A reliability block diagram-based numerical control system evaluation method

Abstract

The application discloses a kind of based on reliability block diagram numerical control system evaluation method, it is related to numerical control system reliability evaluation technical field, including: selecting the key component in numerical control system carries out temperature-humidity double stress accelerated degradation test, obtains degradation data, and establishes performance degradation model and generalized Eyring temperature-humidity double stress coupling acceleration model based on degradation data, obtains reliability function and temperature humidity stress-degradation parameter mapping relationship;The application is accurately calibrated by temperature-humidity double stress accelerated degradation test and generalized Eyring model, again in combination with real-time field environment data, realizes the state monitoring and optimization guidance of the continuous, self-adapting, precision of numerical control system reliability.

Description

Technical Field

[0001] This invention relates to the field of CNC system reliability evaluation technology, and in particular to a CNC system evaluation method based on reliability block diagrams. Background Technology

[0002] The CNC system is the core control unit of a CNC machine tool, primarily used to generate coordinate axis motion control commands, spindle control commands, and auxiliary function control commands based on the machining program. Its reliability directly affects the machining accuracy, operating efficiency, and production safety of the machine tool. Existing CNC systems typically consist of a CNC device, servo drive unit, spindle drive unit, input / output unit, communication bus, power module, detection unit, and key components such as hybrid integrated circuits and connectors. These functional units have reliability logic relationships such as series connection, parallel connection, and resource sharing. Current CNC system reliability evaluation methods typically employ statistical analysis of field failure data, component reliability prediction, or single-life distribution models. By statistically analyzing parameters such as the number of failures, failure interval time, or mean time between failures, the failure rate and reliability of the CNC system or its functional units are estimated.

[0003] Existing methods for evaluating the reliability of CNC systems still have shortcomings. Many existing methods rely on a single data source, or perform statistical fitting based solely on field fault data, or use only component reliability predictions. This makes it difficult to obtain stable and reliable evaluation results when the sample size of field fault data is limited. Furthermore, existing methods typically do not fully consider the impact of environmental stresses such as temperature and humidity on the degradation process of critical components like hybrid integrated circuits and connectors, lacking a quantitative mapping relationship between environmental stresses and component degradation parameters. Simultaneously, complex series and parallel logical relationships exist between the functional units within a CNC system. Simply treating the system as a series system or only performing overall fault statistics fails to accurately reflect the impact of system structure on reliability and is also detrimental to identifying weak links that significantly affect system reliability. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a CNC system evaluation method based on reliability block diagrams to solve the problem of insufficient accuracy in existing CNC system reliability evaluations.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a method for evaluating CNC systems based on reliability block diagrams, comprising: decomposing the CNC system into functional levels, identifying the series and parallel logical relationships between functional units, and drawing a reliability block diagram of the CNC system; collecting fault data during the on-site operation of the CNC system, and simultaneously collecting on-site environmental data; selecting key components in the CNC system for temperature-humidity dual-stress accelerated degradation tests to obtain degradation data, and establishing a performance degradation model and a generalized Eyring temperature-humidity dual-stress coupled acceleration model based on the degradation data to obtain a reliability function and a temperature-humidity stress-degradation parameter mapping relationship; calculating the acceleration factor of the on-site environment relative to the reference environment based on the on-site environmental data, the generalized Eyring temperature-humidity dual-stress coupled acceleration model, and the temperature-humidity stress-degradation parameter mapping relationship, and using the acceleration factor to correct the reliability function for the on-site environment to obtain the equivalent reliability of key components; using the fault data, the equivalent reliability of key components, and the reliability block diagram as inputs for reliability evaluation, using an exponential distribution lifetime model to calculate the system reliability of the CNC system under a given task time, and outputting a list of weak links and corresponding reliability improvement suggestions; When new fault data and field environment data reach the preset update threshold, the field environment is corrected, system reliability is calculated, and weak links in reliability are identified again, and the field reliability evaluation results of the CNC system are dynamically updated.

[0007] As a preferred embodiment of the CNC system evaluation method based on reliability block diagrams described in this invention, the functional hierarchical decomposition of the CNC system is performed according to two dimensions: hardware functions and software functions. The CNC system includes a position control module, a communication function module, a PLC module, a feed drive unit, a spindle drive unit, a user interface module, an interpolation calculation module, a programming function module, external storage, a detection unit, an electrical system, a CNC panel, a machine tool operation panel, a monitoring and diagnostic module, an NC panel, an MCP unit, an IPC unit, a switching power supply, an NCUC bus, a CNC device, a servo system, and a spindle system.

