CN115931112A - Vibration monitoring system for electric motor - Google Patents
Vibration monitoring system for electric motor Download PDFInfo
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Abstract
The vibration monitoring system comprises a vibration sensing device, a signal conversion device and a servo device, wherein the vibration sensing device can sense a vibration measurement signal of the motor facility and convert the vibration measurement signal into motor frequency spectrum characteristic data, and the servo device can perform abnormity analysis, residual life analysis, health degree analysis and fault analysis according to the received motor frequency spectrum characteristic data and then send out notification information according to the content of an analysis result.
Description
Technical Field
The present application relates to a vibration monitoring system for an electric motor, and more particularly, to a system capable of converting a vibration measurement signal into a motor spectrum characteristic data according to the vibration measurement signal of the electric motor, performing an anomaly analysis, a remaining life analysis, a health degree analysis, and a fault analysis, and sending a notification and a maintenance guide.
Background
Generally, a machine has progressive faults under the operation condition, and when abnormal signs appear in the initial stage, if the abnormal signs are not processed in time, the subsequent serious faults can be caused.
The traditional maintenance method is passive maintenance, and when the machine is abnormal, a maintenance engineer can perform fault diagnosis according to the state of the machine. Due to the need for industry maintenance techniques, the focus of repair research has gradually moved towards the areas of condition monitoring, predictive repair, and early diagnosis of faults.
The adoption of the technology also affects the whole industry and the semiconductor manufacturing industry, and obviously, the traditional maintenance method is not suitable for industries such as the semiconductor manufacturing industry and the like.
In view of the above circumstances, the present application can collect and monitor the vibration measurement signal of the motor, and can perform anomaly analysis, remaining life analysis, health degree analysis and fault analysis, so that before serious problems occur, a warning notification can be sent, in addition, maintenance guidance can be provided for the analyzed problems, and thus, occurrence of serious faults due to problem accumulation can be avoided, and the present application should be an optimal solution.
Disclosure of Invention
The vibration monitoring system for the motor is applied to more than one motor facility, and comprises at least one vibration sensing device which is connected with the motor facility and used for sensing a vibration measuring signal of the motor facility; at least one network device for receiving data and transmitting the data in a network transmission mode; at least one signal conversion device, electrically connected to the vibration sensing device and the network device, for receiving the vibration measurement signal detected by the vibration sensing device, converting the vibration measurement signal into a motor frequency spectrum characteristic data, and transmitting the measurement time data of the vibration measurement signal and the motor frequency spectrum characteristic data through the network device; a server capable of receiving the measurement time data and the motor spectrum feature data transmitted by the network device, the server having at least one processor and at least one computer readable recording medium storing at least one monitoring and analyzing application, a normal vibration data and a plurality of situation comparison files, wherein the computer readable recording medium further stores computer readable instructions which, when executed by the processors, cause the server to perform the following procedures: comparing the received motor frequency spectrum characteristic data with the normal vibration data through a monitoring analysis application program to output an abnormal judgment result; the system is used for continuously storing the frequency band characteristic region data and establishing a trend model for estimating the time trend of a total vibration value, and then outputting equipment available service life data according to the time trend and the measurement time data; comparing the received motor frequency spectrum characteristic data with different situation comparison files by using a similar probability, and outputting a fault analysis judgment result by using the situation comparison file with the highest probability; the device is used for sending out a notification message according to the abnormal judgment result, the available service life data of the equipment or/and the content of the fault analysis judgment result.
More specifically, the vibration measurement signal is a sinusoidal vibration waveform or a shock wave waveform.
More specifically, the motor spectrum characteristic data can be divided into a plurality of frequency band characteristic region data according to different frequency bands.
More specifically, the normal vibration data are one or more default characteristic warning values, and the monitoring analysis application program can compare the normal vibration data with the motor frequency spectrum characteristic data according to the default characteristic warning values, and output the abnormal judgment result if the default characteristic warning values are reached.
More specifically, the normal vibration data is data collected under long-term normal operation, a judgment model is trained in a machine learning mode according to the data, the judgment model is compared with the motor frequency spectrum characteristic data, and if the difference is too large, the judgment abnormal result is output.
More specifically, the monitoring analysis application program can continuously store the frequency band characteristic region data into total vibration historical data according to measurement time data, establish the trend model according to the total vibration historical data, estimate the time trend of the total vibration value through the trend model, set a preset total vibration upper limit value according to the equipment machine, judge available upper limit time data of equipment according to the preset total vibration upper limit value and the time trend of the total vibration value, and output the available service life data of the equipment through the available upper limit time data of the equipment and the measurement time data.
