Measurement device and mass spectrometer

Abstract

To provide a measuring device and a mass spectrometer that make it possible to estimate the range of abnormal area that is the source of noise superimposed on a detection signal.SOLUTION: A measuring device includes a measurement unit that measures a measurement target and outputs a measurement signal, a detector that detects the measurement signal and outputs a detection signal, an analysis processing unit that calculates and outputs analysis results from the detection signal, an analysis result display device that displays analysis results, and a device control module that controls the measurement unit and outputs a control signal, and further includes an abnormal detection module that detects noise in the measuring device, and the abnormal detection module stores drive intervals of each component of the measuring device in a memory resource, and for each component of the measuring device, whether noise is mixed is determined on the basis of the corresponding drive interval stored in the memory resource and the analysis result of the analysis processing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a measuring device and a mass spectrometer. [Background technology]

[0002] In recent years, measuring instruments such as mass spectrometers have been used in various fields of life science. In particular, mass spectrometers, which detect measurement signals output by a measurement unit and analyze the components of samples, have become indispensable for drug discovery and drug testing in the medical and pharmaceutical fields.

[0003] An example of a prior art document in this technical field is Patent Document 1. Patent Document 1 describes a means for preventing the waveform of a detection signal for ion species of the same mass-to-charge ratio (m / z) from becoming a series of multiple peaks due to the spread of ion species of the same m / z during flight and the response time of a microchannel plate (MCP) in an ion analyzer, thereby improving the ability to distinguish between signal waveforms and noise. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2019-114528 Summary of the Invention [Problem to be solved by the invention]

[0005] Patent Document 1 describes noise reduction for a specific noise, but does not describe how to identify the noise source from among multiple noises.

[0006] In particular, measurement devices that obtain measurement signals by identifying noise components and improving the signal strength of the detection target pose the following challenges. Noise that degrades the S / N ratio of a measurement unit includes detection signals from sensors within the measurement unit that are not the detection target, thermal noise at ambient temperatures, noise generated by other electrical equipment inside the measurement device that houses the measurement unit, and exogenous noise incident from outside the measurement device. Examples of noise-generating electrical equipment inside the measurement device include switching power supplies and digital circuits. Furthermore, exogenous noise incident from outside the measurement device includes, for example, other measurement devices located near the measurement device, communication radio waves, and broadcasting radio waves.

[0007] The noise superimposed on the measurement unit from these numerous noise sources varies depending on the operating environment of the measurement device. Noise that deviates from the expected noise components and noise levels can be superimposed on the measurement unit, degrading the S / N ratio and making normal measurements difficult. For example, this can occur when a portable wireless communication device is placed near the measurement device, or when the device cover is removed and reinstalled during maintenance, resulting in a reduction in the expected radio wave shielding effect of the device cover. Also, when a cable connector is removed and reinstalled, the installation can be insufficient. When such a situation occurs, it is necessary to check each and every part of the device that has been worked on to ensure normal measurements, which can take a lot of time to identify the problem.

[0008] Therefore, the present invention has been made to solve the above problem, and its purpose is to make it possible to estimate the range of abnormal parts that are the source of noise superimposed on the detection signal of a measuring device. [Means for solving the problem]

[0009] One example of the present invention is a measuring device having a measurement unit that measures an object to be measured and outputs a measurement signal, a detector that detects the measurement signal and outputs a detection signal, an analysis processing unit that calculates and outputs an analysis result from the detection signal, an analysis result display that displays the analysis result, and a device control module that controls the measurement unit and outputs a control signal, and the measuring device has an abnormality detection module that detects noise in the measuring device, and the abnormality detection module stores the drive intervals of each component of the measuring device in a memory resource, and determines whether or not noise has been mixed in for each component of the measuring device based on the corresponding drive interval stored in the memory resource and the analysis results of the analysis processing unit. [Effects of the Invention]

