CN114446098A - Predictive maintenance teaching method and device - Google Patents
Predictive maintenance teaching method and device Download PDFInfo
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- CN114446098A CN114446098A CN202111568345.3A CN202111568345A CN114446098A CN 114446098 A CN114446098 A CN 114446098A CN 202111568345 A CN202111568345 A CN 202111568345A CN 114446098 A CN114446098 A CN 114446098A
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/02—Electrically-operated educational appliances with visual presentation of the material to be studied, e.g. using film strip
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B9/00—Simulators for teaching or training purposes
Abstract
The invention discloses a predictive maintenance teaching method and a device thereof, wherein the method comprises the following steps: starting a teaching device, rotating teaching courses, wiring teaching, teaching course teaching, monitoring teaching operation, feeding back teaching operation, displaying experimental principles and finishing teaching; the teaching device comprises a hardware device and a software system which are matched with each other; the hardware device can realize data acquisition, data transmission and signal simulation, and the software system can realize signal processing. According to the predictive maintenance teaching method and device provided by the invention, the position and severity of the fault can be accurately positioned by acquiring and processing the equipment state data, a predictive maintenance teaching device meeting national standards is provided for the students, scientific research personnel, engineering personnel and the like to carry out predictive maintenance practical training, predictive maintenance flow teaching and the like, and the teaching is carried out by a corresponding teaching method.
Description
Technical Field
The invention relates to the technical field of industrial predictive maintenance, in particular to a predictive maintenance teaching method and a device thereof.
Background
Predictive maintenance is an important technical basis for the transition and upgrade of the traditional manufacturing industry, and is based on state maintenance, which determines the state of equipment by performing periodic (or continuous) state monitoring on system components, predicts the future development trend of the equipment state, and makes a predictive maintenance plan in advance according to the state development trend and possible failure modes of the equipment. At present, with the development and progress of predictive maintenance technology, certain attention and application are paid to various industries.
GB/T40571 general requirement for intelligent service predictive maintenance national standard standardizes the standardized predictive maintenance flow and the functional model.
However, concepts of industries, enterprises and public institutions and the like for predictive maintenance implementation are not unified, actual operability of personnel for technology is not enough, and predictive maintenance implementation capability of personnel needs to be improved, so that a relevant teaching device and a teaching method need to be researched and developed to be matched with national standards for use, and normalized progress and scientization of the predictive maintenance technology are effectively promoted.
Disclosure of Invention
The invention aims to provide a predictive maintenance teaching method and a device thereof. The invention has the advantage of being capable of training the professional skills of the personnel based on the national standard.
The technical scheme of the invention is as follows: a predictive maintenance teaching device teaching method comprising the steps of:
A. starting a teaching device: after the teaching device is connected, turning on a power supply and starting a teaching program;
B. selecting teaching courses: selecting a required teaching course from a software system of a teaching device;
C. and (3) wiring teaching: judging whether wiring is needed according to the teaching course; if yes, conducting wiring teaching; if not, skipping;
D. course teaching: providing corresponding course teaching according to the teaching course: the course teaching is based on a mode comprising characters, pictures and videos;
E. monitoring teaching operation: monitoring the operation of personnel in the teaching process in real time;
F. teaching operation feedback: performing real-time feedback according to the operation monitored in the step E;
G. and (3) experimental principle display: displaying corresponding experimental principles in the teaching process and at the end of teaching;
H. finishing the teaching: and sequentially closing the power supplies of the software system and the teaching device and removing the connection.
In the method for teaching a predictive maintenance teaching device, the teaching device in step a starts a specific process as follows:
a1, preparation of teaching device hardware: the PC and the sensor are respectively connected with the corresponding PC and the corresponding sensor through a network cable and a sensor cable, and are connected with a 220V AC power supply and started;
a2, network connection: setting a static IP address of the teaching device, opening an application program 'PDM demo.exe' and connecting a network, and displaying a 'connection state' indicator lamp as blue if the network connection is successful.
A predictive maintenance teaching device comprising a hardware device and a software system in cooperation; the hardware device can realize data acquisition, data transmission and signal simulation, and the software system can realize signal processing;
the hardware device at least comprises a box body, a power supply interface, a control switch, a motor, a physical simulation key, a display lamp, a fan and an interface end;
the software system at least comprises a function module system, an environment simulation module system, a sensor module system, an industrial protocol module system, a help module system and a connection module system.
