Multi-sensor data online fault detection system and method based on large heavy-load numerical control static pressure rotary table

Abstract

The invention provides a multi-sensor data online fault detection system and method based on a large heavy-load numerical control static pressure rotary table. Comprising the following modules: a workbench detection module, a mandrel detection module, an oil path detection module and a driving part detection module. The method comprises the following steps: S1, reasonably installing various sensors such as laser displacement, temperature, pressure, vibration, strain gauges and current according to a detected part of a rotary table; s2, acquiring multiple signals such as a working table floating amount, a mandrel deformation amount, hydraulic oil temperature and pressure, a vibration current of a driving system and the like of the large-scale heavy-load numerical control static pressure rotary table according to the multiple sensors mounted in the step S1; s3, according to the multi-signal data collected in the S2, online fault detection is carried out on the problems that whether a working table floats uniformly or not, the temperature deformation change of a mandrel, the oil temperature and pressure of hydraulic oil entering and exiting an oil cavity, and the abrasion abnormal position of a driving part; according to the method, the fault position and the fault reason can be quickly positioned, so that an efficient fault maintenance mode can be conveniently determined.

Description

Technical Field

[0001] The invention belongs to the field of state monitoring and fault diagnosis of large-scale heavy-load CNC hydrostatic turntables, and specifically relates to a multi-sensor data online fault detection system and method based on large-scale heavy-load CNC hydrostatic turntables. Background Art

[0002] The CNC rotary table is called the fourth "axis" of advanced CNC machine tools. It realizes multi-degree-of-freedom linkage processing of parts and plays an important role in the manufacturing field. As the workpieces are developed towards large and heavy, the rotary table needs to be upgraded to improve its load-bearing capacity and performance. When a large and heavy-loaded CNC hydrostatic rotary table is in long-term operation, the worktable will float unevenly, the mandrel will deform and expand due to heat accumulation, and even swell and die. The hydraulic oil is affected by temperature and blockage, and cannot normally provide the required pressure to the hydrostatic guide rail oil chamber. The frequent speed and direction changes of the drive system will make the worm, bearings and other parts very easy to wear or loosen.

[0003] In view of the above problems, the unhealthy operation of the components of the large-scale heavy-load CNC hydrostatic turntable will seriously affect its processing efficiency, processing accuracy and load capacity. Therefore, in order to ensure the processing stability of the large-scale heavy-load CNC hydrostatic turntable, fast and accurate fault diagnosis of each component has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to propose a multi-sensor data online fault detection system and method based on a large-scale heavy-load CNC hydrostatic turntable, so as to solve the problem that the unhealthy operation of various components of the heavy-load CNC hydrostatic turntable will seriously affect the processing efficiency, processing accuracy and load capacity of the turntable itself.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A multi-sensor data online fault detection system based on a large heavy-load CNC hydrostatic turntable. It includes a workbench detection module, a spindle detection module, an oil circuit detection module and a drive component detection module. The workbench detection module includes a plurality of laser displacement sensors respectively arranged on the workbench. The laser displacement sensors are sequentially arranged around the workbench and correspond to the positions of the hydrostatic oil chambers on the base.

[0007] The mandrel detection module includes a plurality of platinum resistance temperature sensors and strain gauges respectively arranged on the mandrel, and the platinum resistance temperature sensors and strain gauges are arranged on the inner wall of the mandrel in sequence;

[0008] The oil circuit detection module includes a plurality of pressure detection modules arranged at the oil inlet of the static pressure oil chamber, and a temperature detection module for detecting the temperature of the oil circuit and the oil tank;

[0009] The driving component detection module includes a plurality of three-axis vibration acceleration sensors respectively arranged on the turntable worm pair and a three-phase current sensor of the driving motor. The three-axis vibration acceleration sensors are arranged in sequence on the supporting bearing seats and static pressure copper sleeves of the turntable driving worm and the anti-backlash worm, and the power cable of the driving motor is placed in the open magnetic ring of the three-phase current sensor.