[0010] According to the present invention, by making it possible to estimate the range of an abnormal portion from the analysis results, it is possible to shorten the time required to identify the defective portion of the measuring device and reduce maintenance time. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a configuration block diagram of a measurement device according to a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating a noise source in a measurement device. [Figure 3] 10A and 10B are diagrams illustrating an example of noise superimposed on the analysis results of the measurement device. [Figure 4] 3A and 3B are diagrams illustrating the operation principle of a synchronization selector in the first embodiment. [Figure 5] FIG. 10 is a process flow diagram of a synchronization determiner in the first embodiment. [Figure 6] FIG. 2 is a diagram illustrating the operation principle of the noise collator in the first embodiment. [Figure 7] FIG. 10 is a diagram showing an example of a result determined by a noise determination method in the second embodiment. [Figure 8] FIG. 10 is a block diagram showing the configuration of a mass spectrometer according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. [Example]

[0013] Figure 1 is a block diagram of the measurement device of this embodiment. In Figure 1, measurement device 100 includes a measurement module 110 that measures an object to be measured and displays the results, an anomaly detection module 120 that determines the source of noise that enters the measurement device during measurement, and a control PC 116.

[0014] The instrument module 110 comprises a measurement unit 111 that measures an object to be measured and outputs a measurement signal, an instrument control module 112 that controls the measurement unit 111, a detector 113 that detects the measurement signal and outputs a detection signal, an analysis processing unit 114 that calculates and outputs an analysis result from the detection signal, and an analysis result display 115 that displays the analysis result. The instrument control module 112 transmits a control signal to the measurement unit 111 through control operation using a communication signal from a control PC 116. Here, the analysis result display 115 may be included in the control PC 116.

[0015] The device control module 112 includes, for example, a power supply 204 that provides power to components such as some or all of the units and modules that make up the measurement device 100, a driver 201 that drives the measurement unit 111, a measurement controller 203 that controls the driver 201, a monitor 202 that monitors the operating status of the measurement unit 111, and a communicator 205 that communicates with the control PC 116.

[0016] The anomaly detection module 120 is software processing that realizes various functions by the CPU executing operating programs that realize the various functions. FIG. 1 illustrates the anomaly detection module 120 as a functional block diagram. In FIG. 1, the anomaly detection module 120 includes a synchronization selector 121, a synchronization determiner 122, a noise collator 123, and a matching result display 124. The synchronization selector 121 has a memory resource (described later) that stores drive intervals, which are the operating periods of some or all of the components included in the measurement device 100. The synchronization selector 121 outputs a detection control signal to the detector 113 at the operating period of the drive interval of the component specified by a selection signal input from the control PC 116. The synchronization determiner 122 outputs a synchronization determination result based on the analysis result and the analysis period signal output from the analysis processing unit 114. The noise collator 123 outputs a matching result, which is a noise source estimated from the synchronization determination result and the selection signal. The matching result display 124 displays the matching result from the noise collator 123. The matching result display 124 may be included in the control PC 116.

[0017] Next, we will discuss the noise that may be superimposed on the analysis results and its sources. The noise that may be superimposed on the analysis results can be broadly divided into internal noise that originates from the components, i.e., all units and modules, that are equipped in the measurement device, and external noise that originates from outside the measurement device.

[0018] Fig. 2 is a diagram illustrating noise sources of the measuring device. In Fig. 2, internal noise is, for example, noise transmitted from 204 to measuring unit 111 and noise transmitted from communicator 205 to detector 113. External noise is, for example, a wireless mobile device placed near measuring device 100. Here, noise transmission includes both conduction, in which noise flows through metal parts such as the cover of each module or measuring device 100, and propagation, in which radio waves are transmitted within space.

[0019] The measuring device 100 implements noise countermeasures to prevent each noise from affecting the analysis results. For example, the cover of the measuring device 100 has an electromagnetic shielding structure to shield external noise, the detector 113 is housed in an electromagnetic shielding structure to shield noise from each unit or module, and power supply noise is suppressed by inserting ferrite cores into the power cables connecting the power supply to each unit or module.

[0020] However, during operation of the measuring device 100, the effectiveness of the above noise countermeasures may be reduced or lost, resulting in noise being superimposed on the analysis results. For example, this occurs when replacing regularly scheduled parts during maintenance or when replacing a faulty module. In such cases, the user must open the cover of the measuring device, disconnect the cables connected to the target module, remove the old module, install a new module, connect the cables to the new module, and then close the cover of the measuring device. During this process, if the cable connectors become loose or the cover of the measuring device has poor contact, the effectiveness of the noise countermeasures may be reduced or lost.

