Temperature real-time estimation and fault monitoring method for motorized spindle temperature unmeasurable area

Abstract

The invention discloses a temperature real-time estimation and fault monitoring method for a motorized spindle temperature unmeasurable area, and the method comprises the steps: constructing a heat capacity and heat resistance network model of a to-be-monitored machining center motorized spindle, and carrying out the finite element preliminary simulation, and obtaining the prediction data of a transient temperature field; and according to the transient temperature field prediction data and the local actually measured temperature data, parameter joint optimization is carried out on the heat capacity and heat resistance network model to obtain a motorized spindle transient temperature field real-time prediction model, and a real-time motorized spindle temperature distribution diagram is generated after grid refinement processing and is used for abnormal heating fault real-time judgment. According to the method, the transient temperature field of the motorized spindle under the current working condition can be completely and accurately predicted in real time, and the safety of the motorized spindle is better protected.

Description

Technical Field

[0001] The present invention relates to a technology in the field of machine tool spindle control, in particular to a method for real-time temperature estimation and fault monitoring in an unmeasurable temperature area of ​​an electric spindle based on mechanism-data fusion. Background Art

[0002] The lack of strong cooling measures in the unmeasurable temperature area inside the electric spindle of the machine tool may cause irreversible spindle damage or destruction. Therefore, it is necessary to use an indirect temperature estimation method to predict the temperature field in the unmeasurable temperature area inside the electric spindle in real time to realize intelligent monitoring and fault detection of the production process. Summary of the invention

[0003] In view of the difficulties in fault monitoring caused by the existence of temperature monitoring blind spots inside the existing electric spindles and the shortcomings that only the temperature of the spatially measurable area can be predicted in time, the present invention proposes a real-time temperature estimation and fault monitoring method for the unmeasurable temperature area of ​​the electric spindle, which can predict the transient temperature field of the electric spindle under the current working conditions in real time, completely and accurately, and better protect the safety of the electric spindle.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to a method for real-time temperature estimation and fault monitoring in an area where the temperature of an electric spindle cannot be measured. A heat capacitance and thermal resistance network model of an electric spindle of a machining center to be monitored is constructed and a preliminary finite element simulation is performed to obtain transient temperature field prediction data. The heat capacitance and thermal resistance network model is jointly optimized according to the transient temperature field prediction data and local measured temperature data to obtain a real-time prediction model for the transient temperature field of the electric spindle. A real-time electric spindle temperature distribution diagram is generated after mesh refinement processing and is used for real-time judgment of abnormal heating faults.

[0006] The thermal capacitance and thermal resistance network model is obtained by the following method: the heat transfer coefficient of the flow coupling interface and the kinematic sub-interface is improved to a dynamic value that changes with the spindle speed and real-time temperature to adapt to the complex and changeable machine tool working conditions. Finally, a thermal capacitance and thermal resistance network model of the electric spindle of the machining center to be monitored is established, which takes the spindle working condition and ambient temperature as input and the temperature at each node of the thermal capacitance and thermal resistance network as output.

[0007] The local measured data refers to: arranging a number of temperature sensors in the temperature measurable area of ​​the electric spindle of the machining center to be monitored to record the dynamic temperature changes of the spindle under different time-varying working conditions.

[0008] The joint optimization of parameters includes: dividing the parameters of the heat capacitance and thermal resistance network model into constants, static parameters and time-varying functions, optimizing the model parameters through a two-step parameter estimation method based on a genetic algorithm, bringing the optimized model parameters into the heat capacitance and thermal resistance network model, and inputting the current spindle working condition data and the real-time monitored ambient temperature change data into the heat capacitance and thermal resistance network model through the real-time communication means of the numerical control system, so as to realize the real-time prediction of the temperature at each node of the heat capacitance and thermal resistance network.