More specifically, the monitoring analysis application program can use the ratio of the total vibration history data to the default total vibration upper limit value as a first judgment value, and then adapt a simple linear regression according to the time trend of the total vibration value to obtain a stability, and use the stability as a second judgment value, then use the ratio of the equipment available life data to the equipment available upper limit time data as a third judgment value, and finally use the first judgment value, the second judgment value and the third judgment value to obtain a health degree data through weight distribution.
More specifically, the monitoring analysis application program can compare the received frequency band feature area data with different situation comparison files, and output the result of the fault analysis determination according to the situation comparison file with the highest probability, and if the received frequency band feature area data is determined to be close to each situation comparison file with a probability lower than a predetermined standard, establish the received frequency band feature area data as a new situation comparison file.
More specifically, the monitoring and analyzing application program can provide a report interface for reporting a successful judgment result or a failed judgment result through the report interface after the monitoring and analyzing application program provides the failure analysis judgment result, and the monitoring and analyzing application program can report according to the successful judgment result or the failed judgment result to improve the accuracy of the failure analyzer.
More specifically, the computer readable recording medium stores a maintenance guide profile established according to different situation comparison profiles, and if the fault analysis determination result is analyzed, the monitoring analysis application program can find out the corresponding maintenance guide profile from the maintenance suggestion storage to provide a troubleshooting sequence of maintenance and part inspection.
More specifically, the network transmission mode is a wireless network transmission mode or a wired network transmission mode.
More specifically, the notification message can be sent by mail, communication software or short message technology.
Other features and embodiments of the present application will be described in detail below with reference to the drawings.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1A is a schematic diagram of an apparatus configuration of the vibration monitoring system for an electric motor according to the present application.
Fig. 1B is a schematic diagram of the connection between the network device and the servo device of the vibration monitoring system for the motor according to the present invention.
Fig. 1C is a schematic diagram of the internal architecture of the servo device of the vibration monitoring system for an electric motor according to the present application.
Fig. 1D is an architectural diagram of a monitoring analysis application of the vibration monitoring system for an electric motor of the present application.
Fig. 2A is a schematic data processing diagram of the vibration monitoring system for an electric motor according to the present application.
Fig. 2B is a schematic data processing diagram of the vibration monitoring system for an electric motor according to the present application.
Fig. 2C is a data processing schematic of the vibration monitoring system for an electric motor of the present application.
Fig. 3 is a flow chart of an abnormality detection analysis of the vibration monitoring system for an electric motor according to the present application.
Fig. 4 is a diagram illustrating an example of quantitative analysis of abnormality detection analysis of the vibration monitoring system for the motor according to the present application.
Fig. 5A is a schematic diagram illustrating an example of qualitative analysis of abnormality detection analysis of the vibration monitoring system for an electric motor according to the present application.
Fig. 5B is a schematic diagram illustrating an example of qualitative analysis of abnormality detection analysis of the vibration monitoring system for an electric motor according to the present application.
Fig. 5C is a schematic diagram illustrating an example of qualitative analysis of abnormality detection analysis of the vibration monitoring system for an electric motor according to the present application.
Fig. 5D is a schematic diagram illustrating an example of qualitative analysis of abnormality detection analysis of the vibration monitoring system for an electric motor according to the present application.
FIG. 6 is a flowchart illustrating the remaining life and health analysis of the vibration monitoring system for an electric motor according to the present application.
Fig. 7A is a schematic diagram illustrating a health analysis of the vibration monitoring system for an electric motor according to the present application.
Fig. 7B is a schematic diagram illustrating a health analysis of the vibration monitoring system for an electric motor according to the present application.
Fig. 7C is a schematic diagram illustrating a health analysis of the vibration monitoring system for an electric motor according to the present application.
FIG. 8 is a schematic diagram illustrating an example health analysis of the vibration monitoring system for an electric motor according to the present application.
Fig. 9 is a flow chart of a fault analysis of the vibration monitoring system for an electric motor of the present application.