[0021] Figure 3 shows an example of an analysis result when the effectiveness of noise countermeasures has been reduced or lost. In Figure 3, analysis result 301 shows a normal analysis result, while analysis result 302 shows an analysis result when periodic pulse-shaped noise 303 is superimposed. When analysis result 302 occurs after replacing a regularly replaced part during maintenance or after replacing a faulty module, in the past, a lot of work was required to double-check that there were no errors in the work performed at the time of replacement, and to check one by one whether there were any problems with the regularly replaced part or module that was replaced, which required a lot of work time.

[0022] Next, we will explain a method for estimating the noise source of noise superimposed on the analysis result by the anomaly detection module 120 in this embodiment. When the analysis result 302 shown in Figure 3 occurs, the anomaly detection module 120 functions as a noise determination means that displays the estimated noise source on the comparison result display 124.

[0023] 4 is a diagram illustrating the operating principle of the synchronization selector 121 in the anomaly detection module 120 in this embodiment. In FIG. 4, the synchronization selector 121 has a memory resource 401 and a detection control signal generator 402, and switches the detection control signal that controls the detection timing of the detector 113 based on a selection signal input from the control PC 116. The memory resource 401 stores the drive intervals, i.e., drive cycles, of some or all of the components that are units and modules provided in the measurement device 100. For example, these are the drive intervals of the measurement controller 203, the monitor 202, and the communicator 205. The synchronization selector 121 enables the detector 113 to perform detection at the same cycle as the components selected by the control PC 116.

[0024] 1, detector 113 outputs a detection signal, which is the result of detecting a measurement signal in synchronization with a detection control signal input from synchronization selector 121, and a detection period signal, which is the detection control signal, to analysis processing unit 114. Analysis processing unit 114 processes the input detection signal and outputs the analysis result to analysis result display 115 and synchronization determiner 122. It also outputs the input detection period signal to synchronization determiner 122 as an analysis period signal.

[0025] 5 is a processing flow diagram of the synchronization determiner 122 in the anomaly detection module 120 in this embodiment. Based on the input analysis result and analysis period signal, the synchronization determiner 122 outputs the frequency component of the analysis result and a synchronization determination result that is information on the synchronism between the frequency component and the analysis period signal. Hereinafter, the method of calculating and determining the frequency component and synchronism will be described with reference to FIG. 5.

[0026] The analysis result A is time series data of M points as shown in the following formula (1).

[0027]

number

[0028] In step S151, this time series analysis result A is divided by the analysis period signal. If the analysis period is L and the number of M points divided by L is N (N=M / L), the time series data obtained by dividing the analysis result by N by the analysis period is expressed by the following equation (2).

[0029]

number

[0030] From the analysis results divided into N parts, enhanced spectral data and baseline spectral data are calculated after statistical calculation to enhance the components of the driving interval.

[0031] In step S152, the enhanced spectral data is first calculated by calculating the ensemble average of the N-divided analysis results, and the time-series data TA of the ensemble average is calculated as shown in the following equation (3).

[0032]

number

[0033] Each point of the ensemble average time series data is calculated using the following equation (4).

[0034]

number

[0035] Next, in step S153, the ensemble average time series data is converted into frequency components to obtain enhanced spectral data SS as shown in the following equation (5): The conversion into frequency components is performed using, for example, FFT (Discrete Fourier Transform) or the like.

[0036]

number

[0037] By obtaining enhanced spectral data through the above-described calculations, among the frequency components contained in the enhanced spectral data, frequency components synchronized with the analysis periodic signal maintain the same intensity as the analysis result, frequency components occurring randomly with respect to the analysis periodic signal are attenuated by √N, and frequency components of signals having periodicity on the time axis that occur asynchronously with respect to the analysis periodic signal are attenuated to less than √N.

[0038] In step S154, the baseline spectrum data is first divided into N analysis results, A1 to A N are converted into frequency components by frequency analysis as shown in the following equation (6): The conversion into frequency components uses the same calculation as the frequency analysis used for the enhanced spectrum data.

