CN113419487A - Automatic fault diagnosis system for numerical control machine tool - Google Patents
Automatic fault diagnosis system for numerical control machine tool Download PDFInfo
- Publication number
- CN113419487A CN113419487A CN202110751755.5A CN202110751755A CN113419487A CN 113419487 A CN113419487 A CN 113419487A CN 202110751755 A CN202110751755 A CN 202110751755A CN 113419487 A CN113419487 A CN 113419487A
- Authority
- CN
- China
- Prior art keywords
- module
- equipment
- value
- fault diagnosis
- preprocessing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
- G05B19/4063—Monitoring general control system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/37—Measurements
- G05B2219/37533—Real time processing of data acquisition, monitoring
Landscapes
- Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Manufacturing & Machinery (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Numerical Control (AREA)
Abstract
The invention discloses an automatic fault diagnosis system for a numerical control machine, belongs to the field of equipment diagnosis, relates to a model establishment technology, and is used for monitoring equipment parameters of the numerical control machine so as to avoid the occurrence of equipment problems and the occurrence of an unknown condition of the numerical control machine. The invention collects the working parameters of the equipment through the parameter acquisition module; the preprocessing module preprocesses the working parameters of the equipment; the model training module trains the preprocessing result of the preprocessing module to obtain a training model; the fault diagnosis module receives the working parameters of the equipment pretreated by the pretreatment module in real time and substitutes the working parameters into the training model to calculate the normal index of the equipment; comparing the normal index of the equipment with the normal index threshold of the equipment to output an early warning signal; the fault alarm module receives the early warning signal and carries out fault alarm so as to complete automatic fault diagnosis and automatic alarm of the numerical control machine tool; the problem that the numerical control machine tool is incapable of knowing is effectively avoided.
Description
Technical Field
The invention belongs to the field of equipment diagnosis, relates to a model building technology, and particularly relates to an automatic fault diagnosis system for a numerical control machine tool.
Background
The numerical control machine tool is a digital control machine tool for short, and is an automatic machine tool provided with a program control system. The control system is capable of logically processing and decoding a program having control codes or other symbolic instructions, representing the program in coded digital form, and inputting the coded digital form into the numerical control device through the information carrier. After operation, the numerical control device sends out various control signals to control the action of the machine tool, and the parts are automatically machined according to the shape and the size required by the drawing.
Because the machining requirements on the numerical control machine are strict, most factories place strict emphasis on parts machined by the numerical control machine to control the parts, and equipment parameters of the numerical control machine are not monitored, so that the numerical control machine has equipment problems and is not self-aware in many times.
Therefore, the automatic fault diagnosis system for the numerical control machine tool is provided.
Disclosure of Invention
The invention provides an automatic fault diagnosis system for a numerical control machine, which is used for monitoring equipment parameters of the numerical control machine and avoiding the situation that the numerical control machine has equipment problems and is not known by self. The invention collects the working parameters of the equipment through the parameter acquisition module; the preprocessing module preprocesses the working parameters of the equipment; the model training module trains the preprocessing result of the preprocessing module to obtain a training model; the fault diagnosis module receives the working parameters of the equipment pretreated by the pretreatment module in real time and substitutes the working parameters into the training model to calculate the normal index of the equipment; comparing the normal index of the equipment with the normal index threshold of the equipment to output an early warning signal; the fault alarm module receives the early warning signal and carries out fault alarm so as to complete automatic fault diagnosis and autonomous alarm of the numerical control machine; the problem that the numerical control machine tool is incapable of knowing is effectively avoided.