Description of the symbols
1: the motor facility 2: vibration sensing device
3: signal conversion device 31: connecting wire
4: the network device 5: servo device
51: the processor 52: computer readable recording medium
521: monitoring analysis application 5211: data processor
5212: the abnormality detector 5213: residual life judging device
5214: health degree determiner 5215: fault analyzer
5216: the alarm notifier 5217: using a mouthpiece
522: the data storage unit 53: information receiver/transmitter
Detailed Description
The positional relationship described in the following embodiments includes: the top, bottom, left and right, unless otherwise indicated, are based on the orientation of the elements in the drawings.
Referring to fig. 1A to 1D, which are a schematic diagram of an apparatus configuration, a schematic diagram of a connection between a network device and a servo device, a schematic diagram of an internal architecture of a servo device, and a schematic diagram of an architecture of a monitoring analysis application program of the vibration monitoring system for a motor according to the present application, it can be seen that the vibration monitoring system for a motor is applied to a motor facility 1 on an apparatus table in a factory building, the motor facility 1 is connected to the vibration sensing device 2, and the connection method is not limited to being screwed on the motor or being adhered on the surface of the motor, and different connection methods (connection mainly can be close to a vibration source of the motor) are provided according to the type of the motor facility 1, wherein the motor facility 1 can be a vibration motor or a pneumatic motor, a vibration measurement signal of the vibration motor is a sinusoidal vibration waveform, and a vibration measurement signal of the pneumatic motor is a shock wave waveform.
The electric motor installation 1 can be a constant pressure pump, a fan motor, a compressor, an induction motor, while the electric motor installation 1 is a motor not limited to steady state & transient state.
The other end of the vibration sensing device 2 is connected to the signal conversion device 3, the signal conversion device 3 is used to receive the vibration measurement signal detected by the vibration sensing device 2, convert the vibration measurement signal into a motor spectrum feature data, and then connect the measurement Time data and the motor spectrum feature data of the vibration measurement signal with the network device 4 (the network device 4 refers to any device with internet transmission function, such as an ethernet gateway or/and a network sharer) through a connection line 31 and transmit them in a network transmission manner (wireless network transmission manner or wired network transmission manner), as shown in fig. 2A, after the vibration sensing device 2 obtains the vibration measurement signal, a Time Domain map (Time Domain) is converted into motor spectrum feature data through the signal conversion device 3, as shown in fig. 2B, the motor spectrum feature data is a Frequency Domain map (Frequency Domain), and the Time Domain map is converted into a Frequency Domain map, and a fourier transform algorithm and the like can be used to convert a Time Domain signal into corresponding amplitude and phase at different frequencies, and the Frequency Domain signal represents the Frequency Domain signal under the Frequency spectrum.
In addition, the motor spectrum characteristic data can perform spectrum characteristic extraction according to different frequency bands to obtain a plurality of frequency Band characteristic region data, and the spectrum characteristic extraction can be performed by the signal conversion apparatus 3 or the servo device 5, as shown in fig. 2B and fig. 2C, the motor spectrum characteristic data is divided into a plurality of sections by frequency interval regions, wherein fig. 2C clearly marks the amplitude value of each frequency Band region, and wherein the frequency range of Band1 is the frequency range of the total amount of vibration specified with reference to iso, and the frequency ranges of Band2 to Band 8 are defined as follows (the following definition of different frequency ranges is only an example of one implementation, and the actual implementation can be performed according to different devices, and has different Hz definition ranges):
(1)Band2:55Hz～59Hz
(2)Band3:115Hz～117Hz
(3)Band4:170Hz～174Hz
(4)Band5:227Hz～231Hz
(5)Band6:285Hz～290Hz
(6)Band7:300Hz～1000Hz
(7)Band8:100Hz～2000Hz
the network device 4 can be connected to a server 5, so that the signal conversion device 3 can transmit the measurement time data and the motor spectrum feature data to the server 5 through the network device 4, the server 5 has a processor 51, an information receiver/transmitter 53, and a computer readable recording medium 52, wherein the information receiver/transmitter 53 receives the measurement time data and the motor spectrum feature data in a network transmission manner, and the computer readable recording medium 52 stores at least one monitoring analysis application 521 and a data storage unit 522, and the data storage unit 522 stores therein a plurality of normal vibration data, a plurality of situation comparison files, total vibration history data, and a plurality of maintenance guide files (established according to different situation comparison files);
the monitoring analysis application 521 includes:
(1) A data processor 5211 for receiving the measured time data and the motor spectral feature data, and dividing the motor spectral feature data into a plurality of segments by frequency intervals to form a plurality of frequency band feature region data;
(2) An anomaly detector 5212, coupled to the data processor 5211, is used to perform a quantitative analysis or a qualitative analysis on the frequency band feature region data, as shown in fig. 3, for example and as follows:
(a) Quantitative analysis:
(a1) Carrying out quantitative analysis 302 on the motor frequency spectrum characteristic data 301, then formulating the normal vibration data, wherein the normal vibration data is one or more default characteristic warning values 303, finally comparing the normal vibration data with the frequency band characteristic region data of the motor frequency spectrum characteristic data according to the default characteristic warning values, and if the default characteristic warning values are reached, outputting the abnormal judgment result and sending warning information 304;
(a2) As shown in fig. 4, the total vibration of the ice water pump motor is used as a quantitative analysis result chart, and the process is as follows:
A. recording the real-time data of the total vibration quantity (irregular oscillation curve in the figure);
B. the vibration control limit is established according to ISO-10816 (unit: mm/s), wherein a vibration value < =0.7 represents Good, if the vibration value < =0.7 represents Acceptable, if the vibration value < =1.8 represents unsecititious factor, if the vibration value of 1.8< 4.5 represents unsecititious factor, if the vibration value of 4.5 represents unsecceptable;
C. the figure shows that the vibration value exceeds 1.8mm/s for 52 times (the area above the transverse line), which indicates that the vibration is larger under the condition of partial operation, and the vibration is not frequently generated but should be noticed;
D. recording the data one by a real-time system and warning the intervention confirmation of related personnel;
E. in addition to using ISO as a reference, several regulatory logics are provided: average value n, n =1,2 \8230; mean + n standard deviation, n =3,4.; median + n IQR, n =1.5,3, \8230; or can be self-defined.
(b) And (3) qualitative analysis:
(b1) Performing qualitative analysis 305 on the motor spectrum characteristic data 301, wherein the normal vibration data is data 306 collected under long-term normal operation conditions, training a judgment model 307 in a machine learning manner according to the data, providing a user with interface selection sensitivity (high, standard and low) 308, comparing 309 the judgment model with new motor spectrum characteristic data, if the difference is too large (exceeds a model decision boundary), judging as abnormal 310, outputting and recording the judgment abnormal result and sending out warning information 311, otherwise, if the comparison result is within the model decision boundary, judging as abnormal 312;
(b2) The method used for qualitative analysis is Isolation Forest, which is briefly described as follows:
A. the points which are easy to be isolated are outliers; data which are distributed sparsely and are far away from the high density are outliers;
B. continuously and randomly cutting the data set until 1 point remains in each subspace;
C. repeating the data cutting action for multiple times;
D. after multiple random cutting, calculating abnormal scores, if the abnormal scores are close to 1, the abnormal points are more likely to be found, and if all the scores are about 0.5, the abnormal points are not found in the possible data;
(b3) As shown in fig. 5A to 5D, the process is shown as follows, in which the total vibration amount of a certain ice water pump motor is taken as a qualitative analysis result chart:
A. taking the impeller vibration data of the ice water pump motor as an example, and collecting two data sets, namely Normal Set (which is the operation data in the Normal state, as shown in fig. 5A) and Testing Set (which is the data in the abnormal state of the impeller, which refers to the data of the imbalance and the load of the impeller, as shown in fig. 5C);
B. wherein a Training Set is used in combination with Isolation Forest machine learning method to obtain a model, and its analysis Score (data in normal state of impeller, as shown in fig. 5B) is calculated, and a threshold value is selected from the analysis Score, which is 0.7236, and the model is recorded;
C. apply the model to the Testing Set and calculate the Anomaly Score (Table one below)
| value | Update time | score |
| 0.3001953 | 2021-02-19 15:46:36 | 0.724 |
| 0.2445313 | 2021-02-19 15:46:37 | 0.620 |
| 0.3119141 | 2021-02-19 15:46:38 | 0.724 |
| 0.3158203 | 2021-02-19 15:46:39 | 0.724 |
| 0.3382812 | 2021-02-19 15:46:40 | 0.724 |
| 0.2855469 | 2021-02-19 15:46:41 | 0.724 |
| 0.2835937 | 2021-02-19 15:46:42 | 0.724 |
| 0.2010742 | 2021-02-19 15:46:43 | 0.587 |
| 0.1849609 | 2021-02-19 15:46:44 | 0.596 |
| 0.1844727 | 2021-02-19 15:46:45 | 0.594 |
Table-vibration value vs anomallly Score
D. With 0.7236 as the threshold, an Anomaly Score of the Testing Set greater than 0.7236 is an abnormal data point (e.g., the data point in the upper region in fig. 5D), whereas a Score less than 0.7236 is a normal point;
E. recording the abnormal data points in a database, and informing related engineering units to query;
F. in this example, if the abnormal data points continuously appear, it should be noted whether the impeller has abnormal conditions, which results in a large difference from the original normal data.