[0009] The mesh refinement process includes: mapping a half-section of the electric spindle of the machining center to be monitored to a square mesh space, thereby obtaining a subdivided mesh model of a heat capacitance and thermal resistance network; assigning the node temperature predicted by the heat capacitance and thermal resistance network model to the equivalent temperature center corresponding to the subdivided mesh area, and obtaining the stable temperature of all unassigned meshes through fuzzy operator loop iteration calculation, and drawing a thermal map of the temperature values ​​of each mesh, that is, a real-time electric spindle temperature distribution map under the current working condition.

[0010] The real-time judgment of abnormal heating fault includes: collecting the real-time measured temperature through the embedded temperature sensor arranged at the interface between the internal temperature unmeasurable area and the external temperature measurable area, obtaining the real-time predicted temperature of the embedded temperature sensor position through the heat capacitance and thermal resistance network model, and comparing the real-time measured temperature with the real-time predicted temperature. If the deviation between the real-time measured temperature and the real-time predicted temperature is greater than the set threshold, it is considered that an abnormal heating fault has occurred.

[0011] The present invention relates to a system for implementing the above method, comprising: a perception unit, a model unit, a display unit and an execution unit, wherein: the perception unit obtains the current spindle working condition through the real-time communication means of the numerical control system, and monitors the environmental temperature change information in real time through the environmental temperature sensor; the model unit predicts the temperature at each node in real time through a thermal capacitance and thermal resistance network model according to the information obtained by the perception unit; the display unit performs grid refinement processing according to the temperature information at each node predicted by the model unit, obtains the real-time electric spindle temperature distribution diagram under the current working condition and displays it on the image interface; the execution unit calculates the threshold judgment processing with the embedded temperature sensor according to the temperature information at each node predicted by the model unit, obtains the fault judgment result and sends an execution signal to the subsequent fault handling process. Technical Effects

[0012] The present invention combines the advantages of finite element simulation method, experimental data driven modeling method and thermal capacitance and thermal resistance network model to establish a real-time prediction model of the transient temperature field of the electric spindle with complete spatial distribution, accurate temperature prediction and good real-time performance. Compared with the prior art, the present invention can predict the transient temperature field of the electric spindle with complete spatial distribution and accurate numerical value in real time, with high fault detection rate and fast time response characteristics, and can better protect the safety of the electric spindle. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of the present invention;

[0014] In the figure: 1 spindle housing, 2 spindle motor stator, 3 spindle coolant, 4 spindle installation reference, 5 front bearing seat, 6 rear bearing, 7 spindle motor rotor, 8 rotating shaft, 9 front bearing, 10 tool holder taper sleeve, 11 embedded temperature sensor, 12 temperature unmeasurable area;

[0015] Figure 2 Schematic diagram of thermal capacitance and thermal resistance network model of the embodiment;

[0016] Figure 3 A real-time temperature distribution diagram under a certain working condition predicted by the embodiment;

[0017] Figure 4 A comparison diagram of the fault detection of this method and the traditional method under different working conditions and fault degrees of Monte Carlo simulation in the embodiment;

[0018] Figure 5 This is a comparison diagram of the delay time of the fault detection moment of this method and the traditional method when a serious fault reaching the damage risk temperature occurs in the Monte Carlo simulation in the embodiment. DETAILED DESCRIPTION

[0019] This embodiment relates to a method for real-time temperature estimation and fault monitoring of an electric spindle temperature unmeasurable area based on mechanism-data fusion, comprising:

[0020] The first step is to Figure 1 The thermal characteristics of a typical electric spindle of a machining center shown in the figure are analyzed. The dynamic heat generation power of the spindle motor and bearings under complex working conditions and the dynamic convection heat transfer characteristics of the spindle coolant and air are considered, and the following are established respectively. Figure 2 The thermal capacitance and thermal resistance network model shown describes the heat generation, transfer and dissipation process of the electric spindle.

[0021] The thermal capacitance and thermal resistance network model includes: 25 thermal capacitors, 29 conduction thermal resistances, 13 basic convection resistances, 13 convection interfaces, 3 static convection heat transfer coefficients, 1 time-varying convection heat transfer coefficient and 4 spindle heat source time-varying heating powers.

