CN115187677A - Image diagnosis analysis system and method thereof - Google Patents

Abstract

The invention provides an image diagnosis and analysis system, which is applied to a servo motor driving system and comprises a data acquisition module, a graph establishing module, a sampling module, a conversion module and an analysis module. The image diagnosis and analysis system receives the normal operation data and the instant operation data of the servo motor driving system, establishes a normal operation oscillogram and an instant operation oscillogram, samples the normal operation sampling data and the instant operation sampling data, converts a first RGB value and a second RGB value, and generates a normal operation image and an instant operation image according to the first RGB value and the second RGB value. The image diagnosis and analysis system can further analyze the real-time running image according to the normal running image to judge whether the servo motor driving system is in an abnormal state or not, so as to achieve the effect of real-time detection by utilizing the image.

Description

Image diagnosis analysis system and method thereof

Technical Field

The present invention relates to a system and a method, and more particularly, to an image diagnosis and analysis system and a method thereof.

Background

A motor, also called as an electric motor or an electric motor, is an electrical apparatus that converts electric energy into kinetic energy and can be used to drive other devices, and is widely used in the life today. The principle of the motor is the same as that of the generator, with the main difference being the difference in energy conversion.

The servo motor driving system generally comprises a servo motor and a driver, and is widely applied to the fields of elevators, cranes, oil well pumps and the like, wherein the oil-gas exploration field to which the oil well pump belongs is an important application field of the servo motor system, namely land oil-gas exploration or offshore oil-gas exploration. Problems often occur with drives operating for long periods of time, such as: the deterioration of the capacitor, the damage of the element, the intrusion of foreign matters, etc., which in turn causes the abnormality of the servo motor driving system. An abnormality in the servomotor drive system may cause problems in the field of application, safety or comfort of the machine, such as the above-mentioned elevators, cranes, etc. If the oil well pump in the oil and gas exploration field is stopped because of the abnormality of the servo motor driving system, the loss of rough estimation per day can be as high as 14 ten thousand dollars. Therefore, reliability and real-time detection and diagnosis of the servo motor driving system are increasingly important.

Disclosure of Invention

In view of the problems of the prior art, the drive of the servo motor driving system is caused by long-time operation, and the abnormality of the servo motor driving system and various problems derived therefrom are solved. It is a primary objective of the claimed invention to provide an image diagnostic and analysis system and method thereof to solve at least one problem in the prior art.

The present invention is directed to solve the problems of the prior art, and the technical means necessary for the present invention is to provide an image diagnostic analysis system, which is applied to a servo motor driving system and comprises a data acquisition module, a graph creation module, a sampling module, a conversion module and an analysis module. The data acquisition module receives M normal operation data and M instant operation data of the servo motor driving system. The graph establishing module is used for receiving the M normal operation data and the M instant operation data and establishing a normal operation oscillogram and an instant operation oscillogram. The sampling module samples N normal operation data groups and N immediate operation data groups respectively according to the normal operation oscillogram and the immediate operation oscillogram, each normal operation data group comprises O normal operation sampling data sampled from M normal operation data, and each immediate operation data group comprises O immediate operation sampling data sampled from M immediate operation data, wherein N is less than M and O is less than M. The conversion module is used for receiving N times O normal running sampling data and N times O instant running sampling data, converting each normal running sampling data into a first RGB value through color code conversion, converting each instant running sampling data into a second RGB value through color code conversion, and generating a normal running image and an instant running image according to the first RGB value. The analysis module analyzes the real-time running image by using the normal running image, and generates an abnormal signal when the difference between the second RGB value and the first RGB value is analyzed to be in accordance with an abnormal condition. Wherein, the color code conversion is to multiply the value of each normal operation sampling data and each real-time operation sampling data by 255 and convert them to form the first RGB value and the second RGB value, respectively.

Based on the above-mentioned necessary technical means, a subsidiary technical means derived from the present invention is to make the abnormal condition in the image diagnostic analysis system indicate that the first RGB values of the normal operation image do not correspond to the second RGB values of the real-time operation image.

