KR102306244B1 - Method and apparatus for generating fault detecting model for device - Google Patents

Abstract

The present invention relates to a method for generating a model which uses a vibration signal to detect a defect from a device. According to the present invention, the method comprises the following steps: collecting first vibration signals of first devices and second vibration signals of second devices from vibration sensors which are attached to the first devices determined to be defective and the second devices determined to be free, respectively, and are arranged to sense vibration of at least three orthogonal axes while the first and second devices operate; extracting first statistical characteristic values and second statistical characteristic values from the first and second vibration signals converted into a frequency domain, determining a main axis and a statistical item by comparing the first statistical characteristic values with the second statistical characteristic values, and generating training data with the first statistical characteristic value and the second statistical characteristic value of the main axis and the statistical item; and training a defect detection model in map learning method by using the training data.

Description

Method and apparatus for generating a defect detection model of a device

The present invention relates to a method and apparatus for generating a defect detection model for detecting a defect using a vibration signal of a device. More specifically, the present invention relates to a method and apparatus for generating a defect detection model more efficiently by selecting information useful for detecting a defect among vibration signals of a device.

With the recent development of industrial technology, as rotary machines become larger and more precise, failures in these facilities can cause enormous economic loss or damage to human life, so fault monitoring of these rotary machines has become very important.

Since the machine including these rotating driving parts is driven under physical friction conditions, it is necessary to perform maintenance at frequent intervals to prevent breakdowns and accidents. As such, the rotating machine requires frequent maintenance, so there is a problem that maintenance cost and time are required.

Due to these problems, condition-based maintenance, which performs maintenance only when there is a possibility of a failure, has recently attracted attention. Because condition-based maintenance performs maintenance only when there is a possibility of a failure, it reduces operating costs and increases profits by reducing maintenance costs and time.

Accordingly, Korean Patent Application Laid-Open No. 2020-0101507 also disclosed a system for diagnosing and predicting a malfunction of a rotating machine using machine learning.

The fields where fault diagnosis can be applied to have great effects can be systems that require high maintenance costs, and gearboxes such as high-speed railways and wind turbines are representative.

Since the gearbox is a basic element used to transmit rotational power in a machine, research on detecting and diagnosing defects through various sensor signals is being actively conducted.

The above-mentioned background art is technical information possessed by the inventor for derivation of the present invention or acquired in the process of derivation of the present invention, and cannot necessarily be said to be a known technique disclosed to the general public prior to the filing of the present invention.

An object of the present invention is to more efficiently generate a defect detection model capable of detecting a defect by analyzing a vibration characteristic generated in a device.

An object of the present invention is to solve a problem in which computational resources are wasted by including unnecessary information in generating a model for detecting a device defect using statistical analysis and machine learning.

The problem to be solved by the present invention is not limited to the above-mentioned problems, and other problems and advantages of the present invention not mentioned can be understood by the following description, and more clearly understood by the embodiments of the present invention will be In addition, it will be appreciated that the problems and advantages to be solved by the present invention can be realized by means and combinations thereof indicated in the claims.

As a method of generating a model for detecting a device defect using a vibration signal according to an embodiment of the present disclosure, a neural network model is generated by extracting only useful information related to defect determination among a plurality of vibration signals obtained from devices. It is characterized by training, and tagging the type of information used for training to the trained model.

A method of generating a model for detecting a device defect using a vibration signal according to an embodiment of the present disclosure is attached to first devices determined to be defective and second devices determined to be free from defects, respectively. collecting the first vibration signals of the first devices and the second vibration signals of the second devices by vibration sensors arranged to sense vibration of at least three orthogonal axes while the first and second devices operate; Converting the collected first and second vibration signals into a frequency domain through a Fourier transform, and from the first vibration signals converted to the frequency domain for vibration signals of each of the first devices and each axis Extracting first statistical characteristic values, and extracting second statistical characteristic values for each of the second devices and vibration signals of each axis from the second vibration signals converted to the frequency domain, the same statistical item on the same axis determining an axis and a statistical item having a difference greater than or equal to a threshold value by comparing the first statistical characteristic values with the second statistical characteristic values, among the first statistical characteristic values and the second statistical characteristic values, the determined axis and statistics It may include generating training data by labeling the presence or absence of a device defect on the first statistical characteristic value and the second statistical characteristic value of the item, and training the defect detection model in a supervised learning method using the training data. .

Here, the first vibration signals and the second vibration signals may include vibration signals of a diagonal axis defined by three orthogonal axes in addition to the vibration signals of three axes.

In addition, the step of collecting the vibration signals may include comparing the combination of the vibration signals of the three axes with the vibration signals of the diagonal axis to evaluate whether the vibration signal of the three axes is abnormal, and based on the result of the evaluation, the vibration signal of the three axes It may include the step of collecting only the vibration signal in the normal case.

Here, the first statistical characteristic values and the second statistical characteristic values are a mean value, a median value, a minimum value, a maximum value, and a kurtosis of the vibration signals of each axis converted to the frequency domain. It may include one or more of a kurtosis value, a skewness value, a standard deviation value, and a peak-to-peak value.

In addition, the step of determining the axis and the statistical item includes a range of first kinds of statistical values for vibration signals on the first axis among the first statistical characteristic values and the vibration signals on the first axis among the second statistical characteristic values. Comparing the ranges of the first type of statistical values for each, repeating the comparing step for all types of statistical values of the vibration signals of all axes, the gap between the ranges of the statistical values compared during the repeating step It may include listing the axes and statistics items in the largest order and the smallest overlapping range, and selecting any number of upper axes and statistics items among the listed axes and statistics items.

In the method for generating a defect detection model of a device according to an embodiment of the present disclosure, in addition to the above steps, the defect detection model is added to the defect detection model so as to inform the user of axes and statistical items to be inputted to the user in the process of applying the trained defect detection model. The method may further include tagging the axis and the statistical item.

Here, the first devices and the second devices may be devices of the same model.

A method of detecting a defect in a device using a vibration signal according to another embodiment of the present disclosure includes collecting vibration signals of the device by vibration sensors attached to the device and arranged to sense vibration of at least three orthogonal axes. , converting the vibration signals of the axis determined in the above defect detection model generation method among the collected vibration signals into the frequency domain through Fourier transform, from the vibration signals converted to the frequency domain determined in the above defect detection model generation method The method may include inputting statistical characteristic values of statistical items into the defect detection model generated above, and outputting a result of whether the device is defective through the defect detection model.

