, local temperature information, local humidity information), and the newly acquired information on the type of impeller surface deposits, the system can select the initial adjustment suggestion that best matches the current situation. For example, if the deposits are identified as viscous oil, adjustment strategies with dissolving or stripping effects may be prioritized; if the deposits are hard scale, strategies such as mechanical removal or vibration stripping at a specific frequency may need to be considered.

[0066] This application's solution incorporates information about the type of sediment on the impeller surface, making the screening process for balance adjustment recommendations more refined and intelligent. Traditionally, when providing balance adjustment recommendations during operation, the cause of imbalance might be inferred solely based on vibration performance and operating conditions, resulting in relatively general adjustment strategies. However, different types of sediments may exhibit drastically different responses to the same adjustment measures. For example, for viscous sediments, simple mechanical scraping may be ineffective, and could even lead to redistribution of the sediments on the impeller surface, altering the imbalance rather than eliminating it; while for brittle sediments, specific high-frequency vibrations may effectively promote their removal. Because this application can identify the specific type of sediment, the system can accurately match the most suitable adjustment scheme from a pre-set adjustment strategy library to the current sediment characteristics. This precise matching mechanism ensures that the provided adjustment recommendations directly target the root cause of the imbalance, thereby improving the efficiency and success rate of adjustments.

[0067] Through the above technical solution, this application can significantly improve the pertinence and effectiveness of balance adjustment recommendations during operation. Because it can accurately identify the type of deposits on the impeller surface, the initial adjustment recommendations are no longer general but are matched to the specific material characteristics causing the imbalance. This not only avoids resource waste and time delays caused by using unsuitable adjustment methods but also increases the probability of successful adjustment on the first attempt, thereby reducing the number of repeated adjustments. Furthermore, by providing more targeted recommendations, the balance of the rotor system can be restored more effectively, extending the service life of the equipment, reducing maintenance costs, and ensuring the stable and reliable operation of the motor in complex industrial environments.

[0068] In some preferred embodiments, assuming a motor used for smoke extraction, whose impeller exhibits a continuously increasing imbalance value in vibration performance information after long-term operation, the system needs to provide suggestions for balance adjustment during operation based on the type of warning information (e.g., determined to be long-term cumulative imbalance) and current operating parameters (e.g., motor speed of 1500 rpm, local temperature near the impeller of 80°C, and local humidity of 60%).

[0069] Based solely on the type of warning information and operating parameters, the system might select some general adjustment suggestions from the strategy library, such as attempting minor counterweight adjustments. However, the solution in this application further continuously acquires information on the type of deposits on the impeller surface. Specifically, through image recognition of the impeller surface, the system detects a large amount of black, viscous oil-fume mixture adhering to the impeller surface, whose characteristics match the preset type of "viscous hydrocarbon deposits."

[0070] Based on this, the system comprehensively analyzes the type of early warning information, current operating parameters, and the identified "viscous hydrocarbon deposit" type. From the pre-set adjustment strategy library, the system no longer simply filters for weight adjustment suggestions, but prioritizes initial adjustment suggestions that match the characteristics of the viscous hydrocarbon deposits. For example, the system might suggest, under specific operating conditions, using a specific solvent or cleaning agent combined with short-term high-frequency vibration to soften and peel off these viscous deposits, thereby restoring impeller balance. This targeted approach more effectively addresses imbalances caused by specific types of deposits, avoiding the inefficiency and risks of blindly trying general adjustment strategies.

[0071] Specifically, the steps for adapting the initial adjustment recommendations to the physical properties of the sediments include: Continuously acquire the current physical parameters of the sediment, including viscosity, elastic modulus, and density; Obtain the type and intensity of the initial adjustment recommendations; Based on the type and intensity of the initial adjustment recommendations, and the current physical parameters of the sediments, determine the deformation and stripping trends of the sediments under the adjustment. Based on the deformation trend and peeling trend, the changes in rotor balance state are predicted.

[0072] Sediments refer to substances with both viscous and elastic properties that adhere to the surface of motor shafts or impellers, such as sludge, dust, and moisture mixtures. Their physical parameters, such as viscosity, elastic modulus, and density, are crucial for assessing their response to external forces. These parameters can be continuously acquired through various methods, such as real-time monitoring using online sensors or periodic sampling for laboratory analysis. Specifically, viscosity can be understood as the sediment's resistance to shear deformation, elastic modulus as its resistance to elastic deformation, and density directly affects its mass distribution.