Based on the above-mentioned necessary technical means, a subsidiary technical means derived from the present invention is to make the abnormal condition in the image diagnostic analysis system mean that the second RGB value of the live operation image does not have the first RGB value of the normal operation image.

Based on the above-mentioned necessary technical means, an accessory technical means derived by the present invention is that the data acquisition module in the image diagnostic analysis system comprises an analog-to-digital conversion unit, and the analog-to-digital conversion unit is used for converting the data format of the normal operation data and the real-time operation data from an analog format to a digital format.

Based on the above-mentioned necessary technical means, an accessory technical means derived from the present invention is to make the data acquisition module in the image diagnostic analysis system further comprise a normalization unit, wherein the normalization unit is electrically connected to the adc for normalizing the normal operation data and the real-time operation data.

Based on the above-mentioned necessary technical means, an accessory technical means derived from the present invention is to make the data acquisition module in the image diagnosis and analysis system further comprise a standardization unit, wherein the standardization unit is electrically connected with the analog-to-digital conversion unit and is used for standardizing the normal operation data and the real-time operation data.

Based on the above-mentioned necessary technical means, an accessory technical means derived by the present invention is that the sampling module in the image diagnostic analysis system comprises a frame sampling unit, wherein the frame sampling unit utilizes a frame to move in the normal operation waveform diagram and the real-time operation waveform diagram respectively, so as to sample the normal operation sampling data and the real-time operation sampling data respectively.

Based on the above-mentioned necessary technical means, an accessory technical means derived from the present invention is that the sampling module in the image diagnostic analysis system further comprises a frame setting unit, wherein the frame setting unit is electrically connected to the frame sampling unit and is used for operatively setting a sampling width of the frame.

Based on the above-mentioned necessary technical means, an accessory technical means derived from the invention is to make the image diagnosis analysis system further comprise a display module, wherein the display module is electrically connected with the conversion module and used for receiving and displaying the normal operation image and the real-time operation image.

Based on the above-mentioned necessary technical means, an accessory technical means derived from the present invention is to make the image diagnosis analysis system further comprise an alert module, wherein the alert module is electrically connected to the analysis module and is used for generating an alert message when receiving the abnormal signal.

In order to solve the problems of the prior art, the invention adopts necessary technical means to provide an image diagnosis and analysis method, and comprises the following steps: receiving M normal operation data and M instant operation data by using a data acquisition module; receiving M normal operation data and M immediate operation data by using a graph establishing module, and establishing a normal operation oscillogram and an immediate operation oscillogram according to the M normal operation data and the M immediate operation data; sampling O normal operation sampling data and O immediate operation sampling data by using a sampling module according to the normal operation oscillogram and the immediate operation oscillogram; using a conversion module to receive O normal operation sampling data and O instant operation sampling data, converting a first RGB value and a second RGB value, and generating a normal operation image and an instant operation image according to the first RGB value and the second RGB value; and utilizing an analysis module to generate an abnormal signal when the difference between the second RGB value and the first RGB value is analyzed to be in accordance with the abnormal condition.

Based on the above-mentioned necessary technical means, an ancillary technical means derived from the present invention is a diagnostic imaging analysis method, further comprising the steps of: a display module is used to display the normal operation image and the real-time operation image.

In view of the above, the image diagnosis and analysis system and method provided by the present invention utilize the data acquisition module, the graph establishment module, the sampling module, the conversion module and the analysis module, compared with the prior art, the present invention can utilize the normal operation image and the real-time operation image to immediately analyze whether the difference between the second RGB value and the first RGB value meets the abnormal condition, so as to further know whether the servo motor driving system is in the abnormal state, and further immediately maintain and process the abnormal state.

