A lithium battery homogenate equipment state monitoring method and system

Abstract

The present application belongs to the technical field of data processing, and particularly relates to a lithium battery homogenate equipment state monitoring method and system, which comprises the following steps: collecting a torque signal of a stirring shaft, an angular velocity, a strain signal of a slurry tank wall and a slurry solid content set value, calculating slurry effective viscosity and equipment equivalent stiffness, constructing a stirring system dynamics equation containing a fluid damping term and a structure stiffness term, decoupling to obtain a fluid resistance component and a mechanical resistance component, under different operating stages and solid content conditions, performing kernel smoothing regression modeling on standard working condition data, matching a corresponding model according to a current working condition, combining a real-time fluid resistance component to obtain a mechanical resistance benchmark value, and calculating a mechanical resistance residual; by comparing the difference between the residual and a historical normal distribution, determining an equipment health index, and realizing graded monitoring and fault early warning of the homogenate equipment state. The present application can effectively separate process fluctuations and mechanical abnormalities, and improve monitoring accuracy and reliability.

Description

Technical Field

[0001] This invention relates to the field of data processing technology. More specifically, this invention relates to a method and system for monitoring the status of lithium battery homogenizing equipment. Background Technology

[0002] Lithium-ion batteries are the core power source for new energy vehicles, energy storage systems, and consumer electronics. The quality of their electrode slurry preparation directly affects the battery's energy density, cycle life, and safety. In the front-end processes of lithium-ion battery production, slurry homogenization is a crucial step. Its goal is to uniformly mix active materials, conductive agents, binders, and solvents to form a stable and homogeneous suspension slurry. This process is typically completed in a closed mixing tank, relying on high-shear stirring equipment to achieve powder wetting, particle dispersion, and system homogenization. Due to the non-Newtonian fluid characteristics of the slurry, its high solids content, and high viscosity, the equipment is subjected to complex alternating loads during the stirring process, making it prone to mechanical failures such as bearing wear, transmission loosening, and impeller deformation. Simultaneously, deviations in process parameters, such as inaccurate material feeding, solvent evaporation, and abnormal temperature control, can also lead to abnormal rheological properties of the slurry. Therefore, real-time and accurate monitoring of the slurry homogenization equipment's operating status has become an urgent need to ensure slurry consistency and equipment reliability.

[0003] Currently, on-site condition monitoring of slurry homogenizing equipment in industrial settings mainly relies on traditional methods such as motor current monitoring, vibration signal analysis, or periodic manual inspections. Some companies indirectly reflect load changes by collecting current signals from the spindle drive motor to determine whether there is overload or jamming; other systems install vibration sensors on the equipment casing and use spectrum analysis to identify bearing fault characteristic frequencies.

[0004] Existing methods can detect serious faults to some extent, but due to the multi-stage dynamic changes inherent in the homogenization process, the slurry viscosity fluctuates dramatically with the mixing process, leading to significant natural changes in motor load and vibration levels. Traditional monitoring methods struggle to distinguish between normal process evolution and abnormal equipment degradation. For example, a rapid increase in slurry viscosity during the wetting period is often misjudged as equipment overload, while the slight increase in resistance caused by early bearing wear may be masked by process fluctuations, resulting in missed detections. Summary of the Invention

[0005] To address the technical problem that existing methods for monitoring the condition of homogenizing equipment are unable to effectively distinguish between fluctuations in process parameters and load changes caused by mechanical faults, leading to misjudgments and missed reports, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for monitoring the status of a lithium battery homogenizing device, comprising:

[0007] The system collects torque and angular velocity signals of the stirring shaft, strain signals of the slurry tank wall, and the setpoint of the slurry solid content during the operation of the homogenizing equipment. Based on the torque and angular velocity signals, the effective viscosity of the slurry is calculated. The equivalent stiffness of the equipment is calculated by combining the contact force converted from the strain and torque signals. A dynamic equation for the stirring system, including fluid damping and structural stiffness terms, is constructed based on the effective viscosity of the slurry and the equivalent stiffness of the equipment. The fluid resistance and mechanical resistance components are decoupled and obtained. Kernel smoothing regression modeling is performed on the fluid resistance and mechanical resistance components under standard operating conditions at different operating stages and under different slurry solid content conditions to establish a standard relational database. Based on the kernel regression model corresponding to the current operating condition in the standard relational database, a benchmark value of mechanical resistance is obtained based on the real-time fluid resistance component. The mechanical resistance residual is obtained based on the difference between the real-time mechanical resistance residual and the mechanical resistance residual under historical standard operating conditions. The equipment health index is determined based on the distribution difference between the real-time mechanical resistance residual and the mechanical resistance residual under historical standard operating conditions. The status of the homogenizing equipment is monitored based on the equipment health index.

[0008] This invention acquires the torque signal, angular velocity, strain signal of the slurry tank wall, and the set value of the slurry solid content of the stirring shaft during the operation of the homogenizing equipment. This allows for the simultaneous acquisition of the equipment's mechanical response and the slurry's rheological properties. Based on the relationship between torque and rotational speed, the effective viscosity reflecting the slurry's mixing state is calculated. The equivalent stiffness of the equipment structure is assessed by combining the contact force calculated from the tank wall strain and torque, achieving a dual characterization of both the process and mechanical states. Furthermore, a dynamic model of the stirring system, incorporating fluid damping and structural stiffness, is constructed. This model decouples the total torque into a fluid resistance component dominated by slurry characteristics and a mechanical resistance component dominated by equipment state, effectively separating the coupled effects of process fluctuations and mechanical anomalies. According to different... A kernel smoothing regression model library is established based on standard operating condition data under different operating phases and solid content conditions. This enables the system to adaptively match the optimal benchmark relationship for the current operating conditions. By comparing the deviation between the measured mechanical resistance component and the model prediction value, the mechanical resistance residual is obtained, which accurately reflects the degree to which the equipment deviates from the normal state. Finally, by analyzing the statistical difference between the residual sequence and the historical normal distribution, an equipment health index is generated to assess the health status of the homogenizing equipment. This improves the early fault identification capability and the accuracy of condition monitoring, and avoids false alarms caused by process changes misjudging as equipment failures or mechanical deterioration being masked by process fluctuations. This ensures the stability of the lithium battery slurry preparation process and the reliability of equipment operation.

[0009] Preferably, the effective viscosity of the slurry satisfies the expression: In the formula, The effective viscosity of the slurry; This is a torque signal; For impeller geometry; This refers to the real-time angular velocity of the stirring shaft. The diameter is the blade diameter.

[0010] Preferably, the equivalent stiffness of the device satisfies the expression: In the formula, Equivalent stiffness of the equipment; The contact force between the slurry and the tank wall is determined by the torque signal. Calculated using the lever principle , Where is the radius of the tank; This is the strain signal of the slurry tank wall; This is the characteristic length of the tank.

[0011] Preferably, the kinetic equation of the stirring system is: In the formula, Let the system's rotational inertia be denoted by . This is angular displacement; This is the fluid damping coefficient; Equivalent stiffness of the equipment; This is a torque signal; Indicates angle The first derivative with respect to time; Indicates angle The second derivative with respect to time; Indicates the moment of inertia; Indicates fluid damping torque; It represents the elastic restoring torque.

[0012] This invention establishes the dynamic equations of the stirring system, comprehensively considering the inertial torque, fluid damping torque, and elastic restoring torque during the system's rotation. It includes various mechanical forces acting on the stirring shaft during operation. The inertial torque reflects the resistance characteristics of the rotating parts of the equipment to changes in acceleration, the fluid damping torque reflects the resistance effect of the slurry viscosity on the stirring motion, and the elastic restoring torque characterizes the stiffness characteristics of the equipment structure after deformation under stress, thus providing a basis for decoupling the process load from the state of the equipment itself.

[0013] Preferably, the decoupling to obtain the fluid resistance component and the mechanical resistance component includes: the fluid resistance component. Satisfying the expression: In the formula, This is the fluid damping coefficient; Real-time angular velocity of the stirring shaft; mechanical resistance component Satisfying the expression: In the formula, This is a torque signal.

