A device health state analysis method, system, apparatus and storage medium

Abstract

This invention relates to the field of equipment health status analysis technology, specifically to a method, system, device, and storage medium for equipment health status analysis. The method includes acquiring operating parameters of one or more components of an equipment and determining the parameter types of these operating parameters; calculating the operating state of the components based on the operating parameters and parameter types; calculating the rate of change of the component's health status based on its operating state and establishing a health status dynamic equation for the component based on this rate of change; and establishing a system flow graph based on the component's operating parameters, operating state, rate of change, and health status dynamic equation, and calculating the equipment health status based on the system flow graph. The system dynamics-based health status analysis of this invention can effectively reflect the changes in the health status of equipment during actual operation and has the beneficial effect of good engineering applicability.

Description

Technical Field

[0001] This invention relates to the field of equipment health status analysis technology, and specifically to an equipment health status analysis method, system, device, and storage medium. Background Technology

[0002] For complex equipment in modern industry, aerospace, and high-end manufacturing, accurate assessment of its health status is of great research significance. Simulation-based equipment health status assessment methods can evaluate the health status of equipment under different operating conditions, which is a core prerequisite for achieving predictive maintenance (PHM), reducing life-cycle costs, and ensuring operational safety. However, the inherent system complexity of high-end equipment typically makes changes in its health status a non-linear process, and these non-linear changes are of great value in determining whether maintenance is required.

[0003] Current methods primarily rely on models based on physical failure mechanisms. While these methods offer clear mechanisms, their model construction is highly dependent on precise material parameters and boundary conditions, resulting in massive computational demands and difficulty in capturing system-level chain reactions involving multiple components and coupled physical fields. To address the limitations of physical models, data-driven models, such as Long Short-Term Memory networks or Convolutional Neural Networks (CNNs), have made significant progress in tasks like predicting remaining service life by learning degradation characteristics from massive amounts of historical sensor data, becoming one of the current mainstream solutions. However, these methods still suffer from the "black box" problem, with their predictions often lacking clear physical meaning and mechanistic interpretability. More importantly, data-driven models heavily rely on high-quality, large-sample "full lifecycle" data, especially failure data, which is extremely scarce in real-world industrial scenarios, particularly for high-reliability equipment.

[0004] It is worth noting that the health degradation of complex devices is not the independent evolution of individual components, but rather a complex system full of nonlinear coupling and dynamic feedback. Existing physical and data-driven models lack the ability to effectively describe this system-level "dynamic feedback mechanism."

[0005] System dynamics theory is a discipline specifically designed to study and solve feedback problems in complex systems. Its core advantage lies in characterizing the dynamic structure of a system through stocks, flows, and feedback loops. This theory has been widely applied in socioeconomic and ecological fields, demonstrating strong long-term trend simulation capabilities. However, given the significant domain differences between industrial equipment systems and socioeconomic systems, current attempts to apply system dynamics to equipment health simulation are still in their infancy. Existing preliminary applications often rely on expert experience to qualitatively construct causal relationships, lacking a standardized process that uses the "specific operating parameters" of equipment subsystems as influences on stocks and flows in the system dynamics model. This results in difficulties in accurately quantifying the models and a disconnect between simulation results and physical reality. Therefore, establishing a systematic method to combine the key characteristic parameters of equipment subsystems with the dynamic modeling framework of system dynamics to achieve effective simulation of equipment health status remains a core challenge in current research. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, device, and storage medium for analyzing equipment health status, in order to solve the technical problem that the simulation results of equipment health status are disconnected from the physical reality in the prior art.

[0007] The technical solution adopted by this invention to solve its technical problem is: a method for analyzing equipment health status, comprising the following steps: S1. Obtain the operating parameters of one or more components of the equipment, and determine the parameter type of the operating parameters of the components; S2. Calculate the operating status of the component based on the component's operating parameters and the parameter types of the operating parameters; S3. Calculate the rate of change of the component's health status based on the component's operating status, and establish the component's health status dynamic equation based on the rate of change of the component's health status. S4. Establish a system flow diagram based on the component's operating parameters, operating status, health status change rate, and health status dynamic equation, and calculate the equipment health status based on the system flow diagram.

