Vibration Feedback Control System of Gantry Slicing Machine

Abstract

The present invention discloses a vibration feedback control system of a gantry-type dicing machine, and the present invention relates to the technical field of vibration measurement and control of dicing machines. The vibration feedback control system of the gantry-type dicing machine includes a vibration detection module, a vibration signal processing module, a vibration analysis module, and a vibration control decision generation module. The vibration detection module is composed of sensors arranged at the spindle, the body, and the processing surface of the dicing machine, a signal adjustment unit, and a data acquisition card. The vibration analysis module includes a vibration model and an analysis unit. The present invention realizes the integration of multiple groups of vibration signals, which facilitates the subsequent identification unit to perform overall identification of the correlation coefficient waveforms of multiple vibration sources. Based on the overall identification of a single vibration source, the identification time and identification process are greatly shortened and simplified, and the integration of dicing machine vibration and the shortening of signal data processing time are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of vibration measurement and control of a dicing machine, and in particular to a vibration feedback control system of a gantry-type dicing machine. Background Art

[0002] The dicing machine is a precision CNC equipment that integrates water-gas-electric, air-static high-speed spindle, precision mechanical transmission, sensor and automatic control technologies. It is mainly used for dicing and cutting materials such as silicon integrated circuits, light-emitting diodes, lithium niobate, piezoelectric ceramics, gallium arsenide, sapphire, aluminum oxide, iron oxide, quartz, glass, ceramics, solar cells, etc. Among them, the grinding wheel dicing machine is also called precision grinding wheel cutting machine in China.

[0003] The vibration generated during the processing of traditional dicing machines will cause vibration marks on the workpiece processing surface, reducing the processing accuracy and surface quality of the workpiece, and will produce waviness during low-frequency vibration; vibration will cause tool chipping and cutting and accelerate the wear of the grinding wheel; vibration will loosen the connection part of the fixture, affecting the working performance of the kinematic pair and causing the dicing machine to lose accuracy. Therefore, it is necessary to complete vibration feedback and vibration control in real time during the precision processing of the dicing machine. The current existing technology uses sensors for the collection of vibration data. Sensors are present on the spindle, the x, y and z axes of the moving surface, the body and the processing surface. In the process of sensor transmission of data for processing, it is difficult to integrate and cannot be controlled and adjusted in real time with the processing components. In addition, because the existing technology often uses the NCS mounting structure to control the spindle blade height, it is difficult to coordinate the spindle control in real time according to the vibration size. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a vibration feedback control system for a gantry-type dicing machine, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a vibration feedback control system of a gantry-type dicing machine, including a vibration detection module, a vibration signal processing module, a vibration analysis module, and a vibration control decision generation module. The vibration detection module is composed of a sensor, a signal adjustment unit, and a data acquisition card arranged at the spindle, the body, and the processing surface of the dicing machine. The vibration signal processing module includes an identification unit for receiving a signal transmitted by the sensor through the data acquisition card, a monitoring unit for continuously detecting the vibration signal, and a control unit for processing the vibration signal. The vibration analysis module includes a vibration model and an analysis unit.

[0006] A further improvement of the technical solution of the present invention is that the vibration detection module obtains vibration signals from sensors arranged on the main shaft, the fuselage and the processing surface, and the vibration signals are converted into output signals that can be output through the sensors. The output signals are amplified by the signal adjustment unit, and the high-frequency signals interfering in the output signals are suppressed. The obtained secondary processed vibration signals are collected by the data acquisition card and transmitted to the vibration signal processing module, wherein the signal adjustment unit amplifies the vibration signals received for the first time through circuit design, and matches them with the analog converter A / D in the data acquisition card, and compresses the vibration signal frequency band to reduce the sampling frequency, thereby avoiding signal aliasing, thereby achieving the purpose of suppressing interfering high-frequency signals.