[0014] This invention decomposes the total torque measured during stirring into fluid resistance and mechanical resistance components. This effectively separates the load caused by the rheological properties of the slurry from the resistance caused by the mechanical structure of the equipment. The fluid resistance component is calculated based on the damping characteristics corresponding to the effective viscosity of the slurry and the real-time rotational speed of the stirring shaft, accurately reflecting the viscous resistance level of the slurry under current process conditions. The mechanical resistance component is obtained by subtracting the fluid contribution from the total torque, centrally reflecting the mechanical wear state of the equipment body, such as bearing friction, transmission component wear, and structural loosening. The extracted mechanical resistance component can be used as an independent health feature for subsequent modeling and analysis, significantly improving the robustness of condition monitoring and providing reliable technical support for early warning of process-independent equipment failures.

[0015] Preferably, the standard operating condition refers to the operating condition under which the equipment produces qualified slurry when there are no mechanical failures and the ambient temperature is stable.

[0016] Preferably, the mechanical resistance reference value satisfies the expression: ;in, For the current moment, This is the current reference value for mechanical resistance; This represents the fluid resistance component at the current moment; This is a kernel regression model for the setpoint of slurry solid content under current operating conditions in the current operating stage.

[0017] Preferably, determining the equipment health index includes: constructing a reference window for the current moment; and acquiring historical data that corresponds to the current operating condition, has the same slurry solids content setpoint, and is in the same operating phase. The mean and standard deviation of the mechanical resistance residuals under standard operating conditions. The preset standard operating condition sample size; the current equipment health index. Satisfying the expression: In the formula, This is the mean of all mechanical resistance residuals within the reference window corresponding to the current moment; This represents the average value of the mechanical resistance residuals under historical standard operating conditions. This represents the standard deviation of the mechanical resistance residuals under historical standard operating conditions. It is a function with maximum value. It is an exponential function with the natural constant as its base; This is the attenuation coefficient.

[0018] This invention constructs a sliding reference window at the current moment and combines it with historical standard operating condition data that matches the current process conditions and operating stage to obtain the statistical distribution characteristics of mechanical resistance residuals under normal conditions. It uses the mean and standard deviation of historical data to reflect the fluctuation range of typical mechanical resistance residuals under fault-free conditions. By calculating the offset of the current mechanical resistance residual mean relative to the historical normal distribution, it obtains the equipment health index, realizing adaptive assessment of equipment health status. This effectively improves the sensitivity and reliability of the monitoring system, enabling maintenance personnel to accurately judge the degree of equipment deterioration based on the changing trend of the health index and take graded response measures to ensure the stability of the homogenization process and the safety of equipment operation.

[0019] Preferably, the step of monitoring the homogenizing equipment status based on the equipment health index includes: responding to The current homogenizing equipment status is marked as normal; in response to This triggers an early warning, notifying equipment maintenance personnel to conduct a preliminary inspection and record operating parameters; in response to This triggers an alarm, notifying equipment maintenance personnel to conduct a detailed inspection and arrange preventative maintenance; in response to The equipment maintenance personnel were notified to immediately stop the machine for inspection and repair. This represents the device's health index at the current moment.

[0020] This invention achieves graded monitoring of the operating status of homogenizing equipment by dividing the equipment health index into multiple intervals and corresponding to different status judgment and response strategies. This improves the timeliness and accuracy of fault response, avoids the problems of over-maintenance or under-maintenance, and significantly enhances the reliability, safety and operation and maintenance efficiency of the lithium battery homogenizing process.

[0021] Secondly, the present invention provides a lithium battery homogenizing equipment status monitoring system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned lithium battery homogenizing equipment status monitoring method is implemented.

[0022] By adopting the above technical solution, a computer program is generated from the above-mentioned lithium battery homogenizing equipment status monitoring method and stored in the memory so that it can be loaded and executed by the processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.