[0073] The initial adjustment suggestions may include, but are not limited to, mechanical scraping, high-pressure flushing, heating softening, and chemical dissolution. The intensity of the action refers to the specific parameters of these actions, such as the force of mechanical scraping, the water pressure and flow rate of high-pressure flushing, the temperature and duration of heating, and the concentration of the chemical solvent. This information is usually obtained directly from the initial adjustment suggestions selected from a pre-defined adjustment strategy library.

[0074] The steps to determine the deformation and abrasion tendencies of sediments under conditioning are designed to simulate or calculate the dynamic response of sediments under specific conditioning actions. For example, when mechanical scraping is applied, based on the sediment's viscosity, elastic modulus, and density, as well as the scraping force, it can be predicted whether the sediment will undergo plastic deformation, elastic deformation, or direct abrasion. When heating is applied, viscosity may decrease, and the elastic modulus may change, thus affecting the ease of deformation and abrasion. This determination process can be accomplished using finite element analysis, fluid dynamics simulations, or predictive models based on empirical data. Deformation tendency refers to the tendency of sediments to change shape or volume under conditioning, while abrasion tendency refers to the tendency of sediments to detach from the rotor surface.

[0075] Once the deformation and stripping trends of the sediments are determined, the impact of these changes on the overall mass distribution of the rotor can be further assessed. For example, if localized deformation of the sediments occurs, its center of mass may shift slightly; if partial stripping occurs, the mass of the stripped portion will be removed from the rotor system, directly altering the rotor's imbalance. By substituting these changes in mass distribution into the rotor dynamics model, specific changes in the rotor's equilibrium state, such as the magnitude and phase angle of the imbalance, can be predicted.

[0076] This application's approach, through detailed analysis of the physical properties of sediments and their response to specific adjustments, enables a more accurate assessment of the potential impact of initial adjustment recommendations on rotor balance. Specifically, by continuously acquiring key physical parameters such as sediment viscosity, elastic modulus, and density, accurate input data can be provided for subsequent simulations and predictions. Subsequently, by combining the type and intensity of the initial adjustment recommendations, the deformation and stripping trends of the sediments can be scientifically determined based on physical models or empirical data. It is precisely this in-depth understanding and quantification of these microscopic changes that allows the prediction of rotor balance changes to move beyond simple empirical judgments to precise deductions based on physical mechanisms. This avoids poor adjustment effects or the creation of new imbalances due to insufficient consideration of sediment characteristics.

[0077] The above technical solution significantly improves the accuracy and reliability of adaptive assessments of operational balance adjustment recommendations. Traditional assessment methods may rely solely on experience or simplified models, failing to fully consider the complex responses of sediments under different operating conditions and adjustment actions. This application, by introducing specific physical parameters of the sediments and quantitatively predicting their deformation and stripping trends, makes the assessment results closer to reality. This not only helps avoid secondary imbalances or equipment damage caused by improper adjustments but also optimizes adjustment strategies, ensuring optimal balance with minimal intervention, thereby extending equipment lifespan, reducing maintenance costs, and improving operational safety.

[0078] Specifically, the steps for predicting changes in rotor balance state based on deformation and peeling trends include: Obtain the current structural stiffness information of the rotor system; Based on the deformation trend, the local changes in stress distribution inside the sediment are analyzed to obtain the local stress changes, and the first impact of the local stress changes on the overall structural stiffness of the rotor is evaluated. Based on the peeling trend, the stress release in the peeling area is analyzed, and the secondary impact of stress release on the overall structural stiffness of the rotor is evaluated. By combining the current structural stiffness information of the rotor system, the first influence, the second influence, and the changes in the mass distribution of the sediment, the changes in the rotor balance state are predicted.

[0079] Obtaining the current structural stiffness information of the rotor system refers to acquiring the overall structural stiffness parameters of the rotor system under current operating conditions in real time or periodically through methods such as sensors, finite element analysis, or historical data. This structural stiffness information is a key indicator for evaluating the rotor's resistance to external loads and changes in internal stress.