[0007] The further improvement of the technical solution of the present invention is that: the identification unit completes the fusion processing and pre-storage of the secondary processed vibration signal based on the data fusion technology, obtains the energy waveform with multiple vibration sources, and the identification unit performs characteristic analysis of the amplitude, frequency and phase of the correlation coefficient waveform. The characteristic analysis value is input into the MLFNN network for parameter training, and the multiple current vibration source types transmitted by the fusion processing signal are identified according to the parameter training results. Among them, the MLFNN network recognition algorithm based on the time-frequency transformation feature is relatively mature at present, specifically, the vibration signal is used to calculate the autocorrelation coefficient. The autocorrelation is to use the part with high signal energy as the autocorrelation template, and the autocorrelation coefficient is decomposed by wavelet and the energy proportion of the wavelet of the specified layer is calculated. The energy proportion is used as an optimized MLFNN input for training, which will not be repeated;

[0008] The fusion processing of the secondary processing vibration signal is different from the existing technology. The current vibration source determination and vibration signal processing often process the vibration signal data transmitted by a single sensor separately, which makes it difficult to integrate the resonance of the dicing machine and shorten the signal data processing time. Therefore:

[0009] Set the vibration signal detected by n sensors , , ..., Perform overall correlation calculation to achieve the integration of multiple groups of vibration signals, as shown in the following formula:

[0010] , where N is the total number of vibration signal data, m=0,1,..., k is the time coordinate movement value of the vibration signal for overall correlation calculation, and the energy formula of a single vibration signal is: ,in The signal energy obtained after calculation of each signal during overall integration. Combining the above two formulas, we can know that the total energy of the total number of i sensor signals is: That is, the sum of the signal energies obtained by accumulating the signal measured by the ith sensor and the signals measured by other sensors in sequence, which facilitates the subsequent recognition unit to perform overall recognition of the correlation coefficient waveforms of multiple vibration sources, and recognizes a single vibration source based on the overall recognition, greatly shortening and simplifying the recognition time and recognition process.

[0011] A further improvement of the technical solution of the present invention is that the control unit and the monitoring unit display, draw and issue status alarms based on Visual Basic according to the recognition results obtained by the recognition unit, and the alarm limit sets a warning threshold. When the integrated waveform exceeds the warning threshold, the monitoring unit turns on the alarm light of the corresponding area through the control unit according to the judgment result of the recognition unit, wherein the vibration signal data is displayed in a graphical form in a picture frame in the Visual Basic graphic display area.

[0012] A further improvement of the technical solution of the present invention is that: the vibration model establishes a displacement function according to the displacement of the spindle, the fuselage and the processing surface. It should be noted that in the energy waveform obtained by the vibration signal processing module, the distance between the highest point and the lowest point of each regional regular segment is the displacement of the spindle, the fuselage and the processing surface respectively. The displacement function has two boundary conditions according to the law of vibration, and the vibration of the dicing machine comes from the spindle processing. The vibration model is composed of a period of spindle rotation period T 1 The spindle cutting chatter sub-model and the random vibration sub-model of the machine body and the processing surface are composed of the pre-saved secondary processed vibration signal data input into the vibration model to match the vibration change law. The analysis unit introduces the fault tree model into the vibration control decision generation module according to the vibration change law, wherein the two boundary adjustments of the vibration law include the maximum vibration boundary and the minimum vibration boundary, and the maximum vibration boundary is ,in is the main axis displacement function, and the minimum vibration boundary is , where For the main shaft vibration function, the maximum boundary of vibration corresponds to the warning threshold set in the above alarm limit, and the minimum boundary of vibration is the minimum value detected by the sensor. If the vibration signal data collected by a single sensor exceeds the minimum boundary, it means that the sensor fails or is abnormal.