[0023] The beneficial effects of this invention are as follows: By simultaneously collecting the stirring shaft torque, angular velocity, tank wall strain, and process setting parameters during the operation of the homogenizing equipment, this invention achieves coordinated perception of the dynamic response of the mechanical system and the rheological characteristics of the slurry. Based on physical mechanisms, it extracts the effective viscosity of the slurry reflecting the mixing state and the mechanical equivalent stiffness characterizing the structural integrity of the equipment, providing a basis for distinguishing between process fluctuations and mechanical anomalies. This invention constructs a dynamic model of the stirring system that includes inertia, fluid damping, and structural elasticity effects, and decouples the total load into a fluid resistance component dominated by slurry characteristics and a mechanical resistance component dominated by equipment state, effectively eliminating the interference of process changes on equipment monitoring. This invention establishes a phased and process-specific kernel smoothing regression model library for different operating stages and slurry solid content conditions, forming an adaptive mechanical resistance benchmark prediction capability. By comparing the residuals of measured values ​​and expected values, it accurately identifies trends in equipment deviation from normal conditions. Combining sliding window statistics with the distribution comparison of historical standard operating conditions, it obtains the equipment health index and sets multi-level thresholds to trigger corresponding maintenance responses. This not only improves the sensitivity to early mechanical failures such as bearing wear and structural loosening, but also avoids misjudgments caused by process factors such as material feeding deviations and viscosity fluctuations. It significantly enhances the accuracy and robustness of lithium battery slurry process status monitoring, providing reliable technical support for preventive maintenance. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a method for monitoring the status of a lithium battery homogenizing device according to the present invention;

[0025] Figure 2 It schematically illustrates the torque signal of the stirring shaft;

[0026] Figure 3 It is a line graph schematically showing the mechanical resistance residual;

[0027] Figure 4 It is a line graph that schematically illustrates the health indicators of the equipment. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0030] This invention discloses a method for monitoring the status of a lithium battery homogenizing device, referring to... Figure 1This includes steps S1-S5:

[0031] S1. Collect the torque signal, angular velocity, strain signal of the slurry tank wall, and solid content setting of the stirring shaft during the operation of the homogenizing equipment. Calculate the effective viscosity of the slurry based on the torque signal and angular velocity, and calculate the equivalent stiffness of the equipment by combining the contact force converted from the strain signal and torque signal.

[0032] S101. Collect the torque signal, angular velocity, strain signal of the slurry tank wall, and slurry solid content set value of the stirring shaft during the operation of the homogenizing equipment.

[0033] It should be noted that the operation of a homogenizing equipment is essentially an interaction process between the mixing system and the non-Newtonian slurry. In order to accurately capture the dynamic changes in the equipment status and process execution, it is necessary to capture the fluid characteristics and mechanical response simultaneously.

[0034] Specifically, the torque signal of the stirring shaft is collected. angular velocity Strain signals of the slurry tank wall Ambient temperature Slurry solids content set value .

[0035] Among them, the torque signal of the stirring shaft This refers to the spindle output torque monitored in real time by a strain gauge torque sensor, with units of... Strain signal of slurry tank wall This refers to the radial strain value obtained in real time through resistance strain gauges attached to key locations on the tank body. The torque signal of the stirring shaft and the strain signal of the slurry tank wall need to be collected synchronously to ensure phase alignment. In this embodiment, the sampling frequency is 200Hz. In other embodiments, the implementer can set it according to the actual implementation situation. However, it should be noted that the strain signal of the slurry tank wall directly reflects the fluid pressure distribution of the slurry on the tank wall and is a key input for analyzing rheological characteristics. Its sampling frequency needs to be higher than the characteristic frequency of slurry flow to avoid aliasing effect, and should usually be at least twice the characteristic frequency of slurry flow.

[0036] For example, Figure 2 This is the torque signal for the stirring shaft.

[0037] S102. Calculate the effective viscosity of the slurry based on torque signal and angular velocity, and calculate the equivalent stiffness of the equipment by combining the contact force converted from strain signal and torque signal.

