WO2019163433A1

WO2019163433A1 - Signal analysis system, method and program

Info

Publication number: WO2019163433A1
Application number: PCT/JP2019/002723
Authority: WO
Inventors: 徹矢澤
Original assignee: 徹矢澤; 霜田幸雄; 株式会社シンフォディアフィル
Priority date: 2018-02-21
Filing date: 2019-01-28
Publication date: 2019-08-29
Also published as: JP6935655B2; JP2019144102A

Abstract

[Problem] To provide a signal analysis system suitable for detecting a state of a vibration body. [Solution] According to the present invention, provided is a signal analysis system including: a signal sampling unit which samples, during a prescribed period, voltage values of a signal to be analyzed, and acquires time series data on the voltage values; a threshold voltage setting unit which creates a frequency distribution indicting the frequencies of appearance of positive voltage values composing the time series data, and sets a threshold voltage on the basis of a voltage value, the frequency of appearance of which is rapidly reduced; a peak time acquisition unit which compares each voltage value composing the time series data with the threshold voltage in time series order, and samples voltage values larger than the threshold voltage to thereby acquire, as a peak time, a time at which the maximum value is sampled among voltage values sampled during a subsequent first short period; a DFA target data generation unit which generates DFA target data on the basis of the peak time; and a DFA execution unit which executes a DFA on the basis of the DFA target data to acquire a scaling index.

Description

Signal analysis system, method and program

The present invention relates to a signal analysis system, and more particularly to a signal analysis system suitable for detecting an abnormality of a vibrating body.

Conventionally, an acoustic emission method (Acoustic Emission, AE) is known as a method for diagnosing a damaged state of a rolling bearing. For example, Japanese Patent Application Laid-Open No. 8-159151 (Patent Document 1) measures the ultrasonic wave generated from a bearing with a microphone and compares the component intensity of the frequency of interest of the acoustic signal with a master curve prepared in advance. Disclosed is a bearing diagnosis method for predicting the damage state and estimating the remaining life.

JP-A-8-159151

The present invention has been made in view of the above prior art, and an object thereof is to provide a novel signal analysis system suitable for detecting the state of a vibrating body.

In the process of examining the configuration for detecting the state of the vibrating body, the present inventor has a scaling index obtained by analyzing the fluctuation of the peak interval of the vibration waveform generated by the vibrating body by DFA (Detrended Fluctuation Analysis). We found that it can be used as an indicator of the state of Based on this discovery, the present inventor succeeded in developing an apparatus that automatically obtains a peak interval from a vibration waveform and outputs a scaling index, and arrived at the present invention.

That is, according to the present invention, the voltage value of the analysis target signal is sampled for a predetermined period to obtain time-series data of the voltage value, and the appearance frequency of the positive voltage value constituting the time-series data is represented. A threshold voltage setting unit that creates a frequency distribution and sets a threshold voltage on the basis of a voltage value at which the appearance frequency rapidly decreases, and compares each voltage value constituting the time series data with the threshold voltage in time series order. A peak time acquiring unit that samples a voltage value exceeding the voltage and samples a maximum value among the voltage values sampled in the first minute period thereafter as a peak time; and a DFA based on the peak time A DFA target data generation unit that generates target data; a DFA execution unit that executes a DFA based on the DFA target data and obtains a scaling index; Signal analysis system comprising provided.

As described above, according to the present invention, a novel signal analysis system suitable for detecting the state of a vibrating body is provided.

The figure which shows the signal analysis system of this embodiment. The flowchart of the process which the signal analysis system of this embodiment performs. The conceptual diagram for demonstrating the threshold voltage setting process of this embodiment. The flowchart of the threshold voltage setting process of this embodiment. The conceptual diagram for demonstrating the threshold voltage setting process of this embodiment. The flowchart of the peak time acquisition process of this embodiment. The conceptual diagram for demonstrating the peak time acquisition process of this embodiment. The flowchart of the DFA object data generation process of this embodiment. The conceptual diagram for demonstrating the DFA object data generation process of this embodiment. The flowchart of DFA of this embodiment. The conceptual diagram for demonstrating DFA of embodiment. The graph which shows the result of a present Example.