[0013] A further improvement of the technical solution of the present invention is that the fault tree model is an independent event E in the torque transmission system, spindle processing system, assembly system and station system of the dicing machine. 1 and the torque transmission system, spindle processing system, assembly system and another independent event E in the dicing machine system 2 , and their failure rates are and And the probability of single failure and continuous failure under vibration, where it should be noted that E is checked during the entire operation of the dicing machine. 1 Check that E 2 A check is performed, which means that for E 2 To check, we need to 1 Check; then in a task cycle interval time is T 0 =T 1 ; for E 2 The time interval between two checks is E 2 =nT 0 =T 2 ; Therefore, in summary, during task i, E 1 and E 2 If a fault occurs at the same time, the system will not be able to operate, so during the inspection, E 1 and E 2 The probability that the failures occur one after another is: If E 1 A failure occurs, before task i, E 2 A failure also occurred, with probability: According to probability theory, the probability of task i is: ; in E 2 The total failure probability between two consecutive repairs is, ; Therefore, the final time probability is obtained, ; In the above formula, T 0 n=T 2 That is, E 2 Time for two inspections; T 0 =T 1 That is, E 1 The time for two inspections; finally the failure rate in this vibration environment is obtained .

[0014] The further improvement of the technical solution of the present invention is that: the spindle cutting chatter sub-model is constructed based on the neural network algorithm and combined with the mathematical representation of the instantaneous cutting force, cutting force coefficient, workpiece material and cutting conditions. The random vibration sub-model is constructed based on the neural network algorithm and combined with the cutting force fluctuation frequency and the spindle speed, wherein the spindle cutting chatter sub-model is constructed based on the mathematical representation of the instantaneous cutting force, cutting force coefficient, workpiece material and cutting conditions, so the instantaneous cutting force , where is the instantaneous cutting force, is the cutting force coefficient, which is determined by experiments based on the workpiece material and cutting conditions and will not be repeated here. A is the cutting material. According to the principle of metal cutting, when the hardness of the workpiece is different from the hardness of the workpiece under experimental conditions, the change in force is expressed as an exponential relationship between the hardness of the workpiece, that is, , where is the experimental hardness, is the actual hardness, and then consider the effect of the cutting depth of the dicing machine on the volume A of the workpiece, , so the vibration fluctuation between the spindle and the workpiece is: At this time, the relative displacement between the spindle and the workpiece due to vibration is introduced Finally, the required spindle cutting chatter sub-model is obtained: The pre-saved secondary processed vibration signal data is input into the vibration model to match the vibration variation law of the main shaft.

[0015] The further improvement of the technical solution of the present invention is as follows: In summary, the random vibration sub-model is constructed based on the neural network algorithm and combined with the cutting force fluctuation frequency and the spindle speed. The random vibration sub-model is based on the cutting force determination. , where N is .

[0016] A further improvement of the technical solution of the present invention is that the present invention also provides a vibration optimization structure of a gantry-type dicing machine, including a sensor, a receiver, a processor, and a memory. The sensors are arranged on the main shaft, the fuselage and the processing surface. The receiver is used to receive sensor signals, and the memory stores computer-readable instructions thereon.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: by performing overall correlation calculation on the vibration signals detected by n sensors, the integration of multiple groups of vibration signals is realized, which facilitates the subsequent identification unit to perform overall identification of the correlation coefficient waveforms of multiple vibration sources, and based on the overall identification of a single vibration source, the identification time and the identification process are greatly shortened and simplified, thereby realizing the integration of the dicing machine vibration and shortening the signal data processing time;

[0018] By setting the maximum vibration boundary and the minimum vibration boundary, corresponding to the warning threshold in vibration control, and the minimum vibration boundary is the minimum value detected by the sensor, according to the vibration signal data collected by a single sensor exceeding the minimum boundary, it is judged whether the corresponding sensor fails or is abnormal, and the rapid monitoring of the rapid sensor is completed;

[0019] The fault tree model is introduced through the obtained vibration law, and the preset failure rate of independent events is given to realize the prediction of intelligent control such as spindle torque and tool processing height under the NCS installation structure, so as to carry out real-time control and adjustment;

[0020] Through circuit design, the vibration signal received for the first time is amplified and matched with the analog converter A / D in the data acquisition card, and the vibration signal frequency band is compressed to reduce the sampling frequency to avoid signal aliasing, thereby achieving the purpose of suppressing interfering high-frequency signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the overall vibration feedback control system of the gantry type dicing machine;

[0022] Figure 2 It is a schematic diagram of the principle of the vibration detection module and the vibration signal processing module in the vibration feedback control system of the gantry type dicing machine;

[0023] Figure 3 It is a schematic diagram of a vibration detection module in a gantry-type dicing machine and a vibration feedback control system;

[0024] Figure 4 It is a schematic diagram of a vibration signal processing module in a vibration feedback control system of a gantry-type dicing machine;

[0025] Figure 5 It is a schematic diagram of the vibration analysis module in the vibration feedback control system of the gantry type dicing machine. DETAILED DESCRIPTION

[0026] Various exemplary embodiments, features and aspects of the present application 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 are not necessarily drawn to scale unless otherwise specified.