[0038] It should be noted that changes in slurry viscosity reflect changes in slurry rheological properties. The effective viscosity of the slurry is dominated by process parameters and is a key indicator characterizing process conditions such as feed ratio and solid content. Equipment stiffness attenuation reflects changes in the structural integrity of the mixing system. The equivalent stiffness of the equipment is dominated by the equipment condition and is a core parameter characterizing mechanical failures such as bearing wear and structural loosening. Therefore, obtaining accurate effective slurry viscosity and equivalent equipment stiffness is the physical basis for distinguishing between process deviations and mechanical failures. This invention obtains effective slurry viscosity and equivalent equipment stiffness based on the principles of fluid mechanics and structural mechanics.

[0039] Specifically, the effective viscosity of the slurry is calculated based on Newton's law of internal friction. :

[0040]

[0041] In the formula, The effective viscosity of the slurry is expressed in units of 1000 liters. ; For torque signal, unit ; This refers to the geometric coefficient of the impeller, in units of... ; This refers to the real-time angular velocity of the stirring shaft, in units of... ; The blade diameter is expressed in units of 1000 mm. .in, The values ​​are obtained from the calibration of the impeller geometry and flow field distribution, and are determined through computational fluid dynamics simulation. The simulation requires constructing a 1:1 three-dimensional flow field model of the actual equipment. By changing the impeller speed and slurry viscosity, the mapping relationship between torque and viscosity is fitted, and finally, the results are inversely derived. This coefficient essentially reflects the effective contact efficiency between the blade surface area and the fluid. The larger the blade pitch and the more blades, the better. The larger the value.

[0042] Furthermore, the equivalent stiffness of the equipment is calculated based on the strain signal of the slurry tank wall. :

[0043]

[0044] In the formula, For the equivalent stiffness of the equipment, unit ; The contact force between the slurry and the tank wall, per unit. From torque signal Calculated using the lever principle , Where is the radius of the tank; This is the strain signal of the slurry tank wall; The characteristic length of the tank, in units of This represents the equivalent distance from the installation location of the resistance strain gauge to the center of the tank. Equivalent stiffness of the equipment. It can characterize the structural integrity of the stirring system, and its decay directly indicates the degree of bearing wear.

[0045] S2. Based on the effective viscosity of the slurry and the equivalent stiffness of the equipment, construct the dynamic equation of the stirring system, which includes fluid damping terms and structural stiffness terms, and decouple to obtain the fluid resistance components and mechanical resistance components.

[0046] It should be noted that the homogenization process involves the coupling of fluid resistance and mechanical resistance. The torque signal contains information on both the rheological properties of the slurry and the mechanical state of the equipment. Without decoupling, it is impossible to distinguish the physical causes of process deviations and equipment failures. Therefore, this invention establishes the dynamic equation of the stirring system based on classical mechanics principles in order to decouple the torque signal and obtain the fluid resistance component and the mechanical resistance component.

[0047] Specifically, the dynamic equations of the stirring system are established:

[0048]

[0049] In the formula, Let the system's moment of inertia be expressed in units of... ; Angular displacement, unit ; The fluid damping coefficient, in units of ; For the equivalent stiffness of the equipment, unit ; The driving torque, i.e., the torque signal of the stirring shaft, is expressed in units of... ; Indicates angle The first derivative with respect to time, i.e., angular velocity ; Indicates angle The second derivative with respect to time, i.e., angular acceleration; It represents the moment of inertia, which reflects the resistance of the system's rotational inertia to angular acceleration; It represents the fluid damping torque, which reflects the resistance of fluid viscous resistance to angular velocity; It represents the elastic restoring torque, which reflects the restoring force of the equipment structure stiffness to angular displacement.

[0050] Among them, fluid damping coefficient The relationship between the viscosity and the effective viscosity of the slurry is linear, and the specific expression is as follows:

[0051]

[0052] In the formula, This is the damping proportionality coefficient, in units of... ; The effective viscosity of the slurry, in units Among them, the damping proportionality coefficient The effective contact area between the fluid and the stirring structure is obtained through calibration using the impeller's geometric parameters and flow field simulation. These parameters include the impeller blade surface area and the clearance between the impeller blade and the tank; a larger impeller blade surface area and a smaller clearance between the impeller blade and the tank are considered more efficient. The larger.

