Measurement equipment, measurement system, measurement method, and measurement program

Abstract

To reduce measurement data while increasing the rate of restoration of the original signal. The present invention includes a preprocessing unit 40 that preprocesses a time-varying measured signal a(t), and a random measurement unit 1 that randomly measures the preprocessed signal s(t) preprocessed by the preprocessing unit 40 based on a first random matrix Φ, and the preprocessing unit 40 downsamples the measured signal a(t) to a signal that is frequency-shifted downward by a specific frequency. The preprocessing unit 40 also downsamples the measured signal a(t) to a signal that is frequency-shifted downward by a specific frequency. The present invention also includes a receiving unit 55 that receives a random measurement vector y that is a vector representation of the randomly measured random measurement value from the random measurement unit 1, and a random measurement vector y that is a vector representation of the random measurement value measured by the random measurement unit 1 based on the first random matrix Φ and a basis vector {Ψ i When the n×n orthogonal basis matrix ψ with columns} is expressed as the product Φψx of its coefficient x, the regularization coefficient λ0 is the coefficient x={x i and an estimation unit 61 that estimates {}.

Description

[Technical field]

[0001] The present invention relates to a measuring instrument, a measuring system, a measuring method, and a measuring program, and, for example, to a measuring instrument for evaluating the vibration of an ultrasonic horn. [Background technology]

[0002] Vibrating equipment such as ultrasonic horns and rotating equipment has a resonance point due to the material and structure. Resonance characteristics are often evaluated by spectral distribution. In addition, these vibrating equipment inevitably break down due to material defects, fatigue, aging, etc. Breakdowns lead to equipment downtime and economic losses. For this reason, it is important to diagnose faults in rotating equipment and keep it in good condition. Vibration evaluation and fault diagnosis of vibrating equipment are performed using various sensing methods.

[0003] For example, Non-Patent Document 1 discloses a technology that combines compressed sensing technology with a method of measuring at a constant frequency with a random measurement start time (Random Start Uniform Sampling Method, hereinafter referred to as RSUSM). Compressive sensing theory is a technology that can accurately restore a signal from much less measurement data than is normally required, if it satisfies the conditions that the measurement data has sparsity with respect to a certain basis (e.g., Fourier basis) and that the basis is incoherent (random sampling is performed). As a result, the technology in Non-Patent Document 1 reduces the measurement data required for monitoring. Furthermore, Patent Document 1 discloses a measurement data providing service system that assigns IDs to sensors, centrally manages them in a server system, and performs temperature compensation and linearity compensation processing according to the situation. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Yoshimoto Kato, "Propeller Fault Diagnosis Using Sub-Nyquist Sampling and Compressive Sensing", 19th Symposium on Evaluation and Diagnosis, Japan Society of Mechanical Engineers, December 2-3, 2021 (date of event) [Patent documents]

[0005] [Patent Document 1] Patent No. 6252669 Summary of the Invention [Problem to be solved by the invention]

[0006] The compressed sensing theory used in Non-Patent Document 1 requires that the measurement data has sparsity with respect to the Fourier basis. However, the frequency spectrum may be broadened (i.e., sparsity is low), making it impossible to restore the original signal with the required accuracy.

[0007] The present invention has been made in consideration of the above problems, and has an object to provide a measuring instrument, a measuring system, a measuring method, and a measuring program that can increase the restoration rate of the original signal while reducing measurement data. [Means for solving the problem]

[0008] The above-mentioned object of the present invention can be achieved by the following means. 1. A measuring device comprising: a preprocessing unit that preprocesses a time-varying signal under test; and a measuring unit that randomly measures the preprocessed signal preprocessed by the preprocessing unit based on a predetermined first random matrix Φ, wherein the preprocessing unit downsamples the signal under test to a signal obtained by frequency-shifting the signal under test downward by a specific frequency. Effect of the Invention

[0009] According to the present invention, it is possible to reduce the amount of measurement data while increasing the rate of restoration of the original signal. [Brief description of the drawings]