[0008] The significant advantages of this invention are as follows: This solution considers the impact of various operating parameters of key equipment components on the equipment's health status, calculates the parameter operating status using real-time operating parameters and parameter types, ensuring that the input data is consistent with physical reality. Furthermore, by combining system dynamics theory to establish a dynamic equation for the equipment's health status and a system flow graph, a new method for evaluating equipment health status is proposed. This effectively solves the technical problem of the disconnect between simulation results and physical reality regarding equipment health, significantly improving the confidence and accuracy of equipment health status analysis results.

[0009] Furthermore, in step S1, the parameter types include small and excellent, medium and excellent, and large and excellent. For small and excellent, the operating parameter is in a healthy state when it is less than the corresponding health threshold. For medium and excellent, the operating parameter is in a healthy state when it is within the health threshold range corresponding to the operating parameter. For large and excellent, the operating parameter is in a healthy state when it is greater than the corresponding health threshold.

[0010] By subdividing equipment operating parameters into three types—small and optimal, medium and optimal, and large and optimal—the study fully considers the differentiated characteristics of the impact of different physical parameters (such as temperature, pressure, vibration, speed, etc.) on equipment health and standardizes the parameters of different types.

[0011] Furthermore, in step S2, the operating parameters are calculated using the following formula for the operating state of the small and efficient component:

[0012] In the formula, For components with small and optimized operating parameters The running status at any given moment, It is a natural constant. This is the maximum value of the running parameters. This is the minimum value of the running parameters. For running parameters in The actual running value at any given time; The formula for calculating the operating status of a medium-to-superior component is as follows:

[0013] In the formula, Components with optimal operating parameters in medium to high performance The running status at any given moment, These are the standard operating values ​​for the operating parameters; The formula for calculating the operating status of a large and superior component is as follows:

[0014] In the formula, For components with large and optimized operating parameters The operational status at any given moment.

[0015] It achieves dimensionless processing of various operating parameters. It transforms raw operating parameters with different physical units and orders of magnitude into operating status values ​​with uniform measurement, effectively eliminating the weight bias caused by the difference in parameter magnitude in the evaluation results.

[0016] Further, step S3 includes: S301. Calculate the variable weight coefficients for each operating parameter; S302. Calculate the rate of change of health status of each component of the equipment based on the variable weighting coefficient:

[0017] In the formula, The initial health status change rate of the component. For the rate of change adjustment parameter, The initial time. The first factor affecting the health status of this component The health status of each component at the initial moment. For nonlinear influence parameters, The number of other components that affect the health status of this component. The number of operating parameters for this component. For variable weighting coefficients, For the first The running state at the initial moment of each running parameter. For parameters related to the decline in health status; S303. Establish the component health status dynamic equation based on the rate of change of health status:

[0018] In the formula, In order to be in Time of the first The health status of each component Adjusting parameters for rate of change in health status For the first The initial health status of each component; S304, according to t The component health status and component operating parameter status are updated at any time, and the component health status change rate is updated.

[0019] By introducing the rate of change of component health status and constructing a dynamic equation, the equipment health assessment has been transformed from static analysis to dynamic trend analysis.

[0020] Furthermore, the calculation steps for the variable weighting coefficients include: A1. Sort the operating parameters; and calculate the weight coefficient of each operating parameter based on the sorted parameters: A2. Calculate the variable weighting coefficients of the operating parameters based on the weighting coefficients:

[0021] In the formula, For the first The variable weighting coefficients of each operating parameter For the first The running status of each running parameter For the first The weighting coefficients of each operating parameter. The number of operating parameters for the component. No. The weighting coefficients of each operating parameter. The importance of the operating parameters in the overall state.