Hereinafter, the present invention will be described with reference to embodiments shown in the drawings, but the present invention is not limited to the embodiments shown in the drawings. In the drawings referred to below, the same reference numerals are used for common elements, and the description thereof is omitted as appropriate.

FIG. 1 shows a signal analysis system 100 according to an embodiment of the present invention. The signal analysis system 100 according to the present embodiment uses a vibrating body as an analysis target, and includes a vibration sensor 10 and a computer 20. The analysis target here includes a moving body that emits periodic vibrations as well as a moving body that emits periodic vibrations (for example, a rotating body such as a rolling bearing).

The vibration sensor 10 is means for converting the vibration generated by the vibration body to be analyzed into a voltage waveform signal and outputting the voltage waveform signal. It may be a type sensor.

On the other hand, the computer 20 is an information processing apparatus for executing DFA (Detrended Fluctuation Analysis) based on the voltage waveform signal input from the vibration sensor 10, and includes a signal sampling unit 21, a moving average processing unit 22, The threshold voltage setting unit 23, the peak time acquisition unit 24, the storage unit 25, the DFA target data generation unit 26, the DFA execution unit 27, and the analysis result output unit 28 are configured.

The signal sampling unit 21 is a unit that samples a voltage waveform signal (hereinafter referred to as an analysis target signal) input from the vibration sensor 10 over a predetermined period and acquires time-series data of voltage values.

The moving average processing unit 22 is means for performing a moving average process on the time-series data of the voltage values acquired by the signal sampling unit 21.

The threshold voltage setting unit 23 is a means for setting a threshold voltage for detecting the peak of the analysis target signal.

The peak time acquisition unit 24 is a means for acquiring the occurrence time of the peak of the analysis target signal.

The DFA target data generation unit 26 is means for generating DFA (Detrended Fluctuation Analysis) target data (hereinafter referred to as DFA target data) based on the peak time acquired by the peak time acquisition unit 24.

The DFA execution unit 27 is a means for executing the DFA based on the DFA target data and acquiring the scaling index.

The analysis result output unit 28 is a means for outputting the scaling index acquired by the DFA execution unit 27 as an analysis result.

In this embodiment, the computer 20 functions as the above-described units by executing a predetermined program.

As described above, the functional configuration of the signal analysis system 100 of the present embodiment has been outlined. Next, processing executed by the signal analysis system 100 will be described based on the flowchart shown in FIG. In the following description, FIG. 1 will be referred to as appropriate.

In step 101, the signal sampling unit 21 samples the analysis target signal input from the vibration sensor 10 for a predetermined period to obtain time-series data (discrete data) of voltage values.

In subsequent step 102, the moving average processing unit 22 performs moving average processing on the time-series data of the voltage values acquired by the signal sampling unit 21 to smooth the data, thereby reducing noise.

In subsequent step 103, the threshold voltage setting unit 23 executes the threshold voltage setting process based on the time-series data of the voltage value after the moving average process.

FIG. 3 shows time-series data of voltage values after the moving average process. Here, paying attention to the positive voltage value of the time-series data of voltage values, the number of data below the noise level is large, and the number of data sharply decreases around the noise level. This means that it is reasonable to set the threshold voltage _VTh with reference to a voltage value at which the frequency of appearance of data decreases rapidly in detecting a positive peak of a signal. In the present embodiment, a threshold voltage V _Th for detecting a signal peak is set based on this concept.

Hereinafter, the threshold voltage setting process executed by the threshold voltage setting unit 23 will be described based on the flowchart shown in FIG. Here, a case where a positive threshold voltage is set will be described as an example.

First, in step 201, a frequency distribution representing the frequency of appearance is created for a plurality of voltage values constituting time-series data of voltage values. Specifically, after defining a class of positive voltage values having an appropriate class width, the positive voltage value among the acquired voltage values is assigned to each class to obtain the frequency.

In the following step 202, for each class of the frequency distribution created in the previous step 201, a frequency difference between the class adjacent to the class is obtained, and this is associated with the class. For the last class, there is no adjacent class, so the frequency difference value is blank. The “adjacent class” here is either “adjacent class in ascending order” or “adjacent class in descending order” (hereinafter the same).