[0022] like Figure 1 The dotted box is the area where the temperature of the spindle cannot be measured.

[0023] In the second step, a finite element transient thermal model is established for the electric spindle of the machining center, and the transient temperature field simulation is performed based on the empirically estimated finite element boundary conditions to obtain transient temperature field prediction data with complete spatial distribution including the unmeasurable spindle temperature area, but with inaccurate temperature values.

[0024] In the third step, several temperature sensors are arranged in the temperature measurable area of ​​the electric spindle of the machining center to record the dynamic temperature changes of the spindle under different time-varying working conditions, that is, the local measured temperature data with incomplete spatial distribution but accurate temperature values ​​that do not include the unmeasurable area of ​​the spindle temperature.

[0025] The fourth step is to perform parameter joint optimization on the thermal capacitance and thermal resistance network model established in the first step based on the transient temperature field prediction data obtained in the second step and the local measured temperature data obtained in the third step, and establish a real-time prediction model for the transient temperature field of the electric spindle, which specifically includes:

[0026] 4.1) The parameters of the thermal capacitance and thermal resistance network model are divided into constants, static parameters and dynamic parameters;

[0027] The constant mentioned means that the heat capacity can be determined directly and accurately based on the structure of the spindle and the specific heat capacity of the material. Therefore, the heat capacity is classified as a constant.

[0028] The static parameters are parameters that do not change with working conditions, including 29 conduction resistances, 13 basic convection resistances, 13 convection interface areas and 3 static convection heat transfer coefficients. These static parameters mainly describe the heat transfer relationship between temperature nodes.

[0029] The dynamic parameters are: a time-varying convection heat transfer coefficient and a time-varying heating power of four spindle heat sources that vary with the spindle working condition and real-time temperature.

[0030] 4.2) The model parameters are optimized through a two-step parameter estimation method based on a genetic algorithm. Specifically, the dynamic parameters in the heat capacitance and thermal resistance network model are first replaced by the empirically estimated finite element boundary conditions, and the static parameters are optimized to make them fit the transient temperature field prediction data of the finite element transient thermal model as closely as possible. Then, the optimized static parameters are substituted into the heat capacitance and thermal resistance network model, and the dynamic parameters are optimized to make them fit the local measured temperature data of the thermal characteristics experiment as closely as possible.

[0031] 4.3) The optimized model parameters are brought into the heat capacitance and thermal resistance network model. The current spindle operating data and the real-time monitored ambient temperature change data are input into the heat capacitance and thermal resistance network model through the real-time communication means of the CNC system, so that the real-time temperature prediction at each node of the heat capacitance and thermal resistance network can be realized.

[0032] The fifth step is to further subdivide the temperature at each node of the heat capacitance and thermal resistance network obtained in the fourth step, and determine the temperature of the subdivided grid by the temperature between two adjacent nodes to solve the problem that the nodes of the heat capacitance and thermal resistance network are sparse and the predicted temperature only represents the precision of the equivalent center temperature of a certain area in the electric spindle, thereby obtaining the temperature field data at a certain moment, including:

[0033] 5.1) The half-section of the electric spindle is mapped to a 100*21 square grid space to obtain a subdivided grid model of the thermal capacitance and thermal resistance network, with a total of 1264 grids, and the initial grid temperature is set to 20℃.

[0034] 5.2) Assign the node temperature predicted by the thermal capacitance and thermal resistance network model to the equivalent temperature center corresponding to the subdivided grid area, and reduce the temperature difference of the subdivided grid through the Gaussian fuzzy algorithm.

[0035] 5.3) Repeat step 5.2 until the temperature of the subdivided grid is stable.

[0036] 5.4) Draw the final temperature result of the subdivided grid symmetrically along the center axis of the main axis as shown in the figure Figure 3 The thermal diagram of the temperature values ​​of each grid is shown to obtain the real-time temperature distribution diagram of the electric spindle under the current working conditions.