[0053] Furthermore, the dynamic equations of the stirring system are decoupled to obtain the fluid resistance components. With mechanical resistance component .

[0054] Among them, the fluid resistance component Satisfying the expression:

[0055]

[0056] In the formula, This represents the fluid resistance component, in units of... ; The fluid damping coefficient, in units of ; The real-time angular velocity of the stirring shaft, in units of .

[0057] The mechanical resistance component Satisfying the expression:

[0058]

[0059] In the formula, This is the mechanical resistance component, in units of... This includes the equipment's own resistance, such as bearing friction and structural vibration. This is the torque signal of the stirring shaft, in units of... , This represents the fluid resistance component, in units of... .

[0060] It should be noted that, Mainly affected by the rheological properties of the slurry, Mainly affected by the mechanical state of the equipment, the two constitute an orthogonal feature space.

[0061] S3. Under different operating stages and slurry solid content conditions, kernel smoothing regression modeling is performed on the fluid resistance component and mechanical resistance component under standard operating conditions to establish a standard relation library for different stages and processes.

[0062] It should be noted that the homogenization process exhibits distinct stage-specific characteristics. For example, during the feeding stage, the slurry is in a non-uniform state, with violent and irregular fluctuations in the torque signal, and the slurry viscosity has not yet reached a stable value. During the wetting stage, the liquid and powder are initially mixed, and the slurry gradually forms a viscoelastic body, with the torque signal showing a significant upward trend and the slurry viscosity increasing rapidly. During the dispersion stage, the particles disperse under high shear force, the torque signal tends to stabilize but still fluctuates slightly, and the slurry viscosity reaches the required range. During the homogenization stage, the slurry reaches a uniform state, the torque signal fluctuates smoothly, and the slurry viscosity stabilizes near the target value. The coupling mechanism between the fluid and mechanical system differs significantly across these stages: during the feeding stage, solid particle collisions dominate; during the wetting stage, liquid-solid interface formation dominates; during the dispersion stage, particle deagglomeration dominates; and during the homogenization stage, maintaining uniform dispersion is the primary objective. Therefore, this invention employs a staged local kernel regression modeling method to construct an adaptive mechanical resistance benchmark calculation model to accurately reflect the coupling mechanism between the fluid and mechanical system at different stages.

[0063] Specifically, obtaining different solid content set values ​​for the slurry. Below is a historical record of different stages of homogenization. The fluid resistance and mechanical resistance components under standard operating conditions are defined as follows: standard operating conditions refer to conditions where the equipment is free from mechanical faults (bearing wear less than 0.01 mm) and the ambient temperature is stable at [temperature range missing]. Under the given conditions, a qualified slurry (viscosity within the standard range) was prepared. The operating conditions of ). Among them, The preset standard working condition sample size is set to 100 in this embodiment. In other embodiments, the implementer can set it according to the actual implementation situation, but it is necessary to ensure that the sample size of each stage and each solid content interval is not less than 50 groups to ensure the statistical reliability of the model.

[0064] Using the mechanical resistance component as the dependent variable and the fluid resistance component as the independent variable, kernel smoothing regression modeling is performed on the mechanical resistance component and fluid resistance component under standard operating conditions to obtain the kernel regression model for each stage.

[0065] Record different solid content settings for slurry A standard relational library was constructed for kernel regression models at different stages. Each 0.5% solids content interval in the standard relational library corresponds to a kernel regression model for four stages, covering a typical solids content range of 55%-65%, ensuring that the corresponding model can be matched under different process conditions. For the current operating condition falling between two solids content intervals, a linear interpolation method was used to obtain the kernel regression model parameters corresponding to the current operating stage for the current solids content setpoint.

[0066] S4. Based on the kernel regression model corresponding to the current working condition in the standard relational database, obtain the mechanical resistance benchmark value based on the real-time fluid resistance component, and obtain the mechanical resistance residual based on the difference between the real-time mechanical resistance component and the mechanical resistance benchmark value.