An apparatus for generating a model for detecting a device defect using a vibration signal according to another embodiment of the present disclosure includes at least one processor and at least one code operatively connected to the processor and executed by the processor. a memory to store; the memory, when executed through the processor, causes the processor to be attached to first and second devices determined to be defective and to second devices determined to be non-defective, respectively. The first vibration signals of the first devices and the second vibration signals of the second devices are collected by vibration sensors arranged to sense vibration of at least three orthogonal axes during operation, and the collected first vibration signals and Transforming the second vibration signals into the frequency domain through Fourier transform, and extracting first statistical characteristic values for each of the first devices and the vibration signals of each axis from the first vibration signals converted to the frequency domain, From the second vibration signals converted to the frequency domain, second statistical characteristic values are extracted for each of the second devices and vibration signals of each axis, and the first statistical characteristic values and the second statistical characteristic values for the same statistical item on the same axis An axis and a statistical item having a difference greater than or equal to a threshold value are determined by comparing the characteristic values, and a first statistical characteristic value and a second statistical characteristic of the determined axis and the statistical item among the first statistical characteristic values and the second statistical characteristic values It is possible to generate training data by labeling the values with the presence or absence of defects in the device, and store the code that causes the training data to train the defect detection model in a supervised learning method.

In addition to this, another method for implementing the present invention, another system, and a computer-readable recording medium storing a computer program for executing the method may be further provided.

Other aspects, features and advantages other than those described above will become apparent from the following drawings, claims, and detailed description of the invention.

According to embodiments of the present disclosure, a defect detection model capable of diagnosing a defect in the number of devices according to a change in device state can be more efficiently and effectively generated using only data related to defect determination.

In addition, by efficiently preparing more important and normal data as training data for generating a defect detection model using statistical analysis, it is possible to generate a defect detection model by using computational resources more effectively.

In addition, in using the defect detection model generated according to an embodiment of the present disclosure, by filtering and inputting data important for determining a defect rather than a lot of data, defect detection can be performed more effectively.

Effects of the present invention are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a block diagram schematically illustrating a configuration of an apparatus for generating a model for detecting a device defect according to the present embodiment.
FIG. 2 is an exemplary view illustrating an output direction of a vibration signal output by the vibration sensor in FIG. 1 .
3 is an exemplary diagram illustrating a vibration signal output from a device in a normal state and a failure state according to the present embodiment.
4 is a diagram illustrating a result of preprocessing a vibration signal output from a device in a normal state and a failure state according to the present embodiment.
5 is a diagram illustrating a result of calculating a statistical characteristic value from a preprocessing result for a vibration signal output from a device in a normal state and a failure state according to the present embodiment.
6 is a diagram illustrating distributions of maximum values and kurtosis values among statistical characteristic values from pre-processing results for vibration signals output from devices in steady state and failure state according to the present embodiment.
7 is a flowchart illustrating a method for generating a defect detection model of a device according to the present embodiment.

Advantages and features of the present invention, and methods for achieving them will become apparent with reference to the detailed description in conjunction with the accompanying drawings. However, it should be understood that the present invention is not limited to the embodiments presented below, but may be implemented in a variety of different forms, and includes all transformations, equivalents, and substitutes included in the spirit and scope of the present invention. . The embodiments presented below are provided to complete the disclosure of the present invention, and to fully inform those of ordinary skill in the art to the scope of the present invention. In describing the present invention, if it is determined that a detailed description of a related known technology may obscure the gist of the present invention, the detailed description thereof will be omitted.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof. Terms such as first, second, etc. may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

Hereinafter, embodiments according to the present invention will be described in detail with reference to the accompanying drawings. decide to do

1 is a block diagram schematically illustrating a configuration of an apparatus for generating a model for detecting a device defect according to the present embodiment.

Referring to FIG. 1 , an apparatus 200 for generating a model for detecting a device defect may be connected to devices 100 that generate vibration, such as a rotating machine.

The defect detection model generating apparatus 200 may include at least one processor and a memory for storing at least one code executed by a processor operatively connected to the processor.

The preprocessor 220 , the calculator 230 , the comparison unit 240 , and the training unit 250 described below may correspond to at least one processor.

The devices 100 may include first devices 110 and second devices 120 , wherein the first devices 110 are defective and faulty devices, and the second devices 120 . ) are normal devices without defects. Here, the first devices 110 and the second devices 120 may be mechanical devices of the same model. For example, the first devices 110 may be device A, device B, and device C with faults, and the second devices 120 may be device A', device B', and device C' without faults. have.

These devices may include a gearbox used to transmit rotational power to a mechanical system. The gearbox may include a structure in which various shaft groups and gear groups (not shown) are disposed in order to transmit power required to rotate the drive shaft. Such a gearbox can be used in a wide variety of industries. And by using the gears of the gear group, a small rotational force can be converted into a large rotational force, or a low rotational force can be converted into a high-speed rotation. In the gearbox, a housing (not shown) of a certain shape forms the exterior. The housing may be formed to have a predetermined size and shape according to various mechanical devices or vehicles in which the gearbox is used. In addition, various gears (not shown), a driving shaft (not shown), a rotating shaft (not shown), etc. may be disposed inside the housing to transmit a driving motor (not shown) and driving force for generating driving force.

On the other hand, the gearbox generates various vibrations in the gearbox due to unstable movement in the process of generating and transmitting the driving force using the driving motor, and these vibrations may be transmitted to the drive shaft and the housing forming the gearbox. In addition, when the shaft vibration of the drive shaft is transmitted to the housing, in addition to the vibration of the housing itself, vibration of other components connected to the housing may be caused, and noise due to the vibration may be induced.

In the past, experts analyzed the change in vibration amount and vibration characteristics of the gearbox to find the cause of the defect and set up appropriate countermeasures. However, due to the lack of experience of experts and changes in diversified facility characteristics, it is difficult to make an accurate diagnosis, so the method of setting the size of vibration and notifying the manager of an abnormal condition only when vibration exceeding the size occurs has been used.

However, this method can only determine the degree of failure of a device such as a rotating machine, and since the cause or progress of the failure cannot be known, it cannot be actively dealt with, so the judgment is left to experts again. Therefore, when a defect occurs in a device such as a rotating machine, a technology is needed to prevent an accident in advance by identifying the cause of the defect in advance and predicting the progress at the stage before the failure. Accurate diagnosis is required.

Recently, with the development of machine learning technology, a technology capable of diagnosing using a deep neural network model is being used. However, in order to generate such a defect detection model, it is necessary to prepare a large amount of training data, and a considerable amount of computational resources and training period are required to train the model with such a large amount of training data.

In the present disclosure, in order to efficiently and effectively generate a defect detection model, information useful for determining a defect may be determined, and a defect detection model may be generated using the useful information. According to this method of the present disclosure, it is possible to generate an effective defect detection model with much less computational resources and time to generate the defect detection model.

The apparatus 200 for generating a defect detection model may generate a model capable of detecting whether a device is defective by performing statistical analysis and machine learning by receiving the vibration signal generated from the devices 110 and 120 as an input. Here, whether the device is defective may be determined depending on whether the device is operating normally or when the device is broken. In this embodiment, the apparatus 200 for generating a defect detection model may include a preprocessor 220 , a calculator 230 , a comparison unit 240 , and a training unit 250 .