Drawings

FIG. 1 is a block diagram of an image diagnostic analysis system according to a preferred embodiment of the present invention;

FIG. 2 is a diagram showing a normal operation waveform established by the graph establishing module;

FIG. 3 is a diagram of another normal operating waveform established by the graph establishing module;

FIG. 4 is a schematic diagram illustrating a sampling module sampling according to a normal operation waveform;

FIGS. 5A and 5B are schematic diagrams illustrating normal operation images;

FIG. 6 shows a graph of instantaneous operating waveforms established by the graph establishment module;

FIG. 7 is a diagram of another instantaneous operating waveform established by the graph establishing module;

FIG. 8 is a schematic diagram illustrating the sampling module sampling according to the instantaneous operating waveform;

FIGS. 9A and 9B are schematic diagrams illustrating live running images; and

FIG. 10 is a flowchart illustrating an image diagnostic analysis method according to a preferred embodiment of the present invention.

The reference numbers illustrate:

1 image diagnosis and analysis system

11 data acquisition module

111 analog-to-digital conversion unit

112 normalization unit

113 standardization unit

Graph creation Module

13 sampling module

131 picture frame sampling unit

132 picture frame setting unit

14 conversion module

15 analysis Module

16 display module

17 warning module

2 servo motor driving system

D, sampling direction

FN, FN': normal operation waveform diagram

F1, F1' real-time running waveform diagram

IN1, IN2 Normal running image

I1, I2 instant running image

S is a square frame

T sample width

Detailed Description

The following describes in more detail embodiments of the present invention with reference to the schematic drawings. Advantages and features of the present invention will become apparent from the following description and from the scope of the claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.

Referring to fig. 1, fig. 1 is a block diagram of an image diagnostic analysis system according to a preferred embodiment of the invention. As shown in the figure, an image diagnostic analysis system 1 is applied to a servo motor driving system 2, and includes a data acquisition module 11, a graph creation module 12, a sampling module 13, a conversion module 14, and an analysis module 15. In this embodiment, the image diagnostic analysis system 1 further includes a display module 16 and an alarm module 17. The servo motor driving system 2 generally includes a servo motor and a driver, wherein the driver generally adopts a frequency converter, which is not different from the prior art, and thus, the description thereof is omitted.

The data acquiring module 11 is used for receiving and acquiring M normal operation data and M real-time operation data of the servo motor driving system 2, and includes an adc 111, a normalizer 112, and a normalizer 113.

The adc 111 converts the data format of the received normal operation data and the real-time operation data from an analog format to a digital format for subsequent operations.

The normalization unit 112 is used to normalize the normal operation data and the real-time operation data for the subsequent operation. Data normalization is a common data processing method used to convert a value into a range from 0 to 1 without changing the distribution of data.

The normalization unit 113 is similar to the normalization unit 112 and is used for normalizing the normal operation data and the real-time operation data for the subsequent operation. The normalization is a statistical method that converts the value into a value between 0 and 1 by using a formula without changing the distribution of the data.

It should be noted that the normalizing unit 113 and the normalizing unit 112 perform similar data processing, and although both are drawn in the figure, they are only for illustration, and the image diagnosis analysis system may include one of the two.

The graph establishing module 12 is electrically connected to the data obtaining module 11, and is configured to receive the M normal operation data and the M real-time operation data, and accordingly establish a normal operation waveform graph and a real-time operation waveform graph.

The sampling module 13 is electrically connected to the graph establishing module 12, and respectively samples N normal operation data sets and N real-time operation data sets according to the normal operation waveform diagram and the real-time operation waveform diagram, where each normal operation data set includes O normal operation sampling data sampled from M normal operation data sets, and each real-time operation data set includes O real-time operation sampling data sampled from M real-time operation data sets, where N is less than M and O is less than M. In this embodiment, the sampling module 13 further includes a frame sampling unit 131 and a frame setting unit 132.

The conversion module 14 is electrically connected to the sampling module 13, and is configured to receive N times O normal operation sampling data and N times O real-time operation sampling data, convert each normal operation sampling data into a first RGB value through a color code conversion, convert each real-time operation sampling data into a second RGB value through a color code conversion, and generate a normal operation image and a real-time operation image.

The analysis module 15 is electrically connected to the conversion module 14, and analyzes the real-time running image by using the normal running image, and generates an abnormal signal when it is analyzed that the difference between the second RGB value and the first RGB value conforms to an abnormal condition.