[0067] Specifically, the mechanical resistance reference value satisfies the expression:

[0068]

[0069] in, For the current moment, The current mechanical resistance reference value, in units of ; This represents the fluid resistance component at the current moment; This is a kernel regression model for the setpoint of slurry solid content under current operating conditions in the current operating stage.

[0070] Furthermore, based on the difference between the current mechanical resistance component and the mechanical resistance reference value, the mechanical resistance residual at the current moment is determined:

[0071]

[0072] In the formula, The current moment; This represents the mechanical resistance residual at the current moment; This represents the mechanical resistance component at the current moment; This is the current mechanical resistance reference value.

[0073] For example, Figure 3 This is a line graph of the mechanical resistance residual.

[0074] S5. Based on the difference in distribution between the real-time mechanical resistance residual and the mechanical resistance residual under historical standard working conditions, determine the equipment health index, and monitor the status of the homogenizing equipment based on the equipment health index.

[0075] It should be noted that abnormal equipment status manifests as follows: The statistical characteristics of the residuals between the measured and expected values ​​deviate. Therefore, this invention obtains the equipment health index at the current moment by constructing the difference between the statistical characteristics of the residuals within a dynamic window and the historical normal distribution.

[0076] Specifically, the mechanical resistance residuals at all times within the current operating phase are used to construct a mechanical resistance residual sequence, and the length of the mechanical resistance residual sequence is obtained. The length of the mechanical resistance residual sequence. Greater than or equal to Using the mechanical resistance residual at the current moment as the last data point within the window, construct... The size of the reference window; the length of the residual sequence in response to mechanical resistance. Less than Using the mechanical resistance residual at the current moment as the last data point within the window, construct... A reference window for the size. Among them, The sliding window size is set to 60 in this embodiment. In other embodiments, implementers can set it according to the actual implementation situation.

[0077] Obtain historical data for the same slurry solids content setpoint and the same operating stage as the current operating condition. The mean and standard deviation of the mechanical resistance residuals under standard operating conditions. This is the preset number of standard operating condition samples.

[0078] Furthermore, based on the difference in distribution between the mechanical resistance residual within the reference window at the current moment and the mechanical resistance residual under historical standard operating conditions, the equipment health index at the current moment is obtained:

[0079]

[0080] In the formula, The current device health index; This is the mean of all mechanical resistance residuals within the reference window corresponding to the current moment; This represents the average value of the mechanical resistance residuals under historical standard operating conditions. This represents the standard deviation of the mechanical resistance residuals under historical standard operating conditions. It is a function with maximum value. It is an exponential function with the natural constant as its base; This is the attenuation coefficient, used to control the sensitivity of the health index to abnormalities; its empirical value is... The implementers can set it according to the actual implementation situation. EHI reflects the overall health status of the equipment's mechanical system, with a value ranging from 0 to 100. The higher the value, the healthier the equipment is.

[0081] Furthermore, in response to The current homogenizing equipment status is marked as normal; in response to This triggers an early warning, notifying equipment maintenance personnel to conduct a preliminary inspection and record operating parameters; in response to This triggers an alarm, notifying equipment maintenance personnel to conduct a detailed inspection and arrange preventative maintenance; in response to Notify equipment maintenance personnel to immediately stop the machine for inspection and repair to avoid production interruption or safety accidents caused by equipment failure.

[0082] For example, Figure 4 A line graph of equipment health indicators.

[0083] In one embodiment, in response to The mechanical resistance component at the current moment is added as the dependent variable, along with the fluid resistance component increment, to the kernel regression model of solid content and the corresponding stage to update the model parameters.

[0084] This invention also discloses a lithium battery homogenizing equipment status monitoring system, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a lithium battery homogenizing equipment status monitoring method according to the present invention is implemented.