Each device is provided with a vibration sensor to detect and output a vibration signal generated by the device. In this embodiment, the vibration sensor may include first to third vibration sensors arranged to sense vibration of at least three orthogonal axes. In addition, the vibration sensor may include a fourth vibration sensor arranged to sense vibration of a diagonal axis defined by three orthogonal axes in addition to the vibration signals of three axes.

Here, in the defect detection model generating apparatus, the signal coming from the first devices 110 and the signal coming from the second devices 120 can be distinguished, and the first device 110 is a device without a defect and the second It is known that devices 120 are defective devices.

The preprocessor 200 converts the vibration signals in the time domain into the frequency domain. The calculator 230 may calculate statistical characteristic values for the vibration signals in the frequency domain. The comparator 240 compares the vibration signal from the defective device and the vibration signal from the non-defective device, and among these vibration signals, the statistical value of the vibration signal of the axis is more than a threshold value (axis and statistics) item) can be selected.

When the value of the selected statistical item of the vibration signal of the axis selected by the comparison unit 240 is labeled according to whether the device is defective, training data may be prepared. The training unit 250 may train the initial model using the prepared training data.

According to this training method, it is possible to train the model in a short time with less computational resources than when training the neural network model using the values of the vibration signals of all axes.

FIG. 2 is an exemplary view illustrating an output direction of a vibration signal output by the vibration sensor of FIG. 1 . Referring to FIG. 2 , the first vibration sensor may output a first vibration signal (g_x vibration signal) by detecting vibration in the horizontal (x) direction generated in the device. The second vibration sensor may output a second vibration signal (g_y vibration signal) by detecting vibration in the vertical (y) direction generated in the device. The third vibration sensor may output a third vibration signal (g_z vibration signal) by detecting vibration in the axis (z) direction generated in the device. The fourth vibration sensor detects the vibration in the diagonal (T) direction, which is a combination of the horizontal (x) direction, the vertical (y) direction, and the axial (z) direction generated in the device to output a fourth vibration signal (g_T vibration signal). can

3 is an exemplary diagram illustrating a vibration signal output from a device in a normal state and a failure state according to the present embodiment. Referring to FIG. 3, FIG. 3A shows a first vibration signal (g_x vibration signal) generated in the device 100 in a normal state, and FIG. 3B is a rotating machine 100 in a broken state. The generated first vibration signal (g_x vibration signal) is shown. In particular, FIG. 3B illustrates a first vibration signal generated in the device 100 when a tooth is broken.

The preprocessor 220 may convert a time domain vibration signal output from the vibration sensor into a frequency domain vibration signal. The vibration signal in the frequency domain includes more unique information that can be used to distinguish between normality and failure of the device 100 . In this embodiment, the preprocessor 220 performs fast Fourier transform (FFT) on the first to fourth vibration signals of the time domain output from the first vibration sensor to the fourth vibration sensor to generate the frequency domain. The first vibration signal to the fourth vibration signal may be output. The Fourier transform of a time function is a complex-valued function of frequency, the magnitude (absolute value) indicates the amount of frequency present in the original function, and the argument may be an offset of the fundamental sine wave at that frequency.

In this embodiment, the preprocessor 220 may include a first preprocessor 221 to a fourth preprocessor 224 . The first preprocessor 221 may convert a first vibration signal (g_x vibration signal) of the time domain output from the first vibration sensor into a first vibration signal (g_x FFT signal) of the frequency domain. The second preprocessor 222 may convert a second vibration signal (g_y vibration signal) of the time domain output from the second vibration sensor into a second vibration signal (g_y FFT signal) of the frequency domain. The third preprocessor 223 may convert the third vibration signal g_z vibration signal of the time domain output from the third vibration sensor into a third vibration signal g_z FFT signal of the frequency domain. The fourth preprocessor 224 may convert a fourth vibration signal (g_T vibration signal) of the time domain output from the fourth vibration sensor into a fourth vibration signal (g_T FFT signal) of the frequency domain.

4 is a diagram illustrating a result of preprocessing a vibration signal output from a device in a normal state and a failure state according to the present embodiment. Referring to FIG. 4 , FIG. 4A shows that a first vibration signal (g_x vibration signal) in the time domain generated in the device 100 in a normal state is preprocessed and output as a first vibration signal (g_x FFT signal) in the frequency domain. An example is shown, and Figure 4b is a first vibration signal (g_x FFT signal) in the frequency domain by pre-processing the first vibration signal (g_x vibration signal) in the time domain generated in the rotating machine 100 in a broken state (g_x FFT signal) An example output is shown.

The calculator 230 may calculate a statistical characteristic value for the vibration signal of the frequency domain output from the preprocessor 220 . The vibration signal of the frequency domain output from the preprocessor 220 can be directly input into the deep neural network model to be described later, but since it requires a complex structure and a long training time when working with high-dimensional input data, training the deep neural network model In order to reduce the input data for this purpose, a statistical characteristic value may be calculated. For a model trained by the method of the present disclosure, the same type of input value may be input in a subsequent application step.

Meanwhile, the pre-processing unit may be configured to select and collect first to fourth vibration signals received from the first to fourth vibration sensors.

Among the received vibration signals, there may be vibration signals that are not suitable for evaluating the state of the machine due to noise or other external environment changes, internal processing errors, and the like. Therefore, it may be necessary to evaluate the suitability of the vibration signal and select and collect only the appropriate signal.

For example, the evaluation of the vibration signals is performed by comparing a combination of first to third vibration signals collected from the first to third vibration sensors with a fourth vibration signal collected from the fourth vibration sensor to determine the second vibration signal. It is performed by evaluating whether the first to fourth vibration signals are abnormal, and such selective collection may be performed when the first to fourth vibration signals are normal based on the evaluation.

For example, the first to third axes are x, y, and z axes orthogonal to each other. If the fourth axis is a diagonal axis defined by the x, y, and z axes, the square value of the magnitude of the vibration signal (first vibration signal) of the first axis in a set of vibration signals (that is, from one device) and the square of the magnitude of the vibration signal (second vibration signal) of the second axis and the square of the magnitude of the vibration signal (third vibration signal) of the third axis is the diagonal of the square of the magnitude of the vibration signal of the fourth axis should be equal to the value.

If, in one set of vibration signals, the sum of the square value of the magnitude of the first vibration signal, the square value of the magnitude of the second vibration signal, and the square value of the magnitude of the third vibration signal is the magnitude of the fourth vibration signal If there is a difference of more than a certain degree from the square value of , it can be seen that these sets of vibration signals are not accurately observed values. Therefore, the processor of the defect detection model generating apparatus may not include the vibration signals in this case in the collection.

