A kind of ultra-precision grinding machine tool liquid static pressure guide rail fault monitoring method

Abstract

The application discloses a kind of ultra-precision grinding machine tool liquid static pressure guide rail fault monitoring methods, comprising: synchronously collecting each measuring point signal on liquid static pressure guide rail, and the signal collected is preprocessed;According to the signal after pre-processing, mechanism characteristics for representing the health state of liquid static pressure guide rail are constructed, and the mechanism characteristics include oil cavity pressure balance coefficient, flow imbalance coefficient, temperature difference gradient coefficient, motion accuracy error index, equivalent stiffness index and load fluctuation coefficient;The mechanism characteristics are normalized and weight calculation;According to the weight, the normalized mechanism characteristics are weighted and fused to obtain weighted health deviation index, and state determination is carried out according to the weighted health deviation index and preset threshold value.Can fuse guide rail oil supply state, oil film bearing capacity, motion accuracy and load fluctuation to realize state determination, improve the accuracy and sensitivity of fault monitoring.

Description

Technical Field

[0001] This invention relates to the field of equipment fault diagnosis technology, and in particular to a method for monitoring faults in the hydrostatic guide rails of ultra-precision grinding machine tools. Background Technology

[0002] Existing performance monitoring methods for hydrostatic guideways used in machine tool service primarily rely on threshold alarms or compensations based on a single performance index. For example, patent application number 202210747471.3 discloses a method, system, and medium for thermal error compensation of hydrostatic guideways in a follow-up grinding machine. After obtaining the temperature of the hydrostatic guideway through a sensor, it determines whether the temperature value is within a set threshold, alarms are triggered for temperatures exceeding the threshold, and thermal error calculation and compensation are performed. Another example is patent application number 201510209802.8, which discloses a control system and method for oil film thickness of hydrostatic guideways based on oil temperature compensation. This system uses a control algorithm to calculate the difference signal obtained by comparing the theoretical oil film thickness with the actual oil film thickness detected by the sensor, compensating for changes in hydrostatic guideway oil pressure caused by temperature variations.

[0003] The above solutions rely on threshold alarms based on single parameters such as temperature, pressure, and oil film thickness, or on thermal error compensation and oil film thickness compensation based on the deviation between a single parameter and the theoretical model. However, they have limited ability to identify early and minor faults under multi-factor coupled operating conditions.

[0004] The hydrostatic guide rail system has a complex structure, and its operation is easily affected by multiple factors, including insufficient oil supply pressure, inconsistent oil chamber pressure, partial blockage of the throttle, abnormal oil viscosity, local thermal drift, and fluctuations in drive load. These factors collectively affect the guide rail's oil film load-bearing capacity, linearity of motion, and positioning accuracy; in extreme cases, they may even lead to rigid contact of the guide rail pair, surface wear, or even jamming failure.

[0005] From the perspective of fault characterization, guide rail abnormalities are usually accompanied by fluctuations in oil chamber pressure, imbalances in oil supply and return flow, abnormal increases in oil temperature, increased displacement error, and enhanced fluctuations in motor load. However, due to the simultaneous existence of dynamic changes in motion parameters, environmental disturbances, and structural coupling relationships under actual operating conditions, existing monitoring methods based on single pressure or temperature thresholds lack the accuracy and sensitivity for identifying early, subtle faults, making it difficult to detect potential faults in a timely manner. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a method for fault monitoring of hydrostatic guideways in ultra-precision grinding machine tools, which can integrate guideway oil supply status, oil film bearing capacity, motion accuracy and load fluctuation to achieve status determination, thereby improving the accuracy and sensitivity of fault monitoring.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for fault monitoring of hydrostatic guideways in ultra-precision grinding machine tools includes: The system synchronously acquires signals from various measuring points on the hydrostatic guide rail and preprocesses the acquired signals. The signals from each measuring point include oil supply flow rate, oil return flow rate, oil chamber pressure in at least two oil chambers, temperature at at least two locations, relative displacement between the slider and the guide rail, drive motor current, and drive motor speed. Based on the preprocessed signal, a mechanism feature for characterizing the health status of the hydrostatic guide rail is constructed. The mechanism feature includes the oil chamber pressure balance coefficient, flow imbalance coefficient, temperature gradient coefficient, motion accuracy error index, equivalent stiffness index, and load fluctuation coefficient. The aforementioned mechanistic features are normalized and weighted. The normalized mechanistic features are weighted and fused according to the weights to obtain a weighted health deviation index. The state is determined based on the weighted health deviation index and a preset threshold.