The purpose of the invention can be realized by the following technical scheme:
a fault automatic diagnosis system of a numerical control machine tool comprises a controller, a parameter acquisition module for receiving signals of the controller, a preprocessing module for preprocessing working parameters of equipment, a model training module for establishing a training model, a fault diagnosis module and a fault alarm module;
the controller is used for sending signals to other modules and controlling the other modules to act;
the parameter acquisition module is used for acquiring equipment working parameters of the numerical control machine tool and sending the equipment working parameters to the preprocessing module;
the preprocessing module is used for preprocessing the working parameters of the equipment;
the model training module is used for receiving the preprocessing result of the preprocessing module and training according to the preprocessing result to obtain a training model;
the fault diagnosis module is used for receiving the equipment working parameters preprocessed by the preprocessing module in real time and substituting the equipment working parameters into the training model to calculate the normal index of the equipment;
if the normal index of the equipment for three consecutive times is lower than the normal index threshold of the equipment for one time, a primary early warning signal is sent to a fault warning module; if the normal index of the equipment for three times is not lower than the normal index threshold of the equipment for two times, a secondary early warning signal is sent to a fault warning module;
and the fault alarm module is used for receiving the early warning signal and carrying out fault alarm so as to complete automatic fault diagnosis and autonomous alarm of the numerical control machine tool.
Further, the equipment working parameters comprise vibration frequency, current value, pressure value, flow value and temperature value.
Further, the other modules comprise a parameter acquisition module, a preprocessing module, a model training module, a fault diagnosis module and a fault alarm module.
Furthermore, the parameter acquisition module adopts a periodic sampling mode with the sampling duration of T and the sampling interval of T when acquiring the working parameters of the equipment.
Further, the process of preprocessing the device operating parameters by the preprocessing module includes:
the preprocessing module marks the vibration frequency, the current value, the pressure value, the flow value and the temperature value as Pti, Iti, Yti, Lti and Wti respectively; i represents the collection times in the period t, i is 1, …, n;
the preprocessing module preprocesses the vibration frequency, performs descending order arrangement on the vibration frequency Pti, selects the maximum value, namely the vibration frequency peak value as the vibration frequency value in the sampling duration t, and marks the vibration frequency value as Ptf;
the preprocessing module preprocesses the current value, sums the current value Iti and takes the average value as a temperature value It in the sampling duration t;
the preprocessing module preprocesses the pressure values, carries out descending order arrangement on the pressure values Yti, and selects a maximum value Ytmax and a minimum value Ytmin; calculating an average value Yt of the maximum value Ytmax and the minimum value Ytmin as (Ytmax + Ytmin)/2 to be used as a pressure value Yt in the sampling time length t;
the preprocessing module preprocesses the flow value and sums the flow value Lti to be used as a current value Lt within the sampling time t;
the preprocessing module preprocesses the temperature value, sums the temperature values Wti and takes the average value as the temperature value Wt in the sampling time length t.
Further, the process of training the model by the model training module comprises:
the preprocessing module sends the processed vibration frequency, current value, pressure value, flow value and temperature value to the model training module, the model training module sets the equipment normal index Szc, and the model training module trains the equipment parameter information to obtain a training model.
Furthermore, the parameter acquisition module is also used for acquiring the working parameters of the equipment in real time and also acquiring a periodic sampling mode.
Further, the fault diagnosis module is used for carrying out fault diagnosis on the numerical control machine tool, and comprises the following steps:
obtaining the vibration frequency, the current value, the pressure value, the flow value and the temperature value which are processed by the preprocessing module; the vibration frequency, the current value, the pressure value, the flow value and the temperature value are sent to a fault diagnosis module;
the fault diagnosis module marks the vibration frequency, the current value, the pressure value, the flow value and the temperature value as Ptj, Itj, Ytj, Ltj, Wtj, j representing the number of times of acquisition performed according to the sampling interval T; j is 1, …, m;
the fault diagnosis module acquires a training model from the data storage module, substitutes Ptj, Itj, Ytj, Ltj and Wtj into the training model to calculate the equipment normal index Szcj:
setting a normal index threshold value of the equipment, if the normal index Szcj of the equipment for three times continuously exceeds the normal index threshold value of the equipment, indicating that the equipment is normal, and if the normal index Szcj of the equipment for three times continuously exists and is lower than the normal index threshold value of the equipment for one time, sending a primary early warning signal to a fault warning module; and if the normal index Szcj of the equipment for three times exceeds the normal index threshold value of the equipment for two times, sending a secondary early warning signal to the fault warning module.