[0010] [Figure 1] 1 is a configuration diagram of a measurement system according to a first embodiment of the present invention. [Diagram 2] FIG. 1 is a configuration diagram showing an evaluation target facility for evaluating a measurement target used in a first embodiment of the present invention. [Diagram 3] FIG. 2 is a configuration diagram of a preprocessing unit used in the measurement system according to the first embodiment of the present invention. [Figure 4] 6A to 6C are diagrams illustrating an example of waveforms at each part in the pre-processing unit. [Diagram 5] FIG. 13 is a diagram for explaining the relationship between a parameter setting period (first period T1) and a measurement period (second period T2) after the parameter is set. [Figure 6] 4 is a flowchart for explaining the operation of the measurement system according to the first embodiment of the present invention. [Figure 7] FIG. 11 is a configuration diagram of a measurement system according to a second embodiment of the present invention. [Figure 8] 6 is a flowchart for explaining the operation of a measurement system according to a second embodiment of the present invention. [Figure 9] FIG. 11 is a diagram showing a frequency spectrum measured by a measurement system according to a second embodiment of the present invention. [Figure 10] FIG. 11 is a configuration diagram of a measurement system according to a third embodiment of the present invention. [Figure 11] FIG. 11 is a configuration diagram of a measurement system according to a fourth embodiment of the present invention. [Figure 12] FIG. 13 is a configuration diagram of a measurement system according to a fifth embodiment of the present invention. [Figure 13] FIG. 13 is a configuration diagram showing an evaluation target facility for evaluating a measurement target used in a fifth embodiment of the present invention. [Figure 14] 10A and 10B are diagrams for explaining a difference in a measurement signal depending on whether a rotation speed is high or low. [Figure 15A] FIG. 4 is a diagram showing a change in a signal under measurement over time. [Figure 15B] FIG. 4 is a diagram showing a change over time in the rotational phase of a rotating device. [Figure 15C] FIG. 4 is a diagram showing the relationship between a measurement signal and a rotation phase. [Figure 16] FIG. 13 is a configuration diagram of a measurement system according to a sixth embodiment of the present invention. [Figure 17] FIG. 1 is a diagram showing frequency characteristics of a measurement microphone, a high-frequency measurement microphone, and a general-purpose microphone made of MEMS. [Figure 18] FIG. 13 is a configuration diagram of a measurement system according to a seventh embodiment of the present invention. [Figure 19] FIG. 1 is a configuration diagram of a measurement system that is a comparative example of the present invention. [Figure 20] FIG. 13 is a configuration diagram of a preprocessing unit used in a measurement system according to a modified example of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. Note that each drawing is merely a schematic illustration to allow a sufficient understanding of the present invention. Therefore, the present invention is not limited to the illustrated examples. In addition, in each drawing, common components or similar components are given the same reference numerals, and duplicated explanations thereof will be omitted.

[0012] (First embodiment) FIG. 1 is a configuration diagram of a measurement system according to an embodiment of the present invention. The measurement system 100 measures a characteristic (e.g., vibration velocity) of a measurement object 30 using a sensor 20, and a random measurement device 10 and a measurement and calculation device 50 are communicably connected. The measurement object 30 is, for example, an ultrasonic horn. The sensor 20 is, for example, a Doppler vibrometer, which detects the vibration velocity of the measurement object 30 and outputs a measured signal a(t) that changes over time.

[0013] The random measurement device 10 preprocesses the measured signal a(t) output by the sensor 20 and randomly samples the preprocessed signal s(t). The random measurement device 10 is configured with a preprocessing unit 40, a random measurement unit 1 as a measurement unit, a random matrix setting unit 2 as a setting unit, and a receiving unit 4. In other words, the measurement method executed by the random measurement device 10 includes a preprocessing process and a random measurement process.

[0014] FIG. 2 is a configuration diagram showing the evaluation target facility for evaluating the measurement target. The evaluation target facility 150 includes a measurement target 30 and a sensor 20. The measurement target 30 is, for example, an ultrasonic horn, and includes a vibrator 35 and a horn 36. The vibrator 35 includes a piezoelectric element 31 and a bolt 32. The piezoelectric element 31 is fragile (brittle) against tension, and is used in a state where it is fastened with the bolt 32 and subjected to a compressive load. The horn 36 amplifies the vibration. The measurement target 30, which is an ultrasonic horn, is measured while leaning up and down. The sensor 20 is, for example, a laser Doppler vibrometer that detects the vibration velocity of the measurement target 30. The sensor 20 measures the vibration velocity by applying laser light to the top surface of the vibrator 35 of the leaned measurement target 30.