In the predictive maintenance teaching device described above, the fans include a normal fan and a faulty fan;
the interface end comprises an IO input/output interface, a temperature input interface, a strain input interface, a sound vibration input interface and an analog output interface.
Compared with the prior art, the predictive maintenance teaching method and the device thereof can accurately position the fault occurrence position and severity degree by acquiring and processing equipment state data, provide a predictive maintenance teaching device meeting national standards for carrying out predictive maintenance training, predictive maintenance flow teaching and the like by student groups, scientific research personnel, engineering personnel and the like, and carry out teaching by a corresponding teaching method.
Therefore, the invention has the advantage of being capable of training the professional skills of the personnel based on the national standard.
Drawings
FIG. 1 is a flow chart of a method of the present teachings;
FIG. 2 is a block diagram of a teaching apparatus according to the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples, which are not to be construed as limiting the invention.
Examples are given. A predictive maintenance teaching method, as shown in fig. 1, comprising the steps of:
A. starting a teaching device: after the teaching device is connected, turning on a power supply and starting a teaching program;
B. selecting teaching courses: selecting a required teaching course from a software system of a teaching device;
C. and (3) wiring teaching: judging whether wiring is needed according to the teaching course; if yes, performing wiring teaching; if not, skipping;
D. course teaching: providing corresponding course teaching according to the teaching course: the course teaching is based on a mode comprising characters, pictures and videos;
E. monitoring teaching operation: monitoring the operation of personnel in the teaching process in real time;
F. teaching operation feedback: performing real-time feedback according to the operation monitored in the step E;
G. and (3) experimental principle display: displaying corresponding experimental principles in the teaching process and at the end of teaching;
finishing the teaching: and sequentially closing the power supplies of the software system and the teaching device and removing the connection.
Step A, the teaching device starts the specific flow as follows:
a1, preparation of teaching device hardware: the PC and the sensor are respectively connected with the corresponding PC and the corresponding sensor through a network cable and a sensor cable, and are connected with a 220V AC power supply and started;
a2, network connection: setting a static IP address of the teaching device, opening an application program 'PDM demo.exe' and connecting a network, and displaying a 'connection state' indicator lamp as blue if the network connection is successful.
A predictive maintenance teaching device, as shown in fig. 2, comprising a hardware device and a software system cooperating with each other; the hardware device can realize data acquisition, data transmission and signal simulation, and the software system can realize signal processing;
the hardware device at least comprises a box body, a power supply interface, a control switch, a motor, a physical simulation key, a display lamp, a fan and an interface end;
the software system at least comprises a function module system, an environment simulation module system, a sensor module system, an industrial protocol module system, a help module system and a connection module system.
The fan comprises a normal fan and a fault fan;
the interface end comprises an IO input/output interface, a temperature input interface, a strain input interface, a sound vibration input interface and an analog output interface.
The functional module at least comprises a temperature signal acquisition module, a vibration sound signal acquisition module, an analog voltage output module, a strain signal acquisition module and a DI/DO module;
the environment simulation comprises a plurality of environment simulation units formed by hardware devices, and each environment simulation unit at least comprises a fan fault simulator, four LEDs, four switch keys and a motor.
The temperature signal acquisition module supports access of a thermocouple and a PT100 platinum resistance sensor; the vibration sound signal acquisition module supports data analysis experiments based on vibration and sound signals; the analog voltage output module supports 0-5V voltage output; the strain signal acquisition module is a strain input channel; the DI/DO module supports digital signal non-sum digital output control;
the fan fault simulator can simulate a balanced fan scene and an unbalanced fan scene and output vibration and rotating speed signals;
the LED can be used as a DO output display lamp and an alarm display lamp;
the switch button can be used as a trigger input and a DI input;
the adjustable rotating speed of the motor can be used for simulating a temperature rise scene and a noise scene.