In this embodiment, the calculator 230 calculates a mean value, a median value, a minimum value, a maximum value, a kurtosis value, and a skewness of the vibration signal in the frequency domain. A statistical characteristic value including at least one of a value, a standard deviation value, and a peak-to-peak value may be calculated. This is because using these statistical values rather than the vibration signal itself reflects the characteristics of the vibration signal while training the model with smaller size data.

Here, the kurtosis is an index indicating the degree of sharpness of the probability distribution, and the kurtosis may increase as the distribution of signal values gathers near a specific value to form a sharp shape. In addition, skewness is an indicator indicating the asymmetry of the probability distribution, and as the signal bias (the degree to which the distribution of signal values shifts to one side with respect to the average of the signal) increases, the skewness may also increase. The positive amplitude (peak-to-peak) is an index indicating the overall width of the signal and may indicate the difference between the smallest value and the largest value in the signal.

In this embodiment, the calculator 230 may include a first calculator 231 to a fourth calculator 234 . The first calculator 231 may calculate the above-described eight statistical characteristic values for the first vibration signal g_x FFT signal of the frequency domain output from the first preprocessor 221 . The second calculator 232 may calculate the above-described eight statistical characteristic values for the second vibration signal g_y FFT signal of the frequency domain output from the second preprocessor 222 . The third calculator 233 may calculate the above-described eight statistical characteristic values for the third vibration signal g_z FFT signal of the frequency domain output from the third preprocessor 223 . The fourth calculation unit 234 may calculate the above-described eight statistical characteristic values for the fourth vibration signal g_T FFT signal of the frequency domain output from the fourth preprocessor 224 .

The comparison unit 240 compares the calculation results of the statistical characteristic values output from the first calculation unit 231 to the fourth calculation unit 234, and compares the data ( It is possible to determine the statistic value of which statistic item of the vibration signal of which axis).

In the above-described example, the first calculator 231 to the fourth calculator 234 calculates eight statistical feature values, respectively, so the comparator 240 receives eight statistical feature values for each of the four axes and calculates one value. A total of 32 statistical characteristic values can be received per device.

The comparison unit 240 may determine an axis and a statistical item having a difference greater than or equal to a threshold value by comparing the first statistical characteristic values and the second statistical characteristic values for the same statistical item on the same axis.

For example, the comparator 240 compares each of the eight statistical characteristic values of the x-axis vibration signal from the devices A, B, and C to the eight statistical values of the x-axis vibration signal from the devices A', B', and C'. Each of the characteristic values can be compared.

That is, the comparator 240 determines the range of kurtosis values of the vibration signals on the x-axis from devices A, B, and C, which are defective devices, and the vibrations on the x-axis, from devices A', B', and C', which are devices without defects. A range of kurtosis values of a signal can be compared.

The comparison unit 240 may repeat the above comparison for all statistical items of the vibration signals of all axes. Through this iteration, the gap between the ranges of statistical values of the devices with and without defects being compared (if the ranges are far apart), or the size of the overlapping ranges (if the ranges overlap with each other) can be calculated. can

The comparison unit 240 lists axes and statistical items in the order of the largest gap between the ranges of statistical values of defective devices and non-defective devices according to the result of this operation and the smallest overlapping range can do.

Referring to FIG. 5 , a gap between statistical values of defective devices and non-defective devices in the kurtosis (kur) value among statistical characteristic values for the vibration signal on the x-axis (refer to FIG. 5(a)) This is the largest, followed by a large gap between the ranges of statistical values of defective and non-defective devices in skew value. Next, although they overlap each other, the overlapping range of the statistical values of the devices and the defect-free devices in the max value is small.

Accordingly, in the case of FIG. 5 , when the top three axes and statistical items are listed, the kurtosis value of the x-axis, the skewness value of the x-axis, and the maximum value of the x-axis may be arranged in the order.

The values of these three axes and statistical items can be selected as the index data that best shows the case where the device is defective and the case where there is no defect.

Here, the number of higher-order items may be determined as an arbitrary number according to device performance such as processing power and memory capacity of the device for generating a defect detection model. That is, the larger the processing power and memory capacity of the model generating apparatus, the greater the number of items can be determined.

After the type of data to be used for training the defect detection model (which statistical value of which axis) is determined, the training unit 250 may prepare the training data by labeling the determined type of data. Since it is known to the defect detection model generating apparatus 200 that the first devices 110 are defective devices and the second devices 120 are non-defective devices, each data can be displayed as normal or malfunctioning. Labeling is possible.

The training unit 250 may train the initially set deep neural network model in a supervised learning method using the labeled training data. Here, the initially set deep neural network model is an initial model designed to be configured as a model capable of detecting device defects, and parameter values are set to arbitrary initial values.

The initial model may be completed as a defect detection model capable of accurately detecting a device defect while optimizing parameter values while training through the above-described training data.

Here, since the training unit 250 is trained to detect a device defect using statistical characteristic values calculated from the frequency domain vibration signal without using the time domain vibration signal, training complexity can be reduced. In addition, since training is performed using only data determined to be useful for judging defects, not all statistical characteristic values of all axes, training can be performed more quickly and at a lower cost.

For example, the deep neural network model includes a first vibration signal (g_x FFT signal) in the frequency domain, a second vibration signal (g_y FFT signal) in the frequency domain, and a third vibration signal (g_z FFT signal) in the frequency domain, It may be configured to determine whether a device has failed based on a maximum value and a kurtosis value among statistical characteristic values for the fourth vibration signal (g_T FFT signal) in the frequency domain.

5 is a diagram illustrating a result of calculating a statistical characteristic value from a preprocessing result for a vibration signal output from a device in a normal state and a failure state according to the present embodiment.

Referring to FIG. 5 , FIG. 5A is a first vibration signal in the frequency domain obtained by preprocessing the first vibration signal g_x vibration signal in the time domain generated in the device 100 in a normal state and a broken state. The distribution of statistical characteristics calculated from the x-axis vibration signal, g_x FFT signal) is shown.

5B is a second vibration signal (g_y) in the frequency domain obtained by pre-processing the second vibration signal (y-axis vibration signal, g_y vibration signal) of the time domain generated in the device 100 in the normal state and the broken state. The distribution of statistical characteristics calculated from the FFT signal) is shown.

5C is a first vibration signal (g_z) in the frequency domain obtained by preprocessing the third vibration signal (z-axis vibration signal, g_z vibration signal) of the time domain generated in the device 100 in the normal state and the broken state. The distribution of statistical characteristics calculated from the FFT signal) is shown.

5D is a fourth vibration signal in the frequency domain obtained by pre-processing the fourth vibration signal in the time domain (the vibration signal of the diagonal T axis, the g_T vibration signal) generated in the device 100 in the normal state and the broken state. The distribution of statistical characteristics calculated from (g_T FFT signal) is shown.