[0008] Optionally, before synchronously acquiring signals from each measuring point on the hydrostatic guide rail, the measuring points are arranged on the hydrostatic guide rail, and the arrangement of the measuring points is obtained by the following method: Define fault variable vector ,in, , , These represent different fault types of the hydrostatic guide rail; define the corresponding system response vector. ,in, , , These represent the signal response quantities at each corresponding measuring point; The fault-response sensitivity matrix is ​​obtained from the fault variable vector and the system response vector. ,in, Indicates the first Type of fault change on the first The degree of influence of each response quantity; Definition of the first Importance index of candidate measurement points The calculation formula is as follows:

[0009] in, For the first Importance weights for different types of faults; Define the correlation coefficient between the i-th and j-th measurement points. ,in, Let i be the signal response at the i-th measurement point. Let J be the signal response at the j-th measurement point; Constructing the objective function for selecting measurement points The calculation formula is as follows:

[0010] in, This is a Boolean variable indicating whether to select the first option. One measurement point; This is the redundancy penalty coefficient; For installation costs; The penalty coefficient is the cost. The final arrangement of measurement points is obtained based on the objective function.

[0011] Optionally, the weight calculation for the mechanistic features specifically includes: The comprehensive sensitivity of each mechanism feature is obtained from the fault-response sensitivity matrix. ; The feature weights are obtained based on the aforementioned mechanism characteristics. The calculation method is as follows:

[0012] Optionally, after normalizing and weighting the mechanistic features, the method further includes: The dominant fault sources are analyzed based on weights and normalized mechanistic characteristics, calculated as follows:

[0013]

[0014] in, As the primary source of the fault, For the first The fault contribution of each mechanism characteristic The normalized mechanistic characteristics.

[0015] Optionally, the preset threshold includes a health threshold. and fault threshold The specific calculation method is as follows:

[0016]

[0017] in, This represents the mean of the health deviation index for the healthy sample. The standard deviation of the healthy sample; The specific steps for performing a status determination include: When the weighted health deviation index is less than the health threshold When the weighted health deviation index is greater than or equal to the health threshold, it is considered to be in a healthy state; when the weighted health deviation index is greater than or equal to the health threshold, it is considered to be in a healthy state. And less than the fault threshold When the weighted health deviation index is greater than or equal to the fault threshold, it is determined to be in a warning state; When this occurs, it is determined to be a fault state.

[0018] Optionally, before synchronously acquiring signals from each measuring point on the hydrostatic guide rail, the measuring points are arranged on the hydrostatic guide rail. Specifically, this includes: arranging an oil supply pressure sensor, an oil supply temperature sensor, and an oil supply flow sensor at the hydraulic station outlet of the hydrostatic guide rail; arranging a return oil flow sensor in the return oil main pipeline; arranging pressure sensors at the four different oil chambers of the slider; arranging a relative displacement sensor between the slider and the guide rail; and arranging a current sensor and a speed sensor at the drive motor.

[0019] Optionally, constructing mechanistic features to characterize the health status of the hydrostatic guide rail based on the preprocessed signal specifically includes: The oil chamber pressure balance coefficient is obtained based on the oil chamber pressure of at least two oil chambers. The flow imbalance coefficient is obtained based on the oil supply flow rate and the oil return flow rate. The temperature gradient coefficient is obtained based on the oil supply temperature. The motion accuracy error index is obtained based on the relative displacement between the slider and the guide rail. Based on oil chamber pressure and motion accuracy error indicators Obtain the equivalent stiffness index The load fluctuation coefficient is obtained based on the drive motor current. The aforementioned coefficients and indicators are combined to form a liquid mechanism feature vector. .