Further, the diagnosis method of the automatic fault diagnosis system of the numerical control machine tool comprises the following steps:
the parameter acquisition module acquires equipment working parameters;
the preprocessing module preprocesses the working parameters of the equipment;
the model training module trains the preprocessing result of the preprocessing module to obtain a training model;
the fault diagnosis module receives the working parameters of the equipment pretreated by the pretreatment module in real time and substitutes the working parameters into the training model to calculate the normal index of the equipment;
comparing the normal index of the equipment with the normal index threshold of the equipment to output an early warning signal;
and the fault alarm module receives the early warning signal and carries out fault alarm so as to finish automatic fault diagnosis and automatic alarm of the numerical control machine tool.
Compared with the prior art, the invention has the beneficial effects that:
1. the controller of the invention sets a sampling time length T and a sampling interval T, and sends a data acquisition signal to a parameter acquisition module; the parameter acquisition module carries out equipment working parameters according to the sampling duration T and the sampling interval T of the controller; the working parameters of the equipment are sampled periodically, so that repeated collection of a large amount of data and material and manpower waste are avoided, and data calculation can be reduced by the aid of the intermittent sampling data.
2. The preprocessing module respectively adopts different processing modes for the vibration frequency, the current value, the pressure value, the flow value and the temperature value, so that the real data condition can be more accurately reflected, the equipment problem of the numerical control machine can be effectively reflected, and a data basis is provided for the automatic diagnosis of the numerical control machine in the later period through the modeling mode.
3. Setting a normal index threshold value of equipment, if the normal index Szcj of the equipment for three times continuously exceeds the normal index threshold value of the equipment, indicating that the equipment is normal, and if the normal index Szcj of the equipment for three times continuously is lower than the normal index threshold value of the equipment for one time, sending a primary early warning signal to a fault warning module; if the normal index Szcj of the equipment for three times exceeds the normal index threshold value of the equipment for two times, a secondary early warning signal is sent to the fault warning module, different early warning modes are adopted, misjudgment is avoided firstly, and different maintenance service personnel can be dispatched according to different warning levels secondly.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a diagnostic flowchart of an automatic fault diagnosis system for a numerically-controlled machine tool according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With the continuous improvement of the requirements on the machining precision of mechanical parts, the machining strength is continuously increased, fatigue phenomena are more easily generated on machine tool components, and the occurrence frequency of machine tool faults is improved. Once the fault occurs, the parts can be scrapped, the production can be stopped, and economic losses of enterprises and the like can be caused. Real-time diagnosis of machine tool faults has become an important requirement in machine tool applications.
Many universities and research institutes in China are engaged in remote diagnosis of equipment failures, such as college of unity, Shanghai transportation university, and the like. The CIMS research center of Tongji university establishes a remote fault maintenance system based on multimedia remote service in Shanghai public automobile limited company; the BMEI-AMS-1 advanced manufacturing system designed by Beijing electromechanical research is easy to be connected with the Internet, and can realize remote diagnosis, remote service and real-time monitoring. In addition, research on FMS remote fault diagnosis based on the Internet is also conducted by beijing university of science and technology and south kyo university of aerospace.
At present, the fault diagnosis of the machine tool mostly adopts a method of carrying out wired networking by adding a PC machine through an equipment layer, and has the defects of complex wiring, interference on the movement of machine tool components, interference on the operation of the machine tool, signal loss caused by electromagnetic interference, increased equipment investment and the like. Based on the development of the Internet of things, big data and cloud platform technologies, the intelligent degree of remote fault diagnosis of the machine tool can be improved by combining the fault diagnosis technology with the fault diagnosis technology. Common internet of things wireless communication technologies mainly include bluetooth, ZigBee, narrowband internet of things (NB-IoT) technologies, and the like, and are widely applied to smart city construction.