[0010] A multi-sensor data online fault detection method based on a large-scale heavy-load CNC hydrostatic turntable, the detection method is applicable to the unhealthy operation of the CNC hydrostatic turntable, and specifically includes the following operation steps:

[0011] S1. According to the number of oil cavities of the static pressure main rail of the turntable, the magnetic base of the laser displacement sensor of the workbench detection module is adsorbed around the workbench; the platinum resistance temperature sensor and strain gauge of the core shaft detection module are pasted on the inner wall of the core shaft; the pressure sensor of the oil circuit detection module is installed at the oil inlet and outlet of the static pressure oil cavity using the oil pipe three-way joint, and the platinum resistance temperature sensor is placed in the oil at the oil inlet and outlet of the oil circuit; the magnetic base of the three-way vibration sensor of the drive detection module is respectively pressed against the supporting bearing seat and the static pressure copper sleeve of the drive worm and the anti-backlash worm, and the open magnetic ring of the three-phase current sensor is buckled on the power cable of the motor;

[0012] S2, the laser displacement sensor is used to collect the floating amount of each hydrostatic guide rail oil cavity of the turntable, the platinum resistance temperature sensor is used to collect the temperature signal of the inner wall of the core shaft and each oil circuit of the turntable, the strain gauge is used to collect the strain signal of the core shaft structure change, the pressure sensor is used to collect the pressure signal of the oil pipe corresponding to each oil cavity, the three-way vibration acceleration sensor is used to collect the three-way vibration signal of the worm and the bearing, and the three-phase current sensor is used to collect the three-phase current signal of the servo motor;

[0013] S3. By analyzing the collected data, the displacement signal measured by the laser displacement sensor is used to detect whether the workbench floats evenly, the temperature signal measured by the platinum resistance temperature sensor is used to detect the temperature of the inner wall of the mandrel and the temperature of the oil in and out of the oil chamber, the strain gauge is used to detect the deformation of the inner wall of the mandrel due to temperature rise, the pressure signal collected by the pressure sensor is used to detect the supporting effect of the hydrostatic guide rail oil chamber, the vibration signal measured by the three-way vibration sensor is used to detect the abnormal wear position of the driving component, and the current signal measured by the three-phase current sensor is used to detect the load change during a fault;

[0014] It includes a control component for controlling the workbench detection module, the spindle detection module, the oil circuit detection module and the drive component detection module to be activated as needed, and the control component includes a connected controller and a data processor connected to an alarm module;

[0015] The temperature, pressure, displacement, deformation and other signals in step S3 are determined by setting thresholds based on the summary of previous test studies. If they exceed or fall below the corresponding thresholds, an alarm will be issued to indicate that the component has failed.

[0016] The strain value and temperature signal are used to provide a fault alarm in advance when the core shaft is working and the temperature rise causes a large amount of structural deformation, which affects the processing.

[0017] The vibration signal analysis in step S3 uses a triangular topology aggregation algorithm to optimize variational mode decomposition (TTAO-VMD) method, which specifically includes the following steps:

[0018] S101, obtaining an original fault vibration signal x(i) (i=1, 2, ...N), and setting algorithm parameters;

[0019] S102, using triangular topology aggregation optimization to find the optimal individual with minimum envelope entropy as the objective function, and determine the optimal number of decomposition layers and penalty coefficient;

[0020] S103, performing variational mode decomposition using the optimal number of decomposition layers and penalty coefficients found in the previous step;

[0021] S104. Select sensitive components with kurtosis values ​​greater than 3 from the decomposed signal to reconstruct the signal, and then perform envelope analysis to find out the characteristic frequency corresponding to the fault and determine where the fault occurs.

[0022] In S102, the minimum envelope entropy is used as the objective function, and the envelope entropy calculation formula is:

[0023]

[0024] where p i is the normalized form of a(i); a(i) is the envelope signal of signal x(i) after Hilbert transform demodulation.

[0025] The fault characteristic frequencies in S104 include the rotational frequency and frequency multiples of the driving worm and the anti-backlash worm, the fault frequency and frequency multiples of the bearing, and the gear meshing frequency and frequency multiples. The kurtosis calculation formula is:

[0026]

[0027] where μ(i) is the mean of the vibration signal x(i); σ(i) is the standard deviation of the vibration signal x(i); and E[·] is the expected function.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The present invention cooperates with the workbench detection module, the spindle detection module, the oil circuit detection module and the drive component detection module, and the multi-type sensor acquisition tool acquires diverse and complementary perception information, which can monitor the status of the turntable in real time. The staff can find and locate the fault in the first time and carry out targeted repairs;

[0030] 2. This method enables users to add or delete corresponding acquisition sensors and channels to collect corresponding data according to their own needs. Each acquisition channel can run independently without interfering with each other. Therefore, this method has good scalability.