[0039]

number

[0040] Next, in step S155, the frequency-analyzed A1 to A N By calculating the sample average, the base spectrum data BS is obtained as shown in the following equation (7).

[0041]

number

[0042] Each point of the base spectrum data BS is calculated using the following equation (8).

[0043]

number

[0044] By obtaining base spectrum data through the above calculation, the frequency components contained in the base spectrum data become the average intensity of the N-divided analysis results, regardless of the synchronism with the analysis periodic signal for the frequency components contained in the analysis results.

[0045] In step S156, the intensities of the frequency components of the enhanced spectral data SS and the base spectral data BS are compared to determine whether the enhanced spectral data SS is synchronized with the analysis periodic signal. The intensities of the frequency components of the enhanced spectral data SS are then compared with the intensities of the frequency components of the base spectral data BS. That is, frequency components whose intensities, √N times the intensities of the enhanced spectral data SS, are greater than the intensities of the base spectral data BS are determined to be synchronized with the analysis periodic signal. Frequency components whose intensities, √N times the intensities of the enhanced spectral data SS, are less than the intensities of the base spectral data BS are determined to be asynchronous with the analysis periodic signal.

[0046] The frequency components of the analysis results that are synchronized with the analysis period signal can be estimated to be noise frequency components originating from the drive interval selected by the control PC.Furthermore, the frequency components of the analysis results that are asynchronous with the analysis period signal can be estimated not to be noise frequency components originating from the drive interval selected by the control PC.

[0047] In step S157, it is determined whether or not the process has been completed for all frequencies, and if not, the process of step S156 is repeated until the process is completed.

[0048] Fig. 6 is a diagram illustrating the operating principle of the noise collator 123 in the anomaly detection module 120 in this embodiment. In Fig. 6, the noise collator 123 collates the frequency component determined to be synchronized with the analysis period signal with noise data stored in memory resources 602 using a noise collation controller 601 based on a selection signal input from the control PC 116, and outputs the corresponding noise information as the collation result.

[0049] 6, the noise data stored in the memory resource 602 is, for example, a list of noise frequency components for each drive interval stored in the memory resource 401 of the synchronization selector 121. Also, noise information is stored for each noise frequency component of each drive interval. The noise information may include radio frequency band information and modulation information.

[0050] The noise information may be, for example, the name of the noise source module, the identification number of the cable or connector, the step number in the maintenance manual that describes the work steps for implementing noise countermeasures, etc. The work method for implementing noise countermeasures may also be displayed.

[0051] The matching result display 124 in the anomaly detection module 120 displays the matching results input from the noise matching unit 123 to the operator. When displaying the results, the measuring device may issue an alert using a buzzer or lamp. The timing for displaying the matching results and issuing the alert may be immediately after the matching results are input, or the operator may set the timing. In addition, the operator may set in advance the types of matching results to be displayed, so that only the matching results that the operator wants to display are displayed. Furthermore, a memory resource may be provided, and the matching results may be saved in the memory resource as log information when displayed.

[0052] By executing the noise collator 123 for all drive intervals stored in the memory resource 401, the synchronicity of noise superimposed on the analysis results for all modules stored in the memory resource 401 can be determined.

[0053] As described above, according to this embodiment, it is possible to estimate the noise sources that are superimposed on the analysis results for all of the components, such as units and modules, that are provided in the measuring device and are assumed to be noise sources. This makes it possible to shorten the work time required to identify the noise sources and provide a measuring device that enables reduced maintenance time. [Example]

[0054] In the first embodiment, the drive intervals of all the components, i.e., all the expected units and modules, are stored in the memory resources of the synchronization selector, and the synchronization with the internal noise superimposed on the analysis results is determined to estimate the noise source. In this embodiment, a noise determination method for noise that has flowed in from outside the measurement device is described.

[0055] The anomaly detection module, which is the noise determination means in this embodiment, has the same structure as the anomaly detection module in embodiment 1. However, the drive interval information stored in memory resource 401 of synchronization selector 121 includes all possible noise sources within the measurement device. Also, memory resource 602 of noise collator 123 stores noise information on possible external noise, and noise collation controller 601 includes means for storing the type of selected signal for which noise determination was performed and the synchronization determination result.