The color code conversion multiplies 255 the values of the normal operation sampling data and the real-time operation sampling data and converts the values to form the first RGB value and the second RGB value respectively.

Next, please refer to fig. 1 to 5B, wherein fig. 2 shows a normal operation waveform established by the graph establishing module; FIG. 3 is a diagram of another normal operating waveform established by the graph establishing module; FIG. 4 is a schematic diagram illustrating a sampling module sampling according to a normal operation waveform; fig. 5A and 5B are schematic diagrams illustrating normal operation images. As shown, the data acquisition module 11 receives M normal operation data of the servo motor driving system 2. The normal operation data may be a voltage value, a current value or pulse width modulation data, and fig. 2 shows the current value schematically, but not limited thereto.

The graph creating module 12 creates a normal operation waveform graph according to the M normal operation data. When the M normal operation data are not normalized or standardized, the graph creating module 12 creates a normal operation waveform graph FN as shown in fig. 2; preferably, when the M normal operation data are normalized or normalized so that the value is between 0 and 1, the graph creating module 12 creates the normal operation waveform FN' as shown in fig. 3.

Then, the sampling module 13 uses a block S to move along a sampling direction D on the normal operation waveform pattern FN', so as to sample the normal operation sampling data. It should be noted that, each time, one normal operation data set is sampled in the block S, and each normal operation data set includes a plurality of normal operation sampling data sampled from the M normal operation data sets. The frame setting unit 132 is operative to set a sampling width T of the frame S, which is set to be substantially the same as the waveform period or one-fourth, one-half, and so on.

Generally, for the sake of sampling continuity, the number of all normal operation sampling data is larger than the number of normal operation data. Mathematically, for example, the normal operating sample data may form a matrix of values 9728 by 1 (M = 9278), while the normal operating sample data may form a matrix of values 3243 by 1298 (N =1298, o = 3243).

The conversion module 14 converts each of the normal operation sample data of each of the normal operation data sets into a first RGB value through color code conversion, and generates a normal operation image according to the first RGB value. Each of the normal operation data sets forms a normal operation image, and as shown IN the figure, the conversion module 14 generates a normal operation image IN1 according to one of the normal operation data sets and generates another normal operation image IN2 according to the other normal operation data set.

In practice, the simpler method of color code conversion may use the interface of "programmer/programmer" of the small abacus in the Microsoft system to perform conversion, multiply the normal operation sampling data by 255 to form the color code, and then convert the color code to form the first RGB value. Color code conversion, also referred to as image color digitization, converts values into corresponding RGB values to represent colors corresponding to the RGB values.

Next, please refer to fig. 1, fig. 6 to fig. 9B, wherein fig. 6 shows a waveform diagram of the real-time operation established by the graph establishing module; FIG. 7 is a diagram of another instantaneous operating waveform established by the graph establishing module; FIG. 8 is a schematic diagram illustrating the sampling module sampling according to the instantaneous operating waveform; and, fig. 9A and 9B are schematic diagrams showing live running images. As shown, the data acquisition module 11 receives M real-time operation data of the servo motor driving system 2.

The graph creating module 12 creates an instantaneous operation waveform graph according to the M instantaneous operation data. When the M pieces of instantaneous operation data are not normalized or standardized, the graph creating module 12 creates an instantaneous operation waveform diagram F1 as shown in fig. 6; preferably, when the M normal operation data are normalized or normalized so that the value is between 0 and 1, the graph creating module 12 creates the real-time operation waveform F1' as shown in fig. 7. FIG. 7 shows only an instantaneous operation waveform diagram F1 'at the time of occurrence of an abnormality'

Then, the sampling module 13 utilizes the block S to move along the sampling direction D on the real-time running waveform diagram F1', so as to sample the real-time running sampling data. It should be noted that, each time, the block S samples a real-time operation data set, and each real-time operation data set includes a plurality of real-time operation sample data sampled from the M real-time operation data sets.