[0031] 3. The present invention can quickly locate the fault location and cause of the fault through a comprehensive inspection of each position of the large-scale heavy-load CNC hydrostatic turntable, thereby facilitating the determination of an efficient fault repair method. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a fault diagnosis flow chart for a large, heavy-loaded CNC hydrostatic turntable.

[0033] Figure 2 This is a flow chart of vibration signal fault diagnosis. DETAILED DESCRIPTION

[0034] In order to clarify the technical problems, technical solutions, implementation processes and performance demonstrations, the present invention is further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are for explanation only. The present invention is not intended to limit the present invention. Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0035] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0036] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0037] Example 1

[0038] like Figure 1 and Figure 2As shown, a multi-sensor data online fault detection system based on a large heavy-load CNC hydrostatic turntable. It includes a workbench detection module, a spindle detection module, an oil circuit detection module and a drive component detection module. The workbench detection module includes a plurality of laser displacement sensors respectively arranged on the workbench. The laser displacement sensors are sequentially arranged around the workbench and correspond to the positions of the hydrostatic oil chambers on the base.

[0039] The mandrel detection module includes a plurality of platinum resistance temperature sensors and strain gauges respectively arranged on the mandrel, and the platinum resistance temperature sensors and strain gauges are arranged on the inner wall of the mandrel in sequence;

[0040] The oil circuit detection module includes a plurality of pressure detection modules arranged at the oil inlet of the static pressure oil chamber, and a temperature detection module for detecting the temperature of the oil circuit and the oil tank;

[0041] The driving component detection module includes a plurality of three-axis vibration acceleration sensors respectively arranged on the turntable worm pair and a three-phase current sensor of the driving motor. The three-axis vibration acceleration sensors are arranged in sequence on the supporting bearing seats and static pressure copper sleeves of the turntable driving worm and the anti-backlash worm, and the power cable of the driving motor is placed in the open magnetic ring of the three-phase current sensor.

[0042] A multi-sensor data online fault detection method based on a large-scale heavy-load CNC hydrostatic turntable, characterized in that it is applicable to the unhealthy operation of the CNC hydrostatic turntable, and specifically includes the following operating steps:

[0043] S1. According to the number of oil cavities of the static pressure main rail of the turntable, the magnetic base of the laser displacement sensor of the workbench detection module is adsorbed around the workbench; the platinum resistance temperature sensor and strain gauge of the core shaft detection module are pasted on the inner wall of the core shaft; the pressure sensor of the oil circuit detection module is installed at the oil inlet and outlet of the static pressure oil cavity using the oil pipe three-way joint, and the platinum resistance temperature sensor is placed in the oil at the oil inlet and outlet of the oil circuit; the magnetic base of the three-way vibration sensor of the drive detection module is respectively pressed against the supporting bearing seat and the static pressure copper sleeve of the drive worm and the anti-backlash worm, and the open magnetic ring of the three-phase current sensor is buckled on the power cable of the motor;

[0044] S2, the laser displacement sensor is used to collect the floating amount of each hydrostatic guide rail oil cavity of the turntable, the platinum resistance temperature sensor is used to collect the temperature signal of the inner wall of the core shaft and each oil circuit of the turntable, the strain gauge is used to collect the strain signal of the core shaft structure change, the pressure sensor is used to collect the pressure signal of the oil pipe corresponding to each oil cavity, the three-way vibration acceleration sensor is used to collect the three-way vibration signal of the worm and the bearing, and the three-phase current sensor is used to collect the three-phase current signal of the servo motor;

[0045] S3. By analyzing the collected data, the displacement signal measured by the laser displacement sensor is used to detect whether the workbench floats evenly, the temperature signal measured by the platinum resistance temperature sensor is used to detect the temperature of the inner wall of the core shaft and the temperature of the oil in and out of the oil chamber, the strain gauge is used to detect the deformation of the inner wall of the core shaft caused by temperature rise, the pressure signal collected by the pressure sensor is used to detect the supporting effect of the hydrostatic guide rail oil chamber, the vibration signal measured by the three-way vibration sensor is used to detect the abnormal wear position of the driving component, and the current signal measured by the three-phase current sensor is used to detect the load change during a fault.