[0015] FIG. 3 is a configuration diagram of a preprocessing unit 40 used in the measurement system according to the first embodiment of the present invention. The pre-processing unit 40 pre-processes the measured signal a(t) output by the sensor 20, and outputs a pre-processed signal s(t). The pre-processing unit 40 converts the frequency so that a specific frequency range f1-f2 is displayed in the range of 0-f (frequency range Hz). This conversion causes the width Δ=f2-f1 to be enlarged M times (Δ·M=f) to the width of f. This function is called the zoom function. The pre-processing unit 40 includes a sampling unit 41, a band-pass filter 45, a frequency shift 42, a low-pass filter 43, and a down-sampling unit 44.

[0016] The sampling section 41 digitally converts the measured signal a(t) at a first sampling frequency (for example, 256 kHz) and outputs a sampling signal SP(t). The bandpass filter 45 passes the zoomed frequency range f1 to f2 and outputs a bandpass signal BPF(t). The frequency shifter 42 performs a frequency shift of a specific frequency (fm=30 kHz) on the bandpass signal BPF(t). In other words, the frequency shifter 42 is a multiplier that multiplies the sampling signal SP(t) of the first sampling frequency fs1=256 kHz by a sine wave signal M(t) of a specific frequency (fm=30 kHz). This multiplier outputs both signals (output signals) of the signal component FSh(t) of the upper frequency (Fs1 / 2+fm=128 kHz+30 kHz) and the signal component FSl(t) of the lower frequency (Fs1 / 2-fm=128 kHz-30 kHz).

[0017] That is, the signal component FSh(t) is obtained by frequency-shifting the sampling signal SP(t) upward by a specific frequency (fm = 30 kHz), and the signal component FSl(t) is obtained by frequency-shifting the sampling signal SP(t) downward by a specific frequency (fm).

[0018] The low-pass filter 43 blocks the upper frequency component FSh(t) of the upper frequency (fs1+fm) and outputs the lower frequency component FSl(t) of the lower frequency (fs1-fm). That is, the low-pass filter 43 outputs a signal obtained by shifting the sampling signal SP(t) downward in frequency by a specific frequency (fm). This allows the sampling frequency to be lowered, thereby reducing the number of samples (Zoom function). The downsampling unit 44 downsamples the lower frequency component FSl(t) at a second sampling frequency (e.g., fs2=640 Hz) lower than the first sampling frequency (e.g., fs1=256 kHz), and outputs the preprocessed signal s(t).

[0019] FIG. 4 is a diagram showing an example of waveforms at each part in the pre-processing part. (a) is a sampling signal SP(t) obtained by sampling the measured signal a(t) at the first sampling frequency by the sampling section 41. (b) is a signal FS(t) frequency-shifted by 30 kHz. (c) is the lower frequency component FS1(t) that has passed through the low-pass filter 43. (d) is a pre-processed signal s(t) obtained by down-sampling the lower frequency component FS1(t).

[0020] Returning to the explanation of Fig. 1, the random measurement unit 1 outputs (transmits) a signal of a random measurement vector y = Φs, which is a vector representation of random measurement values ​​obtained by randomly sampling the preprocessed signal s(t) according to a first random matrix Φ, to the measurement calculation device 50. Here, the measurement period is divided into a parameter setting period (first period) in which measurement parameters (for example, a regularization coefficient λ described later) are set, and a measurement period (second period) after the parameters are set.

[0021] 5 is a diagram for explaining the relationship between a parameter setting period (first period T1) and a measurement period after parameter setting (second period T2). The first period T1 is from t=0 to t1, and the second period T2 is from t=t2 onwards. The period from t1 to t2 is a measurement interruption period.

[0022] The random measurement vector measured in the first period T1 is the first random measurement vector y1=Φs, and the random measurement vector measured in the second period T2 (second random measurement vector) is y=Φs. Here, the first random matrix Φ can be a matrix Φr indicating that all the time series signal columns (discrete time signals s) of the pre-processed signal s(t) have been randomly thinned out, or a matrix Φrsu indicating that measurements are made multiple times to randomize the measurement start timing of the discrete time signal s. The matrix Φ indicating random sampling r indicates that the preprocessed signal s(t) is measured so that the sequence of all time-series signals (discrete-time signals s) acquired at equal intervals is randomly thinned out.