As shown in fig. 1, the automatic diagnosis process of the automatic diagnosis system for failure of a numerical control machine may include the steps of:
the method comprises the following steps: the controller sets a sampling duration T and a sampling interval T, and sends a data acquisition signal to the parameter acquisition module; the parameter acquisition module carries out equipment working parameters according to the sampling duration T and the sampling interval T of the controller;
step two: the parameter acquisition module acquires the vibration frequency, the current value, the pressure value, the flow value and the temperature value of the numerical control machine tool and sends the vibration frequency, the current value, the pressure value, the flow value and the temperature value acquired by the parameter acquisition module to the preprocessing module; the preprocessing module preprocesses the working parameters of the equipment;
step three: the preprocessing module sends the processed vibration frequency, current value, pressure value, flow value and temperature value to a model training module, the model training module sets an equipment normal index Szc, and the model training module trains equipment working parameters to obtain a training model;
step four: obtaining the vibration frequency, the current value, the pressure value, the flow value and the temperature value which are processed by the preprocessing module; the vibration frequency, the current value, the pressure value, the flow value and the temperature value are sent to a fault diagnosis module; the fault diagnosis module marks the vibration frequency, the current value, the pressure value, the flow value and the temperature value as Ptj, Itj, Ytj, Ltj and Wtj, j represents the collection times executed according to the sampling interval T; j is 1, …, m;
step five: the fault diagnosis module acquires a training model from the data storage module, substitutes Ptj, Itj, Ytj, Ltj and Wtj into the training model to calculate the equipment normal index Szcj, and the calculation formula is as follows:
step six: setting a normal index threshold value of the equipment, if the normal index Szcj of the equipment for three times continuously exceeds the normal index threshold value of the equipment, indicating that the equipment is normal, and if the normal index Szcj of the equipment for three times continuously is lower than the normal index threshold value of the equipment for one time, sending a primary early warning signal to a fault warning module; and if the normal index Szcj of the equipment for three times is lower than the normal index threshold value of the equipment for two times, sending a secondary early warning signal to the fault warning module.
Preferably, the parameter acquisition module is used for acquiring the working parameters of the equipment of the numerical control machine tool and sending the acquired working parameters of the equipment to the preprocessing module, the preprocessing module preprocesses the working parameters of the equipment, and the preprocessing process comprises the following steps:
the controller sets a sampling duration T and a sampling interval T, and sends a data acquisition signal to the parameter acquisition module;
the parameter acquisition module carries out equipment working parameters according to the sampling duration T and the sampling interval T of the controller;
the parameter acquisition module acquires the vibration frequency, the current value, the pressure value, the flow value and the temperature value of the numerical control machine tool and sends the vibration frequency, the current value, the pressure value, the flow value and the temperature value acquired by the parameter acquisition module to the preprocessing module;
the preprocessing module marks the vibration frequency, the current value, the pressure value, the flow value and the temperature value as Pti, Iti, Yti, Lti and Wti respectively; i represents the collection times in the period t, i is 1, …, n;
the preprocessing module preprocesses the vibration frequency, performs descending order arrangement on the vibration frequency Pti, selects the maximum value, namely the vibration frequency peak value as the vibration frequency value in the sampling duration t, and marks the vibration frequency value as Ptf;
the preprocessing module preprocesses the current value, sums the current value Iti and takes the average value as a temperature value in the sampling duration t;
the preprocessing module preprocesses the pressure values, carries out descending order arrangement on the pressure values Yti, and selects a maximum value Ytmax and a minimum value Ytmin; calculating an average value Yt of the maximum value Ytmax and the minimum value Ytmin as (Ytmax + Ytmin)/2 to be used as a pressure value Yt in the sampling time length t;
the preprocessing module preprocesses the flow value and sums the flow value Lti to be used as a current value Lt within the sampling time t;
the preprocessing module preprocesses the temperature value, sums the temperature values Wti and takes the average value as the temperature value Wt in the sampling time length t.
Preferably, the preprocessing module sends the processed vibration frequency, current value, pressure value, flow value and temperature value to the model training module, the model training module sets the equipment normal index Szc, and the model training module trains the equipment working parameters to obtain a training model;
and sending the training model to a data storage module, wherein the training model is a preset coefficient value, and the preset coefficient value is calculated by substituting the value for multiple times.