[0080] Furthermore, based on the deformation trend, the local changes in stress distribution within the sediment are analyzed to derive local stress variations, and the first impact of these local stress variations on the overall structural stiffness of the rotor is assessed. Specifically, when sediments deform, stress redistribution occurs within them. These local stress variations affect the local stiffness of the rotor body through the coupling effect between the sediment and the rotor interface, thereby affecting the overall structural stiffness of the rotor. The first impact is a quantitative assessment of this change in rotor structural stiffness caused by sediment deformation.

[0081] Furthermore, based on the peeling trend, the stress release in the peeled area was analyzed, and the secondary impact of stress release on the overall structural stiffness of the rotor was assessed. When deposits peel off from the rotor surface, stress release occurs in the original attachment area. This stress release also changes the local stress state and stiffness distribution of the rotor body, thus having a secondary impact on the overall structural stiffness of the rotor.

[0082] Therefore, by combining the current structural stiffness information of the rotor system, the first influence, the second influence, and the changes in the mass distribution of sediments, the changes in the rotor's equilibrium state can be predicted. This means that when predicting the rotor's equilibrium state, not only are the changes in the mass of sediments considered, but also the dual effects of sediment deformation and stripping on the rotor's structural stiffness are comprehensively taken into account, thus making the prediction results more comprehensive and accurate.

[0083] This application's solution addresses the potential inaccuracies of predictions based solely on sediment deformation and stripping trends by incorporating current structural stiffness information of the rotor system when predicting rotor balance state changes and by analyzing in detail the specific impacts of sediment deformation and stripping on rotor structural stiffness. Specifically, by assessing the first impact of local stress changes on the overall rotor structural stiffness, and the second impact of stress release in the stripping region on the overall rotor structural stiffness, this solution can more comprehensively capture the profound effects of sediment dynamics on rotor mechanical behavior. It is precisely this refined consideration of structural stiffness changes that allows for a more accurate reflection of the actual situation in rotor balance state predictions, providing a more reliable basis for subsequent balance adjustments.

[0084] Through the above technical solution, this application can significantly improve the prediction accuracy of changes in the dynamic balance state of the motor shaft. Compared with only considering the deformation and peeling trend of deposits, this solution integrates the current structural stiffness information of the rotor system and quantitatively evaluates the specific impact of deposit deformation and peeling on the overall structural stiffness of the rotor, making the prediction results closer to the actual operating conditions. This more accurate prediction capability helps to identify potential imbalance problems earlier and more accurately, thereby providing more timely and effective in-operation balance adjustment suggestions, avoiding over-adjustment or under-adjustment due to prediction deviations, extending equipment service life, and reducing maintenance costs.

[0085] In some preferred embodiments, it is assumed that the rotor of a large wind turbine has deposits adhering to its surface during operation. The deformation and peeling trends of these deposits have been determined using the method described above. To more accurately predict changes in rotor balance, this embodiment will further perform the following steps: First, accelerometers and strain gauges mounted on the rotor bearing housings are used to acquire the current structural stiffness information of the rotor system in real time. This data is then input into a pre-defined finite element model to calculate the equivalent stiffness of the rotor under the current operating conditions.

[0086] Secondly, based on the deformation trend of the sediments, coupled computational fluid dynamics and structural mechanics simulations were used to analyze the local variations in stress distribution within the sediments. For example, when the sediments undergo slight deformation under the action of high-speed airflow, shear stress is generated within them. This stress is transmitted through the interface between the sediments and the impeller surface, resulting in a slight change in the local stiffness of the impeller. This local stiffness change was assessed as the primary impact on the overall structural stiffness of the rotor.

[0087] Secondly, based on the deposition stripping trend, for example under specific operating conditions, some deposits begin to detach from the impeller surface, releasing stress in the stripped area. The secondary impact of stress release on the overall rotor structural stiffness is assessed by monitoring changes in the vibration spectrum and surface morphology of the stripped area. For example, stripping may lead to localized mass loss and abrupt stiffness changes, thereby altering the rotor's natural frequencies and mode shapes.

[0088] Finally, the acquired information on the current structural stiffness of the rotor system, the first and second effects, and the data on sediment mass distribution changes obtained through mass sensors or image recognition technology are input into the dynamic equilibrium prediction model. This model comprehensively considers these factors to predict changes in the rotor's equilibrium state over a future period, such as the magnitude and phase angle of the imbalance, thus providing precise guidance for subsequent addition of balance blocks or sediment removal.