[0023] The matrix Φrsu is a one-time measurement with a constant frequency f rsu (f rsu << sampling frequency fs) or constant frequency frsu M p Assume that data for each point is taken. As a result, the number of samples that the random measurement unit 1 samples of the preprocessed signal s(t) is reduced compared to the number of discrete-time signals s. The random measurement unit 1 performs compressed sensing (CS) of the preprocessed signal s(t), so it can be manufactured more cheaply than a unit that outputs a sequence of all time-series signals (discrete-time signals s).

[0024] The random matrix setting unit 2 sets the first random matrix Φ used in the random measurement unit 1. The first random matrix Φ may be fixed, but in this embodiment, the second random matrix Φ0 is set as an initial value, and the second random matrix Φ0 is updated to become the first random matrix Φ. The receiving unit 4 receives update data (first random matrix Φ) of the random matrix setting unit 2 from the measurement calculation device 50.

[0025] In the second period T2 (FIG. 5), the measurement and calculation device 50 restores a discrete-time signal s from the random measurement values ​​(random measurement vector y=Φs) received from the random measurement device 10. In other words, the measurement and calculation device 50 restores all data (discrete-time signal s) from a small amount of data (y=Φs) received from the random measurement device 10. Here, the discrete-time signal s is expressed by a basis vector {Ψ i} is the column of the n×n orthogonal basis matrix ψ, where x is the coefficient, and s is expressed as s=ψx. Here, the basis vector {Ψ i} is, for example, a Fourier basis vector with time t as a variable. Note that the time resolution of the Fourier basis vector is, for example, (downsampling frequency / downsampling number) / compression rate of compression sampling.

[0026] The measurement calculation device 50 is a PC (Personal Computer) including a receiving unit 55, a control unit 60, and a transmitting unit 56. The receiving unit 55 receives data of a random measurement vector y=Φs from the random measurement device 10. The control unit 60 is a computer having a CPU (Central Processing Unit), and realizes the functions of an estimation unit 61, a regularization coefficient setting unit 62, and a restoration unit 63 by executing a measurement calculation program.

[0027] The estimation unit 61 estimates the coefficient x of the n×n orthogonal basis matrix ψ by using the random measurement vector y=Φs received by the measurement calculation device 50. Specifically, the estimation unit 61 estimates the coefficient x={x i} is estimated.

number

[0028] Here, the norms in the formulas are defined in the following formulas (2) and (3).

number

number

[0029] Σ|x i | is x from i=1 to N i means adding the absolute value of Σ|x i | 2 is x from i=1 to N i This means adding the square of the absolute value of . Note that (y-Φψx), obtained by subtracting the product Φψx of the first random matrix Φ and the n×n orthogonal basis matrix ψ and its coefficient x from the random measurement vector y, represents an error.

[0030] Before estimating the coefficient x of the n×n orthogonal basis matrix ψ in the second period T2 (FIG. 5), the regularization coefficient setting unit 62 sets the value of the regularization coefficient λ0 in the first period T1 in advance so that it becomes the maximum value within the standard deviation of the cross-validation for the regularization coefficient λ with the smallest error. At this time, the first random matrix Φ uses the initial value (second random matrix Φ0) stored in the random matrix setting unit 2 of the random measuring device 10, but may be changed as appropriate.

[0031] In the second period T2, the restoration unit 63 calculates the n×n orthogonal basis matrix ψ and the coefficient x={x i} to restore the discrete-time signal s=ψx. In other words, the restoration unit 63 can restore the entire discrete-time signal s by using the randomly sampled random measurement vector y=Φs.

[0032] When the regularization coefficient setting unit 62 determines the value of the regularization coefficient λ0, the transmission unit 56 does not use the second random matrix Φ0, and when the regularization coefficient λ0 is changed, transmits the changed first random matrix Φ to the random measurement device 10. As a result, the value of the random matrix setting unit 2 of the random measurement device 10 is updated from the second random matrix Φ0 to the first random matrix Φ.