[0008] As a preferred embodiment of the CNC system evaluation method based on reliability block diagram described in this invention, the identification of the series-parallel logical relationships between functional units includes the power supply subsystem and the control subsystem being connected in series, and the control subsystem being connected in parallel with the X-axis servo drive unit, the Y-axis servo drive unit, the Z-axis servo drive unit, and the spindle drive unit, respectively; each drive unit and the detection unit are considered to be connected in series in terms of reliability logic, and the communication bus is considered as a shared resource and is connected in series with each mounted unit, and a CNC system reliability block diagram is drawn according to the series-parallel logical relationships.

[0009] As a preferred embodiment of the CNC system evaluation method based on reliability block diagram described in this invention, the fault data is obtained by using a timed truncation test scheme to track the fault occurrence time, fault unit identification and fault mode of the CNC system; the field environmental data includes ambient temperature and ambient relative humidity.

[0010] As a preferred embodiment of the CNC system evaluation method based on reliability block diagrams described in this invention, the step of selecting key components in the CNC system for temperature-humidity dual-stress accelerated degradation tests to obtain degradation data is as follows: Hybrid integrated circuits and connectors in the CNC system were selected as key components. A temperature-humidity dual-stress accelerated degradation test scheme combining orthogonal design method and step stress method was adopted, and multiple combinations of temperature stress and relative humidity stress were set up. Accelerated degradation tests were conducted on hybrid integrated circuits and connectors under each combination of temperature stress and relative humidity stress, and data on current changes in hybrid integrated circuits and contact resistance changes in connectors were collected to obtain degradation data.

[0011] As a preferred embodiment of the CNC system evaluation method based on reliability block diagrams described in this invention, the specific steps for establishing a performance degradation model and a generalized Eyring temperature-humidity dual-stress coupling acceleration model based on degradation data to obtain the reliability function and the mapping relationship between temperature and humidity stress and degradation parameters are as follows: Determine the performance degradation amount, initial degradation amount, and failure threshold of key components based on degradation data; A performance degradation model based on the Wiener process is constructed based on degradation data, and the stochastic degradation process of the performance degradation of key components over time is described. The failure lifetime of key components is obtained based on the time when the performance degradation first reaches the failure threshold. The cumulative probability distribution function of failure lifetime is obtained based on the failure lifetime of key components, and the reliability function is obtained based on the cumulative probability distribution function. Based on degradation data, a generalized Eyring temperature-humidity dual-stress coupling acceleration model is established. Parameters of the performance degradation model and the generalized Eyring temperature-humidity dual-stress coupling acceleration model are estimated to obtain the parameters of the performance degradation model and the generalized Eyring temperature-humidity dual-stress coupling acceleration model. The mapping relationship between temperature and humidity stress and degradation parameters is obtained by using the parameters of the performance degradation model and the parameters of the generalized Eyring temperature-humidity dual-stress coupling acceleration model.

[0012] As a preferred embodiment of the CNC system evaluation method based on reliability block diagram described in this invention, the acceleration factor of the calculated field environment relative to the reference environment is calculated by setting the reference environment temperature and reference environment relative humidity, inputting the field environment data, reference environment temperature and reference environment relative humidity into the generalized Eyring temperature-humidity dual stress coupling acceleration model, and calculating the acceleration factor according to the temperature-humidity stress-degradation parameter mapping relationship.

[0013] As a preferred embodiment of the CNC system evaluation method based on reliability block diagrams described in this invention, the equivalent reliability of key components is obtained by using an acceleration factor to modify the reliability function of key components obtained from the performance degradation model based on the Wiener process under field environment conditions.

[0014] As a preferred embodiment of the CNC system evaluation method based on reliability block diagrams described in this invention, the method uses fault data, equivalent reliability of key components, and reliability block diagrams as inputs for reliability evaluation. An exponential distribution lifetime model is used to calculate the system reliability of the CNC system under a given task time, and a list of weak points and corresponding reliability improvement suggestions are output. The specific steps are as follows: Based on the fault unit identifier in the fault data, the fault data is assigned to the corresponding functional unit in the CNC system reliability block diagram. Based on the number of faults and the cumulative running time of each functional unit, the failure rate of each functional unit is calculated. For functional units with sparse fault data, the failure rate of the functional unit is corrected by using the equivalent reliability of key components. Based on the failure rate of each functional unit, the reliability of each functional unit under a given task time is calculated using an exponential distribution lifetime model. Input the reliability of each functional unit under a given task time into the CNC system reliability block diagram, and calculate the system reliability of the CNC system under a given task time according to the series and parallel logic relationship; Based on the failure rate, reliability, and failure percentage of each functional unit, the impact of each functional unit on the system reliability is analyzed to identify weak links in reliability, and a list of weak links and corresponding reliability improvement suggestions are output.