[0056] The method for determining noise that has flowed in from outside the measurement device is to first execute the processing in the synchronization determiner 122 in the first embodiment for all drive intervals stored in the memory resource 401, thereby determining the synchronism of noise superimposed on the analysis results for all units and modules stored in the memory resource 401. Next, the noise matching controller 601 of the noise matcher 123 references the synchronization determination results for each stored selection signal, and if there is a frequency component of the noise superimposed on the analysis results that is asynchronous with all drive intervals, it determines that this frequency component is external noise and outputs the noise information of the external noise stored in the memory resource 602 as the matching result. Furthermore, for frequency components of the noise superimposed on the analysis results that are determined to be synchronous with one or more drive intervals, it determines that this noise information is internal noise and outputs the noise information as the matching result.

[0057] FIG. 7 shows an example of the results determined using the noise determination method of this embodiment. In FIG. 7, the analysis result 700 in the upper row shows an example where a normal analysis result would have a constant relative intensity of 100%, but external noise is superimposed on the internal noise, resulting in a rectangular wave shape. The middle and lower rows of FIG. 7 also show examples of the frequency components of internal noise and external noise obtained using the noise determination method of this embodiment. 701 in the middle row is internal noise, and 702 in the lower row is external noise. Here, the relative intensity [dB] is based on the intensity of a normal analysis result (0 dB) and indicates the frequency intensity of the base spectrum data BS. The frequency component displayed as internal noise 701 is noise due to the communication signal between the control PC 116 and the communication device in the device control module 112, while the frequency component displayed as external noise 702 is noise irradiated from outside the measurement device using an antenna, which is EMC test equipment. This confirms that internal noise and external noise can be distinguished.

[0058] As described above, according to this embodiment, it is possible to determine the noise source of noise that has flowed in from outside the assumed device and is superimposed on the analysis results, thereby shortening the work time required to identify the noise source. [Example]

[0059] Techniques for estimating the source of noise superimposed on analysis results in a measurement device have been described in Examples 1 and 2. In this example, a technique for estimating the source of noise superimposed on analysis results in a mass spectrometer, which is a specific example of a measurement device, will be described.

[0060] Figure 8 is a block diagram of a mass spectrometer 800 according to this embodiment. In Figure 8, the same components as those in Figure 1 are denoted by the same reference numerals, and their description will be omitted. Figure 8 differs from Figure 1 in that the measurement unit 111 is replaced with a mass analysis unit 900.

[0061] 8, mass analysis unit 900 includes an ion source 901 that ionizes the sample to be analyzed sent from the pretreatment unit, a focusing unit 902 that focuses ionized sample 910, a separation unit 903 that filters the focused ionized sample according to its mass-to-charge ratio to pass only the ionized sample to be detected, and a fluorescent unit 904 that causes the ionized sample that has passed through the separation unit to collide with a conversion dynode 909, converting it into electrons 911 corresponding to the amount of ionized sample, and causing the electrons 911 to enter a scintillator 905, thereby outputting photons corresponding to the amount of electrons. Detector 113 outputs an electrical signal corresponding to the photons output from fluorescent unit 904.

[0062] The anomaly detection module 120 stores in the memory resource 401 of the synchronization selector 121 all drive intervals that could be noise sources for components such as units and modules mounted on the mass spectrometer 800, and as described in Examples 1 and 2, can determine the synchronicity of noise superimposed on the analysis results and estimate the noise source.

[0063] Furthermore, if the frequency components of the noise superimposed on the analysis result include a frequency component that is asynchronous with all of the drive intervals, the frequency component can be determined to be external noise.

[0064] As described above, according to this embodiment, it is possible to estimate the noise sources that are superimposed on the analysis results for all of the components, that is, units and modules, that are provided in the mass spectrometer and are assumed to be noise sources, thereby shortening the work time required to identify the noise sources and providing a mass spectrometer that enables reduced maintenance time.

[0065] The present invention has been described above as an example of the present invention, but it can shorten the time required to identify noise sources, thereby reducing maintenance time, improving productivity, and reducing labor costs. Therefore, the present invention contributes to achieving a high level of economic productivity through technological improvement and innovation in order to achieve the Sustainable Development Goals (SDGs), particularly goal 8, "Decent Work and Economic Growth."