Preferably, the fault diagnosis module is used for performing fault diagnosis on the numerical control machine tool, and the specific diagnosis mode includes the following steps:
obtaining the vibration frequency, the current value, the pressure value, the flow value and the temperature value which are processed by the preprocessing module; the vibration frequency, the current value, the pressure value, the flow value and the temperature value are sent to a fault diagnosis module;
the fault diagnosis module marks the vibration frequency, the current value, the pressure value, the flow value and the temperature value as Ptj, Itj, Ytj, Ltj, Wtj, j representing the number of times of acquisition performed according to the sampling interval T; j is 1, …, m;
the fault diagnosis module acquires a training model from the data storage module, substitutes Ptj, Itj, Ytj, Ltj and Wtj into the training model to calculate the equipment normal index Szcj, and the calculation formula is as follows:
setting a normal index threshold value of the equipment, if the normal index Szcj of the equipment for three times continuously exceeds the normal index threshold value of the equipment, indicating that the equipment is normal, and if the normal index Szcj of the equipment for three times continuously exists and is lower than the normal index threshold value of the equipment for one time, sending a primary early warning signal to a fault warning module; and if the normal index Szcj of the equipment for three times exceeds the normal index threshold value of the equipment for two times, sending a secondary early warning signal to the fault warning module.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows: the invention collects the working parameters of the equipment through the parameter acquisition module; the preprocessing module preprocesses the working parameters of the equipment; the model training module trains the preprocessing result of the preprocessing module to obtain a training model; the fault diagnosis module receives the working parameters of the equipment pretreated by the pretreatment module in real time and substitutes the working parameters into the training model to calculate the normal index of the equipment; comparing the normal index of the equipment with the normal index threshold of the equipment to output an early warning signal; the fault alarm module receives the early warning signal and carries out fault alarm so as to complete automatic fault diagnosis and automatic alarm of the numerical control machine tool; the problem that the numerical control machine tool is incapable of knowing is effectively avoided.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and there may be other divisions when the actual implementation is performed; the modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of the embodiment.
It is further evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above examples are only intended to illustrate the technical process of the present invention and not to limit the same, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical process of the present invention without departing from the present invention.
Claims (9)
1. The automatic fault diagnosis system of the numerical control machine tool is characterized by comprising a controller, a parameter acquisition module for receiving signals of the controller, a preprocessing module for preprocessing working parameters of equipment, a model training module for establishing a training model, a fault diagnosis module and a fault alarm module;
the controller is used for sending signals to other modules and controlling the other modules to act;
the parameter acquisition module is used for acquiring equipment working parameters of the numerical control machine tool and sending the equipment working parameters to the preprocessing module;
the preprocessing module is used for preprocessing the working parameters of the equipment;
the model training module is used for receiving the preprocessing result of the preprocessing module and training according to the preprocessing result to obtain a training model;
the fault diagnosis module is used for receiving the working parameters of the equipment preprocessed by the preprocessing module in real time and substituting the working parameters into the training model to calculate the normal index of the equipment;
if the normal index of the equipment for three consecutive times is lower than the normal index threshold of the equipment for one time, a primary early warning signal is sent to a fault warning module; if the normal index of the equipment which is continuously three times is not lower than the normal index threshold of the equipment twice, a secondary early warning signal is sent to the fault warning module;
and the fault alarm module is used for receiving the early warning signal and carrying out fault alarm so as to finish automatic fault diagnosis and automatic alarm of the numerical control machine tool.
2. The automatic fault diagnosis system for the numerical control machine tool as claimed in claim 1, wherein the equipment operating parameters comprise vibration frequency, current value, pressure value, flow value and temperature value.
3. The system of claim 1, wherein the other modules include a parameter acquisition module, a preprocessing module, a model training module, a fault diagnosis module, and a fault alarm module.
4. The automatic fault diagnosis system for the numerical control machine tool according to claim 1, wherein a periodic sampling mode with a sampling duration of T and a sampling interval of T is adopted when the parameter acquisition module acquires the working parameters of the equipment.