The temperature input interface supports a thermocouple sensor, a PT100 sensor (three wire system, two wire system);
the sound vibration input interface parameter is 180kS/s/ch (maximum), synchronous, 16bit, IEPE excitation;
the parameters of the analog output interface are 0-5V output, 4mA driving current (maximum) and the update rate is 1kS/s (maximum);
the strain input interface can be connected with a strain bridge circuit.
The specific teaching example is as follows:
experiment-digital IO experiment
The target is as follows: familiarizing with the switching value signal; input and output of switching value signal controlled by program
Time consumption: for 10min
Wiring: without hardware wiring
Brief introduction: this section learns how to utilize the switching value digital signal to accomplish the desired function. The content in this section mainly uses a Switch (Switch), a Trigger (Trigger) and an LED indicator light of a teaching device to carry out an interactive experiment. The experiment was performed as indicated in the interface.
Click the 'start experiment' button to start the experiment:
1) hardware control mode
When a Switch button in the teaching device is operated, the panel indicator light can be seen to complete corresponding state switching along with the Switch closing; when the Trigger (Trigger) button is pressed, the indicator lights flash alternately in the form of the marquee, and then the marquee stops when the Trigger is pressed.
2) Software control mode
In the mode, the state change of the indicator light of the teaching device is not controlled by the switch button, but controlled by the operation of the upper computer software.
Experiment two temperature acquisition experiment
The target is as follows: familiarity with temperature sensors; acquisition and conditioning of temperature signal
Time consumption: for 10min
Wiring: connecting PT100 platinum resistance temperature sensor to appointed temperature collecting channel (temperature input interface)
Brief introduction: how to acquire and check data of the PT100 temperature sensor is learned in the section, and the temperature overrun alarm is realized based on a set threshold value. And clicking a 'start experiment' button to start the experiment according to the interface indication. And selecting whether to start an alarm function or not and adjusting the alarm threshold values of the two channels to realize temperature acquisition and monitoring.
Experimental three-vibration data acquisition A
Wiring: connecting a piezoelectric vibration sensor to a sound vibration input interface (the teaching device provides 12 sound vibration input interfaces in total, each interface has an IEPE current excitation function and can be connected to the piezoelectric vibration sensor.)
Brief introduction: this section learns how to perform acquisition, analysis, viewing and recording of multi-channel vibration data.
1) After the sensors are connected, selecting corresponding acquisition channels;
2) and selecting whether to start data waveform storage, setting a storage path folder when starting, and automatically storing the data by taking the date as the name.
3) Clicking a 'start experiment' button to start an experiment according to an interface instruction; and clicking a 'stop experiment' button to stop the experiment.
Experimental four-vibration data acquisition B
Wiring: the X, Y acceleration channel of the DSA fan simulator is respectively connected to sound vibration input interfaces ch1 and ch2 through a BNC cable, and is connected to a fan rotating speed control channel through an analog output interface AO0
Note: the fan rotation speed control of the DSA simulator has two modes: 1. BNC signal control; 2. and (4) knob control. Mode switching may be accomplished by a dial switch in the "Fan Speed Control" region. In the experiment, a BNC signal control mode is adopted, and the AO output can be controlled through software to realize program-controlled speed regulation.
And (4) prompting: the DSA simulator can simulate a fan in two states: 1. equilibrium (healthy) state (Balanced Fan); 2. unbalanced (failed) state (Unbalanced Fan). The state switching may be accomplished by a dial switch on the DSA simulator. Different health states of the simulation equipment by switching fan states in the experiment
Brief introduction: this section learns how to acquire and analyze the vibration signal of the faulty fan, and realizes fault early warning based on a set threshold. And clicking a 'start experiment' button to start the experiment according to the interface indication.
1) Adjusting the rotating speed, and observing (hearing) the rotating speed state of the fan;
2) switching the balance/unbalance state of the fan, and observing the time domain waveform and the frequency spectrum change;
3) setting a proper amplitude threshold value, switching the fan state and observing the fault alarm state.
Five signal processing and characteristic engineering experiment
Wiring: an X, Y acceleration channel of the DSA fan simulator is connected to the sound vibration input interfaces ch1 and ch2 through BNC cables respectively, and a rotating speed channel is connected to the sound vibration input interface ch 3. Connected to the fan speed control channel through an analog output interface AO0
Brief introduction: in this section, different signal processing methods are used to analyze different states of the fan, and signal time domain and frequency domain features are extracted.