In the subsequent step 203, for each class of the frequency distribution created in the previous step 201, the frequency difference associated with the class and the "frequency difference" difference associated with the adjacent class (ie, the class) (Difference of “frequency difference” between other classes adjacent to) and associate this with the class. The difference of “frequency difference” obtained here has the same meaning as the change rate of change in the appearance frequency of data. For the classes adjacent in the last order, since the frequency difference value of the last order is blank, the difference value of “frequency difference” is blank.

Finally, in step 204, the class associated with the maximum value of the “frequency difference” is specified, and the threshold voltage V _Th is set based on the class value of the class. Here, the class specified in step 204 is a voltage value range in which the change rate of the change in the appearance frequency of data is maximized, and the class value of the class is a voltage value slightly higher than the noise level. Note that “setting the threshold voltage V _{Th based} on the class value” here means that the class value is set as the threshold voltage V _Th as it is, and a value obtained by adding an appropriate margin to the class value is the threshold voltage. This is a concept including both setting as _VTh .

As described above, the case where the positive threshold voltage is set has been described. However, when the negative threshold voltage is set, the frequency of appearance of the negative voltage value among the plurality of sampled voltage values in the previous step 201 is set. The frequency distribution to be expressed is created, and the threshold voltage V _Th (negative value) may be set in the previous step 204 with reference to the class value of the class associated with the maximum value of the “frequency difference”.

Here, in order to deepen the understanding of the threshold voltage setting process described above, the graph shown in FIG. 5 is referred to. Here, the graph shown in FIG. 5 is a line graph (×) showing the relationship between the class and the frequency difference on the histogram created based on the frequency distribution representing the frequency of occurrence of the voltage value, the class and the “frequency”. This is a graph in which line graphs (♦) showing the relationship of the difference of “no difference” are superimposed.

Referring to the graph shown in FIG. 5, in the threshold voltage setting process described above, when a positive peak is extracted, the threshold voltage is set based on the class value of the class corresponding to the plot point P ₁ of the line graph (♦). When V _Th (positive value) is set and a negative peak is extracted, the threshold voltage V _Th (negative value) is set on the basis of the class value corresponding to the plot point P ₂ of the line graph (♦). Will be.

Subsequently, another method of threshold voltage setting processing will be described based on the flowchart shown in FIG. Will be described.

First, in step 211, a frequency distribution representing the frequency of appearance of voltage values is created in the same procedure as in step 201, and a histogram representing the frequency of appearance of voltage values is created based on the frequency distribution.

In the subsequent step 212, a curve function indicating the relationship between the voltage value and the appearance frequency of data is obtained by curve fitting for the created histogram. The curve function obtained here corresponds to the solid line f in the graph shown in FIG.

In the following step 213, the second derivative is obtained by performing second order differentiation on the obtained curve function. The second derivative obtained here corresponds to a function indicating the relationship between the voltage value and the change rate of the change in the appearance frequency of the data.

In the following step 214, a voltage value giving the maximum value of the obtained second derivative (that is, the maximum value of the change rate of the appearance frequency of data) is obtained, and the threshold voltage V _Th is set based on the obtained voltage value. To do. In step 214, when a positive peak is extracted, a voltage value that gives the maximum value of the second derivative in the domain of the positive voltage value is obtained, and when a negative peak is extracted, a negative peak is obtained. Find the voltage value that gives the maximum value of the second derivative in the domain of the voltage value.

As described above, the threshold voltage V _Th for detecting the peak of the analysis target signal is automatically set to an appropriate value by the procedure described above.

Again, returning to FIG.

When the threshold voltage V _Th is set by the threshold voltage setting process in step 103, the process proceeds to step 104. In the following step 104, the peak time acquisition unit 24 executes a peak time acquisition process. Hereinafter, the peak time acquisition process executed by the peak time acquisition unit 24 will be described with reference to a flowchart shown in FIG. 6 and a conceptual diagram of time-series data of voltage values shown in FIG. The analysis target signal has a positive peak and a negative peak. Here, a case where a positive peak is detected will be described as an example.

First, in step 301, the counter [q] is set to the initial value “1”.