Among the distributions of statistical characteristics calculated from the first vibration signal (g_x FFT signal) in the frequency domain in FIG. 5A , the difference between the kurtosis values of the normal state and the broken state is shown in FIGS. 5B to 5B . A normal state calculated from the second vibration signal (g_y FFT signal) of the frequency domain, the third vibration signal (g_z FFT signal) of the frequency domain, and the fourth vibration signal (g_T FFT signal) of the frequency domain shown in 5d And it can be seen that it is larger than the difference between the kurtosis (max) values of the broken state.

In addition, in the distribution of statistical characteristics calculated from the first vibration signal (g_x FFT signal) in the frequency domain in FIG. 5A , the difference between the skewness values of the normal state and the broken state is shown in FIG. 5B . The normal calculated from the second vibration signal (g_y FFT signal) of the frequency domain, the third vibration signal (g_z FFT signal) of the frequency domain, and the fourth vibration signal (g_T FFT signal) of the frequency domain shown in FIGS. 5D to 5D . ) state and it can be seen that it is larger than the difference between the skewness values of the broken state.

From FIG. 5 , if the arbitrary number of data items serving as criteria for determining failure and normality is two, the kurtosis and skewness values of the x-axis vibration signal will be the criteria for determining whether the device 100 is defective. it can be seen that In addition, if the arbitrary number of data items serving as a criterion for determining a failure and a normality is three, the kurtosis value, skewness value, and maximum value of the x-axis vibration signal are the criteria for determining whether the device 100 is defective. can be

6 is a diagram illustrating distributions of maximum values and kurtosis values among statistical characteristic values from pre-processing results for vibration signals output from devices in steady state and failure state according to the present embodiment.

Referring to FIG. 6 , FIG. 6A is a first vibration signal in the frequency domain obtained by preprocessing the first vibration signal g_x vibration signal in the time domain generated in the rotating machine 100 in a normal state and a broken state. The distribution of the maximum value and the kurtosis value among the statistical characteristic values calculated from (g_x FFT signal) is shown. 6B is a second vibration signal (g_y FFT signal) in the frequency domain obtained by preprocessing the second vibration signal (g_y vibration signal) in the time domain generated in the rotating machine 100 in a normal state and a broken state. The distribution of the maximum value and the kurtosis value among the calculated statistical characteristic values is shown.

Figure 6c shows the third vibration signal (g_z FFT signal) in the frequency domain obtained by preprocessing the third vibration signal (g_z vibration signal) in the time domain generated in the rotating machine 100 in the normal state and the broken state. The distribution of the maximum value and the kurtosis value among the calculated statistical characteristic values is shown.

Figure 6d is from the fourth vibration signal (g_T FFT signal) in the frequency domain pre-processed the fourth vibration signal (g_T vibration signal) in the time domain generated in the rotating machine 100 in a normal state and a broken state The distribution of the maximum value and the kurtosis value among the calculated statistical characteristic values is shown.

In FIG. 6A , the distribution of the maximum (max) value and the kurtosis value of the normal state and the broken state calculated from the first vibration signal (g_x FFT signal) of the frequency domain is identified in FIGS. 6B to The normal calculated from the second vibration signal (g_y FFT signal) of the frequency domain, the third vibration signal (g_z FFT signal) of the frequency domain, and the fourth vibration signal (g_T FFT signal) of the frequency domain shown in FIG. 6D It can be seen that it is clearer than the identification of the distribution of the max and kurtosis values of the state and the broken state.

The fact that the first vibration signal (g_y FFT signal) in the frequency domain from FIG. 6 serves as a criterion for determining whether a device is defective is only an example derived through an experiment and is not limited thereto, and in another embodiment, the first vibration signal (g_y FFT signal) signal) to the fourth vibration signal (g_T FFT signal) may be a reference, and may be other statistical characteristic values of those vibration signals.

Whether a certain statistical characteristic value of a signal of a certain axis is a criterion for determining whether a device is defective may be determined by the method of a flowchart to be described below.

In this embodiment, the defect detection model generating apparatus 200 generates a model for detecting a defect of the device 100, an artificial intelligence (AI) algorithm and/or machine learning (machine learning) in a 5G communication environment. ) to run the algorithm.

Here, artificial intelligence (AI) is a field of computer science and information technology that studies how computers can do the thinking, learning, and self-development that can be done with human intelligence. It could mean making it possible to imitate intelligent behavior.

Also, AI does not exist by itself, but has many direct and indirect connections with other fields of computer science. In particular, in modern times, attempts are being made to introduce artificial intelligence elements in various fields of information technology and use them to solve problems in that field.

Machine learning is a branch of artificial intelligence, which can include fields of study that give computers the ability to learn without explicit programming. Specifically, machine learning can be said to be a technology that studies and builds a system and an algorithm for learning based on empirical data, making predictions, and improving its own performance. Algorithms in machine learning can take the approach of building specific models to make predictions or decisions based on input data, rather than executing strictly set static program instructions.

As a machine learning method of such an artificial neural network, both unsupervised learning and supervised learning may be used.

In addition, deep learning technology, which is a type of machine learning, can learn by going down to a deep level in multiple stages based on data. Deep learning can represent a set of machine learning algorithms that extract core data from a plurality of data as the level increases.

The deep learning structure may include an artificial neural network (ANN). For example, the deep learning structure is composed of a deep neural network (DNN) such as a convolutional neural network (CNN), a recurrent neural network (RNN), and a deep belief network (DBN). can be The deep learning structure according to the present embodiment may use various well-known structures. For example, the deep learning structure according to the present embodiment may include CNN, RNN, DBN, and the like. RNN is widely used in natural language processing, etc., and is an effective structure for processing time-series data that changes with the passage of time. DBN may include a deep learning structure composed of multiple layers of restricted boltzman machine (RBM), a deep learning technique. By repeating RBM learning, when a certain number of layers is reached, a DBN having the corresponding number of layers can be configured. CNNs can include models that simulate human brain functions based on the assumption that when a person recognizes an object, it extracts the basic features of an object, then performs complex calculations in the brain and recognizes an object based on the result. .

7 is a flowchart illustrating a method for generating a defect detection model of a machine according to the present embodiment. In the following description, descriptions of parts overlapping with those of FIGS. 1 to 5 will be omitted.

The steps of FIG. 7 may be performed by a processor of the apparatus 200 for generating a defect detection model.

In step S710 , the detection model generating apparatus 200 may receive vibration signals from vibration sensors attached to the devices 100 . Here, the devices 100 may include the first devices 110 pre-determined to be defective and the second devices 120 pre-determined as having no defects.

Vibration sensors attached to the devices may be arranged to sense vibrations in at least three orthogonal x-axis, y-axis, and z-axis directions. In addition, a vibration sensor for detecting vibration of a diagonal axis defined by the x-axis, the y-axis, and the z-axis (eg, an axis in the (1, 1, 1) direction) may be disposed.