[0015] As a preferred embodiment of the CNC system evaluation method based on reliability block diagram described in this invention, the update threshold includes a threshold for the number of newly added fault data and a threshold for changes in on-site environmental data. When either the number of newly collected fault data or the quarterly average change in field environmental data reaches the update threshold, the field environmental correction, system reliability calculation, and identification of weak links in reliability are carried out again.

[0016] The beneficial effects of this invention are as follows: by using temperature-humidity dual-stress accelerated degradation tests and a generalized Eyring model, the failure patterns of key components can be quickly obtained. Then, by combining real-time field environmental data, the components can be accurately calibrated, thereby efficiently generating component reliability data that fits the actual working conditions. The calibrated component data, field fault data, and system reliability block diagram are fused and calculated to construct a dynamic closed loop of "evaluation-optimization-re-evaluation", realizing continuous, adaptive, and precise state monitoring and optimization guidance for the reliability of CNC systems. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a CNC system evaluation method based on a reliability block diagram.

[0019] Figure 2 This is a flowchart of the temperature-humidity dual-stress accelerated degradation test.

[0020] Figure 3 The flowchart is for obtaining the reliability function.

[0021] Figure 4 This is a flowchart for obtaining the equivalent reliability of key components. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4As one embodiment of the present invention, this embodiment provides a method for evaluating CNC systems based on reliability block diagrams, comprising the following steps: S1. Decompose the CNC system into functional levels, identify the series and parallel logical relationships between each functional unit, and draw a reliability block diagram of the CNC system.

[0026] The functional hierarchy of a CNC system is decomposed according to two dimensions: hardware functions and software functions. A CNC system includes a position control module, a communication function module, a PLC module, a feed drive unit, a spindle drive unit, a user interface module, an interpolation calculation module, a programming function module, external storage, a detection unit, an electrical system, a CNC panel, a machine tool operation panel, a monitoring and diagnostic module, an NC panel, an MCP unit, an IPC unit, a switching power supply, an NCUC bus, a CNC device, a servo system, and a spindle system.

[0027] Specifically, the CNC system is functionally decomposed into 24 subsystems based on two dimensions: hardware functional modules and software functional modules. These 24 subsystems include: position control module, communication module, PLC module, feed drive unit, spindle drive unit, user interface module, interpolation calculation module, programming module, external storage, detection unit, electrical system, CNC panel, machine tool operation panel, monitoring and diagnostic module, NC panel, MCP unit, IPC unit, switching power supply, NCUC bus, CNC device, servo system, and spindle system.

[0028] Identifying the series and parallel logical relationships between functional units includes connecting the power supply subsystem and the control subsystem in series, and connecting the control subsystem in parallel with the X-axis servo drive unit, Y-axis servo drive unit, Z-axis servo drive unit and spindle drive unit respectively; each drive unit and detection unit are considered to be connected in series in terms of reliability logic, and the communication bus is connected in series with each mounted unit as a shared resource. Based on the series and parallel logical relationships, a reliability block diagram of the CNC system is drawn.

[0029] Specifically, based on the CNC system wiring diagram, the logical relationships between the subsystems are analyzed: the power supply subsystem is connected in series with the control subsystem; the control subsystem is connected in parallel with the X-axis servo drive unit, Y-axis servo drive unit, Z-axis servo drive unit, and spindle drive unit, respectively; each drive unit and detection unit are considered to be connected in series in terms of reliability logic; the communication bus, as a shared resource, is connected in series with each mounted unit. Based on this, a CNC system reliability block diagram is drawn.

[0030] It should be noted that the reliability logic relationship refers to the influence relationship formed by the failure of each functional unit on the failure of the corresponding function of the CNC system in the reliability block diagram of the CNC system. When the failure of any functional unit will cause the corresponding function to fail, the relevant functional units are determined to be in series reliability relationship. When at least one of the multiple functional units can maintain the corresponding function if at least one of the functional units is normal, the relevant functional units are determined to be in parallel reliability relationship.