[0066] Furthermore, the present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is also possible to add, delete, or replace part of the configuration of each embodiment with other configurations. [Explanation of symbols]

[0067] 100: measuring device, 110: device module, 111: measuring unit, 112: device control module, 113: detector, 114: analysis processing unit, 115: analysis result display, 116: control PC, 120: anomaly detection module, 121: synchronization selector, 122: synchronization determiner, 123: noise matcher, 124: match result display, 201: driver, 202: monitor, 203: measurement controller, 204: power supply, 205: communication device, 301, 302, 700: analysis result, 303: noise, 401, 602: memory resource, 402: detection control signal generator, 601: noise match controller, 701: internal noise, 702: external noise, 800: mass spectrometer, 900: mass spectrometer unit

Claims

1. A measuring device comprising: a measuring unit that measures an object to be measured and outputs a measurement signal; a detector that detects the measurement signal and outputs a detection signal; an analysis processing unit that calculates and outputs an analysis result from the detection signal; an analysis result display that displays the analysis result; and a device control module that controls the measuring unit and outputs a control signal, an anomaly detection module for detecting noise in the measurement device; The anomaly detection module stores a drive interval of each component of the measurement device in a memory resource, and determines whether or not the noise is mixed in for each component of the measurement device based on the corresponding drive interval stored in the memory resource and the analysis result of the analysis processing unit, The anomaly detection module: determining, for each of the components of the measurement device, synchronism between the corresponding driving interval stored in the memory resource and the frequency components of the analysis result; If there is a frequency component of the noise superimposed on the analysis result that is asynchronous with all of the drive intervals stored in the memory resource, the frequency component is determined to be external noise, and a frequency component that is determined to be synchronous with one or more drive intervals stored in the memory resource is determined to be internal noise; When outputting the noise information of the determined external noise or internal noise as a collation result from a second memory resource in which noise information of expected external noise and internal noise is stored, The determination of synchrony is performed by comparing baseline spectral data with enhanced spectral data after statistical calculations that enhance components of the drive interval; the baseline spectrum data is spectrum data generated by performing a frequency analysis on the analysis result and then performing an ensemble average operation, The measurement device is characterized in that the enhanced spectral data is spectral data generated by dividing the analysis results into multiple sections at the drive interval, performing ensemble averaging, and then performing frequency analysis.

2. 2. The measuring device according to claim 1, The measuring device is characterized in that the abnormality detection module can select the timing and type of output for outputting the noise information as a comparison result, and can issue an alert using a buzzer or lamp.

3. 2. The measuring device according to claim 1, The measurement device according to claim 1, wherein the noise information includes frequency information and modulation information.

4. A mass spectrometer having a measurement unit that measures an object to be measured and outputs a measurement signal, a detector that detects the measurement signal and outputs a detection signal, an analysis processing unit that calculates and outputs an analysis result from the detection signal, an analysis result display that displays the analysis result, and an apparatus control module that controls the measurement unit and outputs a control signal, the measurement unit has an ion source, a focusing section, a separating section, and a fluorescent section; an anomaly detection module for detecting noise in the mass spectrometer; The anomaly detection module: determining, for each of the components of the mass analyzer, whether a corresponding drive interval of the component of the mass analyzer stored in a memory resource is synchronized with a frequency component of the analysis result; If there is a frequency component of the noise superimposed on the analysis result that is asynchronous with all of the drive intervals stored in the memory resource, the frequency component is determined to be external noise, and a frequency component that is determined to be synchronous with one or more drive intervals stored in the memory resource is determined to be internal noise; When outputting the noise information of the determined external noise or internal noise as a collation result from a second memory resource in which noise information of expected external noise and internal noise is stored, The determination of synchrony is performed by comparing baseline spectral data with enhanced spectral data after statistical calculations that enhance components of the drive interval; the baseline spectrum data is spectrum data generated by performing a frequency analysis on the analysis result and then performing an ensemble average operation, The enhanced spectral data is spectral data generated by dividing the analysis results into a plurality of sections based on the drive interval, performing an ensemble average calculation, and then performing frequency analysis.

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