(3) A remaining life determiner 5213, connected to the data processor 5211, configured to continuously store the frequency band feature region data as a total vibration history data according to the measurement time data, establish the trend model according to the total vibration history data, estimate the time trend of the total vibration value through the trend model, set a preset total vibration upper limit value according to the equipment, determine an equipment available upper limit time data according to the preset total vibration upper limit value and the time trend of the total vibration value, and output the equipment available life data through the equipment available upper limit time data and the measurement time data, where the descriptions are as follows:
(a) The method is based on the amplitude value, and an optimal trend model (linear or nonlinear model) is adapted from historical data. Establishing a relation between the vibration OA value and the time series, converting the measured data into predicted time series information as a basis for predicting the performance degradation of the machine;
(b) The operating conditions are as follows:
(b1) The alarm value is defined, wherein the total vibration quantity is referred to the ISO upper limit value, other vibration quantities can be defined by a field, and the field is defined as follows:
(b11) Maximum amplitude (Max) × n, n =2,3, \ 8230;
(b12) Amplitude average n, n =2,3, \ 8230;
(b13) Or is self-defined
(b2) The long-time data modeling is more stable;
(c) The operation is as follows (the following operation example is only an example of one implementation, and the actual implementation may have different non-linear algorithms depending on different devices):
(c1) A trend model is decorated in a linear and a non-linear way
(c11) Linear glm
y＝α+βx
(c12) Nonlinear Exponential Model regression to Exponential distribution
y＝α+e βx
(c2) The modeling model estimates the Time to reach the upper limit, which is End Time (TEnd).
(c3) RUL (remaining Life) = TFail-Tnow
(4) A health degree determinator 5214, connected to the data processor 5211 and the remaining life determinator 5213, for determining a stability by using a ratio of the total vibration history data to the default total vibration upper limit as a first determination value and by adapting a simple linear regression according to a time trend of the total vibration value, and by using the stability as a second determination value, and then by using a ratio of the equipment available life data to the equipment available upper limit time data as a third determination value, and finally by using weight distribution of the first determination value, the second determination value and the third determination value, health degree data is obtained, which is described as follows:
(a) According to the method, the health degree is calculated by the total vibration degree/component vibration value of equipment, model estimation and ISO standard, as shown in FIG. 6, after historical total vibration quantity and operation starting date 601 of motor equipment are recorded, data updating 602 is carried out (if new data comes in, old data and new data are combined), then data cleaning 603 is carried out (for eliminating shutdown state data and abnormal data caused by man-made factors is eliminated), and finally model adaptation is carried out to estimate the health degree and the residual life 604;
(b) A first judgment value (H1) based on the total vibration value
(b1) The upper limit of the ISO-specified total vibration value is (ex: calss 1, <15kw, 4.8mm/s)
(b2) Health degree:
(c) The second criterion (H2) is a Model R2 for the stability
(c1) Defining the upper limit value of vibration total amount
(c2) Adapting a simple linear regression to amplitude vs. time andthe R2 value transformation represents the stability of the dependence time, for example, in FIG. 7A, the R of the adapted line of the measurement curve 2 0.0316 and H2=96.4%, and further taking fig. 7B as an example, the curve is initially stable, and after a certain time, the curve goes upward to represent instability, and R of the fitting line fitted by the curve is measured 2 0.8484, and H2=15.1%,
(c3) A simple linear regression (Y = α + β X + ∈) is illustrated below:
sample data: (y) i ,x i ),i＝1…n
Error:
the minimum error value is calculated by the least square method
An example of the output operation is as follows:
(c4) R2 is between 0 and 1:
0: indicates stability, independent of time, so that the efficacy is stable
1: showing instability, time-dependent, and therefore unstable performance
(d) A third determination value (H3) for estimating the remaining life, as shown in FIG. 7C, in which the measured data is an irregular curve, the other curve is an upward curve, and the time for touching the upper limit value is set as T End And T is 0 To start the time of use, T 1 For the current measurement time, the estimation formula is as follows:
(e) Combining H1& H2& H3, and setting weight ratio by owner
Healthy＝a×H1+b×H2+c×H3,a+b+c＝1
(f) An example is shown in fig. 8, and is described below:
(f1) In an example of a semiconductor factory bench, the upper limit value of the total vibration amount is 6, the y-axis represents the actual vibration data point, the x-axis represents time, and the horizontal line is the linear trend line (y =2.228+ 3.505x10) adapted to this example (y =2.228+ 3.505x10) -7 x), data shows that the equipment vibration tends to be stable, and reaches an upper warning limit after 209 days are estimated;
(f2) The health degree is calculated as follows, wherein H1=52.3%, H2=99.2%, and H3=93.7%, and after average calculation, the overall health degree is estimated to be 81.8%;
(f3) The model adaptation operation of this example is as follows:
(5) A fault analyzer 5215, connected to the data processor 5211, for comparing the received frequency band feature region data with different situation comparison files, and outputting the result of the fault analysis according to the situation comparison file with the highest probability, and if the received frequency band feature region data is determined to be lower than a predetermined criterion, establishing the received frequency band feature region data as a new situation comparison file, which is described as follows:
(a) As shown in fig. 9, the fault analysis program performs vibration data simulation 901 of the motor device, establishes a plurality of context files 902, performs data cleaning, labeling, standardization and other processing 903, performs neural network training, modeling and updating 904, outputs 905 the model, and applies the model for analysis 906;
(b) When new data of the motor device is input 907, then fault analysis and prediction are carried out through the model 908;
(c) After a failure, the system can provide the failure prediction update module 909, and then update the scenario 911 in the scenario library 910 in the data storage unit 522 to improve the accuracy of the failure analyzer.
(6) An alarm notifier 5216, which is connected to the abnormality detector 5212, the remaining life determiner 5213, the health determiner 5214 and the fault analyzer 5215, and is used to send out notification information through the information receiver/transmitter 53 by means of mail, communication software or short message or directly display the notification information on the report interface when determining that there is an abnormal condition.
(7) A user interface 5217, connected to the anomaly detector 5212, the remaining life determiner 5213, the health determiner 5214, the fault analyzer 5215 and the alarm notifier 5216, wherein the user interface 5217 can provide a reporting interface for reporting a successful determination result or a failed determination result through the reporting interface after the fault analysis determination result is provided, and the monitoring and analysis application can report back according to the successful determination result or the failed determination result to improve the accuracy of the fault analysis.
In addition, the data storage unit 522 of the computer readable recording medium 52 stores maintenance guide files created according to different situation comparison files, and if the failure analysis determination result is analyzed, the monitoring analysis application 521 can find out the corresponding maintenance guide file from the maintenance suggestion storage to provide a troubleshooting sequence for maintenance and component inspection.
The vibration monitoring system for the motor that this application provided, when comparing with other prior art each other, its advantage is as follows:
(1) The motor can be collected and monitored through vibration measurement signals of the motor, abnormal analysis, residual life analysis, health degree analysis and fault analysis can be carried out, and warning notification can be sent out before serious problems occur.
(2) The application can provide maintenance guide aiming at the analyzed problems, so that serious faults caused by problem accumulation can be avoided.
The above-described embodiments and/or implementations are only for illustrating the preferred embodiments and/or implementations of the technology of the present application, and are not intended to limit the implementations of the technology of the present application in any way, and those skilled in the art can make modifications or changes to other equivalent embodiments without departing from the scope of the technology disclosed in the present application, but should be construed as technology or implementations substantially the same as the present application.