[0033] Fig. 6 is a flow chart for explaining the operation of the measurement system according to the first embodiment of the present invention. This flow is started first when the measurement target 30 (Figs. 1 and 2) is changed. As a result, the measurement calculation device 50 predetermines parameters (regularization coefficient λ and first random matrix Φ) in the first period T1 (Fig. 5), and starts measurement in the second period T2 (Fig. 5). In addition, a random matrix Φ=Φ0 is set (stored) in advance in the random matrix setting unit 2.

[0034] In the first period T1, the preprocessing unit 40 (FIGS. 1 and 3) of the random measurement device 10 preprocesses the measured signal a(t) from the sensor 20 and outputs a preprocessed signal s(t). Then, the random measurement unit 1 (FIG. 1) of the random measurement device 10 acquires the preprocessed signal s(t) in advance (step S1). That is, the random measurement device 10 acquires a discrete-time signal s downsampled at a second sampling frequency (for example, fs2=640Hz). The random measurement device 10 calculates a random measurement vector y=Φs using the discrete-time signal s sampled in advance (step S2). After the process of step S2, the random measurement device 10 transmits the random measurement vector y to the measurement calculation device 50 (step S3). The measurement calculation device 50 receives the random measurement vector y (step S4) and determines the regularization coefficient λ=λ0 to be the maximum value within the standard deviation of cross-validation at λ with the smallest error (step S5). At this time, the regularization coefficient setting unit 62 (FIG. 1) uses the random matrix Φ=Φ0, but the regularization coefficient λ0 may be determined after appropriately correcting the first random matrix Φ.

[0035] After the processing of step S5, the measurement calculation device 50 transmits the appropriately corrected first random matrix Φ to the random measurement device 10 (step S6). As a result, the first random matrix Φ is set in the random measurement device 10. The random measurement device 10 receives the first random matrix Φ and resets the second random matrix Φ0 set in the random matrix setting unit 2 to the received first random matrix Φ (step S7). As a result, the random measurement device 10 is ready to start measurement.

[0036] In the second period T2 (FIG. 5), the random measurement device 10 randomly acquires a preprocessed signal s(t) from the sensor 20 (step S8). At this time, the random matrix setting unit 2 stores the first random matrix Φ reset in step S7. During execution of step S8, the random measurement device 10 uses the randomly acquired preprocessed signal s(t) to calculate a random measurement vector y=Φs (step S9). After processing in step S9, the random measurement device 10 transmits the random measurement vector y to the measurement calculation device 50 (step S10). The measurement calculation device 50 receives the random measurement vector y (step S11) and calculates the coefficients x={x i After the process of step S12, the measurement and calculation device 50 restores the discrete-time signal s=ψx.

[0037] As described above, according to the measurement system 100 of this embodiment, the pre-processing unit 40 shifts the input signal a(t) to a lower frequency (fs1-fm) lower than the first sampling frequency fs1. This allows the pre-processing unit 40 to perform downsampling at the second sampling frequency fs2. As a result, the pre-processing unit 40 performs downsampling, and the random measurement unit 1 performs random sampling, so that the number of samples is reduced in a cumulative manner. In other words, the number of data points of the random measurement vector y that the random measuring device 10 transmits to the measurement and calculation device 50 becomes extremely small. Conversely, if the number of samples is the same, the coefficient x={x i} frequency resolution is improved.

[0038] Second embodiment In the first embodiment, the regularization coefficient λ=λ0 is determined to be the maximum value within the standard deviation of cross validation at λ with the smallest error (step S5 in FIG. 6). In the present embodiment, the error is calculated as the difference between the sum of the products of power calculated by compressed sampling (CS) and the sum of the products of power calculated by DFT (Discrete Fourier Transform).

[0039] FIG. 7 is a configuration diagram of a measurement system according to a second embodiment of the present invention. The measurement system 101 is configured by connecting a random measurement device 11, a sequential measurement device 71, and a measurement and calculation device 51 so as to be capable of communicating with each other.

[0040] The random measurement device 11 is configured to include the above-mentioned preprocessing unit 40 and the random measurement unit 1 as a random measurement value output unit. In particular, the matrix Φ rsu When the sampling frequency fs=Nf rsu It becomes.

[0041] The sequential measurement device 71 includes the preprocessing unit 40, a sequential measurement unit 73, a DFT calculation unit 74, and a sampling frequency generator 76. The sampling frequency generator 76 generates a clock of the second sampling frequency fs2. The sequential measurement unit 73 sequentially measures the preprocessed signal s(t) using the second sampling frequency fs2. The sequential measurement unit 73 differs from the random measurement unit 1 in that it does not perform random measurements.