[0031] S2. Collect fault data during the on-site operation of the CNC system, and simultaneously collect on-site environmental data.

[0032] The fault data was obtained by using a timed truncation test scheme to track the time of fault occurrence, fault unit identification and fault mode of the CNC system; the on-site environmental data included ambient temperature and relative humidity.

[0033] Specifically, a timed truncation test scheme was adopted to conduct on-site tracking of the CNC systems of 28 machining centers. The data collection period was 27 months, and a total of 55 fault data were obtained. The fault data included the time interval between fault occurrences, description of fault phenomena, fault location, fault mode, and machine tool number information. Based on the fault unit identifier in the fault data, each fault data point is assigned to the corresponding functional unit in the CNC system reliability block diagram, and the number of faults, fault time interval, fault percentage, and cumulative running time of each functional unit are calculated. On-site environmental data can be collected by temperature and humidity sensors, thermal imaging acquisition equipment or other environmental monitoring equipment arranged in the electrical cabinet in the workshop. By statistically processing the ambient temperature and relative humidity, on-site ambient temperature and relative humidity statistical values ​​can be obtained for on-site environmental correction.

[0034] S3. Select key components in the CNC system to conduct temperature-humidity dual-stress accelerated degradation tests, obtain degradation data, and establish a performance degradation model and a generalized Eyring temperature-humidity dual-stress coupled accelerated model based on the degradation data, and obtain the reliability function and the temperature-humidity stress-degradation parameter mapping relationship.

[0035] Hybrid integrated circuits and connectors in the CNC system were selected as key components.

[0036] Specifically, the connector is a DR15 connector; the initial current value of the hybrid integrated circuit is 0.3A, and the contact resistance of the DR15 connector is used to characterize the degree of contact performance degradation of the connector under temperature stress and relative humidity stress.

[0037] An accelerated degradation test scheme combining temperature and humidity dual stress was adopted, using an orthogonal design method and a step stress method, and multiple combinations of temperature stress and relative humidity stress were set up.

[0038] Specifically, the temperature stress range was 50°C to 90°C, the relative humidity stress range was 60% to 90%, the total test time was 1600 hours, and 16 samples were placed under each stress level; the test stress levels included 16 combinations of temperature stress and relative humidity stress.

[0039] Accelerated degradation tests were conducted on hybrid integrated circuits and connectors under each combination of temperature stress and relative humidity stress, and data on current changes in hybrid integrated circuits and contact resistance changes in connectors were collected to obtain degradation data.

[0040] Specifically, under various combinations of temperature stress and relative humidity stress, the current value of the hybrid integrated circuit and the contact resistance value of the DR15 connector are collected according to the sampling period. The current degradation data of the hybrid integrated circuit is obtained based on the change of the current value relative to the initial current value, and the contact resistance degradation data of the DR15 connector is obtained based on the change of the contact resistance value relative to the initial contact resistance value. The current degradation data and the contact resistance degradation data are used as the degradation data of key components.

[0041] Based on degradation data, determine the performance degradation amount, initial degradation amount, and failure threshold of key components.

[0042] A performance degradation model based on the Wiener process is constructed based on degradation data, and the stochastic degradation process of the performance degradation of key components over time is described. The failure lifetime of key components is obtained based on the time when the performance degradation first reaches the failure threshold.

[0043] Specifically, continuous-time random variables The following conditions must be met: when hour, ; The stochastic process has the property of stationary independent increments, and the distribution of the increments is independent of the choice of the starting point of time. For any time increment, the increment follows a normal distribution as follows: ; in, Indicates the time increment The increase in the amount of internal performance degradation; Indicates the time increment; The drift coefficient represents the performance degradation process and reflects the average rate of performance degradation. The diffusion coefficient represents the performance degradation process and reflects the temporal random fluctuations of the performance degradation process; The mean is variance is The normal distribution; The drift coefficient and diffusion coefficient of the performance degradation process were obtained by using parameter estimation methods such as maximum likelihood estimation based on degradation data collected from temperature-humidity dual-stress accelerated degradation tests.

[0044] The performance degradation model based on the Wiener process can be expressed as: ; in, express The amount of performance degradation of critical components at all times; Indicates the initial performance degradation of key components; Indicates time; Indicates the drift coefficient (meaning as before); Indicates the diffusion coefficient (meaning as before); This represents the standard Brownian motion process.