[0020] Optionally, the oil chamber pressure balance coefficient The calculation is as follows:

[0021] in, The average oil chamber pressure of the at least two oil chambers. For the first The oil chamber pressure in each oil chamber; The flow imbalance coefficient The calculation method is as follows:

[0022] in, For oil supply flow rate, This refers to the return oil flow rate; The temperature gradient coefficient The calculation method is as follows:

[0023] in, The highest temperature among the at least many temperatures. The lowest temperature among the at least many temperatures, The average temperature at the at least multiple locations; The motion accuracy error index The calculation method is as follows:

[0024]

[0025] in, For the first One displacement error, For the reference displacement trajectory, the first Displacement characteristics, The first of the relative displacements Displacement characteristics, This represents the total number of displacement features. The load fluctuation coefficient The calculation method is as follows:

[0026] in, The first current of the drive motor Current characteristics, This represents the average value of the drive motor current. This represents the total number of current characteristics.

[0027] Optionally, the equivalent stiffness index The calculation method is as follows:

[0028] in, The average oil chamber pressure of the at least two oil chambers. For motion accuracy error index, A positive number introduced to prevent the denominator from being zero.

[0029] Optionally, after obtaining the weighted health deviation index, the method further includes: calculating the Mahalanobis distance on the preprocessed signal; fusing the weighted health deviation index and the Mahalanobis distance; and then determining the state based on the fusion result and a preset threshold.

[0030] The beneficial effects of this invention are as follows: Multi-source signals such as pressure, temperature, flow rate, displacement, current, and rotational speed are collected and preprocessed. Further, combined with the working mechanism of the hydrostatic guide rail, the mechanism characteristics of oil chamber pressure balance, flow imbalance, temperature gradient, motion accuracy error, equivalent stiffness, and load fluctuation are constructed. These mechanism characteristics can directly characterize its health status. Finally, the mechanism characteristics are standardized and weighted to obtain an interpretable health deviation index for unified evaluation of the multi-source mechanism characteristics, achieving state determination and effectively improving the accuracy and sensitivity of fault monitoring. Attached Figure Description

[0031] Figure 1 The diagram shown is a schematic overall flow chart of the fault monitoring method for the hydrostatic guideway of an ultra-precision grinding machine tool according to an embodiment of the present invention. Figure 2 The diagram shown is a schematic representation of the structure of the hydrostatic guide rail system and the arrangement of sensor measuring points according to an embodiment of the present invention. Figure 3 The diagram shown illustrates the coupling relationship between typical faults and response parameters of a hydrostatic guide rail according to an embodiment of the present invention. Figure 4 The diagram shown illustrates the principle of slider position displacement caused by unbalanced oil cavity pressure distribution in an embodiment of the present invention. Figure 5 The diagram shown is a schematic diagram of the weighted health deviation index meter for determining the state of the hydrostatic guide rail according to an embodiment of the present invention. Detailed Implementation

[0032] To better understand the technical content, objectives, and effects of this invention, the following detailed description, in conjunction with specific embodiments and accompanying drawings, is provided. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. Numerous specific details are set forth in the following description to provide a thorough understanding of the invention; the described embodiments are merely a part of, and not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0033] Please refer to Figures 1-5 As shown, the embodiments of the present invention are as follows: A fault monitoring method for hydrostatic guideways in ultra-precision grinding machine tools is proposed. This method focuses on fault modes that may occur during the service of hydrostatic guideways, such as insufficient oil supply, throttle blockage, abnormal oil viscosity, oil chamber pressure imbalance, thermal drift, and abnormal load. By coordinating the monitoring of oil supply status, oil film bearing status, thermal status, motion status, and load status, a mechanism feature with clear physical meaning is constructed, and on this basis, a health status evaluation and fault determination are completed.

[0034] The method generally includes the following process: First, determine the typical fault modes and candidate measurement points of the hydrostatic guideway; second, complete the measurement point layout based on the fault-response relationship; then, collect multi-source signals such as pressure, temperature, flow rate, displacement, current, and speed, and perform preprocessing such as filtering, noise reduction, drift elimination, and data standardization; further, construct a multi-dimensional mechanism feature vector matrix of oil cavity pressure balance, flow imbalance, temperature gradient, motion accuracy error, equivalent stiffness, and load fluctuation; then, perform feature normalization and weight calculation on the mechanism features; and perform weighted fusion to obtain the health deviation index; finally, complete the health, early warning, and fault status determination based on the threshold; and analyze the dominant fault sources and output the results. A schematic diagram of the overall flow of the fault monitoring method for hydrostatic guideways of ultra-precision grinding machine tools in this embodiment is shown below. Figure 1 As shown.