[0089] This application further proposes steps for predicting changes in rotor balance state based on deformation trends and peeling trends, including: Continuously acquire internal pore structure parameters and fluid permeability parameters of sediments; The variation in the bonding strength between the sediment and the rotor interface was determined based on the deformation trend, peeling trend, internal pore structure parameters, and fluid permeability parameters. The predicted changes in rotor balance state are corrected based on the changes in the bonding strength between the sediment and the rotor interface.

[0090] Specifically, continuously acquiring internal pore structure parameters and fluid permeability parameters of sediments refers to periodically obtaining structural information such as porosity, pore size distribution, and pore connectivity within the sediments, as well as their ability to permeate fluids, through non-contact detection techniques, such as ultrasonic testing, X-ray tomography, or optical microscopy. These parameters reflect the microstructural characteristics of the sediments and their potential for interaction with the external environment (such as air and moisture), aiming to provide a deeper physical basis for assessing the bonding strength between the sediment and the rotor interface.

[0091] The determination of changes in the interfacial bonding strength between sediments and the rotor, based on deformation trends, ablation trends, internal pore structure parameters, and fluid permeability parameters, can be understood as comprehensively considering the macroscopic response (deformation and ablation) of the sediment under adjustment and its microstructural characteristics, quantifying the dynamic changes in interfacial bonding strength through the establishment of physical or machine learning models. For example, high porosity and high fluid permeability may indicate lower interfacial bonding strength, especially when ablation trends are present, as fluid permeation may accelerate the ablation process. The aim is to more accurately assess the adhesion state of the sediment-rotor interface, providing crucial input for subsequent equilibrium state prediction.

[0092] In practical applications, the predicted changes in rotor balance are corrected based on changes in the interfacial bonding strength between the sediment and the rotor. Specifically, this involves adjusting previous predictions based solely on deformation and detachment trends, based on established changes in interfacial bonding strength. For instance, a significant decrease in interfacial bonding strength, even without a pronounced deformation trend, may indicate impending localized sediment detachment, leading to a sudden increase in rotor imbalance. This correction allows for more accurate prediction of actual rotor imbalance changes, thereby improving the reliability and accuracy of the predictions.

[0093] This application's approach, by continuously acquiring internal pore structure parameters and fluid permeability parameters of sediments, enables a more comprehensive understanding of the sediment's physical state. Internal pore structure parameters reflect the density and structural integrity of the sediment's interior, while fluid permeability parameters reveal the sediment's potential for interaction with the surrounding medium (such as moisture in the air). These microscopic parameters are closely related to the adhesion of sediments to the rotor surface. By combining macroscopic manifestations such as deformation and peeling trends, and comprehensively analyzing this multi-scale information, the changes in the bond strength between the sediment and rotor interface can be determined more accurately. For example, high porosity and high permeability may lead to moisture accumulation or corrosion at the interface, thereby weakening the bond strength. Even if macroscopic deformation or peeling trends are not obvious, there may still be a potential risk of bond failure. Therefore, based on more accurate changes in interfacial bond strength, the prediction results of changes in rotor equilibrium state can be corrected, compensating for the shortcomings of predictions relying solely on macroscopic trends, making the prediction results closer to reality.

[0094] Through the above technical solution, this application enables a deeper understanding of the dynamic behavior of sediments under adjustment, particularly regarding the bonding strength between the sediments and the rotor interface. By introducing internal pore structure parameters and fluid permeability parameters, the limitations of traditional methods that rely solely on macroscopic deformation and peeling trends for prediction are overcome, significantly improving the accuracy and reliability of predicting changes in rotor balance. This more refined predictive capability allows subsequent in-operation balance adjustment recommendations to be more precisely tailored to the actual state of the sediments, avoiding over- or under-adjustment, thereby effectively extending the service life of the motor shaft, reducing maintenance costs, and ensuring the stability and safety of equipment operation.

[0095] In some preferred embodiments, it is assumed that a motor shaft has deposits on its impeller surface during operation. Vibration performance information of the rotor system is acquired via vibration sensors and processed in conjunction with operating speed information to obtain a value reflecting the degree of rotor imbalance. When this value shows a continuous upward trend, and in conjunction with early warning information, the system needs to predict changes in the rotor's balance state to provide adjustment suggestions.