[0045] The performance degradation stochastic process first reaches the failure threshold. The time is defined as the failure lifetime of the critical component, and the failure lifetime is expressed as: ; in, Indicates the failure life of key components; Indicates the failure threshold; Denotes the infimum of a set, i.e., the amount of performance degradation. exist The failure threshold is reached for the first time under the condition. The earliest time.

[0046] The cumulative probability distribution function of failure lifetime is obtained based on the failure lifetime of key components, and the reliability function is obtained based on the cumulative probability distribution function.

[0047] Specifically, based on the performance degradation model of the Wiener process, the lifetime of key components follows an inverse Gaussian distribution, that is: ; in, Indicates the failure life of key components; This represents an inverse Gaussian distribution, with the first parameter being the mean parameter and the second parameter being the shape parameter. Indicates the failure threshold; Indicates the drift coefficient; This represents the diffusion coefficient.

[0048] Therefore, the probability density function of the failure lifetime is derived as follows: ; in, The probability density function representing the failure lifetime of a critical component; Indicates time.

[0049] The cumulative probability distribution function of failure lifetime is: ; in, This represents the cumulative probability distribution function of the failure lifetime of a critical component, i.e., the failure lifetime is no greater than... The probability of; The cumulative distribution function represents the standard normal distribution.

[0050] The reliability function of the key components is: ; in, Indicates key components in Reliability at any given time, i.e., failure lifetime greater than The probability of.

[0051] Based on degradation data, a generalized Eyring temperature-humidity dual-stress coupling acceleration model is established. Parameters of the performance degradation model and the generalized Eyring temperature-humidity dual-stress coupling acceleration model are estimated to obtain the parameters of the performance degradation model and the generalized Eyring temperature-humidity dual-stress coupling acceleration model.

[0052] Specifically, the generalized Eyring temperature-humidity dual-stress coupling acceleration model is used to establish the mapping relationship between the performance degradation characteristic parameters of key components and temperature stress and relative humidity stress. It should be noted that the generalized Eyring temperature-humidity dual-stress coupled acceleration model is expressed as: ; in, It represents the drift coefficient that indicates the performance degradation process of key components under the action of temperature stress and relative humidity stress; This represents absolute temperature stress (unit: K). Represents relative humidity stress; Represents the Boltzmann constant; , , , The undetermined coefficients of the generalized Eyring model are estimated using accelerated degradation test data.

[0053] Furthermore, to reduce the complexity of solving the model parameters, the generalized Eyring temperature-humidity dual-stress coupling acceleration model is converted into a log-linear form: ; in, Represents a constant term; Indicates the temperature stress term; Represents the relative humidity stress term; The undetermined coefficients of the temperature-humidity coupling term are represented (obtained through parameter estimation based on degradation data under different combinations of temperature stress and relative humidity stress). Characteristic functions representing temperature stress; Characteristic function representing relative humidity stress; The characteristic function representing the temperature-humidity dual stress coupling.

[0054] The mapping relationship between temperature and humidity stress and degradation parameters is obtained by using the parameters of the performance degradation model and the parameters of the generalized Eyring temperature-humidity dual-stress coupling acceleration model.

[0055] Specifically, based on degradation data collected under different combinations of temperature stress and relative humidity stress, the drift coefficient and diffusion coefficient in the performance degradation model based on the Wiener process are estimated using the maximum likelihood estimation method, and the model undetermined coefficients in the generalized Eyring temperature-humidity dual-stress coupled acceleration model are estimated using the maximum likelihood estimation method.

[0056] Furthermore, the model parameter estimation results were validated. The AD test was used to test the normality of the degradation rate distribution under each temperature stress and relative humidity stress combination. If the degradation rate distribution under each stress level is greater than the significance level of 0.05, the degradation rate distribution is deemed to meet the normality requirement. The Bartlett test was used to test the homogeneity of the degradation rate variance under each temperature stress and relative humidity stress combination. If the test statistic is less than the critical value at the corresponding significance level, the degradation rate variance under different stress level combinations is deemed to meet the homogeneity requirement, and it is determined that the temperature-humidity dual stress accelerated degradation test did not change the failure mechanism of the key components.