[0035] Specifically, the method includes: Step S1: Arrange the measuring points on the hydrostatic guide rail. Specifically, this includes arranging an oil supply pressure sensor at the hydraulic station outlet of the hydrostatic guide rail. Fuel supply temperature sensor and fuel supply flow sensor Install a return oil flow sensor on the main return oil pipeline. Pressure sensors are installed at the four different oil chambers of the slider. , , , Temperature sensors are arranged in four different oil chambers: the left oil chamber, the right oil chamber, the upper oil chamber, and the lower oil chamber. A relative displacement sensor is arranged between the slider and the guide rail. A current sensor is placed at the drive motor. and speed sensor In an extended embodiment, a return oil temperature sensor may be further arranged. Or a throttle deformation sensor. A schematic diagram of the hydrostatic guide rail system structure and sensor measurement point arrangement is shown below. Figure 2 As shown.

[0036] It should be noted that the arrangement of the above measuring points is not an empirical arrangement, but rather determined based on the fault mechanism and response relationship of the hydrostatic guide rail. This arrangement is a preferred configuration chosen by the present invention to achieve a comprehensive characterization of the operating state of the hydrostatic guide rail. Specifically, the arrangement of each measuring point is obtained by the following method: Step S11: To describe the impact of different faults on each response quantity, define a fault variable vector. To represent typical faults of hydrostatic guideways, among which, , , These represent different fault types of hydrostatic guideways. In a specific example, six typical faults are selected. This indicates insufficient fuel supply. This indicates that the throttle is blocked. This indicates abnormal oil viscosity. This indicates an imbalance in the oil chamber pressure. Indicates thermal drift. This indicates an abnormal load. Accordingly, a corresponding system response vector is defined. ,in, , , These represent the signal response quantities at each measurement point, and corresponding to the specific example above, the system response vector. Each component corresponds to a monitoring quantity such as oil supply pressure, flow rate, temperature, oil chamber pressure, displacement, current, and rotational speed.

[0037] Step S12: Assume the system response satisfies the functional relationship:

[0038] Performing a first-order Taylor expansion on the area near the healthy working point yields:

[0039] Therefore, the fault-response sensitivity matrix is ​​obtained. .matrix elements Indicates the first Type of fault change on the first The degree of influence of each response quantity can be used to evaluate the sensitivity of a measurement point to a fault. If a complete analytical model is not available, small-disturbance tests or historical data can be used to approximate the result. ; Step S13: Define the first Importance index of candidate measurement points The calculation formula is as follows:

[0040] in, For the first The importance weight of different types of faults. This formula shows that if a certain measurement point has high sensitivity to multiple types of key faults, then its importance is higher and it is more suitable as the final measurement point.

[0041] Step S14: Considering the potential information overlap between multiple measurement points, to avoid redundancy, define the first... The measuring point and the first The correlation coefficient between the measurement points is:

[0042] in, Let i be the signal response at the i-th measurement point. Let be the signal response at the j-th measurement point.

[0043] Further construct the objective function for measuring point selection The calculation formula is as follows:

[0044] in, This is a Boolean variable indicating whether to select the first option. One measurement point; This is the redundancy penalty coefficient; For installation costs; The penalty coefficient is used as the cost. This objective function allows the final measurement point layout to balance high fault sensitivity, low information redundancy, and installability, thus demonstrating that the measurement point layout in this invention has a clear theoretical basis. In a specific example, a schematic diagram illustrating the coupling relationship between typical faults and response parameters of a hydrostatic guide rail is shown below. Figure 3 As shown.

[0045] Step S15: Obtain the final arrangement of measurement points according to the objective function.

[0046] According to the method of steps S11-S15, the present invention finally obtains the arrangement of the above-mentioned measuring points.

[0047] Step S2: Synchronously collect the signals from each measuring point arranged in step S1, and preprocess the collected signals; the signals from each measuring point include oil supply flow rate, oil supply temperature, return oil flow rate, return oil temperature, oil chamber pressure of four different oil chambers, temperature of four different oil chambers, relative displacement between the slider and the guide rail, drive motor current and drive motor speed.