[0037] The sixth step is to use the temperature at each node of the thermal capacitance and thermal resistance network predicted in the fourth step for real-time monitoring of abnormal heating failure of the electric spindle, specifically including:

[0038] 6.1) Embed the temperature sensor in the position closest to the unmeasurable area inside the spindle, that is, near the outer ring of the front and rear bearings of the spindle, and collect the value of the embedded temperature sensor;

[0039] 6.2) Compare the value of the embedded temperature sensor with the real-time predicted temperature of the corresponding node of the thermal capacitance and thermal resistance network. When the deviation is greater than the set threshold, it is considered that an abnormal heating fault has occurred.

[0040] In the parameter range of spindle speed 0 to 20,000 rpm and fault heating power ratio 0 to 4 times of normal power, 1,000 Monte Carlo simulations of abnormal heating faults of the electric spindle were performed. A comparison chart of fault detection between this method and the traditional method was obtained, as shown in the figure below: Figure 4 The present invention has a detection rate of 68.1% for all faults, of which 100% for serious faults that may cause spindle damage, while the traditional method has a detection rate of only 24.0% and 83.0%. Further analysis of the serious faults that may cause spindle damage revealed that the two methods detected abnormal heating faults at a time when the bearing ball reached the allowable limit temperature of the grease, as shown in Figure 2. Figure 5 As shown. It can be seen that in most cases, this method can detect abnormal heating faults when the bearing ball temperature has not reached the limit; while the traditional method can generally detect abnormal heating faults only after the bearing ball temperature exceeds the limit value.

[0041] Compared with the existing fault monitoring method, which sets a static temperature threshold and considers an abnormal heating fault once the value of the embedded temperature sensor collected in real time is higher than the threshold, the dynamic threshold generated by the model in real time in the present invention has a higher fault detection rate and faster time response characteristics in theory. In summary, the present method has a higher fault detection rate, faster time response characteristics, and can better protect the safety of the electric spindle.

[0042] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principle and purpose of the present invention. The protection scope of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. Each implementation scheme within its scope shall be subject to the constraints of the present invention.

Claims

1. A method for real-time temperature estimation and fault monitoring in an unmeasurable temperature area of ​​an electric spindle, characterized in that: By constructing a heat capacitance and thermal resistance network model of the electric spindle of the machining center to be monitored and performing preliminary finite element simulation, the transient temperature field prediction data is obtained. According to the transient temperature field prediction data and the local measured temperature data, the parameters of the heat capacitance and thermal resistance network model are jointly optimized to obtain the real-time prediction model of the transient temperature field of the electric spindle. After mesh refinement, the real-time electric spindle temperature distribution map is generated and used for real-time judgment of abnormal heating faults.

2. The method for real-time temperature estimation and fault monitoring in the unmeasurable temperature area of ​​the electric spindle according to claim 1 is characterized in that: The thermal capacitance and thermal resistance network model is obtained in the following way: the heat transfer coefficient of the flow coupling interface and the kinematic sub-interface is improved into a dynamic value that changes with the spindle speed and the real-time temperature to adapt to the complex and changeable machine tool working conditions, and finally a thermal capacitance and thermal resistance network model of the electric spindle of the machining center to be monitored is established, which takes the spindle working condition and the ambient temperature as input and the temperature at each node of the thermal capacitance and thermal resistance network as output.

3. The method for real-time temperature estimation and fault monitoring in the unmeasurable temperature area of ​​the electric spindle according to claim 1 is characterized in that: The local measured data refers to: arranging a number of temperature sensors in the temperature measurable area of ​​the electric spindle of the machining center to be monitored to record the dynamic temperature changes of the spindle under different time-varying working conditions.