[0046] It includes a control component for controlling the workbench detection module, the spindle detection module, the oil circuit detection module and the drive component detection module to be activated on demand, and the control component includes a connected controller and a data processor connected to an alarm module.

[0047] The temperature, pressure, displacement, deformation and other signals in step S3 are determined by setting thresholds based on the summary of previous test studies. If they exceed or fall below the corresponding thresholds, an alarm will be issued to indicate that the component has failed.

[0048] The strain value and temperature signal are used to provide a fault alarm in advance when the core shaft is working and the temperature rise causes a large amount of structural deformation, which affects the processing.

[0049] The vibration signal analysis in step S3 uses a triangular topology aggregation algorithm to optimize variational mode decomposition (TTAO-VMD) method, which specifically includes the following steps:

[0050] S101, obtaining an original fault vibration signal x(i) (i=1, 2, ...N), and setting algorithm parameters;

[0051] S102, using triangular topology aggregation optimization to find the optimal individual with minimum envelope entropy as the objective function, and determine the optimal number of decomposition layers and penalty coefficient;

[0052] S103, performing variational mode decomposition using the optimal number of decomposition layers and penalty coefficients found in the previous step;

[0053] S104. Select sensitive components with kurtosis values ​​greater than 3 from the decomposed signal to reconstruct the signal, and then perform envelope analysis to find out the characteristic frequency corresponding to the fault and determine where the fault occurs.

[0054] In S102, the minimum envelope entropy is used as the objective function, and the envelope entropy calculation formula is:

[0055]

[0056] where p iis the normalized form of a(i); a(i) is the envelope signal of signal x(i) after Hilbert transform demodulation.

[0057] The fault characteristic frequencies in S104 include the rotational frequency and frequency multiples of the driving worm and the anti-backlash worm, the fault frequency and frequency multiples of the bearing, and the gear meshing frequency and frequency multiples. The kurtosis calculation formula is:

[0058]

[0059] where μ(i) is the mean of the vibration signal x(i); σ(i) is the standard deviation of the vibration signal x(i); and E[·] is the expected function.

[0060] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A multi-sensor data online fault detection system based on a large-scale heavy-load CNC hydrostatic turntable, characterized in that It includes a workbench detection module, a spindle detection module, an oil circuit detection module and a drive component detection module. The workbench detection module includes a plurality of laser displacement sensors respectively arranged on the workbench. The laser displacement sensors are sequentially arranged around the workbench and correspond to the positions of the static pressure oil chambers on the base. The mandrel detection module includes a plurality of platinum resistance temperature sensors and strain gauges respectively arranged on the mandrel, and the platinum resistance temperature sensors and strain gauges are arranged on the inner wall of the mandrel in sequence; The oil circuit detection module includes a plurality of pressure detection modules arranged at the oil inlet of the static pressure oil chamber, and a temperature detection module for detecting the temperature of the oil circuit and the oil tank; The driving component detection module includes a plurality of three-axis vibration acceleration sensors respectively arranged on the turntable worm pair and a three-phase current sensor of the driving motor. The three-axis vibration acceleration sensors are arranged in sequence on the supporting bearing seats and static pressure copper sleeves of the turntable driving worm and the anti-backlash worm, and the power cable of the driving motor is placed in the open magnetic ring of the three-phase current sensor.