[0022] By calculating the variable weighting coefficients for each operating parameter, the overall importance of each operating parameter of the component is realized. When the operating status value of an operating parameter deviates significantly from the normal value, it usually indicates a significant decrease in the performance of a certain aspect of the component. However, if constant weighting coefficients are used for evaluation, and their weights are relatively small, the overall evaluation of the equipment is usually still at a normal level, thus failing to reflect the true operating status of the equipment.

[0023] Furthermore, in step S4, the formula for calculating the equipment health status is:

[0024] In the formula, For the equipment in Constant health status For the first The weight of the health status of each component This represents the total number of components.

[0025] It realizes the mapping of health status from component level to device level, and obtains the health status of the device by weighted summation of component health status.

[0026] A device health status analysis system, applicable to the above-mentioned device health status analysis method, the system comprising: The component operation status analysis unit is used to acquire the operating parameters of the equipment components and calculate the operating status of the components according to the type of operating parameters. The component health status analysis unit is used to analyze the rate of change of the component's health status based on the component's operating status, and to establish the component's health status dynamic equation based on the rate of change of the component's health status. The equipment health status analysis unit is used to establish a system flow diagram based on the component's operating parameters, operating status, health status change rate, and health status dynamic equation, and to analyze the equipment health status based on the system flow diagram.

[0027] Furthermore, the equipment health status analysis unit calculates the component health status based on the component health status dynamic equation, and establishes a mathematical relationship to obtain the system flow diagram by taking the component health status as the stock, the component health status change rate as the rate variable, the component operating parameters as constants, and the component operating state as auxiliary variables.

[0028] An equipment health status analysis device includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the equipment health status analysis method described above.

[0029] A computer-readable storage medium containing a computer program, wherein the computer program is stored thereon, characterized in that, when the computer program is executed by one or more processors, it implements the steps of the above-described device health status analysis method. Attached Figure Description

[0030] Figure 1 This is a flowchart of the device health status analysis method in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system flow in an embodiment of the present invention; Figure 3 This is a health status change curve of the motor components in an embodiment of the present invention; Figure 4 This is a graph showing the change in the health status of the cooling system in an embodiment of the present invention. Figure 5 This is a health status change curve of the hydraulic system in an embodiment of the present invention; Figure 6 This is a health status change curve of the spindle system in an embodiment of the present invention; Figure 7 This is a simulation curve of the overall health status of the equipment in an embodiment of the present invention; Figure 8 This is a comparison chart of the health status curve of the simulated device and the actual recorded health status in an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, a clear and complete description will be provided below in conjunction with the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0032] See appendix Figure 1 The equipment health status analysis method shown includes the following steps: S1. Obtain the operating parameters of one or more components of the equipment within a preset time period, and determine the parameter type of the operating parameters of the components; wherein the operating parameters are the state parameters of the equipment components during operation, such as the rotation speed, temperature or pressure of the components, which can reflect the health status of the equipment components and can be collected by sensors. When obtaining the operating parameters of the equipment, the operating parameters of the corresponding components are obtained by installing speed sensors, temperature sensors or pressure sensors on the equipment. The parameters of each component of the equipment during operation are monitored by the sensors, and the multiple operating parameters obtained within the preset time period are combined into time series data according to the time sequence.

[0033] Specifically, in this embodiment, the analysis device is a CNC machine tool. When collecting operating parameters, corresponding sensors can be installed on key components such as the spindle motor, cooling system, spindle system, and hydraulic system of the machine tool, and the operating parameters can be collected in real time through these sensors. In this embodiment, when collecting operating parameters, the Vensim-PLE software's built-in functions generate operating parameter data for each component of the CNC machine tool, simulating the actual operation of the equipment and its actual health status during this period. The total time is 7200 seconds (2 hours), with 10-second intervals, generating a total of 720 sets of time-series data. The specific content of generating these parameters using the Vensim-PLE software is existing technology and will not be described in detail here.