Since the defect detection model generating apparatus 200 knows which device is a defective device or a normal device, the defect detection model generating apparatus 200 determines whether the vibration signal to be collected is collected from a defective device or from a normal device. You can tell if it's been collected.

The defect detection model generating apparatus 200 may selectively collect vibration signals by determining whether the vibration signals are normal or not. The defect detection model generating apparatus 200 may compare the combination of the vibration signals of the three orthogonal axes with the vibration signals of the diagonal axes to evaluate whether the vibration signal of the three axes is abnormal. The defect detection model generating apparatus 200 may collect only the vibration signal when the vibration signals of the three axes are normal based on the result of such evaluation.

For example, if the sum of the square values of the sizes of the vibration signals on three orthogonal axes of the device A is different from the square values of the sizes of the vibration signals on the diagonal axis, the vibration signal from the device A may not be a normal signal.

Accordingly, the defect detection model generating apparatus 200 may exclude the signal from the device A in performing the subsequent process.

In step S720 , the collected vibration signals may be converted into vibration signals in the frequency domain through a Fourier transform. In the frequency domain, signal characteristics can be analyzed more clearly.

In operation S730, statistical characteristic values may be extracted for the vibration signals of each axis from the vibration signals converted into the frequency domain. For example, first statistical characteristic values are extracted with respect to the vibration signals of each axis from the first vibration signals converted into the frequency domain, and vibration signals of each axis are extracted from the second vibration signals converted into the frequency domain. It is possible to extract second statistical characteristic values for .

That is, a statistical characteristic value may be extracted for each vibration signal of each axis obtained from each device among the first devices. For example, if there are device A, device B, and device C, 8 statistical characteristic values are extracted for each vibration signal of each of the x, y, and z axes of device A, and this can be performed in both B and C. For example, among the vibration signals from defective devices, 8 statistical characteristic values for the vibration signal on the x-axis, 8 statistical characteristic values for the vibration signal on the y-axis, and 8 statistical characteristic values for the vibration signal on the z-axis Eight statistical characteristic values for the characteristic values and the vibration signal of the T-axis are extracted, and the same type of signals can be extracted from devices without defects.

In step S740 , the defect detection model generating apparatus 200 may compare statistical characteristic values of the vibration signal of the defective device with the statistical characteristic values of the vibration signal of the non-defective device for the same statistical items on the same axis.

For example, to compare the average value of the vibration signals on the x-axis of devices A, B, and C, which are previously determined to be defective, with the average value of the vibration signals on the x-axis of devices, A', B', C', which are previously determined to be free of defects. it means you can

In operation S750 , the apparatus 200 for generating a defect detection model may determine an axis and a statistical item having a difference greater than or equal to a threshold value based on the result of the above comparison.

Here, the step of determining the axis and the statistical item having a difference greater than or equal to the threshold value includes a range of a first type of statistical values for vibration signals of a first axis among the first statistical characteristic values, and a range of the first type of statistical values among the second statistical characteristic values. The range of the first kind of statistical values for the vibration signals of the first axis may be compared, and the step of comparing may be repeated for all kinds of statistical values of the vibration signals of all axes.

During this iterative step, the axes and statistics items are listed in the order of the largest gap between the ranges of statistical values compared and the smallest overlapping ranges, and the upper axis and statistics of any number of the listed axes and statistics items You can select an item.

For example, referring to the case of FIG. 5, among the statistical characteristic values for the vibration signal on the x-axis (refer to FIG. 5(a)), the statistics of devices with and without defects in the kurtosis (kur) value The gap between the ranges of values is the largest, followed by the gap between the ranges of statistical values of defective devices and non-defective devices in skew value. Next, although they overlap each other, the overlapping range of the statistical values of the devices and the defect-free devices in the max value is small.

Accordingly, in the case of FIG. 5 , when the top three axes and statistical items are listed, the kurtosis value of the x-axis, the skewness value of the x-axis, and the maximum value of the x-axis may be arranged in the order.

The values of these three axes and statistical items can be selected as the index data that best shows the case where the device is defective and the case where there is no defect.

Here, the number of higher-order items may be determined as an arbitrary number according to device performance such as processing power and memory capacity of the device for generating a defect detection model. That is, the larger the processing power and memory capacity of the model generating apparatus, the greater the number of items can be determined.

In step S760 , after the type of data to be used for training the defect detection model (which statistical value on which axis) is determined, the defect detection model generating apparatus 200 may generate training data by labeling the determined type of data. Since it is known to the defect detection model generating apparatus 200 that the first devices 110 are defective devices and the second devices 120 are non-defective devices, each data can be displayed as normal or malfunctioning. labeling is possible. According to the above example, normal or faulty labeling can be performed with respect to the kurtosis value, skewness value, and maximum value of the vibration signals of the x-axis among the vibration signals of the first devices 110 and the second devices 120 . have.

In step S770, the initially set deep neural network model may be trained in a supervised learning method using the labeled training data. Here, the initially set deep neural network model is an initial model designed to be configured as a model capable of detecting device defects, and parameter values are set to arbitrary initial values.

The initial model may be completed as a defect detection model capable of accurately detecting a device defect while optimizing parameter values while training through the above-described training data.

Meanwhile, information on the axes and statistical items determined above may be tagged in the defect detection model to inform the user of the axes and statistical items to be input for use of the subsequently generated defect detection model.

The user can collect only the vibration signal of the corresponding axis from the collection stage through the information tagged in the defect detection model and input it into the defect detection model, and thus the computational resources required to apply the model can be efficiently used.

In another embodiment, the computer processor itself may be designed to read the axis and statistical items tagged in the defect detection model, and selectively collect vibration signals according to the input to the defect detection model.

When the defect detection model is generated in this way, the defect of the device may be determined using the completed defect detection model.

The defect detection apparatus in which the defect detection model is stored may collect vibration signals of the device, which is a defect determination target, by vibration sensors attached to the device and disposed to sense vibration of at least three orthogonal axes.

The defect detection apparatus may convert the vibration signals of the axis tagged in the model among the collected vibration signals into the frequency domain through Fourier transform.

Thereafter, the defect detection apparatus may input statistical characteristic values of statistical items that are previously tagged and determined from the vibration signals converted into the frequency domain into the defect detection model trained in the above manner.

The defect detection apparatus may determine whether the device is defective by outputting a result of whether the device is defective through the defect detection model.

Such a defect detection apparatus detects a defect using a deep neural network model trained in advance to determine whether a device is defective using statistical characteristic values of a vibration signal derived from a vibration sensor attached to a device to be determined. In this embodiment, the deep neural network model may be a neural network model trained in advance using training data including statistical characteristic values of vibration signals generated in the device and the state of the device (one of normal and malfunction).