[0057] S4. Based on field environmental data, the generalized Eyring temperature-humidity dual-stress coupling acceleration model, and the temperature-humidity stress-degradation parameter mapping relationship, calculate the acceleration factor of the field environment relative to the reference environment, and use the acceleration factor to correct the reliability function for the field environment to obtain the equivalent reliability of key components.

[0058] Set the reference ambient temperature and reference ambient relative humidity, input the field environmental data, reference ambient temperature and reference ambient relative humidity into the generalized Eyring temperature-humidity dual stress coupling acceleration model, and calculate the acceleration factor based on the temperature-humidity stress-degradation parameter mapping relationship.

[0059] Specifically, the on-site environmental data includes the on-site ambient temperature and the on-site relative humidity; the on-site ambient temperature is denoted as... The reference ambient temperature is denoted as The relative humidity of the on-site environment is recorded as The relative humidity of the reference environment is denoted as The acceleration factor of the field environment relative to the reference environment is calculated using the generalized Eyring acceleration model.

[0060] The expression for the acceleration factor is: ; in, This represents the acceleration factor of the field environment relative to the reference environment. Indicates the ambient temperature (unit: K); Indicates the reference ambient temperature (unit: K); The relative humidity of the ambient environment is expressed in % (unit: %). Relative humidity of the reference environment (unit: %); The activation energy represents the failure process of a critical component; Represents the Boltzmann constant; The exponential coefficient representing temperature stress; The exponential coefficient representing relative humidity stress.

[0061] By using an acceleration factor, the reliability function of key components obtained from the performance degradation model based on the Wiener process is modified by field environment to obtain the equivalent reliability of key components.

[0062] Specifically, the acceleration factor of the field environment relative to the reference environment is used as the field environment correction parameter and applied to the key component reliability function obtained by the performance degradation model based on the Wiener process. This allows the key component reliability function to reflect the influence of field temperature and relative humidity on the degradation process of key components, thereby obtaining the equivalent reliability of hybrid integrated circuits and connectors under field conditions.

[0063] S5. Using fault data, equivalent reliability of key components, and reliability block diagram as inputs for reliability evaluation, the system reliability of the CNC system under a given task time is calculated using an exponential distribution life model, and a list of weak links and corresponding reliability improvement suggestions are output.

[0064] Based on the fault unit identifier in the fault data, the fault data is assigned to the corresponding functional unit in the CNC system reliability block diagram. Based on the number of faults and the cumulative running time of each functional unit, the failure rate of each functional unit is calculated.

[0065] Specifically, the failure count, failure time interval, cumulative running time and failure percentage of each functional unit are counted, and the failure rate of the corresponding functional unit is obtained based on the failure count and cumulative running time.

[0066] For functional units with sparse fault data, the failure rate of the functional unit is corrected by using the equivalent reliability of key components. Based on the failure rate of each functional unit, the reliability of each functional unit under a given task time is calculated using an exponential distribution lifetime model.

[0067] Specifically, for functional units with sufficient fault data, the failure rate is directly estimated using the statistical results of on-site fault data; for functional units with sparse fault data, an exponential distribution lifetime model combined with the aggregated results of component reliability is used for estimation.

[0068] It should be noted that the reliability function of the exponential lifetime model is: ; in, Indicates the functional unit in Reliability at any given moment; The failure rate of a functional unit is calculated by summing the number of failures and the cumulative running time of that functional unit. Indicates the given task time; Represents the natural constant.

[0069] Input the reliability of each functional unit under a given task time into the CNC system reliability block diagram, and calculate the system reliability of the CNC system under a given task time according to the series and parallel logic relationship.

[0070] It should be noted that, for a series logic structure, the system reliability under a given task time is calculated as follows: ; in, This indicates the system reliability of the series structure under a given task time. Indicates the first Each functional unit Reliability at any given moment; This indicates the total number of functional units in a series structure; Indicates the number of the functional unit.

[0071] For a parallel logic structure, the system reliability under a given task time is calculated as follows: ; in, This indicates the system reliability of the parallel structure under a given task time. Indicates the first Each functional unit Reliability at any given moment; This indicates the total number of functional units in a parallel structure.

[0072] Based on the failure rate, reliability, and failure percentage of each functional unit, the impact of each functional unit on the system reliability is analyzed to identify weak links in reliability, and a list of weak links and corresponding reliability improvement suggestions are output.

[0073] Specifically, the functional units that have the greatest impact on system reliability are identified as weak links in reliability, and corresponding reliability improvement suggestions are output based on the failure causes of these weak links.

