Static pressure turntable full-period fault data acquisition system and method based on edge calculation
Through edge computing technology, the full-cycle acquisition and selective storage of static pressure turntable fault data is solved, and the problems of incomplete fault data and large amount of data are realized, and the predictive maintenance of turntable equipment and the optimization of storage system is realized.
Patent Information
- Application Number
- CN202411255586.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-09
- Publication Date
- 2024-12-27
AI Technical Summary
The existing static pressure turntable fault data acquisition technology has problems such as incomplete fault data and large amount of data, which leads to excessive burden on the storage system and is difficult to achieve predictive maintenance.
The full-cycle fault data acquisition system of static pressure rotary table based on edge computing is adopted. The data acquisition unit monitors the status of the rotary table equipment through the data acquisition unit, the edge computing unit conducts data correlation analysis and selective storage, and the remote storage unit conducts data visualization and classified storage.
It realizes full-cycle collection and selective storage of fault data of turntable equipment, reduces the burden on the storage system, and supports predictive maintenance of turntable equipment.
Smart Images
Figure CN119201951A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of hydrostatic turntable data acquisition systems, and more specifically, to a hydrostatic turntable full-cycle fault data acquisition system and method based on edge computing. Background Art
[0002] The hydrostatic turntable has the advantages of strong load-bearing capacity, low friction coefficient, homogenization error, and high machining accuracy, and has gradually become the core component of precision machining. The turntable equipment will inevitably wear out when it is in operation for a long time, which greatly affects the machining accuracy and running stability of the turntable equipment. By continuously monitoring and recording the operating data and fault information of the turntable equipment, potential problems and trends can be discovered in advance and preventive maintenance can be performed. This is conducive to reducing the occurrence of sudden failures and improving the reliability and stability of the equipment.
[0003] Previous research on fault data collection technology for static pressure turntables has the following shortcomings:
[0004] When collecting fault data of a static pressure turntable, data is collected only when an obvious abnormality occurs on the turntable. This will result in incomplete collection of turntable fault data, severely limiting the comprehensive analysis of the turntable equipment's operating status, making it difficult to implement some predictive maintenance strategies, and having a negative impact on the operation and maintenance of the turntable equipment.
[0005] The operation of turntable products is relatively stable. When a turntable device fails, it can still maintain a relatively stable operating state and continue to work for a period of time. This requires a long period of fault data collection to determine the change trend of the turntable device operating parameters under a specific fault. A large amount of fault data will be generated during the collection process, and the storage system needs to have sufficient storage capacity to save this data, which greatly increases the cost of the storage system. Summary of the invention
[0006] The present invention provides a static pressure turntable full-cycle fault data collection system and method based on edge computing to solve the problems of incomplete turntable equipment fault data collection and large data volume.
[0007] To achieve the above object, the present invention provides the following technical solutions:
[0008] A static pressure turntable full-cycle fault data acquisition system based on edge computing, comprising: a data acquisition unit, an edge computing unit and a remote storage unit;
[0009] The data acquisition unit monitors the status of key components inside the turntable equipment through various sensors, and transmits the collected operation information to the edge computing unit through a wireless transmission module;
[0010] The edge computing unit performs correlation analysis on the received operation data, determines the correlation between the current operation information and the historical operation information, and selectively stores the data;
[0011] The remote storage unit includes a data visualization platform and a data storage module; the data visualization platform is used to display the operating data of each turntable device in real time; the data storage module stores the turntable device operating data transmitted by the edge computing unit and provides a download function.
[0012] A method for collecting full-cycle fault data of a static pressure turntable based on edge computing is performed using the static pressure turntable full-cycle fault data collection system based on edge computing, and specifically includes the following steps:
[0013] S1: Install the data acquisition unit near the monitored turntable device; the data acquisition unit obtains real-time operation data of the turntable, including but not limited to vibration signals, temperature signals, current signals, displacement signals, and pressure signals.
[0014] The three-axis vibration acceleration sensor is installed on the driving worm and the anti-backlash worm support seat to obtain the vibration signal of the transmission parts; the temperature sensor is installed inside the turntable oil outlet to obtain the temperature data of the oil in the oil chamber; the current sensor measures the current of the driving motor; the displacement sensor is installed on the turntable table to obtain the floating amount data of the turntable; the pressure sensor is installed at the oil inlet of the oil chamber to obtain the real-time pressure data of the oil chamber.
[0015] The real-time operation data of the turntable is recorded as the first information, and the historical operation data of the turntable is recorded as the second information.
[0016] S2: The edge computing unit directly transmits the low-speed acquisition signal in the first information to the remote storage unit; uses the high-speed acquisition signal in the first information to perform feature conversion through a preset independent variable feature conversion model to obtain a feature vector of the first information; uses the second information to perform feature extraction through a preset dependent variable feature extraction model to obtain historical operation feature information of the turntable device; performs correlation analysis on the feature vector of the first information and the historical operation feature information of the turntable device to determine the correlation value between the feature vector of the first information and the historical operation feature information of the turntable device;
[0017] If the correlation value is greater than or equal to the set threshold, the edge computing unit does not send the current operating data of the turntable device to the remote storage unit and does not update the second information; otherwise, the data is transmitted to the remote storage unit and the current first information is updated as the second information.