1) Adjusting to a proper rotating speed, switching the balance/unbalance state of the fan, observing the change characteristics of a vibration signal probability density curve, an axis locus diagram and a related curve, and thinking reasons;
2) continuously dialing the rotating speed knob, observing the change condition of the vibration signal time-frequency diagram, and summarizing the application of the time-frequency diagram analysis method;
3) continuously shifting a rotating speed knob, comparing the difference between the frequency spectrum and the order spectrum of the vibration signal, and analyzing the reason;
4) the method comprises the steps of extracting a plurality of typical time domain and frequency domain characteristics of a vibration signal by using a common signal processing method (probability density analysis, axis trajectory analysis, correlation analysis, time frequency analysis and order analysis), and thinking how to select the characteristics according to different fault types.
Six-fault diagnosis and machine learning experiment
Wiring: the experiment is based on an off-line data set and does not need to be wired
Brief introduction: in the learning section, different analysis methods are adopted to know the fault characteristics of the rolling bearing/gear, and a machine learning algorithm is adopted to carry out model training on different characteristic combinations.
1) Test objects: rolling bearing
2) Operating according to the indication steps: reading waveforms → displaying characteristics → training models → testing data.
3) Reading a waveform: clicking a button behind a page file path, opening a Windows file manager, selecting a folder of 'C: \ Program Files (x86) \ PDM Demo \ data', selecting a 'bearing _ norm.csv' or 'bearing _ outer.csv' file, clicking to determine, outputting a sampling rate on a Program page, inputting related parameters on a filter parameter setting and frequency upper and lower limit setting interface, finally clicking a 'reading' button, displaying a time domain waveform, a spectrogram and a filtering envelope spectrogram on a right page of the Program, observing and analyzing a time domain, a frequency domain, an envelope and an envelope spectrogram of a fault rolling bearing, and thinking how to select proper characteristics.
4) And (3) feature display: entering a characteristic display page, clicking a characteristic data file selection button at the upper left corner of the right side of the page, opening a Windows file manager, selecting a C: \ Program Files (x86) \ PDM Demo \ data folder, selecting a bearing training set csv file, clicking a confirmation file, pointing out the characteristic required to participate in training, clicking a confirmation button of the page, and calculating to display a selected characteristic trend graph on a Program page.
5) Model training: entering a model training interface, clicking a 'start training' button, performing model training on the program based on selected training set data according to three modes of SVM, Neural Net Work and Logistic Regression to obtain training results of 3 different algorithms, trying to select a certain feature combination and algorithm, and finishing off-line training; .
6) And (3) data testing: the method comprises the steps of entering a data test, firstly selecting a test data file, opening a Windows file manager, selecting a folder of C: \ Program Files (x86) \ PDM Demo \ data, selecting any one of bearing ball fault test data csv and bearing inner ring fault test data csv and bearing outer ring fault test data csv, selecting a type of model, clicking a 'start test' button, then checking a test result on a page, verifying the accuracy, and trying other feature combinations and algorithm effects.
Claims (4)
1. A method of predictive maintenance teaching, comprising the steps of:
A. starting a teaching device: after the teaching device is connected, turning on a power supply and starting a teaching program;
B. selecting teaching courses: selecting a required teaching course from a software system of a teaching device;
C. and (3) wiring teaching: judging whether wiring is needed according to the teaching course; if yes, conducting wiring teaching; if not, skipping;
D. course teaching: providing corresponding course teaching according to the teaching course: the course teaching is based on a mode comprising characters, pictures and videos;
E. monitoring teaching operation: monitoring the operation of personnel in the teaching process in real time;
F. teaching operation feedback: performing real-time feedback according to the operation monitored in the step E;
G. and (3) experimental principle display: displaying corresponding experimental principles in the teaching process and at the end of teaching;
H. finishing the teaching: and sequentially closing the power supplies of the software system and the teaching device and removing the connection.
2. A predictive maintenance teaching method according to claim 1, wherein said teaching device of step a initiates a specific procedure as follows:
a1, preparation of teaching device hardware: the PC and the sensor are respectively connected with the corresponding PC and the corresponding sensor through a network cable and a sensor cable, and are connected with a 220V AC power supply and started;
a2, network connection: setting a static IP address of the teaching device, opening an application program 'PDM demo.exe' and connecting a network, and displaying a 'connection state' indicator lamp as blue if the network connection is successful.