5. The system of claim 1, wherein the preprocessing module preprocesses the operating parameters of the machine tool by:
the preprocessing module marks the vibration frequency, the current value, the pressure value, the flow value and the temperature value as Pti, Iti, Yti, Lti and Wti respectively; i represents the collection times in the period t, i is 1, …, n;
the preprocessing module preprocesses the vibration frequency, performs descending order arrangement on the vibration frequency Pti, selects the maximum value, namely the vibration frequency peak value as the vibration frequency value in the sampling duration t, and marks the vibration frequency value as Ptf;
the preprocessing module preprocesses the current value, sums the current value Iti and takes the average value as the temperature value in the sampling time length t
The preprocessing module preprocesses the pressure values, carries out descending order arrangement on the pressure values Yti, and selects a maximum value Ytmax and a minimum value Ytmin; calculating an average value Yt of the maximum value Ytmax and the minimum value Ytmin as (Ytmax + Ytmin)/2 to be used as a pressure value Yt in the sampling time length t;
the preprocessing module preprocesses the flow value and sums the flow value Lti to be used as a current value Lt in the sampling time length t;
the preprocessing module preprocesses the temperature value, sums and averages the temperature value WtiTaking the mean value as the temperature value in the sampling time length t
6. The system of claim 1, wherein the model training module performs a process of training a model comprising:
the preprocessing module sends the processed vibration frequency, current value, pressure value, flow value and temperature value to the model training module, the model training module sets the equipment normal index Szc, and the model training module trains the equipment parameter information to obtain a training model.
7. The system according to claim 1, wherein the parameter acquisition module is further configured to acquire the working parameters of the device in real time, and also adopts a periodic sampling manner.
8. The automatic fault diagnosis system for the numerical control machine tool is characterized in that the fault diagnosis module is used for carrying out fault diagnosis on the numerical control machine tool and comprises the following steps:
obtaining the vibration frequency, the current value, the pressure value, the flow value and the temperature value which are processed by the preprocessing module; the vibration frequency, the current value, the pressure value, the flow value and the temperature value are sent to a fault diagnosis module;
the fault diagnosis module marks the vibration frequency, the current value, the pressure value, the flow value and the temperature value as Ptj, Itj, Ytj, Ltj and Wtj, j represents the collection times executed according to the sampling interval T; j is 1, …, m;
the fault diagnosis module acquires a training model from the data storage module, substitutes Ptj, Itj, Ytj, Ltj and Wtj into the training model to calculate the equipment normal index Szcj:
setting a normal index threshold value of the equipment, if the normal index Szcj of the equipment for three times continuously exceeds the normal index threshold value of the equipment, indicating that the equipment is normal, and if the normal index Szcj of the equipment for three times continuously is lower than the normal index threshold value of the equipment for one time, sending a primary early warning signal to a fault alarm module; and if the normal index Szcj of the equipment for three times exceeds the normal index threshold value of the equipment for two times, sending a secondary early warning signal to the fault warning module.