[0074] S6. When new fault data and field environment data reach the preset update threshold, the field environment correction, system reliability calculation and weak link identification are carried out again, and the field reliability evaluation results of the CNC system are dynamically updated.

[0075] The update thresholds include the threshold for the number of newly added fault data and the threshold for changes in on-site environmental data.

[0076] It should be noted that the threshold for the number of newly added fault data is no less than five new fault data, and the threshold for changes in field environmental data is that the quarterly average change in field environmental data exceeds five percent. When either the number of newly collected fault data or the quarterly average change in field environmental data reaches the update threshold, the field environmental correction, system reliability calculation, and identification of weak links in reliability are carried out again.

[0077] Specifically, when re-correcting the field environment, the acceleration factor of the field environment relative to the baseline environment is recalculated based on the updated field environment data, and the equivalent reliability of key components is updated using the recalculated acceleration factor; when re-calculating the system reliability, the updated fault data, the updated equivalent reliability of key components, and the CNC system reliability block diagram are used as reliability evaluation inputs to recalculate the system reliability of the CNC system under a given task time; when re-identifying reliability weaknesses, the list of weaknesses and corresponding reliability improvement suggestions are updated based on the updated failure rate, reliability, and impact on system reliability of each functional unit.

[0078] In summary, this invention rapidly obtains the failure patterns of key components through temperature-humidity dual-stress accelerated degradation testing and a generalized Eyring model. It then combines this with real-time field environmental data for precise calibration, thereby efficiently generating component reliability data that closely reflects actual operating conditions. By fusing the calibrated component data, field fault data, and system reliability diagram, a dynamic closed loop of "evaluation-optimization-re-evaluation" is constructed, achieving continuous, adaptive, and precise state monitoring and optimization guidance for the reliability of CNC systems.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating CNC systems based on reliability block diagrams, characterized in that, include: The CNC system is decomposed into functional levels, the series and parallel logical relationships between each functional unit are identified, and a reliability block diagram of the CNC system is drawn. Collect fault data during the on-site operation of the CNC system, and simultaneously collect on-site environmental data; Key components in the CNC system were selected for temperature-humidity dual-stress accelerated degradation tests to obtain degradation data. Based on the degradation data, a performance degradation model and a generalized Eyring temperature-humidity dual-stress coupled accelerated degradation model were established to obtain the reliability function and the temperature-humidity stress-degradation parameter mapping relationship. Based on field environmental data, the generalized Eyring temperature-humidity dual-stress coupling acceleration model, and the temperature-humidity stress-degradation parameter mapping relationship, the acceleration factor of the field environment relative to the reference environment is calculated, and the reliability function is corrected for the field environment using the acceleration factor to obtain the equivalent reliability of key components. Fault data, equivalent reliability of key components, and reliability block diagram are used as inputs for reliability evaluation. The exponential distribution lifetime model is used to calculate the system reliability of the CNC system under a given task time, and the list of weak links and corresponding reliability improvement suggestions are output. When new fault data and field environment data reach the preset update threshold, the field environment is corrected, system reliability is calculated, and weak links in reliability are identified again, and the field reliability evaluation results of the CNC system are dynamically updated.

2. The CNC system evaluation method based on reliability block diagram as described in claim 1, characterized in that, The functional decomposition of the CNC system is carried out according to two dimensions: hardware functions and software functions. The CNC system includes a position control module, a communication function module, a PLC module, a feed drive unit, a spindle drive unit, a user interface module, an interpolation calculation module, a programming function module, external storage, a detection unit, an electrical system, a CNC panel, a machine tool operation panel, a monitoring and diagnostic module, an NC panel, an MCP unit, an IPC unit, a switching power supply, an NCUC bus, a CNC device, a servo system, and a spindle system.

3. The CNC system evaluation method based on reliability block diagram as described in claim 1, characterized in that, The identification of the series and parallel logical relationships between the functional units includes the power supply subsystem and the control subsystem being connected in series, and the control subsystem being connected in parallel with the X-axis servo drive unit, Y-axis servo drive unit, Z-axis servo drive unit and spindle drive unit respectively; each drive unit and the detection unit are considered to be connected in series in terms of reliability logic, and the communication bus is connected in series with each mounted unit as a shared resource. Based on the series and parallel logical relationships, a reliability block diagram of the CNC system is drawn.