Since the types of signals that must be input for defect detection are reduced through the above method, effective defect detection can be performed with less computational resources and time than when all of the vibration signals are used.

The embodiment according to the present invention described above may be implemented in the form of a computer program that can be executed through various components on a computer, and such a computer program may be recorded in a computer-readable medium. In this case, the medium includes a hard disk, a magnetic medium such as a floppy disk and a magnetic tape, an optical recording medium such as CD-ROM and DVD, a magneto-optical medium such as a floppy disk, and a ROM. , RAM, flash memory, and the like, hardware devices specially configured to store and execute program instructions.

Meanwhile, the computer program may be specially designed and configured for the present invention, or may be known and used by those skilled in the computer software field. Examples of the computer program may include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

In the specification of the present invention (especially in the claims), the use of the term "above" and similar referential terms may be used in both the singular and the plural. In addition, when a range is described in the present invention, each individual value constituting the range is described in the detailed description of the invention as including the invention to which individual values belonging to the range are applied (unless there is a description to the contrary). same as

The steps constituting the method according to the present invention may be performed in an appropriate order, unless there is an explicit order or description to the contrary. The present invention is not necessarily limited to the order in which the steps are described. The use of all examples or exemplary terms (eg, etc.) in the present invention is merely for the purpose of describing the present invention in detail, and unless defined by the claims, the scope of the present invention is limited by the examples or exemplary terminology. it's not going to be In addition, those skilled in the art will recognize that various modifications, combinations, and changes may be made in accordance with design conditions and factors within the scope of the appended claims or their equivalents.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and the scope of the spirit of the present invention is not limited to the scope of the scope of the present invention. will be said to belong to

100: devices
200: Defect detection model generation device
220: preprocessor
230: output unit
240: comparison unit
250: training department

Claims (17)