[0048] Signal acquisition and preprocessing specifically include: Let the first The sensor at the first The original data at each sampling time point is The sampling length within an evaluation window is Then the first The signal sequence generated by the sensors within this window is as follows:

[0049] Because hydrostatic guideways are susceptible to noise, drift, and fluctuations in operating conditions during actual service, the original signal needs to be filtered, de-drifted, and standardized. Let the filtered and de-drifted signal be... The signal after zero-mean standardization It can be represented as:

[0050] in, and These represent the mean and standard deviation of the corresponding signal within the evaluation window or the healthy sample database, respectively. This processing mitigates the adverse effects of different dimensions and magnitudes on subsequent feature construction.

[0051] Step S3: Construct mechanistic features to characterize the health status of the hydrostatic guide rail based on the preprocessed signal. These mechanistic features include the oil chamber pressure balance coefficient. Flow imbalance coefficient Temperature gradient coefficient Motion accuracy error index Equivalent stiffness index and load fluctuation coefficient Specifically: (1) The oil chamber pressure balance coefficient The calculation is as follows:

[0052]

[0053] in, , , , The oil chamber pressures of the four different oil chambers are respectively. The average oil chamber pressure is denoted as .

[0054] When the hydrostatic guide rail is working normally, the pressure distribution of the four different oil chambers should remain relatively balanced, such as Figure 4 The diagram shows the principle of slider position displacement caused by unbalanced oil chamber pressure distribution, with the left figure being... , , , Maintaining balance, the slider position is normal, but the oil chamber pressure in the right figure is unbalanced. , Low, , (High) causes the slider position to shift.

[0055] The above oil chamber pressure balance coefficient Essentially, it reflects the degree of dispersion of the oil cavity pressure distribution relative to the average pressure. The larger the value, the more uneven the pressure distribution in the oil cavity, and the more likely there is abnormal oil supply, local throttling blockage, or degradation of the oil film bearing condition.

[0056] (2) The flow imbalance coefficient The calculation method is as follows:

[0057] in, For oil supply flow rate, This refers to the return oil flow rate.

[0058] When a hydrostatic guide rail is operating stably, a relatively stable flow relationship should be maintained between the oil supply and return. The aforementioned flow imbalance coefficient... Used to characterize the degree of deviation between supply and return oil flow rates. The larger the value, the more likely the system is to have leaks, blockages, or abnormal local flow resistance.

[0059] (3) The temperature gradient coefficient The calculation method is as follows:

[0060] in, This represents the highest temperature among all temperatures. The lowest temperature among all temperatures. This represents the average temperature.

[0061] If only two measuring points are set, namely the oil supply temperature and the oil return temperature, a simplified expression can also be used:

[0062] in, For oil supply temperature, This indicates the return oil temperature.

[0063] When a hydrostatic guide rail experiences abnormal thermal conditions, it is often accompanied by uneven temperature distribution. The aforementioned temperature gradient coefficient... The larger the value, the higher the degree of thermal non-uniformity of the system, and the more obvious the risk of thermal drift.

[0064] (4) The motion accuracy error index The calculation method is as follows:

[0065]

[0066] in, For the first One displacement error, For the reference displacement trajectory, the first Displacement characteristics, The first of the relative displacements Displacement characteristics, This represents the total number of displacement features.

[0067] The failure of a hydrostatic guide rail will ultimately manifest in the degradation of the slider's motion accuracy, as mentioned above regarding motion accuracy error indicators. In essence, it is the root mean square value of the displacement error. The larger the value, the worse the linearity of the motion or the positioning accuracy.

[0068] (5) The equivalent stiffness index The calculation method is as follows: hydrostatic guide rail stiffness It is an important parameter for measuring its load-bearing capacity and disturbance resistance, and is basically defined as:

[0069] in, Incremental load-bearing capacity This represents the change in oil film thickness.

[0070] Let the first The effective bearing area of ​​each oil cavity is Then the total bearing capacity It can be approximated as:

[0071] in, For the first The oil chamber pressure of each oil chamber.