Claims (10)
1. A vibration monitoring system for an electric motor, the vibration monitoring system being adapted for use with more than one electric motor installation, the vibration monitoring system for an electric motor comprising:
at least one vibration sensing device connected with the motor facility and used for sensing a vibration measurement signal of the motor facility;
at least one network device for receiving data and transmitting the data in a network transmission mode;
at least one signal conversion device, electrically connected to the vibration sensing device and the network device, for receiving the vibration measurement signal detected by the vibration sensing device, converting the vibration measurement signal into a motor frequency spectrum characteristic data, and transmitting the measurement time data of the vibration measurement signal and the motor frequency spectrum characteristic data through the network device; and
a server capable of receiving the measurement time data and the motor spectrum feature data transmitted by the network device, the server having at least one processor and at least one computer readable recording medium storing at least one monitoring and analyzing application, a normal vibration data and a plurality of situation comparison files, wherein the computer readable recording medium further stores computer readable instructions which, when executed by the processors, cause the server to perform the following procedures: comparing the received motor frequency spectrum characteristic data with the normal vibration data through a monitoring analysis application program to output an abnormal judgment result; the system comprises a frequency band characteristic region data acquisition module, a trend model and a data processing module, wherein the frequency band characteristic region data acquisition module is used for acquiring frequency band characteristic region data of a frequency band; the system is used for comparing the received motor frequency spectrum characteristic data with different situation comparison files to obtain similar probability, and outputting a fault analysis judgment result by using the situation comparison file with the highest probability; the device is used for sending out a notification message according to the abnormal judgment result, the available service life data of the equipment or/and the content of the fault analysis judgment result.
2. A vibration monitoring system for an electric motor according to claim 1, wherein the vibration measuring signal is a sinusoidal vibration waveform or a shock wave waveform.
3. A vibration monitoring system for an electric motor according to claim 1, wherein the motor spectral characteristic data can be divided into a plurality of frequency band characteristic region data according to different frequency bands.
4. A vibration monitoring system for an electric motor according to claim 1, wherein the normal vibration data is one or more default characteristic alarm values, and the monitoring analysis application program is capable of comparing the normal vibration data with the frequency spectrum characteristic data of the electric motor according to the default characteristic alarm values, and outputting the abnormal judgment result if the default characteristic alarm values are reached.
5. The vibration monitoring system of claim 1, wherein the normal vibration data is collected under long-term normal operation, a judgment model is trained by machine learning according to the data, the judgment model is compared with the motor spectrum feature data, and if the difference is too large, the judgment abnormality result is outputted.
6. The vibration monitoring system of claim 1, wherein the monitoring analysis application program is capable of continuously storing the frequency band characteristic region data as a total vibration history data according to a measurement time data, establishing the trend model according to the total vibration history data, estimating a time trend of the total vibration value through the trend model, setting a preset total vibration upper limit value according to the equipment machine, determining an equipment available upper limit time data according to the preset total vibration upper limit value and the time trend of the total vibration value, and outputting the equipment available life data through the equipment available upper limit time data and the measurement time data.
7. The vibration monitoring system according to claim 6, wherein the monitoring analysis application program is capable of obtaining a stability by using a ratio of the total vibration history data to the default total vibration upper limit as a first determination value and by adapting a simple linear regression according to a time trend of the total vibration value, and by using the stability as a second determination value, and by using a ratio of the device usable life data to the device usable upper limit time data as a third determination value, and by using weight distribution of the first determination value, the second determination value and the third determination value, a health data is obtained.
8. The system of claim 1, wherein the monitoring and analyzing application program is capable of comparing the received frequency band feature region data with different situation comparison files and outputting the fault analysis determination result according to the situation comparison file with the highest probability, and if the probability of the received frequency band feature region data being similar to each situation comparison file is determined to be lower than a predetermined criterion, establishing the received frequency band feature region data as a new situation comparison file.
9. The vibration monitoring system according to claim 1, wherein the monitoring and analyzing application provides a reporting interface for reporting a success determination result or a failure determination result through the reporting interface after the monitoring and analyzing application provides the failure analysis determination result, and the monitoring and analyzing application reports back according to the success determination result or the failure determination result for improving the accuracy of the failure analysis.
10. The vibration monitoring system of claim 1, wherein the computer readable recording medium stores maintenance guide files created according to different situation comparison files, and if the failure analysis determination result is analyzed, the monitoring analysis application program can find out the corresponding maintenance guide file from the maintenance suggestion storage to provide a troubleshooting sequence for maintenance and component inspection.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110905591.7A CN115931112A (en) | 2021-08-06 | 2021-08-06 | Vibration monitoring system for electric motor |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110905591.7A CN115931112A (en) | 2021-08-06 | 2021-08-06 | Vibration monitoring system for electric motor |
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|---|---|
| CN115931112A true CN115931112A (en) | 2023-04-07 |
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| CN202110905591.7A Pending CN115931112A (en) | 2021-08-06 | 2021-08-06 | Vibration monitoring system for electric motor |
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| Country | Link |
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| CN (1) | CN115931112A (en) |
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