3. A predictive maintenance teaching device according to any one of claims 1 or 2, wherein: the system comprises a hardware device and a software system which are matched with each other; the hardware device can realize data acquisition, data transmission and signal simulation, and the software system can realize signal processing;
the hardware device at least comprises a box body, a power supply interface, a control switch, a motor, a physical simulation key, a display lamp, a fan and an interface end;
the software system at least comprises a function module system, an environment simulation module system, a sensor module system, an industrial protocol module system, a help module system and a connection module system.
4. A predictive maintenance teaching device according to claim 3 wherein: the fan comprises a normal fan and a fault fan;
the interface end comprises an IO input/output interface, a temperature input interface, a strain input interface, a sound vibration input interface and an analog output interface.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040148350A1 (en) * | 2003-01-28 | 2004-07-29 | Lacy Donald D | System and method for providing instructor services using a plurality of client workstations connected to a central control station |
| CN101354851A (en) * | 2007-07-26 | 2009-01-28 | 浙江天煌科技实业有限公司 | Complete digital control alternating current-direct current velocity modulation and load application system for teaching |
| US20120284606A1 (en) * | 2011-05-06 | 2012-11-08 | David H. Sitrick | System And Methodology For Collaboration Utilizing Combined Display With Evolving Common Shared Underlying Image |
| CN204791709U (en) * | 2015-05-21 | 2015-11-18 | 上海理工大学 | Mould electricity teaching assistance system |
| US20180061269A1 (en) * | 2016-09-01 | 2018-03-01 | Honeywell International Inc. | Control and safety system maintenance training simulator |
| CN109272798A (en) * | 2018-10-19 | 2019-01-25 | 中铁第四勘察设计院集团有限公司 | A kind of electrical teaching training system and its Training Methodology suitable for EMU |
| CN109866209A (en) * | 2018-11-29 | 2019-06-11 | 珠海格力电器股份有限公司 | A kind of remote debugging method, system and computer readable storage medium |
| CN111489609A (en) * | 2020-06-08 | 2020-08-04 | 中国矿业大学 | Fan performance detection experiment teaching system and method based on virtual reality |
| CN113269404A (en) * | 2021-04-29 | 2021-08-17 | 机械工业仪器仪表综合技术经济研究所 | Process industry intelligent safety management system based on industrial network |
-
2021
- 2021-12-21 CN CN202111568345.3A patent/CN114446098A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040148350A1 (en) * | 2003-01-28 | 2004-07-29 | Lacy Donald D | System and method for providing instructor services using a plurality of client workstations connected to a central control station |
| CN101354851A (en) * | 2007-07-26 | 2009-01-28 | 浙江天煌科技实业有限公司 | Complete digital control alternating current-direct current velocity modulation and load application system for teaching |
| US20120284606A1 (en) * | 2011-05-06 | 2012-11-08 | David H. Sitrick | System And Methodology For Collaboration Utilizing Combined Display With Evolving Common Shared Underlying Image |
| CN204791709U (en) * | 2015-05-21 | 2015-11-18 | 上海理工大学 | Mould electricity teaching assistance system |
| US20180061269A1 (en) * | 2016-09-01 | 2018-03-01 | Honeywell International Inc. | Control and safety system maintenance training simulator |
| CN109272798A (en) * | 2018-10-19 | 2019-01-25 | 中铁第四勘察设计院集团有限公司 | A kind of electrical teaching training system and its Training Methodology suitable for EMU |
| CN109866209A (en) * | 2018-11-29 | 2019-06-11 | 珠海格力电器股份有限公司 | A kind of remote debugging method, system and computer readable storage medium |
| CN111489609A (en) * | 2020-06-08 | 2020-08-04 | 中国矿业大学 | Fan performance detection experiment teaching system and method based on virtual reality |
| CN113269404A (en) * | 2021-04-29 | 2021-08-17 | 机械工业仪器仪表综合技术经济研究所 | Process industry intelligent safety management system based on industrial network |
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