[0027] 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.

[0028] In addition, in order to better illustrate the present application, numerous specific details are provided in the specific embodiments below. It should be understood by those skilled in the art that the present application can also be implemented without certain specific details. In some examples, methods, means, and elements well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present application.

[0029] Reference Figure 1 - Figure 5 , the present invention provides 4 technical solutions: Example

[0030] The vibration feedback control system of the gantry-type dicing machine includes a vibration detection module, a vibration signal processing module, a vibration analysis module, and a vibration control decision generation module. The vibration detection module is composed of sensors arranged at the spindle, the body and the processing surface of the dicing machine, a signal adjustment unit and a data acquisition card. The vibration signal processing module includes an identification unit for receiving the signal transmitted by the sensor through the data acquisition card, a monitoring unit for continuously detecting the vibration signal and a control unit for processing the vibration signal. The vibration analysis module includes a vibration model and an analysis unit.

[0031] The vibration detection module obtains vibration signals from sensors installed on the spindle, the fuselage and the processing surface. The vibration signals are converted into output signals through sensors. The output signals are amplified by the signal adjustment unit, and the high-frequency signals interfering in the output signals are suppressed. The obtained secondary processed vibration signals are collected by the data acquisition card and transmitted to the vibration signal processing module, wherein the signal adjustment unit amplifies the vibration signals received for the first time through circuit design, matches them with the analog converter A / D in the data acquisition card, and compresses the frequency band of the vibration signals to reduce the sampling frequency, so as to avoid signal aliasing and achieve the purpose of suppressing interfering high-frequency signals.

[0032] Embodiment 2: Based on embodiment 1, the recognition unit completes the fusion processing and pre-storage of the secondary processed vibration signal based on the data fusion technology, obtains the energy waveform with multiple vibration sources, and performs characteristic analysis of the amplitude, frequency and phase of the correlation coefficient waveform. The characteristic analysis value is input into the MLFNN network for parameter training, and the multiple current vibration source types transmitted by the fusion processing signal are identified according to the parameter training results. Among them, the MLFNN network recognition algorithm based on the time-frequency transformation feature is relatively mature at present, specifically, the vibration signal is used to calculate the autocorrelation coefficient. The autocorrelation is to use the part with high signal energy as the autocorrelation template, and the autocorrelation coefficient is decomposed by wavelet and the energy proportion of the wavelet of the specified layer is calculated. The energy proportion is used as the input of an optimized MLFNN for training, which will not be repeated;

[0033] The fusion processing of the secondary processing vibration signal is different from the existing technology. The current vibration source determination and vibration signal processing often process the vibration signal data transmitted by a single sensor separately, which makes it difficult to integrate the resonance of the dicing machine and shorten the signal data processing time. Therefore:

[0034] Set the vibration signal detected by n sensors , , ..., Perform overall correlation calculation to achieve the integration of multiple groups of vibration signals, as shown in the following formula:

[0035] , where N is the total number of vibration signal data, m=0,1,..., k is the time coordinate movement value of the vibration signal for overall correlation calculation, and the energy formula of a single vibration signal is: ,in The signal energy obtained after calculation of each signal during overall integration, from the above two formulas, the total energy of the total number of i sensor signals is: That is, the sum of the signal energies obtained by accumulating the signal measured by the ith sensor and the signals measured by other sensors in sequence, which facilitates the subsequent recognition unit to perform overall recognition of the correlation coefficient waveforms of multiple vibration sources, and recognizes a single vibration source based on the overall recognition, greatly shortening and simplifying the recognition time and recognition process.