[0072] Correspondingly, the increase in bearing capacity It can be written as:

[0073] Therefore, equivalent stiffness It can be approximated as:

[0074] in, To prevent the denominator from being zero, a small positive number is introduced. Considering the difficulty in obtaining the effective bearing area and precise force increment of each oil cavity in real time during engineering implementation, a simplified equivalent stiffness index is proposed. :

[0075] in, The average oil chamber pressure of the at least two oil chambers. For motion accuracy error index, A small positive number is introduced to prevent the denominator from being zero. The above equivalent stiffness index... It can approximate the stiffness state of the guide rail without significantly increasing the difficulty of parameter calculation.

[0076] (6) The load fluctuation coefficient The calculation method is as follows:

[0077] in, The first current of the drive motor Current characteristics, This represents the average value of the drive motor current. This represents the total number of current characteristics.

[0078] When the friction condition of the hydrostatic guide rail deteriorates or the drive load fluctuates, the current of the drive motor will fluctuate more significantly. The aforementioned load fluctuation coefficient... The larger the value, the more pronounced the fluctuations in the guide rail friction or the drive load.

[0079] Combining the above six types of coefficients and indicators, the mechanistic characteristic vector of the hydrostatic guide rail is constituted:

[0080] Each component in this eigenvector corresponds to a specific physical meaning, thus making it more interpretable than traditional black-box methods based solely on statistics.

[0081] Step S4: Normalize and calculate the weights of the aforementioned mechanistic features; specifically including: Because the dimensions and numerical ranges of the various mechanistic characteristics differ, standardization is required based on the health sample database. Let the number of samples in the health sample database be... The mean of each mechanistic feature is Standard deviation is Then the standardization features Defined as:

[0082] in, This represents the i-th mechanistic feature in the sample to be tested. and Let represent the mean and standard deviation of the i-th mechanistic feature in the health sample database, respectively. This represents the i-th mechanistic feature after standardization.

[0083] This leads to the standardized feature vector. It is expressed as follows:

[0084] To give greater weight to features that are more sensitive to faults in the overall evaluation, the comprehensive sensitivity of each mechanism feature is obtained by combining the aforementioned fault-response sensitivity matrix. And define the weights of the mechanistic features. It is expressed as follows:

[0085] This allows for weighted assessment of the importance of different mechanistic features.

[0086] Step S5: The mechanistic features are weighted according to their respective weights to obtain a weighted health deviation index. The state is then determined based on the weighted health deviation index and a preset threshold. Specifically, this includes: Using an interpretable weighted health deviation index As a core evaluation metric, it is defined as:

[0087] in, The smaller the value, the closer the tested state is to a healthy state; The larger the value, the more significantly the system deviates from a healthy state.

[0088] In one specific implementation, let's assume that the mean of the health deviation index is obtained from the statistics of the healthy sample. and standard deviation Then a health threshold can be taken. and fault threshold They are respectively:

[0089]

[0090] Based on this, the following judgment rule is established: When When, it is determined to be a healthy state; when When, it is determined to be a warning state; when When this occurs, it is determined to be a fault state. For example... Figure 5 The diagram shows the weighted health deviation index of the hydrostatic guide rail for determining its condition.

[0091] In a preferred embodiment, after obtaining the weighted health deviation index, the method further includes: calculating the Mahalanobis distance on the preprocessed signal; specifically including: Let the feature mean vector of the healthy sample be... The covariance matrix is Then the Mahalanobis distance of the sample to be tested for:

[0092] The weighted health deviation index and Mahalanobis distance are fused to obtain the fused health deviation index. The calculation method is as follows:

[0093] in, for The fusion coefficient between them.

[0094] Using the fused health deviation index Instead of the aforementioned weighted health deviation index Perform a status check.

[0095] The above preferred embodiments further consider the correlation between mechanistic features, taking into account both the interpretability of the weighted health deviation index and the ability of Mahalanobis distance to characterize feature correlation, which can further improve the robustness of state recognition under complex working conditions.

[0096] Step S6: To further determine which abnormal factor is dominant, define the first... Fault contribution of each indicator for:

[0097] The main source of the fault It can be determined by the following formula:

[0098] This contribution analysis can identify whether oil chamber pressure imbalance, abnormal flow, abnormal thermal state, degraded motion accuracy, or abnormal load fluctuation is the dominant factor, providing a basis for subsequent maintenance and compensation.