In subsequent step 302, the first voltage value constituting the time-series data of the voltage value is compared with the threshold voltage _VTh . As a result, when the voltage value does not exceed the threshold voltage _VTh (step 303, No), the value of the counter [q] is incremented by 1 (step 307), and the second voltage value is set as the threshold voltage _VTh . Compare (step 302). Thereafter, until the voltage value exceeds the threshold voltage _{V Th,} repeat the above process, when the voltage value exceeds the threshold voltage _{V Th} (step 303, Yes), the process proceeds to step 304.

Following step 304, it identifies the maximum value V _max among the sampled voltage value to the threshold voltage V _Th subsequent minute period by sampling the voltage value exceeding T1, a time at which sample the maximum value V _max acquired as the peak time t, stored in the storage unit 25 it in the form of time-series data {t _i}.

In the subsequent step 305, the number r of data sampled in the minute period T2 continuous to the minute period T1 is added to the value of the counter [q]. Here, r corresponds to a value obtained by multiplying the time length of T2 by the sampling rate of the analysis target signal.

Thereafter, the above-described series of processing is repeated until the value of the counter [q] exceeds the total data number Q of the time-series data of the voltage value (No in Step 306), and the value of the counter [q] When the time is exceeded (step 306, Yes), the peak time acquisition process is terminated.

In the peak time acquisition process described above, the voltage value sampled in the minute period T2 is added in step 305 by adding the number of data r sampled in the minute period T2 to the value of the counter [q]. Sampling time is not acquired as peak time. As a result, it is possible to prevent noise generated during the minute period T2 from being erroneously counted as a peak. Here, the micro periods T1 and T2 have meaning as time window lengths, and may be set to appropriate values according to the frequency characteristics of the vibration to be analyzed.

The case where a positive peak is detected has been described above. However, when a negative peak is detected, a negative value is set in advance as the threshold voltage _VTh , and in the previous step 303, the q-th voltage is set. It is determined whether or not the value is lower than the threshold voltage V _Th (negative value). In the previous step 304, the voltage value sampled in the minute period T1 after sampling the voltage value lower than the threshold voltage V _Th is calculated. The minimum value V _min is specified from the inside, and the time at which the minimum value V _min is sampled may be acquired as the peak time t.

Again, returning to FIG.

When the time series data {t _i } at the peak time t is acquired in step 104, the process proceeds to step 105. In the subsequent step 105, the DFA target data generation unit 26 executes DFA target data generation processing. Hereinafter, processing executed by the DFA target data generation unit 26 will be described based on a flowchart shown in FIG.

In step 401, the time series data {t _i } of the peak time t is loaded from the storage unit 25.

Subsequent step 402, time-series data {t _i} from the A-number of peak time t contained in (A-1) to calculate the number of the peak interval x, and elements (A-1) number of the peak interval x Time series data {x _i } of the peak interval x is generated. FIG. 9A conceptually shows the time series data {x _i } generated in step 402.

In step 403, after calculating the average value _{x ave} of the peak interval x constituting the series data _{{x i}} time, by subtracting the average value _{x ave} from the elements constituting the series data _{{x i}} time, Time series data {(x _i -x _ave ) _i } is generated. FIG. 9B conceptually shows the time series data {(x _i -x _ave ) _i } generated in step 403.

In the subsequent step 404, the time series data {(x _i -x _ave ) _i } is integrated to generate time series data {y _i }. The following formula (1) shows a calculation formula of the time series data {y _i }.

Specifically, the time series data {y _i } is generated by adding the elements of the time series data {(x _i -x _ave ) _i } in time series order. FIG. 9C conceptually shows the time series data {y _i } generated in step 404.

In the subsequent step 405, the time series data {y _i } generated by the above-described series of procedures is stored in the storage unit 25 as DFA target data, and the DFA target data generation process is terminated.

Again, returning to FIG.

When the threshold voltage V _Th is set in step 105, the process proceeds to step 106. In subsequent step 106, the DFA execution unit 27 executes DFA (Detrended Fluctuation Analysis) to obtain a scaling index. Hereinafter, the processing executed by the DFA execution unit 27 will be described based on the flowchart shown in FIG.

First, in step 501, time series data {y _i }, which is DFA target data, is loaded from the storage unit 25.