[0096] At this point, in addition to determining the deformation and ablation trends based on the type and intensity of the initial adjustment recommendations, as well as the viscosity, elastic modulus, and density of the sediment, the system continuously acquires the internal pore structure parameters and fluid permeability parameters of the sediment. For example, by scanning the impeller surface sediment with a miniature X-ray tomography device, internal pore structure parameters such as porosity, average pore size, and pore connectivity are obtained. Simultaneously, fluid permeability parameters are obtained by measuring the sediment's absorption rate of a specific liquid or gas diffusion rate.

[0097] Assuming the analysis results show that the sediment has high porosity and strong fluid permeability, and that deformation trend analysis reveals microcracks in localized areas, the system determines that the bonding strength between the sediment and the rotor interface is gradually weakening. Even though the macroscopic peeling trend is not yet obvious, the risk of localized detachment is high due to internal structural defects and fluid permeability.

[0098] Based on this, the system will correct the original rotor balance state change predictions derived solely from deformation and peeling trends. For example, what was originally predicted as a slight increase in imbalance may be corrected to predict a more significant surge in imbalance in the short term, or even localized deposit detachment. This corrected prediction will guide the system to generate more forward-looking and targeted in-operation balance adjustment recommendations, such as suggesting proactive local cleaning or reinforcement rather than simply performing routine balance block adjustments, thereby effectively avoiding potential equipment failures.

[0099] refer to Figure 2 This application proposes a dynamic balance control system for a motor shaft, the system comprising: The acquisition module is used to acquire vibration performance information of the rotor system and the current operating speed information of the rotor system. The processing module is used to process the vibration performance information based on the vibration performance information and the operating speed information to eliminate the influence of the operating speed on the vibration performance and obtain a value reflecting the degree of rotor imbalance. The analysis module is used to analyze the changes in magnitude over time, determine the operating balance status of the rotor system based on the changes in magnitude over time, and issue early warning information. It is recommended to provide a module that can offer suggestions for balancing adjustments during operation based on early warning information.

[0100] Specifically, the acquisition module can be understood as the hardware and software components responsible for data acquisition. For example, it may include vibration sensors, speed sensors, and corresponding data acquisition cards or interfaces. The vibration sensors are configured to monitor the vibration signals of the rotor system in real time, while the speed sensors are used to acquire the current operating speed information of the rotor. The raw data acquired by these sensors is transmitted to the system for further processing.

[0101] The processing module can be understood as the core unit for data preprocessing and feature extraction. Its main function is to receive the raw vibration data and operating speed data transmitted from the acquisition module and process the vibration performance information using specific algorithms. For example, by using signal processing techniques such as Fourier transform and wavelet analysis, combined with operating speed information, it can filter out or compensate for vibration components caused by changes in operating speed, thereby accurately extracting the values ​​directly related to the degree of rotor imbalance. Its purpose is to ensure the accuracy of subsequent analysis and avoid misjudgments caused by fluctuations in operating speed.

[0102] In practical applications, the analysis module is specifically an intelligent unit responsible for trend analysis and status judgment of the quantities output by the processing module. For example, it can use time series analysis, statistical methods, or machine learning algorithms to continuously monitor the change curves of quantities over time. By identifying rising, falling, fluctuating, or stable trends in the quantities, the analysis module can determine whether the rotor system is currently in a balanced state, a slightly unbalanced state, or a severely unbalanced state. Once an abnormal trend is detected, the analysis module will immediately generate and issue corresponding early warning information to alert the operators.

[0103] Furthermore, the recommendation module can be understood as an output unit for decision support and action guidance. It receives early warning information from the analysis module and, based on the type and severity of the warning, selects or generates specific operational balance adjustment suggestions from a pre-set strategy library. For example, when a warning indicates a slight imbalance, the suggestion might include adjusting operating parameters; when it indicates a severe imbalance, it might suggest stopping the machine for inspection or precise counterweighting. Its purpose is to provide operators with timely and feasible guidance to restore the rotor system's balance.