4. The method for real-time temperature estimation and fault monitoring in the unmeasurable temperature area of ​​the electric spindle according to claim 1 is characterized in that: The joint optimization of parameters includes: dividing the parameters of the heat capacitance and thermal resistance network model into constants, static parameters and time-varying functions, optimizing the model parameters through a two-step parameter estimation method based on a genetic algorithm, bringing the optimized model parameters into the heat capacitance and thermal resistance network model, and inputting the current spindle working condition data and the real-time monitored ambient temperature change data into the heat capacitance and thermal resistance network model through the real-time communication means of the numerical control system, so as to realize the real-time prediction of the temperature at each node of the heat capacitance and thermal resistance network.

5. The method for real-time temperature estimation and fault monitoring in the unmeasurable temperature area of ​​the electric spindle according to claim 1 is characterized in that: The mesh refinement process includes: mapping a half-section of the electric spindle of the machining center to be monitored to a square mesh space, thereby obtaining a subdivided mesh model of a heat capacitance and thermal resistance network; assigning the node temperature predicted by the heat capacitance and thermal resistance network model to the equivalent temperature center corresponding to the subdivided mesh area, and obtaining the stable temperature of all unassigned meshes through fuzzy operator loop iteration calculation, and drawing a thermal map of the temperature values ​​of each mesh, that is, a real-time electric spindle temperature distribution map under the current working condition.

6. The method for real-time temperature estimation and fault monitoring in the unmeasurable temperature area of ​​the electric spindle according to claim 1 is characterized in that: The real-time judgment of abnormal heating fault includes: collecting the real-time measured temperature through the embedded temperature sensor arranged at the interface between the internal temperature unmeasurable area and the external temperature measurable area, obtaining the real-time predicted temperature of the embedded temperature sensor position through the heat capacitance and thermal resistance network model, and comparing the real-time measured temperature with the real-time predicted temperature. If the deviation between the real-time measured temperature and the real-time predicted temperature is greater than the set threshold, it is considered that an abnormal heating fault has occurred.

7. The method for real-time temperature estimation and fault monitoring in the unmeasurable temperature area of ​​the electric spindle according to any one of claims 1 to 6, characterized in that: include: The first step is to analyze the thermal characteristics of a typical electric spindle in a machining center, considering the dynamic heat generation power of the spindle motor and bearings under complex working conditions and the dynamic convection heat transfer characteristics of the spindle coolant and air, and establish a thermal capacitance and thermal resistance network model to describe the heat generation, transfer and dissipation process of the electric spindle; In the second step, a finite element transient thermal model is established for the electric spindle of the machining center, and a transient temperature field simulation is performed based on the finite element boundary conditions estimated empirically, to obtain transient temperature field prediction data with complete spatial distribution including the unmeasurable spindle temperature area, but with inaccurate temperature values. The third step is to arrange several temperature sensors in the temperature measurable area of ​​the electric spindle of the machining center to record the dynamic temperature change of the spindle under different time-varying working conditions, that is, to exclude the local measured temperature data with incomplete spatial distribution but accurate temperature values ​​in the unmeasurable area of ​​the spindle temperature; The fourth step is to perform parameter joint optimization on the thermal capacitance and thermal resistance network model established in the first step according to the transient temperature field prediction data obtained in the second step and the local measured temperature data obtained in the third step, and establish a real-time prediction model for the transient temperature field of the electric spindle; The fifth step is to further subdivide the temperature at each node of the heat capacitance and thermal resistance network obtained in the fourth step, and determine the temperature of the subdivided grid by the temperature between two adjacent nodes, so as to solve the problem that the nodes of the heat capacitance and thermal resistance network are sparse and the predicted temperature only represents the precision of the equivalent center temperature of a certain area in the electric spindle, thereby obtaining the temperature field data at a certain moment; The sixth step is to use the temperature at each node of the thermal capacitance and thermal resistance network predicted in the fourth step for real-time monitoring of abnormal heating failure of the electric spindle, specifically including: 6.1) Embed the temperature sensor in the position closest to the unmeasurable area inside the spindle, that is, near the outer ring of the front and rear bearings of the spindle, and collect the value of the embedded temperature sensor; 6.2) Compare the value of the embedded temperature sensor with the real-time predicted temperature of the corresponding node of the thermal capacitance and thermal resistance network. When the deviation is greater than the set threshold, it is considered that an abnormal heating fault has occurred.