[0034] Based on the impact of the numerical distribution of operating parameters on equipment health, parameter types can be categorized into three types: small-optimal, medium-optimal, and large-optimal. Small-optimal parameters indicate a healthy state when the parameter is below its corresponding health threshold; that is, the lower the actual value of a small-optimal parameter, the better the equipment health. Medium-optimal parameters indicate a healthy state when the parameter falls within its corresponding health threshold range; that is, the best equipment health is achieved when the actual value of a medium-optimal parameter is within this range, and deviations from the threshold will lead to a deterioration in equipment health. Large-optimal parameters indicate a healthy state when the parameter is above its corresponding health threshold; that is, the higher the value of a large-optimal parameter, the better the equipment health.

[0035] S2. Calculate the operating status of the component based on the component's operating parameters and parameter types; perform unified dimensionless processing on the time-series data of the operating parameters for each parameter type to obtain the operating status of each parameter type.

[0036] The formula for calculating the operating status of the small and efficient operating parameter component is as follows:

[0037] In the formula, For components with small and optimized operating parameters The running status at any given moment, This is the maximum value of the running parameters. This is the minimum value of the running parameters. For running parameters in The actual running value at any given time; The formula for calculating the operating status of the medium-optimal operating parameter component is as follows:

[0038] In the formula, Components with optimal operating parameters in medium to high performance The running status at any given moment, These are the standard operating values ​​for the operating parameters; The formula for calculating the operating status of a large and efficient operating parameter component is as follows:

[0039] In the formula, For components with large and optimized operating parameters The running status at any given moment; Specifically, when judging the operating status of each component, the operating status values ​​of the components with small-optimal, medium-optimal, and large-optimal operating parameters are compared with the corresponding judgment thresholds. The operating status of the parameter is obtained based on the comparison results. That is, when the operating status value of the small-optimal operating parameter is less than the small-optimal threshold, the operating status of the component corresponding to the parameter is a safe operating status; when the operating status value of the small-optimal operating parameter is equal to the small-optimal threshold, the operating status of the component corresponding to the parameter is a borderline dangerous operating status; and when the operating status value of the small-optimal operating parameter is greater than the small-optimal threshold, the operating status of the component corresponding to the parameter is a dangerous operating status. When the operating status value of the optimal operating parameter is within the optimal threshold range, the operating status of the component corresponding to the parameter is a safe operating status. When the operating status value of the optimal operating parameter is equal to the endpoint value of the optimal threshold range, the operating status of the component corresponding to the parameter is a dangerous operating status edge state. When the operating status value of the optimal operating parameter is not within the optimal threshold range, the operating status of the component corresponding to the parameter is a dangerous operating status. When the operating status value of the large and superior operating parameter is greater than the large and superior threshold, the operating status of the component corresponding to the parameter is a safe operating status. When the operating status value of the large and superior operating parameter is equal to the large and superior threshold, the operating status of the component corresponding to the parameter is a dangerous operating status edge state. When the operating status value of the large and superior operating parameter is less than the large and superior threshold, the operating status of the component corresponding to the parameter is a dangerous operating status. For example, in the small and efficient operating parameters of this embodiment, when the operating parameters are in Actual running value at time equal to the minimum value of the running parameters At that time, small and optimal operating parameters can be obtained. runtime status When the running parameters are Actual running value at time Equal to the maximum value of the running parameters At that time, it is possible to obtain small and optimal operating parameters. runtime status When the running parameters are Actual running value at time Greater than the maximum value of the running parameters At that time, small and optimal operating parameters are runtime status Therefore, we can define small and efficient operating parameters. runtime status At that time, the component corresponding to the operating parameters is on the verge of a dangerous operating state, and the small and optimal operating parameters are in runtime status At that time, the component corresponding to the operating parameters is in a dangerous operating state.

[0040] S3. Calculate the rate of change of the component's health status based on the component's operating state, and establish the component's health status dynamic equation based on the rate of change of the component's health status; specifically including the following steps: S301. Calculate the variable weight coefficients of each operating parameter; when calculating the variable weight coefficients of each operating parameter, firstly, obtain the constant weight of each operating parameter through the order relation analysis method, and then select the constant weights using the variable weight theory. The specific steps for calculating the variable weight coefficients are as follows: A1. Sort the operating parameters according to their importance; specifically, compare the importance of the collected operating parameters. The importance of each operating parameter can be obtained based on expert experience. The specific content of sorting by importance is existing technology and will not be elaborated here.