[0104] The system solution of this application modularizes and integrates the various steps in the aforementioned dynamic balancing control method for motor shafts, achieving automated and continuous monitoring and control of the rotor system's operating status. Specifically, the acquisition module is responsible for collecting key operating data of the rotor system in real time and accurately, providing reliable input for subsequent analysis. The processing module, based on the acquired raw data, uses intelligent algorithms to refine the vibration performance information, effectively eliminating the interference of operating speed on vibration performance, thus ensuring that the obtained values ​​accurately reflect the degree of rotor imbalance. Based on this, the analysis module can perform in-depth time-series analysis based on these precise values, promptly detecting abnormal changes in the rotor balance state and issuing early warnings. Finally, the suggestion module, based on the early warning information and combined with preset adjustment strategies, provides operators with specific and actionable balance adjustment suggestions. It is precisely this modular and automated collaborative work that enables seamless integration of the entire dynamic balancing control process from data acquisition to decision support, greatly improving control efficiency and accuracy.

[0105] Through the above technical solution, the motor shaft dynamic balance control system of this application can realize real-time, automated monitoring and diagnosis of motor shaft imbalance, significantly reducing the need for manual intervention and potential human error. The system, through its integrated modular design, ensures continuous data acquisition, accurate data processing, and timely early warning judgment, enabling rotor imbalance problems to be detected and effectively intervened at an early stage. Furthermore, the system can automatically provide suggestions for balance adjustment during operation based on early warning information, greatly shortening the response time from problem discovery to solution implementation. This effectively avoids accelerated equipment wear, increased energy consumption, and even safety accidents caused by unbalanced operation, thereby improving equipment operational reliability, extending service life, and reducing maintenance costs.

[0106] In some preferred embodiments, this application is implemented as follows: In a large industrial fan system, a motor shaft dynamic balance control system is deployed to monitor the operating status of the fan impeller. The acquisition module includes an accelerometer installed near the fan bearing housing and a photoelectric speed sensor connected to the motor shaft. These sensors continuously collect vibration signals and real-time speed data during fan operation and transmit them to the central control unit via industrial Ethernet. The processing module inside the central control unit receives this data and uses an adaptive filtering algorithm to separate the speed-related components in the vibration signal, thereby extracting the vibration values ​​caused solely by impeller imbalance. The analysis module then uses a moving average and trend prediction model to analyze these values ​​in real time. For example, when a value is detected to have continuously increased above a preset threshold over the past 24 hours, the analysis module immediately generates a "moderate imbalance" warning and notifies the operator via HMI (Human-Machine Interface) and SMS. The recommended module, based on this warning information and the current operating conditions of the wind turbine (such as air volume and air pressure), should select from a pre-set adjustment strategy library suggestions such as "clean the impeller surface during the next planned shutdown" or "make minor counterweight adjustments via a remote counterweight system without shutting down the turbine," and display these suggestions on the operation interface for operators to refer to and execute. In this way, the system can intelligently monitor, diagnose, and provide suggestions regarding the imbalance of the wind turbine impeller, ensuring the long-term stable and efficient operation of the wind turbine.

[0107] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A method for dynamic balancing control of a motor shaft, characterized in that, The method includes the following steps: To obtain vibration performance information and current operating speed information of the rotor system; Based on the vibration performance information and the operating speed information, the vibration performance information is processed to eliminate the influence of the operating speed on the vibration performance, and a value reflecting the degree of rotor imbalance is obtained. Analyze the changes in magnitude over time, determine the operational balance of the rotor system based on these changes, and issue an early warning. Based on the early warning information, suggestions for balance adjustments during operation are provided.

2. The method for dynamic balancing control of a motor shaft as described in claim 1, characterized in that, The method also includes the following steps: Analyze the long-term changes in the values ​​to identify gradual changes in the overall mass distribution of the rotor system; The system collects local temperature and humidity information near the impeller; the impeller is connected to the motor shaft. Analyze the correlation between local temperature information, local humidity information, and long-term changes in their values; Analyze the fluctuation characteristics of the measured values ​​within a short time window; By combining the long-term changes in the magnitude and the fluctuation characteristics of the magnitude within a short time window, as well as the correlation between local temperature information, local humidity information and the long-term changes in the magnitude, it is possible to distinguish whether the increase in the actual imbalance is due to changes in the original sediments with operating conditions or due to the attachment of new sediments. The original sediments refer to the material that has been attached to the impeller surface for a first time period, while the new sediments refer to the material that has been newly attached relative to the original sediments within a second time period. The second time period is shorter than the first time period. Based on the differentiation results, an early warning message will be issued and maintenance suggestions will be provided.