9. The method for diagnosing the automatic fault diagnosis system of the numerical control machine tool according to claim 1, wherein the method for diagnosing comprises the following steps:
the parameter acquisition module acquires equipment working parameters;
the preprocessing module preprocesses the working parameters of the equipment;
the model training module trains the preprocessing result of the preprocessing module to obtain a training model;
the fault diagnosis module receives the working parameters of the equipment pretreated by the pretreatment module in real time and substitutes the working parameters into the training model to calculate the normal index of the equipment;
comparing the normal index of the equipment with the normal index threshold of the equipment to output an early warning signal;
and the fault alarm module receives the early warning signal and carries out fault alarm so as to finish automatic fault diagnosis and automatic alarm of the numerical control machine tool.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110751755.5A CN113419487A (en) | 2021-07-02 | 2021-07-02 | Automatic fault diagnosis system for numerical control machine tool |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110751755.5A CN113419487A (en) | 2021-07-02 | 2021-07-02 | Automatic fault diagnosis system for numerical control machine tool |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113419487A true CN113419487A (en) | 2021-09-21 |
Family
ID=77720187
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110751755.5A Pending CN113419487A (en) | 2021-07-02 | 2021-07-02 | Automatic fault diagnosis system for numerical control machine tool |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113419487A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115122155A (en) * | 2022-08-31 | 2022-09-30 | 深圳市玄羽科技有限公司 | Machine tool remote diagnosis method and system based on industrial internet big data |
| WO2023173692A1 (en) * | 2022-03-17 | 2023-09-21 | 北京华卓精科科技股份有限公司 | Grading control method for workpiece table, system, device, and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110543142A (en) * | 2019-08-22 | 2019-12-06 | 电子科技大学 | Fault diagnosis and early warning system of numerical control machine tool |
| CN112612247A (en) * | 2020-12-05 | 2021-04-06 | 深圳市云海互联技术有限公司 | Method for diagnosing numerical control machine tool fault by computer simulation analysis software |
-
2021
- 2021-07-02 CN CN202110751755.5A patent/CN113419487A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110543142A (en) * | 2019-08-22 | 2019-12-06 | 电子科技大学 | Fault diagnosis and early warning system of numerical control machine tool |
| CN112612247A (en) * | 2020-12-05 | 2021-04-06 | 深圳市云海互联技术有限公司 | Method for diagnosing numerical control machine tool fault by computer simulation analysis software |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023173692A1 (en) * | 2022-03-17 | 2023-09-21 | 北京华卓精科科技股份有限公司 | Grading control method for workpiece table, system, device, and storage medium |
| CN115122155A (en) * | 2022-08-31 | 2022-09-30 | 深圳市玄羽科技有限公司 | Machine tool remote diagnosis method and system based on industrial internet big data |
| CN115122155B (en) * | 2022-08-31 | 2022-11-22 | 深圳市玄羽科技有限公司 | Machine tool remote diagnosis method and system based on industrial internet big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113419487A (en) | Automatic fault diagnosis system for numerical control machine tool | |
| CN110362037A (en) | A kind of integrated maintenance system platform for numerically-controlled machine tool | |
| CN116189407B (en) | Intelligent early warning system based on data monitoring | |
| CN103034207A (en) | Infrastructure health monitoring system and implementation process thereof | |
| CN114510979A (en) | Underwater equipment inspection system and method based on edge calculation | |
| CN115929566A (en) | Online state monitoring and early warning system and method for variable pitch speed reducer of wind turbine generator | |
| CN111894798B (en) | Method and system for determining running state of fan | |
| CN117092953A (en) | Production data acquisition management and control system based on industrial Internet of things | |
| CN116345687A (en) | User behavior safety early warning system of power monitoring system | |
| CN112612247B (en) | Method for diagnosing numerical control machine tool fault by computer simulation analysis software | |
| CN114320773B (en) | Wind turbine generator system fault early warning method based on power curve analysis and neural network | |
| CN115757569A (en) | New energy multi-type data allocation method and system based on domestic structure | |
| CN112101799B (en) | Standard state dividing method and device based on wind farm data | |
| CN116957531A (en) | Intelligent rail transit operation and maintenance system and method based on state perception | |
| CN112001561A (en) | Electric power industry risk prediction method and system | |
| CN113485279A (en) | Factory equipment start-stop energy-saving management system based on full-view artificial intelligence | |
| CN112486096A (en) | Machine tool operation state monitoring method | |
| CN106897946A (en) | A kind of monitoring system status information section comparison method | |
| Zhang et al. | Distributed Architecture of Power Grid Asset Management and Future Research Directions | |
| CN111245097A (en) | Intelligent power grid management and control system and method | |
| CN113129160A (en) | Electric power communication network inspection method based on equipment state perception and intellectualization | |
| Fan et al. | An intelligent condition-based monitoring and maintenance system for wind turbine | |
| CN117560300B (en) | Intelligent internet of things flow prediction and optimization system | |
| CN114338745B (en) | Converter station data collection and comprehensive analysis system | |
| CN109725596A (en) | A kind of lathe intelligent remote monitoring system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210921 |
|
| RJ01 | Rejection of invention patent application after publication |