[0018] S3: The data visualization platform in the remote storage unit displays the operating data of different turntable devices in real time; the data storage module classifies and stores the operating data from the edge computing and provides a download function.
[0019] In S1, the various equipment operation signals collected by the data collection unit include high-speed collection signals and low-speed collection signals. The high-speed collection signals include vibration, noise and other signals; the low-speed collection signals include pressure, temperature, floating amount and other signals.
[0020] In S2, the edge computing unit performs feature conversion on the first information, and the specific steps are: using the Markov transfer field to convert the collected one-dimensional data into a two-dimensional image, and then using image convolution to extract the feature vector; the edge computing unit performs feature extraction on the second information, and the specific steps are the same as the above steps.
[0021] In S2, the first information and the second information are subjected to correlation analysis, and the analysis formula is:
[0022]
[0023] Among them, γ f (k) is the relationship coefficient of the characteristic vector of the first information of the turntable device relative to the second information; f is the characteristic vector of the first information; k is the characteristic vector of the second characteristic; y(k) is the time information of obtaining the first information corresponding to the characteristic vector; x f (k) is the second information acquisition time information of the turntable device; ρ is the resolution coefficient, and its value range is [0,1].
[0024] In S2, the set threshold value for correlation value comparison is set by turntable equipment research experts based on an empirical formula.
[0025] Compared with the prior art, the present invention has the following beneficial effects:
[0026] The present invention realizes multi-data collection of turntable equipment through a data acquisition unit. Multiple types of sensors obtain diverse and complementary perception information, can monitor the operating status of the turntable equipment, and realize full-cycle collection of turntable equipment fault data, which is of great significance to the operation and maintenance of the turntable equipment.
[0027] The present invention analyzes the correlation between current data and historical data through edge computing, which can realize selective storage of data, solve the problem of stable operation of turntable equipment and unnoticeable initial failure, and effectively alleviate the burden of the storage system. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Figure 1 This is the physical architecture of the static pressure turntable full-cycle fault data acquisition system based on edge computing described in the present invention. DETAILED DESCRIPTION
[0029] 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.
[0030] 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.
[0031] 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.
[0032] Embodiment 1
[0033] A full-cycle fault data acquisition system for static pressure turntable based on edge computing, such as Figure 1 As shown, by establishing a connection between a data acquisition unit, an edge computing unit, and a remote storage unit, data acquisition, transmission, analysis, and selective storage are achieved to reduce the pressure on the storage system. The specific implementation plan is as follows:
[0034] Embodiment 2
[0035] A method for collecting full-cycle fault data of a hydrostatic turntable based on edge computing is performed using the full-cycle fault data collection system of a hydrostatic turntable based on edge computing, and specifically includes the following steps: S1: installing the data collection unit near the monitored turntable equipment; the data collection unit obtains real-time operation data of the turntable, including but not limited to vibration signals, temperature signals, current signals, displacement signals, and pressure signals.
[0036] The three-axis vibration acceleration sensor is installed on the driving worm and the anti-backlash worm support seat to obtain the vibration signal of the transmission parts; the temperature sensor is installed inside the turntable oil outlet to obtain the temperature data of the oil in the oil chamber; the current sensor measures the current of the driving motor; the displacement sensor is installed on the turntable table to obtain the floating amount data of the turntable; the pressure sensor is installed at the oil inlet of the oil chamber to obtain the real-time pressure data of the oil chamber.
[0037] The real-time operation data of the turntable is recorded as the first information, and the historical operation data of the turntable is recorded as the second information.
[0038] S2: The edge computing unit directly transmits the low-speed acquisition signal in the first information to the remote storage unit; uses the high-speed acquisition signal in the first information to perform feature conversion through a preset independent variable feature conversion model to obtain a feature vector of the first information; uses the second information to perform feature extraction through a preset dependent variable feature extraction model to obtain historical operation feature information of the turntable device; performs correlation analysis on the feature vector of the first information and the historical operation feature information of the turntable device to determine the correlation value between the feature vector of the first information and the historical operation feature information of the turntable device;
[0039] If the correlation value is greater than or equal to the set threshold, the edge computing unit does not send the current operating data of the turntable device to the remote storage unit and does not update the second information; otherwise, the data is transmitted to the remote storage unit and the current first information is updated as the second information.
[0040] S3: The data visualization platform in the remote storage unit displays the operating data of different turntable devices in real time; the data storage module classifies and stores the operating data from the edge computing and provides a download function.
[0041] In S1, the various equipment operation signals collected by the data collection unit include high-speed collection signals and low-speed collection signals. The high-speed collection signals include vibration, noise and other signals; the low-speed collection signals include pressure, temperature, floating amount and other signals.