3. The method for dynamic balancing control of a motor shaft as described in claim 1, characterized in that, The steps for analyzing how a quantity changes over time include: Continuously acquire local temperature and humidity information near the impeller; When the value shows an upward trend, analyze the correlation between local temperature information, local humidity information and long-term changes in the value; The source of the upward trend in the values ​​can be determined by the degree of correlation between local temperature information, local humidity information and long-term changes in the values.

4. The method for dynamic balancing control of a motor shaft as described in claim 1, characterized in that, The steps for issuing a warning include: To obtain information on the long-term upward trend of the quantity value; Obtain instantaneous fluctuation characteristics of the quantity; The basic early warning level is determined based on the long-term upward trend of the value. The basic warning level is adjusted based on the instantaneous fluctuation characteristics of the value. Warning information is generated based on the revised basic warning level.

5. The method for dynamic balancing control of a motor shaft as described in claim 1, characterized in that, Based on the early warning information, the steps to provide suggestions for operational balance adjustments include: Obtain the current operating condition parameters, including motor speed, local temperature information near the impeller, and local humidity information; Based on the type of early warning information and the current operating parameters, initial adjustment suggestions are selected from the preset adjustment strategy library; Based on the physical characteristics of the deposits attached to the impeller, an adaptive assessment of the initial adjustment recommendations is conducted, and the adaptive assessment results are obtained. The adaptive assessment includes simulating the potential impact of the initial adjustment recommendations on the rotor balance state. Based on the results of the adaptability assessment, recommendations for operational balance adjustments are generated.

6. The method for dynamic balancing control of a motor shaft as described in claim 5, characterized in that, Based on the type of warning information and the current operating parameters, the steps for selecting initial adjustment suggestions from the preset adjustment strategy library include: Continuously acquire information on the type of deposits on the impeller surface, which is obtained through spectral analysis or image recognition of the impeller surface material; Based on the type of early warning information, current operating parameters, and the type of deposits on the impeller surface, initial adjustment suggestions matching the type of deposits on the impeller surface are selected from a pre-set adjustment strategy library.

7. The method for dynamic balancing control of a motor shaft as described in claim 5, characterized in that, The steps for assessing the adaptability of the initial adjustment recommendations, taking into account the physical characteristics of the deposits adhering to the impeller, include: Continuously acquire the current physical parameters of the sediment, including viscosity, elastic modulus, and density; Obtain the type and intensity of the initial adjustment recommendations; Based on the type and intensity of the initial adjustment recommendations, and the current physical parameters of the sediments, determine the deformation and stripping trends of the sediments under the adjustment. Based on the deformation trend and peeling trend, the changes in rotor balance state are predicted.

8. The method for dynamic balancing control of a motor shaft as described in claim 7, characterized in that, The steps for predicting changes in rotor balance state based on deformation and peeling trends include: Obtain the current structural stiffness information of the rotor system; Based on the deformation trend, the local changes in stress distribution inside the sediment are analyzed to obtain the local stress changes, and the first impact of the local stress changes on the overall structural stiffness of the rotor is evaluated. Based on the peeling trend, the stress release in the peeling area is analyzed, and the secondary impact of stress release on the overall structural stiffness of the rotor is evaluated. By combining the current structural stiffness information of the rotor system, the first influence, the second influence, and the changes in the mass distribution of the sediment, the changes in the rotor balance state are predicted.

9. The method for dynamic balancing control of a motor shaft as described in claim 7, characterized in that, The steps for predicting changes in rotor balance state based on deformation and peeling trends include: Continuously acquire internal pore structure parameters and fluid permeability parameters of sediments; The variation in the bonding strength between the sediment and the rotor interface was determined based on the deformation trend, peeling trend, internal pore structure parameters, and fluid permeability parameters. The predicted changes in rotor balance state are corrected based on the changes in the bonding strength between the sediment and the rotor interface.

10. A dynamic balancing control system for a motor shaft, characterized in that, The system includes: The acquisition module acquires vibration performance information and current operating speed information of the rotor system. The processing module processes the vibration performance information based on the vibration performance information and the operating speed information to eliminate the influence of the operating speed on the vibration performance and obtain a value reflecting the degree of rotor imbalance. The analysis module analyzes the changes in magnitude over time, determines the operational balance of the rotor system based on these changes, and issues early warning information. It is recommended to provide a module that offers suggestions for balancing adjustments during operation based on early warning information.

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