[0036] In the third embodiment, the control unit and the monitoring unit display, draw and alarm the status based on Visual Basic according to the recognition result obtained by the recognition unit, and the alarm limit sets the warning threshold. When the integrated waveform exceeds the warning threshold, the monitoring unit turns on the alarm light of the corresponding area through the control unit according to the judgment result of the recognition unit, wherein the vibration signal data is displayed in a graphical form in the picture frame of the Visual Basic graphic display area. The related programs involved are:

[0037] If i=0 Then

[0038] PictureTemperature.PSet(i, sTemp), QBColor(12) 'Starting point of drawing graphics, red'

[0039] Else

[0040] PictureTemperature.Line-(4*i,sTemp),QBColor(12) 'Draw other data points of the graph, red'

[0041] End if

[0042] i=i+1

[0043] Next nindex

[0044] 'The data is displayed in the text box in the data display area'

[0045] Embodiment 4: The vibration model establishes a displacement function according to the displacement of the spindle, the body and the processing surface. It should be noted that in the energy waveform obtained by the vibration signal processing module, the distance between the highest point and the lowest point of each regional regular segment is the displacement of the spindle, the body and the processing surface respectively. The displacement function has two boundary conditions according to the law of vibration, and the vibration of the dicing machine comes from the spindle processing. The vibration model is composed of a period of spindle rotation period T1 The spindle cutting chatter sub-model and the random vibration sub-model of the machine body and the processing surface are composed. The pre-saved secondary processed vibration signal data is input into the vibration model to match the vibration change law. The analysis unit introduces the fault tree model into the vibration control decision generation module according to the vibration change law. The two boundary conditions of the vibration law are the maximum vibration boundary and the minimum vibration boundary. The maximum vibration boundary is ,in is the main axis displacement function, and the minimum vibration boundary is , where For the main shaft vibration function, the maximum boundary of vibration corresponds to the warning threshold set in the above alarm limit, and the minimum boundary of vibration is the minimum value detected by the sensor. If the vibration signal data collected by a single sensor exceeds the minimum boundary, it means that the sensor fails or is abnormal.

[0046] The fault tree model is composed of independent events E in the torque transmission system, spindle processing system, assembly system and workstation system of the dicing machine. 1 and the torque transmission system, spindle processing system, assembly system and another independent event E in the dicing machine system 2 , and their failure rates are and And the probability of single failure and continuous failure under vibration, where it should be noted that E is checked during the entire operation of the dicing machine. 1 Check that E 2 A check is performed, which means that for E 2 To check, we need to 1 Check; then in a task cycle interval time is T 0 =T 1 ; for E 2 The time interval between two checks is E 2 =nT 0 =T 2 ; Therefore, in summary, during task i, E 1 and E 2 If a fault occurs at the same time, the system will not be able to operate, so during the inspection, E 1 and E 2 The probability that the failures occur one after another is: If E 1 A failure occurs, before task i, E 2 A failure also occurred, with probability: According to probability theory, the probability of task i is: ; in E 2 The total failure probability between two consecutive repairs is, ; Therefore, the final time probability is obtained, ; In the above formula, T 0 n=T 2 That is, E 2 Time for two inspections; T 0 =T 1 That is, E 1 The time for two inspections; finally the failure rate under the vibration condition is obtained .

[0047] The spindle cutting chatter sub-model is constructed based on the neural network algorithm and combined with the mathematical representation of the instantaneous cutting force, cutting force coefficient, workpiece material and cutting conditions. The random vibration sub-model is constructed based on the neural network algorithm and combined with the cutting force fluctuation frequency and spindle speed. The spindle cutting chatter sub-model is constructed based on the mathematical representation of the instantaneous cutting force, cutting force coefficient, workpiece material and cutting conditions. , where is the instantaneous cutting force, is the cutting force coefficient, which is determined by experiments based on the workpiece material and cutting conditions. It will not be elaborated here. A is the cutting material. According to the principle of metal cutting, when the hardness of the workpiece is different from the hardness of the workpiece under experimental conditions, the change of force is expressed as an exponential relationship between the hardness of the workpiece, that is, , where is the experimental hardness, is the actual hardness, and then consider the effect of the cutting depth of the dicing machine on the volume A of the workpiece, , so the vibration fluctuation between the spindle and the workpiece is: At this time, the relative displacement between the spindle and the workpiece due to vibration is introduced Finally, the required spindle cutting chatter sub-model is obtained: The pre-saved secondary processed vibration signal data is input into the vibration model to match the vibration variation law of the main shaft.