In the subsequent step 502, the box size data in the box size range set from the box size data stored in the storage unit 25 is loaded. Here, the box size data means a set of a plurality of box sizes (integers) used in the DFA, the box size means the number of data elements, and the box size range is used in the DFA. It means the range that box size (integer) takes.

In the subsequent step 503, the first box size [N] (for example, [10]) is set from the loaded box size data (integer set).

In the following step 504, the time series data {y _i } loaded in step 501 is divided by the box size [N] set at that time. For example, when the box size set at that time is [10], the time-series data {y _i } is divided into small sections (hereinafter referred to as boxes) including 10 elements. In this case, since the time series data {y _i } is composed of M elements, in step 504, the time series data {y _i } is divided into M / N boxes. FIG. 11A shows time-series data {y _i } divided by the box size [10]. In this case, each box (BOX (1), BOX (2), BOX (3)...) Includes 10 elements.

In the subsequent step 505, for each of the divided boxes (BOX (1), BOX (2), BOX (3)...), An approximate curve is fitted to N data existing in the box, The value on the approximate curve is determined as the local trend y _v for each box. Here, fitting of the approximate curve can be performed by a least square method using a linear function to a quartic function. The approximate curve here is a concept including a straight line. FIG. 11B shows approximate curves y _v (y _v (1), y _v (2), y _v (3) for each box (BOX (1), BOX (2), BOX (3)...). ) ...) is shown in a fitted state.

In the subsequent step 506, for each of the boxes (BOX (1), BOX (2), BOX (3)...), The local trend y _v determined for the box is subtracted from each element constituting the box to obtain a time series. Data {z _i } is generated. The following equation (2) shows an equation of time series data {z _i }, and FIG. 11C shows a graph of the time series data {z _i } generated in step 506.

In the subsequent step 507, each of the boxes (BOX (1), BOX (2), BOX (3)...) Of the time series data {z _i } is indicated by enclosing the box with the first element (dotted circle). ) And the value of the last element (indicated by a dotted square).

In the subsequent step 508, the root mean square [S] of the difference (difference between the beginning and the end) obtained for all boxes is calculated.

In the subsequent step 509, a set of numerical values (N, S) consisting of the root mean square [S] calculated in step 508 and the box size [N] set at that time is recorded.

In subsequent step 510, it is determined whether or not a set (N, S) has been recorded for all box sizes [N] included in the box size data loaded in step 502. As a result, when the recording of the set (N, S) is not completed for all the box sizes [N] (step 510, No), the process proceeds to step 511.

In step 511, the next box size [N] (for example, [11]) is newly set from the values included in the box size data loaded in step 502. Thereafter, the processing returns to step 504 again. Thereafter, until it is determined in step 510 that the set (N, S) has been recorded for all box sizes [N], the above-described series of processing (S504 to S504). S511) is repeated. As a result, finally, a set of numerical values (N, S) corresponding to the number of box sizes is recorded.

Here, the relationship between the box size [N] and the root mean square [S] is defined as a function S (N) shown in the following equation (3). In the following formula (3), “M” indicates the number of elements of the time series data {y _i } that is the DFA target data, “N” indicates the box size, and “z _{jN + N} −z _{jN + 1} ” indicates j The difference (displacement) between the _first element (z _{jN + 1} ) and the last element (z _{jN + N} ) of the th box is shown.

When the set (N, S) is recorded for all box sizes [N] (step 510, Yes), the process proceeds to step 512, and the recorded set (N, S) is plotted on a log-log scale. Fitting a linear function to

Finally, in step 513, the slope of the fitted linear function is acquired as the scaling index α, and the process ends.

As mentioned above, although the DFA execution part 27 has demonstrated the process which calculates | requires the scaling index | exponent α based on DFA object data, the method of DFA mentioned above is excellent at the point which is effective also about the phenomenon in which the scaling index | exponent α exceeds 1. . For details of the modified DFA method originally developed by the present inventor, see “Modified DFA Method for Fluctuation Analysis, Toru Yazawa, 1st Edition, MERCUMAR Co., Ltd., December 20, 2015 See "day".