[0099] In summary, the beneficial effects of the present invention are as follows: The measurement point layout method based on fault-response sensitivity and correlation constraints establishes the relationship between fault variables, response quantities, and measurement point importance, and determines the final measurement point combination with sensitivity, redundancy, and installation cost as constraints, thus providing a clear theoretical basis for sensor layout.

[0100] Based on the mechanism characteristic system construction method of hydrostatic guide rail, this paper proposes mechanism characteristics such as oil cavity pressure balance coefficient, flow unbalance coefficient, temperature gradient coefficient, motion accuracy error index, equivalent stiffness index and load fluctuation coefficient, so that the monitoring index directly corresponds to the guide rail oil supply state, oil film bearing capacity and motion accuracy, and enhances the object specificity and interpretability.

[0101] The multi-source information fusion evaluation method based on the health deviation index uses an interpretable weighted health deviation index to uniformly evaluate the multi-source mechanism characteristics. The model has a simple structure, is easy to implement in engineering, and is suitable for health, early warning and fault determination under limited sample conditions.

[0102] The dominant anomaly identification method based on fault contribution measures the contribution of each mechanism feature to the abnormal state. This can further determine which type of problem is most prominent among pressure imbalance, flow abnormality, thermal state abnormality, motion accuracy degradation, or load fluctuation, thereby improving the interpretability of the condition diagnosis results.

[0103] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Therefore, any modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for monitoring a fault of a hydrostatic guideway of an ultra-precision grinding machine tool, characterized in that, include: The signals from each measuring point on the hydrostatic guide rail are collected synchronously, and the collected signals are preprocessed. The signals at each measuring point include oil supply flow rate, oil return flow rate, oil chamber pressure in at least two oil chambers, temperature at at least two locations, relative displacement between the slider and the guide rail, drive motor current, and drive motor speed. Based on the preprocessed signal, a mechanism feature for characterizing the health status of the hydrostatic guide rail is constructed. The mechanism feature includes the oil chamber pressure balance coefficient, flow imbalance coefficient, temperature gradient coefficient, motion accuracy error index, equivalent stiffness index, and load fluctuation coefficient. The aforementioned mechanistic features are normalized and weighted. The normalized mechanistic features are weighted and fused according to the weights to obtain a weighted health deviation index. The state is determined based on the weighted health deviation index and a preset threshold.

2. The ultra-precision grinding machine tool hydrostatic guideway fault monitoring method of claim 1, wherein, Before synchronously acquiring signals from each measuring point on the hydrostatic guide rail, the process also includes arranging the measuring points on the hydrostatic guide rail. The arrangement of the measuring points is obtained by the following method: Definition of the fault variable vector wherein , , denote different fault types of the hydrostatic guideway; definition of the corresponding system response vector wherein , , denote the signal response quantities for the respective measuring points; A fault-response sensitivity matrix is obtained from the fault variable vector and the system response vector wherein represents the influence degree of the fault change of the i-th fault variable on the j-th response variable ​​ Definition of the importance index of the first candidate measuring point The calculation formula of the importance index of the first candidate measuring point is as follows: ​ wherein, is the importance weight of the class of faults; defining a correlation coefficient between the ith and jth measurement points wherein, is a signal response quantity of the ith measurement point, is a signal response quantity of the jth measurement point; The construction of the measurement point selection objective function The calculation formula is as follows: wherein, is a Boolean variable indicating whether the i-th measurement point is selected; is a Boolean variable indicating whether the i-th measurement point is selected; is a redundancy penalty coefficient; is an installation cost; is a cost penalty coefficient; The final arrangement of measurement points is obtained based on the objective function.

3. The method of claim 2, wherein the method further comprises: The weighting calculation of the aforementioned mechanistic features specifically includes: The comprehensive sensitivity of each mechanism feature is obtained from the fault-response sensitivity matrix. ; The feature weights are obtained based on the aforementioned mechanism characteristics. The calculation method is as follows: 。 4. The method for monitoring faults in the hydrostatic guideway of an ultra-precision grinding machine tool according to claim 3, characterized in that, After normalizing and weighting the aforementioned mechanistic features, the method further includes: The dominant fault sources are analyzed based on weights and normalized mechanistic characteristics, calculated as follows: in, As the primary source of the fault, For the first The fault contribution of each mechanism characteristic The normalized mechanistic characteristics.