4. The CNC system evaluation method based on reliability block diagram as described in claim 1, characterized in that, The fault data was obtained by using a timed truncation test scheme to track the fault occurrence time, fault unit identification and fault mode of the CNC system; the on-site environmental data includes ambient temperature and ambient relative humidity.

5. The CNC system evaluation method based on reliability block diagram as described in claim 1, characterized in that, The key components of the CNC system were selected for temperature-humidity dual-stress accelerated degradation tests to obtain degradation data. The specific steps are as follows: Hybrid integrated circuits and connectors in the CNC system were selected as key components. A temperature-humidity dual-stress accelerated degradation test scheme combining orthogonal design method and step stress method was adopted, and multiple combinations of temperature stress and relative humidity stress were set up. Accelerated degradation tests were conducted on hybrid integrated circuits and connectors under each combination of temperature stress and relative humidity stress, and data on current changes in hybrid integrated circuits and contact resistance changes in connectors were collected to obtain degradation data.

6. The CNC system evaluation method based on reliability block diagram as described in claim 1, characterized in that, The specific steps for establishing a performance degradation model and a generalized Eyring temperature-humidity dual-stress coupling acceleration model based on degradation data, and obtaining the reliability function and the mapping relationship between temperature and humidity stress and degradation parameters are as follows: Determine the performance degradation amount, initial degradation amount, and failure threshold of key components based on degradation data; A performance degradation model based on the Wiener process is constructed based on degradation data, and the stochastic degradation process of the performance degradation of key components over time is described. The failure lifetime of key components is obtained based on the time when the performance degradation first reaches the failure threshold. The cumulative probability distribution function of failure lifetime is obtained based on the failure lifetime of key components, and the reliability function is obtained based on the cumulative probability distribution function. Based on degradation data, a generalized Eyring temperature-humidity dual-stress coupling acceleration model is established. Parameters of the performance degradation model and the generalized Eyring temperature-humidity dual-stress coupling acceleration model are estimated to obtain the parameters of the performance degradation model and the generalized Eyring temperature-humidity dual-stress coupling acceleration model. The mapping relationship between temperature and humidity stress and degradation parameters is obtained by using the parameters of the performance degradation model and the parameters of the generalized Eyring temperature-humidity dual-stress coupling acceleration model.

7. The CNC system evaluation method based on reliability block diagram as described in claim 1, characterized in that, The acceleration factor of the field environment relative to the reference environment refers to setting the reference environment temperature and relative humidity, inputting the field environment data, reference environment temperature and relative humidity into the generalized Eyring temperature-humidity dual stress coupling acceleration model, and calculating the acceleration factor according to the temperature-humidity stress-degradation parameter mapping relationship.

8. The CNC system evaluation method based on reliability block diagram as described in claim 1, characterized in that, The equivalent reliability of key components is obtained by using an acceleration factor to correct the reliability function of key components obtained from the performance degradation model based on the Wiener process for field environment conditions.

9. The CNC system evaluation method based on reliability block diagram as described in claim 1, characterized in that, The method uses fault data, equivalent reliability of key components, and reliability block diagram as inputs for reliability evaluation. An exponential distribution lifetime model is employed to calculate the system reliability of the CNC system under a given task time, outputting a list of weak points and corresponding reliability improvement suggestions. The specific steps are as follows: Based on the fault unit identifier in the fault data, the fault data is assigned to the corresponding functional unit in the CNC system reliability block diagram. Based on the number of faults and the cumulative running time of each functional unit, the failure rate of each functional unit is calculated. For functional units with sparse fault data, the failure rate of the functional unit is corrected by using the equivalent reliability of key components. Based on the failure rate of each functional unit, the reliability of each functional unit under a given task time is calculated using an exponential distribution lifetime model. Input the reliability of each functional unit under a given task time into the CNC system reliability block diagram, and calculate the system reliability of the CNC system under a given task time according to the series and parallel logic relationship; Based on the failure rate, failure percentage, and reliability of each functional unit, the influence of each functional unit on the reliability of the CNC system is analyzed to identify weak links in reliability, and a list of weak links and corresponding reliability improvement suggestions are output.

10. The CNC system evaluation method based on reliability block diagram as described in claim 1, characterized in that, The update thresholds include a threshold for the number of newly added fault data and a threshold for changes in on-site environmental data. When either the number of newly collected fault data or the quarterly average change in field environmental data reaches the update threshold, the field environmental correction, system reliability calculation, and identification of weak links in reliability are carried out again.

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