2. A multi-sensor data online fault detection method based on a large-scale heavy-load CNC hydrostatic turntable, characterized in that: The fault detection method is applicable to the unhealthy operation of a CNC static pressure turntable, and specifically includes the following steps: S1. According to the number of oil cavities of the static pressure main rail of the turntable, the magnetic base of the laser displacement sensor of the workbench detection module is adsorbed around the workbench; the platinum resistance temperature sensor and strain gauge of the core shaft detection module are pasted on the inner wall of the core shaft; the pressure sensor of the oil circuit detection module is installed at the oil inlet and outlet of the static pressure oil cavity using the oil pipe three-way joint, and the platinum resistance temperature sensor is placed in the oil at the oil inlet and outlet of the oil circuit; the magnetic base of the three-way vibration sensor of the drive detection module is respectively pressed against the supporting bearing seat and the static pressure copper sleeve of the drive worm and the anti-backlash worm, and the open magnetic ring of the three-phase current sensor is buckled on the power cable of the motor; S2, the laser displacement sensor is used to collect the floating amount of each hydrostatic guide rail oil cavity of the turntable, the platinum resistance temperature sensor is used to collect the temperature signal of the inner wall of the core shaft and each oil circuit of the turntable, the strain gauge is used to collect the strain signal of the core shaft structure change, the pressure sensor is used to collect the pressure signal of the oil pipe corresponding to each oil cavity, the three-way vibration acceleration sensor is used to collect the three-way vibration signal of the worm and the bearing, and the three-phase current sensor is used to collect the three-phase current signal of the servo motor; S3. By analyzing the collected data, the displacement signal measured by the laser displacement sensor is used to detect whether the workbench floats evenly, the temperature signal measured by the platinum resistance temperature sensor is used to detect the temperature of the inner wall of the core shaft and the temperature of the oil in and out of the oil chamber, the strain gauge is used to detect the deformation of the inner wall of the core shaft caused by temperature rise, the pressure signal collected by the pressure sensor is used to detect the supporting effect of the hydrostatic guide rail oil chamber, the vibration signal measured by the three-way vibration sensor is used to detect the abnormal wear position of the driving component, and the current signal measured by the three-phase current sensor is used to detect the load change during a fault.

3. The multi-sensor data online fault detection method based on a large-scale heavy-load CNC static pressure turntable according to claim 2 is characterized in that , including a control component for controlling the workbench detection module, the spindle detection module, the oil circuit detection module and the drive component detection module to be activated on demand, and the control component includes a connected controller and a data processor connected to an alarm module.

4. The multi-sensor data online fault detection method based on a large-scale heavy-load CNC static pressure turntable according to claim 2 is characterized in that The temperature, pressure, displacement, deformation and other signals in step S3 are determined by referring to the summary of previous test research to set thresholds. If they exceed or fall below the corresponding thresholds, an alarm will be issued to indicate that the component has failed.

5. The method according to claim 4, characterized in that: The strain value and temperature signal are used to provide a fault alarm in advance when the core shaft is working and the temperature rise causes a large amount of structural deformation, which affects the processing.

6. The multi-sensor data online fault detection method based on a large-scale heavy-load CNC static pressure turntable according to claim 2 is characterized in that The vibration signal analysis in step S3 uses a triangular topology aggregation algorithm to optimize variational mode decomposition (TTAO-VMD), which specifically includes the following steps: S101, obtaining an original fault vibration signal x(i) (i=1, 2, ...N), and setting algorithm parameters; S102, using triangular topology aggregation optimization to find the optimal individual with minimum envelope entropy as the objective function, and determine the optimal number of decomposition layers and penalty coefficient; S103, performing variational mode decomposition using the optimal number of decomposition layers and penalty coefficients found in the previous step; S104. Select sensitive components with kurtosis values ​​greater than 3 from the decomposed signal to reconstruct the signal, and then perform envelope analysis to find out the characteristic frequency corresponding to the fault and determine where the fault occurs.

7. The multi-sensor data online fault detection method based on a large-scale heavy-load CNC static pressure turntable according to claim 6 is characterized in that In S102, the minimum envelope entropy is used as the objective function, and the envelope entropy calculation formula is: where p i is the normalized form of a(i); a(i) is the envelope signal of signal x(i) after Hilbert transform demodulation.

8. The multi-sensor data online fault detection method based on a large-scale heavy-load CNC static pressure turntable according to claim 6 is characterized in that The fault characteristic frequencies in S104 include the rotational frequency and frequency multiples of the driving worm and the anti-backlash worm, the fault frequency and frequency multiples of the bearing, and the gear meshing frequency and frequency multiples. The kurtosis calculation formula is: where μ(i) is the mean of the vibration signal x(i); σ(i) is the standard deviation of the vibration signal x(i); and E[·] is the expected function.

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