[0041] A2. Calculate the weight coefficient of each operating parameter based on the sorted operating parameters. Specifically, when calculating the weight coefficient of each operating parameter, first determine the relative importance of two adjacent operating parameters, and then calculate the weight coefficient of that operating parameter based on the relative importance of two adjacent operating parameters. The formula for calculating the weight coefficient is as follows:

[0042]

[0043] In the formula, For the first The weighting coefficients of each operating parameter. For the first The importance of each running parameter For the first The weighting coefficients of each operating parameter. The number of operating parameters for the component; A3. Calculate the variable weight coefficients of each parameter based on the weight coefficients; specifically, when calculating the edge weight coefficients, if the state variable weight vector is regarded as the gradient vector of an n-dimensional real function, then the equilibrium function can be used as another way to construct the variable weight vector, that is, the formula for calculating the edge weight coefficients is:

[0044] In the formula, For the first The variable weighting coefficients of each operating parameter For the first The running status of each running parameter The number of running parameters, The importance of each operating parameter in the overall state, among which The smaller the value, the greater the impact of each indicator on the overall state of the equipment. Preferably, in this embodiment... .

[0045] S302. Calculate the rate of change of health status for each component of the equipment based on the variable weighting coefficient; the formula for calculating the rate of change of health status is:

[0046] In the formula, The initial health status change rate of the component. The rate of change in the health status of a component is nonlinearly affected by the health status of other related components. The rate of change adjustment parameter is preferably specified in this embodiment. , The initial time. The first factor affecting the health status of this component The initial health status of a component is considered. Since the rate of change of a component's health status and its actual health status change over time, the calculation of the rate of change of a component's health status first involves calculating the impact of the health status of other components on the component's health status at the initial moment. The number of other components that affect the health status of this component. The number of operating parameters for this component. The nonlinear influence parameter is used in this embodiment. , The rate of change in the health status of a component is affected by the operating status of its operating parameters. For variable weighting coefficients, For the first The running state at the initial moment of each running parameter. The parameter for health status decay is preferably specified in this embodiment. .

[0047] S303. Establish the component health state dynamic equation based on the rate of change of health state; the expression of the component health state dynamic equation is:

[0048] In the formula, In order to be in Time of the first The health status of each component The initial time. In this embodiment, the parameter for adjusting the rate of change in health status is preferably... , For the first The initial health status of each component.

[0049] S304, according to t The component health status and component operating parameter status are updated at any given time to determine the component's health status change rate. Specifically, after calculating the health status of each component based on the component's health status dynamic equation, the component's health status change rate is updated based on the calculated status of each component. That is, the health status change rate calculation formula in step S302 is used to update the component's health status based on the components affecting that component's health status. The rate of change in the health status of the component is updated in real time.

[0050] S4. Establish a system flow diagram based on the operating parameters, operating states, health status change rates, and health status dynamic equations of the components. Specifically, when establishing the system flow diagram, establish mathematical relationships between the operating parameters, operating states, health status change rates, and health status of each component of the equipment, and create the system flow diagram using the system dynamics modeling software Vensim-PLE.

[0051] The system flow diagram aims to reflect how to quantify the model, requiring the differentiation of different types of variables. In this embodiment, the component health state is calculated using the component health state dynamic equation. The component health state is set as a stock, the rate of change of the component health state as a rate variable, the operating parameters as constants, and the operating state as an auxiliary variable. Mathematical relationships between these variables are established to obtain the system flow diagram. Here, the stock represents a variable that changes over time, the rate variable reflects how quickly the stock changes, the auxiliary variable is an intermediate variable used in the calculation, and the constant is a variable that does not change during the calculation process. The system flow diagram established in this embodiment is attached. Figure 2 As shown, the specific content of the system flow diagram is existing technology and will not be elaborated here.