[0042] In S2, the edge computing unit performs feature conversion on the first information, and the specific steps are: using the Markov transfer field to convert the collected one-dimensional data into a two-dimensional image, and then using image convolution to extract the feature vector; the edge computing unit performs feature extraction on the second information, and the specific steps are the same as the above steps.
[0043] In S2, the first information and the second information are subjected to correlation analysis, and the analysis formula is as follows:
[0044]
[0045] Among them, γ f (k) is the relationship coefficient of the characteristic vector of the first information of the turntable device relative to the second information; f is the characteristic vector of the first information; k is the characteristic vector of the second characteristic; y(k) is the time information of obtaining the first information corresponding to the characteristic vector; x f (k) is the second information acquisition time information of the turntable device; ρ is the resolution coefficient, and its value range is [0,1].
[0046] In S2, the set threshold value for correlation value comparison is set by turntable equipment research experts based on an empirical formula.
[0047] 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 full-cycle fault data acquisition system for a static pressure turntable based on edge computing, characterized in that include: Data acquisition unit, edge computing unit and remote storage unit; The data acquisition unit monitors the status of key components inside the turntable equipment through various sensors, and transmits the collected operation information to the edge computing unit through a wireless transmission module; The edge computing unit performs correlation analysis on the received operation data, determines the correlation between the current operation information and the historical operation information, and selectively stores the data; The remote storage unit includes a data visualization platform and a data storage module; the data visualization platform is used to display the operating data of each turntable device in real time; the data storage module stores the turntable device operating data transmitted by the edge computing unit and provides a download function.
2. A method for collecting full-cycle fault data of a static pressure turntable based on edge computing, characterized in that: The method of using the static pressure turntable full-cycle fault data acquisition system based on edge computing as claimed in claim 1 specifically includes the following steps: S1: installing the data acquisition unit near the monitored turntable device; the data acquisition unit acquires the real-time operation data of the turntable, including but not limited to vibration signals, temperature signals, current signals, displacement signals, and pressure signals; The three-axis vibration acceleration sensor is installed on the driving worm and the anti-backlash worm support seat to obtain the vibration signal of the transmission parts; the temperature sensor is installed inside the turntable oil outlet to obtain the temperature data of the oil in the oil chamber; the current sensor measures the current of the driving motor; the displacement sensor is installed on the turntable table to obtain the floating amount data of the turntable; the pressure sensor is installed at the oil inlet of the oil chamber to obtain the real-time pressure data of the oil chamber; Recording the real-time operation data of the turntable as the first information and the historical operation data of the turntable as the second information; S2: The edge computing unit directly transmits the low-speed acquisition signal in the first information to the remote storage unit; uses the high-speed acquisition signal in the first information to perform feature conversion through a preset independent variable feature conversion model to obtain a feature vector of the first information; uses the second information to perform feature extraction through a preset dependent variable feature extraction model to obtain historical operation feature information of the turntable device; performs correlation analysis on the feature vector of the first information and the historical operation feature information of the turntable device to determine the correlation value between the feature vector of the first information and the historical operation feature information of the turntable device; If the correlation value is greater than or equal to the set threshold, the edge computing unit does not send the current operation data of the turntable device to the remote storage unit and does not update the second information; otherwise, the data is transmitted to the remote storage unit and the current first information is updated as the second information; S3: The data visualization platform in the remote storage unit displays the operating data of different turntable devices in real time; the data storage module classifies and stores the operating data from the edge computing and provides a download function.
3. According to the edge computing-based static pressure turntable full-cycle fault data acquisition method of claim 2, it is characterized by: In S1, the various equipment operation signals collected by the data collection unit include high-speed collection signals and low-speed collection signals; the high-speed collection signals include vibration, noise and other signals; the low-speed collection signals include pressure, temperature, floating amount and other signals.
4. According to the edge computing-based static pressure turntable full-cycle fault data acquisition method of claim 2, it is characterized by: In S2, the edge computing unit performs feature conversion on the first information, and the specific steps are: using the Markov transfer field to convert the collected one-dimensional data into a two-dimensional image, and then using image convolution to extract the feature vector; the edge computing unit performs feature extraction on the second information, and the specific steps are the same as the above steps.
5. According to the edge computing-based static pressure turntable full-cycle fault data acquisition method of claim 2, it is characterized by: In S2, the first information and the second information are subjected to correlation analysis, and the analysis formula is: Among them, γ f (k) is the relationship coefficient of the characteristic vector of the first information of the turntable device relative to the second information; f is the characteristic vector of the first information; k is the characteristic vector of the second characteristic; y(k) is the time information of obtaining the first information corresponding to the characteristic vector; x f (k) is the second information acquisition time information of the turntable device; ρ is the resolution coefficient, and its value range is [0,1].
6. The method for collecting full-cycle fault data of a static pressure turntable based on edge computing according to claim 2 is characterized in that: In S2, the set threshold value for correlation value comparison is set by turntable equipment research experts based on an empirical formula.