The conversion module 14 converts each real-time running sample data of each real-time running data set into a second RGB value through color code conversion, and generates a real-time running image according to the second RGB value. Each of the real-time running data sets forms a real-time running image, and as shown in the figure, the conversion module 14 generates a real-time running image I1 according to one of the real-time running data sets and generates another real-time running image I2 according to the other real-time running data set.

The display module 16 displays the normal operation images (e.g., the normal operation images IN1 and IN2 IN fig. 5A and 5B) and the live operation images (e.g., the live operation images I1 and I2 IN fig. 9A and 9B) for a user to view. Comparing fig. 5A, 5B, 9A and 9B, it is apparent that the live images I1 and I2 of fig. 9A and 9B do not appear yellow. Therefore, the user can immediately know from the viewed image that the servo motor driving system 2 may be in an abnormal state at present.

The analysis module 15 also analyzes the live image (e.g., the live images I1 and I2 of fig. 9A and 9B) by using the normal running image (e.g., the normal running images IN1 and IN2 of fig. 5A and 5B). When the analysis module 15 analyzes that the difference between the second RGB value and the first RGB value satisfies the abnormal condition, an abnormal signal is generated. For example, the analysis module 15 may compare the second RGB values with the first RGB values one by one, and analyze that the abnormal condition is met when the number of the second RGB values different from the first RGB values reaches a number threshold; the analysis module 15 may also sort all the gradation ranges of the first RGB values and the second RGB values, and analyze that the abnormal condition is met when the gradation range of the second RGB value is smaller than the gradation range of the first RGB value and the difference reaches a range threshold value.

It should be noted that, according to the examination criteria, since the present invention mainly uses color images for analysis and display, the corresponding fig. 5A, 5B, 9A and 9B have the necessity of using color pictures, and the technical content of the present invention can be more clearly shown.

When the warning module 17 receives the abnormal signal, it generates a warning message to immediately remind and warn the user that the servo motor driving system 2 may be in an abnormal state. The warning information may be sound, text, light, etc. for warning.

Finally, referring to fig. 10, fig. 10 shows a flowchart of an image diagnostic analysis method according to a preferred embodiment of the invention. An image diagnosis analysis method is implemented by the image diagnosis analysis system 1 shown in fig. 1, and includes the following steps S101 to S108.

Step S101: and receiving normal operation data and instant operation data by using a data acquisition module.

Step S102: and establishing a normal operation oscillogram and an instantaneous operation oscillogram by using the graph establishing module.

Step S103: and sampling normal operation sampling data and real-time operation sampling data by using the sampling module.

Step S104: the conversion module is used for receiving the normal operation sampling data and the real-time operation sampling data, converting a first RGB value and a second RGB value, and generating a normal operation image and a real-time operation image according to the first RGB value and the second RGB value.

Step S105: and analyzing the difference between the second RGB value and the first RGB value by using an analysis module.

Step S106: whether the difference meets an exception condition.

And if yes, the process proceeds to the following step S107. And if not, re-executing step S106 to continuously determine whether the difference between the next second RGB value and the first RGB value meets the abnormal condition.

Step S107: an anomaly signal is generated using an analysis module.

Step S108: a display module is used for displaying the normal operation image and the real-time operation image.

The steps of the image diagnosis and analysis method are described in the foregoing paragraphs, and therefore, the detailed description is omitted.

In summary, the image diagnosis and analysis system and method provided by the present invention utilize the data acquisition module, the graph establishment module, the sampling module, the conversion module and the analysis module, compared with the prior art, the present invention can utilize the normal operation image and the real-time operation image to immediately analyze whether the difference between the second RGB value and the first RGB value meets the abnormal condition, so as to further know whether the servo motor driving system is in the abnormal state, and further immediately perform maintenance, repair and processing on the abnormal state. In addition, the invention can further display the normal operation image and the instant operation image by using the display module, so that a user can intuitively judge whether the servo motor driving system is in an abnormal state or not through the instant operation image.

The foregoing detailed description of the preferred embodiments is intended to more clearly illustrate the features and spirit of the present invention, and is not intended to limit the scope of the invention by the preferred embodiments disclosed above. On the contrary, it is intended to cover various modifications and equivalent arrangements included within the scope of the claims appended hereto.