[0052] After calculating the health status of each component of the equipment based on the component health state dynamic equations in the system flow diagram, the overall equipment health status is obtained through nonlinear mapping. The formula for calculating the equipment health status using the health state dynamic equations is as follows:

[0053] In the formula, For the equipment in Constant health status For the first The weight of the health status of each component is preferably determined in this embodiment. , This represents the total number of components. The equipment is simulated based on a system flow graph, and the health status of the equipment is obtained from the simulation results. The results of the equipment health status analysis are then verified. Specifically, the established system flow graph is simulated and verified in Vensim-PLE software. The collected operating parameter data and the initial health status of the components are input, and the simulation results of the component and equipment health status are obtained.

[0054] The actual collected component operating data is input into the system dynamics model for simulation, and the health status change curves of each component are obtained as follows: Figure 3-6 As shown, the overall health status simulation curve of the equipment is obtained through the weighted summation logic of component health status. Figure 7As shown in the diagram. Specifically, during the normal and stable operation phase from 0 to 3500 seconds, both the simulated health curves of the components and the simulated health status of the overall equipment slowly and steadily decline, remaining above 0.95. When the equipment enters the high-load operation phase (e.g., after 4000 seconds), the operating parameters experience significant disturbances, such as increased motor load rate and motor shaft temperature, leading to a rapid increase in the rate of change of component health status. Consequently, the component health curves show a significant downward trend, consistent with reality. Furthermore, hydraulic components, motors, and transmission components are affected by the health status of other components; when the health status of related components declines, the decline in their overall health status becomes more pronounced.

[0055] To quantify the accuracy of the simulation results, the simulated equipment health status curves are compared with the actual recorded health status scores, such as... Figure 8 As shown, the simulation results are highly consistent with the actual health status changes of the equipment. In summary, this experiment verified the feasibility and accuracy of the proposed method using real equipment operating data, demonstrating that this health status simulation method based on system dynamics can effectively reflect the health status changes of equipment during actual operation and has good engineering applicability.

[0056] This invention also provides an equipment health status analysis system, applicable to the aforementioned equipment health status analysis method. The system includes: a component operation status analysis unit, used to acquire the operation parameters of each component of the equipment and calculate the operation status of the component according to the type of operation parameter; a component health status analysis unit, used to analyze the rate of change of the component's health status based on the component's operation status and establish a health status dynamic equation for the component based on the rate of change of the component's health status; an equipment health status analysis unit, used to establish a system flow diagram based on the component's operation parameters, operation status, rate of change of health status, and health status dynamic equation, and analyze the equipment health status based on the system flow diagram; and an equipment simulation verification unit, used to simulate the equipment based on the system flow diagram and verify the equipment health status based on the simulation results. The equipment health status analysis unit calculates the health status of the component according to the health status dynamic equation, treats the component's health status as a stock, the component's rate of change of health status as a rate variable, the component's operation parameters as constants, and the component's operation status as an auxiliary variable, and establishes a mathematical relationship to obtain the system flow diagram. The specific implementation steps of each unit are consistent with the aforementioned equipment health status analysis method and will not be repeated here.

[0057] The present invention also provides an equipment health status analysis device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described equipment health status analysis method.

[0058] The present invention also provides a computer-readable storage medium containing a computer program, wherein the computer program is stored thereon, and when the computer program is executed by one or more processors, it implements the steps of the above-described device health status analysis method.

[0059] This invention provides a highly versatile and portable framework for equipment health analysis. The analysis process defined by the above technical solution is not limited to a single type of equipment and can be systematically applied to a variety of complex equipment.

[0060] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing the health status of equipment, characterized in that, Includes the following steps: S1. Obtain the operating parameters of one or more components of the equipment, and determine the parameter type of the operating parameters of the components; S2. Calculate the operating status of the component based on the component's operating parameters and the parameter types of the operating parameters; S3. Calculate the rate of change of the component's health status based on the component's operating status, and establish the component's health status dynamic equation based on the rate of change of the component's health status. S4. Establish a system flow diagram based on the component's operating parameters, operating status, health status change rate, and health status dynamic equation, and calculate the equipment health status based on the system flow diagram.