5. The method for monitoring faults in the hydrostatic guideway of an ultra-precision grinding machine tool according to claim 1, characterized in that, The preset threshold includes a health threshold. and fault threshold The specific calculation method is as follows: in, This represents the mean of the health deviation index for the healthy sample. The standard deviation of the healthy sample; The specific steps for performing a status determination include: When the weighted health deviation index is less than the health threshold When the weighted health deviation index is greater than or equal to the health threshold, it is considered to be in a healthy state; when the weighted health deviation index is greater than or equal to the health threshold, it is considered to be in a healthy state. And less than the fault threshold When the weighted health deviation index is greater than or equal to the fault threshold, it is determined to be in a warning state; When this occurs, it is determined to be a fault state.

6. The method for monitoring faults in the hydrostatic guideway of an ultra-precision grinding machine tool according to claim 1, characterized in that, Before synchronously acquiring signals from each measuring point on the hydrostatic guide rail, the process also includes arranging various measuring points on the hydrostatic guide rail. Specifically, this includes: arranging an oil supply pressure sensor, an oil supply temperature sensor, and an oil supply flow sensor at the hydraulic station outlet of the hydrostatic guide rail; arranging a return oil flow sensor in the return oil main pipeline; arranging pressure sensors at the four different oil chambers of the slider; arranging a relative displacement sensor between the slider and the guide rail; and arranging a current sensor and a speed sensor at the drive motor.

7. The method for monitoring faults in the hydrostatic guideway of an ultra-precision grinding machine tool according to claim 1, characterized in that, The mechanistic features for characterizing the health status of hydrostatic guideways, constructed based on the preprocessed signals, specifically include: The oil chamber pressure balance coefficient is obtained based on the oil chamber pressure of at least two oil chambers. The flow imbalance coefficient is obtained based on the oil supply flow rate and the oil return flow rate. The temperature gradient coefficient is obtained based on the oil supply temperature. The motion accuracy error index is obtained based on the relative displacement between the slider and the guide rail. Based on oil chamber pressure and motion accuracy error indicators Obtain the equivalent stiffness index The load fluctuation coefficient is obtained based on the drive motor current. The aforementioned coefficients and indicators are combined to form a liquid mechanism feature vector. .

8. The method for monitoring faults in the hydrostatic guideway of an ultra-precision grinding machine tool according to claim 7, characterized in that, The oil chamber pressure balance coefficient The calculation is as follows: in, The average oil chamber pressure of the at least two oil chambers. For the first The oil chamber pressure in each oil chamber; The flow imbalance coefficient The calculation method is as follows: in, For oil supply flow rate, This refers to the return oil flow rate; The temperature gradient coefficient The calculation method is as follows: in, The highest temperature among the at least many temperatures. The lowest temperature among the at least many temperatures, The average temperature at the at least multiple locations; The motion accuracy error index The calculation method is as follows: in, For the first One displacement error, For the reference displacement trajectory, the first Displacement characteristics, The first of the relative displacements Displacement characteristics, This represents the total number of displacement features. The load fluctuation coefficient The calculation method is as follows: in, The first current of the drive motor Current characteristics, This represents the average value of the drive motor current. This represents the total number of current characteristics.

9. The method for monitoring faults in the hydrostatic guideway of an ultra-precision grinding machine tool according to claim 8, characterized in that, The equivalent stiffness index The calculation method is as follows: in, The average oil chamber pressure of the at least two oil chambers. For motion accuracy error index, A positive number introduced to prevent the denominator from being zero.

10. The method for monitoring faults in the hydrostatic guideway of an ultra-precision grinding machine tool according to claim 1, characterized in that, After obtaining the weighted health deviation index, the process also includes: calculating the Mahalanobis distance on the preprocessed signal; fusing the weighted health deviation index and the Mahalanobis distance; and then determining the state based on the fusion result and a preset threshold.

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