On the other hand, in an embodiment targeting only a phenomenon in which the scaling index α does not exceed 1, the DFA execution unit 27 may be configured to execute a conventional DFA. In this case, the DFA execution unit 27 calculates the average F (N) of variance in each box of the time series data {y _i } based on the following formulas (4) and (5), and the box size N and variance F May be plotted on a logarithmic scale, and the slope of the linear function fitted thereto may be obtained as the scaling index α. In the following formulas (4) and (5), “N” indicates the box size, “M” indicates the number of elements of the time series data {y _i }, and “y _v (jN + k)” indicates the location trend. The fitting function of is shown.

Again, returning to FIG.

When the scaling index α is acquired by the DFA process in Step 106, the process proceeds to Step 107. In the subsequent step 107, the analysis result output unit 28 outputs the acquired scaling index α as the analysis result, and ends the process.

In this embodiment, it is possible to detect an abnormal state of the vibrating body to be analyzed using the output scaling index as an index. More precisely, by comparing the output scaling index with a reference value (scaling index obtained in a normal state), an abnormal state (ie, accompanied by the generation of abnormal noise) detected by the conventional acoustic emission method It is possible to detect a potential abnormal state occurring at a stage before the occurrence of the abnormal state.

As described above, the present invention has been described with the embodiment. However, the present invention is not limited to the above-described embodiment, and the functions and effects of the present invention are within the scope of other embodiments that can be considered by those skilled in the art. As long as it plays, it is included in the scope of the present invention.

Hereinafter, the signal analysis system of the present invention will be described more specifically using examples, but the present invention is not limited to the examples described later.

An experiment was conducted to verify the performance of the signal analysis system of the present invention. In this experiment, four types of bearings shown in Table 1 below are rotated by a motor, and output from a vibration sensor attached to a housing (bearing box) in a state where a force of 40 kgf is applied in a direction perpendicular to the rotation axis. The voltage waveform signal was sampled at a sampling rate of 100 kHz.

In this experiment, seven different conditions shown in Table 2 below were set for the micro period T1 and the micro period T2, the peak time was acquired from the acquired sampling data, and DFA target data was generated. In Table 2 below, the time length of each period is represented by the number of data.

Also, in this experiment, for each DFA target data generated under the condition of 28 (4 × 7), the DFA was executed with the box size range set to 30 to 270, and the scaling index was obtained.

FIG. 12 collectively shows scaling indices obtained under the above-described 28 conditions. As shown in FIG. 12, the scaling index for the three types of bearings (“normal”, “wear”, “foreign matter contamination”) is normal in an environment with a lot of natural vibration in any of the seven conditions related to the minute period. While the value stayed in the vicinity of the value (0.5), the scaling index related to the “insufficient grease” bearing maintained a value close to 0.8 higher than the normal value in any of the seven conditions related to the minute period. This result shows that the “grease-deficient” bearing is in an abnormal state different from that of the other bearings (“normal”, “wear”, “foreign matter contamination”), and the signal analysis system of the present invention has detected it. Means that.

DESCRIPTION OF SYMBOLS 10 ... Vibration sensor 20 ... Computer 21 ... Signal sampling part 22 ... Moving average process part 23 ... Threshold voltage setting part 24 ... Peak time acquisition part 25 ... Storage part 26 ... DFA object data generation part 27 ... DFA execution part 28 ... Analysis result Output unit 100 ... signal analysis system