8. The method for real-time temperature estimation and fault monitoring in the unmeasurable temperature area of ​​the electric spindle according to claim 7 is characterized in that: The fourth step specifically includes: 4.1) The parameters of the thermal capacitance and thermal resistance network model are divided into constants, static parameters and dynamic parameters; The constants mentioned are: Based on the structure of the spindle and the specific heat capacity of the material, the heat capacity can be determined directly and accurately; therefore, the heat capacity is classified as a constant; The static parameters are parameters that do not change with working conditions, including 29 conduction resistances, 13 basic convection resistances, 13 convection interface areas and 3 static convection heat transfer coefficients; these static parameters mainly describe the heat transfer relationship between temperature nodes; The dynamic parameters are: 1 time-varying convection heat transfer coefficient and 4 time-varying heating powers of the spindle heat sources that vary with the spindle working condition and real-time temperature; 4.2) Optimizing the model parameters by a two-step parameter estimation method based on a genetic algorithm, specifically: first, using the empirically estimated finite element boundary conditions to replace the dynamic parameters in the heat capacitance and thermal resistance network model, and optimizing the static parameters to make them fit the transient temperature field prediction data of the finite element transient thermal model as much as possible, then substituting the optimized static parameters into the heat capacitance and thermal resistance network model, and optimizing the dynamic parameters to make them fit the local measured temperature data of the thermal characteristics experiment as much as possible; 4.3) The optimized model parameters are brought into the heat capacitance and thermal resistance network model. The current spindle operating data and the real-time monitored ambient temperature change data are input into the heat capacitance and thermal resistance network model through the real-time communication means of the CNC system, so that the real-time temperature prediction at each node of the heat capacitance and thermal resistance network can be realized.

9. The method for real-time temperature estimation and fault monitoring in the unmeasurable temperature area of ​​the electric spindle according to claim 7, characterized in that: The fifth step specifically includes: 5.1) Map the half-section of the electric spindle to a 100*21 square grid space to obtain a subdivided grid model of the thermal capacitance and thermal resistance network, with a total of 1264 grids, and the initial grid temperature is set to 20℃; 5.2) Assign the node temperature predicted by the thermal capacitance and thermal resistance network model to the equivalent temperature center corresponding to the subdivided grid area, and reduce the temperature difference of the subdivided grid by Gaussian fuzzy algorithm; 5.3) Repeat step 5.2 until the temperature of the subdivided grid is stable; 5.4) The final temperature results of the subdivided grids are symmetrically arranged along the central axis of the spindle, and a thermal diagram of the temperature values ​​of each grid is drawn to obtain the real-time temperature distribution diagram of the electric spindle under the current working conditions.

10. A system for real-time temperature estimation and fault monitoring of an electric spindle in an unmeasurable temperature region for implementing the method described in any one of claims 1 to 9, characterized in that: include: A sensing unit, a model unit, a display unit and an execution unit, wherein: the sensing unit obtains the current spindle working condition through the real-time communication means of the numerical control system, and monitors the ambient temperature change information in real time through the ambient temperature sensor; the model unit predicts the temperature at each node in real time through the thermal capacitance and thermal resistance network model according to the information obtained by the sensing unit; the display unit performs grid refinement processing according to the temperature information at each node predicted by the model unit, obtains the real-time electric spindle temperature distribution diagram under the current working condition and displays it on the image interface; the execution unit calculates the threshold judgment processing with the embedded temperature sensor according to the temperature information at each node predicted by the model unit, obtains the fault judgment result and sends an execution signal to the subsequent fault handling process.

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