[0048] In summary, the random vibration sub-model is constructed based on the neural network algorithm and combined with the cutting force fluctuation frequency and spindle speed. The random vibration sub-model is based on the cutting force determination. , where N is .

[0049] The present invention also provides a vibration optimization structure of a gantry-type dicing machine, including a sensor, a receiver, a processor, and a memory. The sensor is arranged on the main shaft, the body, and the processing surface. The receiver is used to receive the sensor signal, and the memory stores computer-readable instructions.

[0050] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0051] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A vibration feedback control system for a gantry dicing machine, characterized in that: It includes a vibration detection module, a vibration signal processing module, a vibration analysis module, and a vibration control decision generation module. The vibration detection module is composed of sensors arranged at the spindle, the body, and the processing surface of the dicing machine, a signal adjustment unit, and a data acquisition card. The vibration signal processing module includes an identification unit for receiving a signal transmitted by the sensor through the data acquisition card, a monitoring unit for continuously detecting the vibration signal, and a control unit for processing the vibration signal. The vibration analysis module includes a vibration model and an analysis unit. The vibration model establishes a displacement function according to the displacement of the spindle, the body and the machined surface. The displacement function has two boundary conditions according to the law of vibration. The vibration model is composed of a spindle cutting chatter sub-model whose period is the spindle rotation period T1, and a random vibration sub-model of the body and the machined surface. The pre-saved secondary processed vibration signal data is input into the vibration model to match the vibration change law. The analysis unit introduces a fault tree model into the vibration control decision generation module according to the vibration change law. The fault tree model is an independent event E1 in the torque transmission system, spindle processing system, assembly system and workstation system of the dicing machine and another independent event E2 in the torque transmission system, spindle processing system, assembly system and workstation system of the dicing machine, and their failure rates are and And the respective probabilities of single failure and continuous failure under vibration; The spindle cutting chatter sub-model is constructed based on a neural network algorithm and combined with a mathematical representation of instantaneous cutting force, cutting force coefficient, workpiece material and cutting conditions; The random vibration sub-model is constructed based on a neural network algorithm and in combination with the cutting force fluctuation frequency and the spindle speed.

2. The vibration feedback control system of the gantry type dicing machine according to claim 1, characterized in that: The vibration detection module obtains vibration signals from sensors installed on the spindle, the fuselage and the processing surface. The vibration signals are converted into output signals through sensors. The output signals are amplified by the signal adjustment unit, and the high-frequency signals interfering in the output signals are suppressed. The obtained secondary processed vibration signals are collected by the data acquisition card and transmitted to the vibration signal processing module.

3. The vibration feedback control system of the gantry type dicing machine according to claim 1, characterized in that: The identification unit completes the fusion processing and pre-storage of the secondary processed vibration signal based on data fusion technology, and obtains an energy waveform with multiple vibration sources. The identification unit performs characteristic analysis of the amplitude, frequency and phase of the correlation coefficient waveform, and the characteristic analysis value is input into the MLFNN network for parameter training, and the multiple current vibration source types transmitted by the fusion processing signal are identified according to the parameter training results.

4. The vibration feedback control system of the gantry type dicing machine according to claim 3, characterized in that: The control unit and the monitoring unit display, draw and issue status alarms based on Visual Basic according to the recognition results obtained by the recognition unit. The alarm limit sets a warning threshold. When the integrated waveform exceeds the warning threshold, the monitoring unit turns on the alarm light of the corresponding area through the control unit according to the judgment result of the recognition unit, and the vibration signal data is displayed in a graphical form in a picture frame of the Visual Basic graphic display area.

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