Claims

A signal sampling unit that samples the voltage value of the analysis target signal for a predetermined period to obtain time-series data of the voltage value;
Creating a frequency distribution representing the frequency of appearance of positive voltage values constituting the time series data, and a threshold voltage setting unit for setting a threshold voltage with reference to a voltage value at which the frequency of appearance decreases rapidly;
The voltage value constituting the time series data is compared with the threshold voltage in time series order, the voltage value exceeding the threshold voltage is sampled, and the maximum value among the voltage values sampled in the first minute period thereafter. A peak time acquisition unit that acquires the time at which sampling is performed as a peak time;
A DFA target data generation unit that generates DFA target data based on the peak time;
A DFA execution unit that acquires a scaling index by executing DFA based on the DFA target data;
Including signal analysis system.
A signal sampling unit that samples the voltage value of the analysis target signal for a predetermined period to obtain time-series data of the voltage value;
Creating a frequency distribution representing the frequency of appearance of negative voltage values constituting the time-series data, and a threshold voltage setting unit for setting a threshold voltage with reference to a voltage value at which the frequency of appearance decreases rapidly;
The voltage value constituting the time series data is compared with the threshold voltage in time series order, the voltage value lower than the threshold voltage is sampled, and the minimum value among the voltage values sampled in the first minute period thereafter A peak time acquisition unit that acquires the time at which sampling is performed as a peak time;
A DFA target data generation unit that generates DFA target data based on the peak time;
A DFA execution unit that acquires a scaling index by executing DFA based on the DFA target data;
Including signal analysis system.
The threshold voltage setting unit includes:
The threshold voltage is set with reference to the class value of the class in which the difference in the frequency difference between the adjacent other classes in the frequency distribution is maximized,
The signal analysis system according to claim 1 or 2.
The threshold voltage setting unit includes:
A histogram is created based on the frequency distribution, a curve function indicating the relationship between the voltage value and the appearance frequency is obtained by curve fitting with respect to the histogram, a second derivative is obtained by performing second order differentiation on the curve function, and the second Setting the threshold voltage with reference to a voltage value giving the maximum value of the second derivative;
The signal analysis system according to claim 1 or 2.
The peak time acquisition unit
Do not acquire the sampling time of the voltage value sampled in the second minute period that is continuous with the first minute period as the peak time,
The signal analysis system according to any one of claims 1 to 4.
A moving average processing unit that performs a moving average process on the time-series data;
The threshold voltage setting unit sets the threshold voltage based on the time series data after moving average processing,
The peak time acquisition unit acquires the peak time based on the time series data after the moving average process,
The signal analysis system according to any one of claims 1 to 5.
A method for analyzing a signal comprising:
Sampling the voltage value of the signal to be analyzed for a predetermined period to obtain time-series data of the voltage value;
Creating a frequency distribution representing the appearance frequency of positive voltage values constituting the time series data, and setting a threshold voltage with reference to a voltage value at which the appearance frequency suddenly decreases;
The voltage value constituting the time series data is compared with the threshold voltage in time series order, the voltage value exceeding the threshold voltage is sampled, and the maximum value among the voltage values sampled in the first minute period thereafter. A step of acquiring the sampling time as a peak time;
Generating DFA target data based on the peak time;
Performing a DFA based on the DFA target data to obtain a scaling index;
Including methods.
A method for analyzing a signal comprising:
Sampling the voltage value of the signal to be analyzed for a predetermined period to obtain time-series data of the voltage value;
Creating a frequency distribution representing the frequency of appearance of negative voltage values constituting the time-series data, and setting a threshold voltage based on a voltage value at which the frequency of appearance decreases rapidly;
The voltage value constituting the time series data is compared with the threshold voltage in time series order, the voltage value lower than the threshold voltage is sampled, and the minimum value among the voltage values sampled in the first minute period thereafter A step of acquiring the sampling time as a peak time;
Generating DFA target data based on the peak time;
Performing a DFA based on the DFA target data to obtain a scaling index;
Including methods.
The step of setting the threshold voltage includes:
Including the step of setting the threshold voltage with reference to a class value of a class in which a difference in frequency difference between adjacent classes in the frequency distribution is maximum.
The method according to claim 7 or 8.
The step of setting the threshold voltage includes:
A histogram is created based on the frequency distribution, a curve function indicating the relationship between the voltage value and the appearance frequency is obtained by curve fitting with respect to the histogram, a second derivative is obtained by performing second order differentiation on the curve function, and the second Setting the threshold voltage relative to a voltage value that gives a maximum value of the second derivative,
The method according to claim 7 or 8.
In the step of obtaining the peak time,
Do not acquire the sampling time of the voltage value sampled in the second minute period that is continuous with the first minute period as the peak time,
The method according to any one of claims 7 to 10.
Performing a moving average process on the time-series data,
In the step of setting the threshold voltage, the threshold voltage is set based on the time series data after the moving average process,
In the step of acquiring the peak time, the peak time is acquired based on the time series data after the moving average process.
The method according to any one of claims 7 to 11.
A computer-executable program for causing a computer to execute the steps of the method according to any one of claims 7 to 12.