[0085] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A lithium battery homogenizer equipment state monitoring method, characterized by, The method comprises the following steps: Collecting torque signal, angular velocity of stirring shaft, strain signal of slurry tank wall and slurry solid content set value during the operation of homogenizing equipment; Calculating the effective viscosity of slurry based on torque signal and angular velocity, and calculating the equivalent stiffness of equipment combined with contact force converted from strain signal and torque signal; constructing the dynamic equation of stirring system containing fluid damping term and structural stiffness term according to the effective viscosity of slurry and the equivalent stiffness of equipment, and decoupling to obtain fluid resistance component and mechanical resistance component; the stirring system dynamic equation is: ； is the system moment of inertia; is the angular displacement; is the fluid damping coefficient; is the device equivalent stiffness; is the torque signal; denotes the angle is the first derivative with respect to time; denotes the angle is the second derivative with respect to time; denotes the inertial moment; denotes the fluid damping moment; denotes the elastic restoring moment; Decoupling to obtain fluid resistance component and mechanical resistance component, comprising: Fluid resistance component satisfies the expression: , is the fluid damping coefficient; is the real-time angular velocity of the stirring shaft; Mechanical resistance component satisfies the expression: , is a torque signal; Under different operating stages and slurry solid content conditions, the fluid resistance component and the mechanical resistance component under standard working conditions are subjected to kernel smoothing regression modeling to establish a standard relationship library; based on the real-time fluid resistance component, the mechanical resistance reference value is obtained according to the kernel regression model corresponding to the standard relationship library under the current working condition; the mechanical resistance residual is obtained according to the difference between the real-time mechanical resistance component and the mechanical resistance reference value; the mechanical resistance reference value satisfies: ； is a current time, is a mechanical resistance reference value at the current time; is a fluid resistance component at the current time; is a nuclear regression model corresponding to the current operating stage for the slurry solid content set value at the current working condition. According to the distribution difference between the real-time mechanical resistance residual and the mechanical resistance residual under the historical standard working condition, the equipment health index is determined, and the homogenizing equipment state monitoring is carried out according to the equipment health index.

2. The method of claim 1, wherein the method comprises: The effective viscosity of slurry satisfies the expression: ； wherein is the effective viscosity of the slurry; is the torque signal; is the agitator geometry factor; is the real-time angular speed of the agitator shaft; is the blade diameter.

3. The method of claim 1, wherein the method comprises: The equivalent stiffness of equipment satisfies the expression: ； wherein K is the equivalent stiffness of the equipment; F is the contact force of the slurry against the tank wall, derived from the torque signal converted via the lever principle , R is the tank radius; ε is the slurry tank wall strain signal; L is the tank characteristic length.

4. The method of claim 1, wherein the method further comprises: The standard working condition refers to the operating condition of the equipment under the conditions of no mechanical failure and stable environmental temperature, and qualified slurry is prepared.

5. The method of claim 1, wherein the method further comprises: The determination of the equipment health index comprises: construct a reference window of the current time; obtain historical mechanical resistance residuals of the same slurry solid content setting and the same running stage as the current working condition a mean value and a standard deviation of the mechanical resistance residuals of the standard working condition group, a preset standard working condition sample number; a device health index at the current time satisfy the expression: , in the formula, is a mean value of all mechanical resistance residuals in the reference window corresponding to the current time; is a mean value of the mechanical resistance residuals of the historical standard working condition; is a standard deviation of the mechanical resistance residuals of the historical standard working condition; is a maximum function, is an exponential function with a natural constant as a base; is an attenuation coefficient.

6. The method of claim 1, wherein the method further comprises: The homogenizing equipment state monitoring according to the equipment health index comprises: In response to , the current homogenate equipment state is marked as normal; in response to , a pre-warning is triggered to notify the equipment maintenance personnel to conduct a preliminary check and record the operating parameters; in response to , an alarm is triggered to notify the equipment maintenance personnel to conduct a detailed check and arrange preventive maintenance; in response to , the equipment maintenance personnel are notified to immediately shut down for maintenance, wherein, is the equipment health index at the current time.

7. A lithium battery homogenizer plant condition monitoring system characterized by, The method comprises the following steps: A processor and a memory, the memory stores computer program instructions, when the computer program instructions are executed by the processor, a lithium battery homogenizing equipment state monitoring method according to any one of claims 1-6 is realized.

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