A method of generating a model for detecting a defect in a device using a vibration signal, the method comprising:
Vibration sensors attached to the first devices determined to be defective and the second devices determined to be free, respectively, and arranged to sense vibration of at least three orthogonal axes while the first and second devices operate collecting first vibration signals of the first devices and second vibration signals of the second devices by
converting the collected first and second vibration signals into a frequency domain through a Fourier transform;
First statistical characteristic values are extracted with respect to the vibration signals of each axis from the first vibration signals converted into the frequency domain, and the first statistical characteristic values are extracted for the vibration signals of each axis from the second vibration signals converted into the frequency domain. 2 extracting statistical feature values;
determining an axis and a statistical item having a difference equal to or greater than a threshold value by comparing the first statistical characteristic values with the second statistical characteristic values for the same statistical items on the same axis;
generating training data by labeling the presence or absence of a device defect to the determined first and second statistical characteristic values of the axes and statistical items among the first statistical characteristic values and the second statistical characteristic values; and
using the training data to train a defect detection model in a supervised learning method,
The first vibration signals and the second vibration signals include vibration signals of a diagonal axis defined by the three orthogonal axes in addition to the vibration signals of the three axes,
The collecting step is
comparing the combination of the vibration signals of the three axes with the vibration signals of the diagonal axis to evaluate whether the vibration signal of the three axes is abnormal; and
Comprising the step of collecting only the vibration signal in the case of normal among the vibration signals of the three axes based on the result of the evaluation step,
A method for generating a defect detection model of a device. delete The method of claim 1,
The first statistical characteristic values and the second statistical characteristic values are a mean value, a median value, a minimum value, a maximum value, a kurtosis ( Including one or more of a kurtosis value, a skewness value, a standard deviation value, and a peak-to-peak value,
A method for generating a defect detection model of a device. The method of claim 1,
A method of generating a model for detecting a defect in a device using a vibration signal, the method comprising:
Vibration sensors attached to the first devices determined to be defective and the second devices determined to be free, respectively, and arranged to sense vibration of at least three orthogonal axes while the first and second devices operate collecting first vibration signals of the first devices and second vibration signals of the second devices by
converting the collected first and second vibration signals into a frequency domain through a Fourier transform;
First statistical characteristic values are extracted with respect to the vibration signals of each axis from the first vibration signals converted into the frequency domain, and the first statistical characteristic values are extracted for the vibration signals of each axis from the second vibration signals converted into the frequency domain. 2 extracting statistical feature values;
determining an axis and a statistical item having a difference equal to or greater than a threshold value by comparing the first statistical characteristic values with the second statistical characteristic values for the same statistical items on the same axis;
generating training data by labeling the presence or absence of a device defect to the determined first and second statistical characteristic values of the axes and statistical items among the first statistical characteristic values and the second statistical characteristic values; and
using the training data to train a defect detection model in a supervised learning method,
The step of determining the axis and the statistical item is,
The range of the first kind of statistical values for the vibration signals on the first axis among the first statistical characteristic values and the statistical values of the first kind for the vibration signals on the first axis among the second statistical characteristic values comparing ranges;
repeating the comparing step for all kinds of statistical values of vibration signals of all axes;
listing the axes and statistical items in the order of the largest gap between the ranges of statistical values compared during the repeating step and the smallest overlapping range; and
selecting any number of top axes and statistics items from among the listed axes and statistics items;
A method for generating a defect detection model of a device. 5. The method of claim 4,
Prior to the selecting step,
detecting a computing power and a memory capacity of a device performing fault detection model generation; and
determining the arbitrary number based on the computing power and the memory capacity,
A method for generating a defect detection model of a device. A method of generating a model for detecting a defect in a device using a vibration signal, the method comprising:
Vibration sensors attached to the first devices determined to be defective and the second devices determined to be free, respectively, and arranged to sense vibration of at least three orthogonal axes while the first and second devices operate collecting first vibration signals of the first devices and second vibration signals of the second devices by
converting the collected first and second vibration signals into a frequency domain through a Fourier transform;
First statistical characteristic values are extracted with respect to the vibration signals of each axis from the first vibration signals converted into the frequency domain, and the first statistical characteristic values are extracted for the vibration signals of each axis from the second vibration signals converted into the frequency domain. 2 extracting statistical feature values;
determining an axis and a statistical item having a difference equal to or greater than a threshold value by comparing the first statistical characteristic values with the second statistical characteristic values for the same statistical items on the same axis;
generating training data by labeling the presence or absence of a device defect to the determined first and second statistical characteristic values of the axes and statistical items among the first statistical characteristic values and the second statistical characteristic values; and
using the training data to train a defect detection model in a supervised learning method,
The method further comprising the step of tagging the axis and statistical items determined in the determining step to the defect detection model so as to inform the user of the axes and statistical items to be input in the process of applying the trained defect detection model,
A method for generating a defect detection model of a device. The method of claim 1,
The first devices and the second devices are devices of the same model,
A method for generating a defect detection model of a device. A method of detecting a device defect using a vibration signal, comprising:
collecting vibration signals of the device by vibration sensors attached to the device and arranged to sense vibration of at least three orthogonal axes;
converting the vibration signals of the axis determined in claim 1 among the collected vibration signals into a frequency domain through a Fourier transform;
inputting statistical characteristic values of the statistical items determined in claim 1 from the vibration signals converted into the frequency domain into the defect detection model of claim 1; and
outputting a result of whether the device is defective through the defect detection model,
The vibration signals include a vibration signal of a diagonal axis defined by the three orthogonal axes in addition to the vibration signals of the three axes,
The collecting step is
comparing the combination of the vibration signals of the three axes with the vibration signals of the diagonal axis to evaluate whether the vibration signal of the three axes is abnormal; and
Comprising the step of collecting only the vibration signal in the case of normal among the vibration signals of the three axes based on the result of the evaluation step,
A method for detecting defects in a device. delete 9. The method of claim 8,
The statistical characteristic values are a mean value, a median value, a minimum value, a maximum value, a kurtosis value, and a skewness value of the vibration signals of each axis converted to the frequency domain. , including at least one of a standard deviation value and a peak-to-peak value,
A method for detecting defects in a device. A computer-readable recording medium storing a computer program for executing the method of any one of claims 1, 3 to 8, and 10 using a computer. An apparatus for generating a model for detecting a device defect by using a vibration signal, comprising:
at least one processor; and
a memory operatively coupled to the processor and storing at least one code executed by the processor;
The memory, when executed by the processor, causes the processor to:
Vibration sensors attached to the first devices determined to be defective and the second devices determined to be free, respectively, and arranged to sense vibration of at least three orthogonal axes while the first and second devices operate Collecting the first vibration signals of the first devices and the second vibration signals of the second devices by
converting the collected first and second vibration signals into a frequency domain through a Fourier transform;
From the first vibration signals converted to the frequency domain, first statistical characteristic values are extracted with respect to the vibration signals of each of the first devices and each axis, and from the second vibration signals converted to the frequency domain, the second vibration signals are extracted. Extracting second statistical characteristic values for each of the two devices and the vibration signals of each axis,
determining an axis and a statistical item having a difference greater than or equal to a threshold value by comparing the first statistical characteristic values and the second statistical characteristic values for the same statistical items on the same axis;
generating training data by labeling the presence or absence of a device defect to the determined first and second statistical characteristic values of the axes and statistical items among the first statistical characteristic values and the second statistical characteristic values;
store a code causing to train a defect detection model in a supervised learning manner using the training data;
The first vibration signals and the second vibration signals include vibration signals of a diagonal axis defined by the three orthogonal axes in addition to the vibration signals of the three axes,
The memory, when executed by the processor, causes the processor to:
Comparing the combination of the vibration signals of the three axes with the vibration signals of the diagonal axis to evaluate whether the vibration signal of the three axes is abnormal,
Further storing a code causing to collect only the vibration signal in the normal case among the vibration signals of the three axes based on the result of the evaluating step,
An apparatus for generating a defect detection model of a device. delete 13. The method of claim 12,
The first statistical characteristic values and the second statistical characteristic values are a mean value, a median value, a minimum value, a maximum value, a kurtosis ( Including one or more of a kurtosis value, a skewness value, a standard deviation value, and a peak-to-peak value,
An apparatus for generating a defect detection model of a device. An apparatus for generating a model for detecting a device defect by using a vibration signal, comprising:
at least one processor; and
a memory operatively coupled to the processor and storing at least one code executed by the processor;
The memory, when executed by the processor, causes the processor to:
Vibration sensors attached to the first devices determined to be defective and the second devices determined to be free, respectively, and arranged to sense vibration of at least three orthogonal axes while the first and second devices operate Collecting the first vibration signals of the first devices and the second vibration signals of the second devices by
converting the collected first and second vibration signals into a frequency domain through a Fourier transform;
From the first vibration signals converted to the frequency domain, first statistical characteristic values are extracted with respect to the vibration signals of each of the first devices and each axis, and from the second vibration signals converted to the frequency domain, the second vibration signals are extracted. Extracting second statistical characteristic values for each of the two devices and the vibration signals of each axis,
determining an axis and a statistical item having a difference greater than or equal to a threshold value by comparing the first statistical characteristic values and the second statistical characteristic values for the same statistical items on the same axis;
generating training data by labeling the presence or absence of a device defect to the determined first and second statistical characteristic values of the axes and statistical items among the first statistical characteristic values and the second statistical characteristic values;
store a code causing to train a defect detection model in a supervised learning manner using the training data;
The first statistical characteristic values and the second statistical characteristic values are a mean value, a median value, a minimum value, a maximum value, a kurtosis ( It includes at least one of a kurtosis value, a skewness value, a standard deviation value, and a peak-to-peak value,
The memory, when executed by the processor, causes the processor to:
The range of the first kind of statistical values for the vibration signals on the first axis among the first statistical characteristic values and the statistical values of the first kind for the vibration signals on the first axis among the second statistical characteristic values compare ranges,
repeating the comparing step for all kinds of statistical values of vibration signals of all axes,
List the axes and statistical items in the order of the largest gap between the ranges of statistical values compared during the repeated operation and the smallest overlapping range,
further storing code for causing the selection of any number of upper axes and statistics items of the listed axes and statistics items;
An apparatus for generating a defect detection model of a device. An apparatus for generating a model for detecting a device defect by using a vibration signal, comprising:
at least one processor; and
a memory operatively coupled to the processor and storing at least one code executed by the processor;
The memory, when executed by the processor, causes the processor to:
Vibration sensors attached to the first devices determined to be defective and the second devices determined to be free, respectively, and arranged to sense vibration of at least three orthogonal axes while the first and second devices operate Collecting the first vibration signals of the first devices and the second vibration signals of the second devices by
converting the collected first and second vibration signals into a frequency domain through a Fourier transform;
From the first vibration signals converted to the frequency domain, first statistical characteristic values are extracted with respect to the vibration signals of each of the first devices and each axis, and from the second vibration signals converted to the frequency domain, the second vibration signals are extracted. Extracting second statistical characteristic values for each of the two devices and the vibration signals of each axis,
determining an axis and a statistical item having a difference greater than or equal to a threshold value by comparing the first statistical characteristic values and the second statistical characteristic values for the same statistical items on the same axis;
generating training data by labeling the presence or absence of a device defect to the determined first and second statistical characteristic values of the axes and statistical items among the first statistical characteristic values and the second statistical characteristic values;
store a code causing to train a defect detection model in a supervised learning manner using the training data;
The memory, when executed by the processor, causes the processor to:
The trained defect detection model further stores a code causing the axes and statistical items to be tagged so as to inform the user of the axes and statistical items to be input in the application process,
An apparatus for generating a defect detection model of a device. 13. The method of claim 12,
The first devices and the second devices are devices of the same model,
An apparatus for generating a defect detection model of a device.

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