2. The equipment health status analysis method according to claim 1, characterized in that, In step S1, the parameter types include small and excellent, medium and excellent, and large and excellent. For small and excellent, the operating parameter is in a healthy state when it is less than the corresponding health threshold. For medium and excellent, the operating parameter is in a healthy state when it is within the range of the corresponding health threshold. A healthy state is defined as a system with large and high performance parameters whose operating parameters exceed the corresponding health threshold.

3. The equipment health status analysis method according to claim 2, characterized in that, In step S2, the operating parameters are calculated using the following formula for the operating state of the small and efficient component: In the formula, For components with small and optimized operating parameters The running status at any given moment, It is a natural constant. This is the maximum value of the running parameters. This is the minimum value of the running parameters. For running parameters in The actual running value at any given time; The formula for calculating the operating status of a medium-to-superior component is as follows: In the formula, Components with optimal operating parameters in medium to high performance The running status at any given moment, These are the standard operating values ​​for the operating parameters; The formula for calculating the operating status of a large and superior component is as follows: In the formula, For components with large and optimized operating parameters The operational status at any given moment.

4. The equipment health status analysis method according to claim 1, characterized in that, Step S3 includes: S301. Calculate the variable weight coefficients for each operating parameter; S302. Calculate the rate of change of health status of each component of the equipment based on the variable weighting coefficient: In the formula, Initial moment of component t 0% health status change rate For the rate of change adjustment parameter, The first factor affecting the health status of this component The health status of each component at the initial moment. For nonlinear influence parameters, The number of other components that affect the health status of this component. The number of operating parameters for this component. For variable weighting coefficients, For the first The running state at the initial moment of each running parameter. For parameters related to the decline in health status; S303. Establish the health status dynamic equation of the component based on the rate of change of health status: In the formula, In order to be in Time of the first The health status of each component Adjust parameters for the rate of change in health status. For the first The initial health status of each component; S304, according to t The component health status and component operating parameter status are updated at any time, and the component health status change rate is updated.

5. The equipment health status analysis method according to claim 4, characterized in that, The calculation steps for variable weighting coefficients include: A1. Sort the operating parameters; and calculate the weight coefficient of each operating parameter based on the sorted parameters: A2. Calculate the variable weighting coefficients for each parameter based on the weighting coefficients: In the formula, For the first The variable weighting coefficients of each operating parameter For the first The running status of each running parameter For the first The weighting coefficients of each operating parameter. The number of operating parameters for the component. No. The weighting coefficients of each operating parameter. The importance of the operating parameters in the overall state.

6. The equipment health status analysis method according to claim 1, characterized in that, In step S4, the formula for calculating the equipment health status is: In the formula, For the equipment in Constant health status For the first The weight of the health status of each component This represents the total number of components.

7. A device health status analysis system, applicable to the device health status analysis method as described in any one of claims 1-6, characterized in that, The system includes: The component operation status analysis unit is used to acquire the operating parameters of the equipment components and calculate the operating status of the components according to the type of operating parameters. The component health status analysis unit is used to analyze the rate of change of the component's health status based on the component's operating status, and to establish the component's health status dynamic equation based on the rate of change of the component's health status. The equipment health status analysis unit is used to establish a system flow diagram based on the component's operating parameters, operating status, health status change rate, and health status dynamic equation, and to analyze the equipment health status based on the system flow diagram.

8. The equipment health status analysis system according to claim 7, characterized in that, The equipment health status analysis unit calculates the component health status based on the component health status dynamic equation, and establishes mathematical relationships to obtain the system flow diagram by treating the component health status as inventory, the component health status change rate as rate variable, the component operating parameters as constants, and the component operating state as auxiliary variables.

9. A device for analyzing equipment health status, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the device health status analysis method according to any one of claims 1-6.

10. A computer-readable storage medium containing a computer program, wherein the computer program is stored thereon, characterized in that, When the computer program is executed by one or more processors, it implements the steps of the device health status analysis method according to any one of claims 1-6.

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