Claims (12)

1. An image diagnosis and analysis system applied to a servo motor driving system comprises:

the data acquisition module is used for receiving M normal operation data and M instant operation data of the servo motor driving system;

the graph establishing module is used for receiving the M normal operation data and the M instant operation data and establishing a normal operation waveform graph and an instant operation waveform graph according to the M normal operation data and the M instant operation data;

a sampling module, which samples N normal operation data groups and N immediate operation data groups respectively according to the normal operation oscillogram and the immediate operation oscillogram, wherein each normal operation data group comprises O normal operation sampling data sampled from the M normal operation data groups, and each immediate operation data group comprises O immediate operation sampling data sampled from the M immediate operation data groups, wherein N is less than M and O is less than M;

a conversion module for receiving N times O normal operation sampling data and N times O real-time operation sampling data, converting each of the plurality of normal operation sampling data into a first RGB value through color code conversion, converting each of the plurality of real-time operation sampling data into a second RGB value through the color code conversion, and generating a normal operation image and a real-time operation image; and

the analysis module is used for analyzing the instant running image by utilizing the normal running image and generating an abnormal signal when the difference between the second RGB value and the first RGB value is analyzed to be in accordance with an abnormal condition;

the color code conversion multiplies the value of each normal operation sampling data and each real-time operation sampling data by 255 and converts the values to form the first RGB value and the second RGB value respectively.

2. The diagnostic imaging analysis system of claim 1, wherein the abnormal condition is that the first RGB values of the normal running image do not correspond to the second RGB values of the live running image.

3. The image diagnostic analysis system of claim 2, wherein the abnormal condition is that the second RGB values of the live running image do not appear to the first RGB values of the normal running image.

4. The image diagnostic and analysis system of claim 1, wherein the data acquisition module comprises an analog-to-digital conversion unit, and the analog-to-digital conversion unit is configured to convert the data format of the normal operation data and the real-time operation data from an analog format to a digital format.

5. The image diagnostic analysis system of claim 4, wherein the data acquisition module further comprises a normalization unit electrically connected to the ADC unit for normalizing the normal operation data and the real-time operation data.

6. The image diagnostic analysis system of claim 4, wherein the data acquisition module further comprises a normalization unit electrically connected to the adc for normalizing the normal operation data and the instant operation data.

7. The system of claim 1, wherein the sampling module comprises a frame sampling unit, and the frame sampling unit is configured to move between the normal operation waveform and the real-time operation waveform by using a frame to sample the normal operation sample data and the real-time operation sample data respectively.

8. The image diagnostic analysis system of claim 7, wherein the sampling module further comprises a frame setting unit electrically connected to the frame sampling unit for operatively setting a sampling width of the frame.

9. The image diagnostic analysis system of claim 1, further comprising a display module electrically connected to the conversion module for receiving and displaying the normal operation image and the live operation image.

10. The image diagnostic analysis system of claim 1, further comprising an alert module electrically connected to the analysis module for generating alert information when the abnormal signal is received.

11. An image diagnostic analysis method implemented by the image diagnostic analysis system according to claim 1, comprising the steps of:

(a) Receiving the M normal operation data and the M instant operation data by using the data acquisition module;

(b) Receiving the M normal operation data and the M immediate operation data by using the graph establishing module, and establishing a normal operation oscillogram and an immediate operation oscillogram according to the M normal operation data and the M immediate operation data;

(c) Sampling the O normal operation sampling data and the O immediate operation sampling data by using the sampling module according to the normal operation oscillogram and the immediate operation oscillogram;

(d) Receiving the O normal operation sample data and the O immediate operation sample data by using the conversion module, converting the first RGB values and the second RGB values, and generating the normal operation image and the immediate operation image;

(e) And generating the abnormal signal by using the analysis module when the difference of the second RGB value compared with the first RGB value is analyzed to accord with the abnormal condition.

12. The image diagnostic analysis method of claim 11, further comprising the steps of:

(f) And displaying the normal operation image and the instant operation image by using a display module.

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