[0042] The DFT (Discrete Fourier Transform) calculation unit 74 performs a discrete Fourier transform on the discrete time signal s output by the sequential measurement unit 73, and calculates the calculation result (Fourier coefficient xf={xf i}) is output to the measurement calculation device 52. The calculation for the preprocessed signal s(t) is referred to as ZoomDFT.

[0043] The measurement and calculation device 51 calculates the Fourier coefficient xf={xf i}, judges sparsity, and sets a parameter (regularization coefficient λ). Specifically, in the first period T1, the measurement and calculation device 51 confirms that n>m and n / s>n / m, where c is a constant for compressive sensing. Here, the number of samples after compression is m, the number of samples before compression is n, and the number of non-zero q (frequency) components is s. After that, the measurement and calculation device 51 sets a parameter (regularization coefficient λ).

[0044] Furthermore, in the second period T2 (FIG. 5), the measurement and calculation device 51, like the measurement and calculation device 50 (FIG. 1), recovers all data (discrete-time signal s) from the small amount of random measurement values ​​(random measurement vector y=Φs) received from the random measuring device 11.

[0045] The measurement calculation device 51 is a PC, and by executing a measurement calculation program, it realizes the functions of an estimation unit 61, a regularization coefficient setting unit 62, a restoration unit 63, a restoration error calculation unit 54, an anomaly degree calculation unit 57, and an alarm unit 58.

[0046] In the first period T1, the restoration error calculation unit 54 calculates the Fourier coefficient xf={xf i} and coefficient x={x i} to obtain the power difference between the discrete-time signal s and the first random measurement value (the vector representation of which is the first random measurement vector y1). Furthermore, the restoration error calculation unit 54 divides the power difference by the square sum of the coefficient x to calculate the restoration error r (equation (4)). The restoration error calculation unit 54 checks whether the restoration error r falls within a predetermined range (for example, 0.01%, 0.1%, 1%, 10%, 20%), and corrects the regularization coefficient λ=λ0 if it does not fall within the predetermined range.

[0047]

number

[0048] The abnormality degree calculation unit 57 calculates the abnormality degree using, for example, the Mahalanobis distance (online) during the second period. The notification unit 58 notifies the user when the abnormality degree calculation unit 57 detects an abnormality, that is, when the abnormality degree using the Mahalanobis distance exceeds a threshold value.

[0049] FIG. 8 is a flowchart for explaining the operation of the measurement system according to the second embodiment of the present invention. In the first period T1 (FIG. 5), in the sequential measurement device 71, the sequential measurement unit 73 sequentially acquires the preprocessed signal s(t) at a predetermined second sampling frequency fs2 (step S21). After the process of step S21, the sequential measurement device 71 has the DFT calculation unit 74 calculate the Fourier coefficients xf={xf i} (step S22), and transmits the Fourier coefficient xf to the measurement and calculation device 51 (step S23).

[0050] In the measurement calculation device 51, the regularization coefficient setting unit 62 uses the first random measurement vector y1=Φs to determine the regularization coefficient λ0 (step S24). In the measurement calculation device 51, the Fourier coefficient xf is received (step S25), and the recovery error calculation unit 54 calculates the recovery error r and determines whether the recovery error r is within a predetermined range (step S26). When the recovery error r is outside the predetermined range (outside the predetermined range in step S26), the measurement calculation device 51 returns the process to step S24 and causes the regularization coefficient setting unit 62 to reset the regularization coefficient λ0. On the other hand, when the recovery error r is within the predetermined range (within the predetermined range in step S26), the measurement system 101 transitions to the second period T2 (FIG. 5).

[0051] In the second period T2, in the random measurement device 11, the random measurement unit 1 randomly acquires the measurement value of the preprocessed signal s(t) (step S27) and transmits the random measurement vector y=Φs to the measurement calculation device 51 (step S28). In the measurement calculation device 51, the random measurement vector y=Φs is received (step S29), and the estimation unit 61 estimates the coefficient x={x i} is estimated (step S30). The estimation for the preprocessed signal s(t) is called ZoomCS.

[0052] After the process of step S30, in the measurement calculation device 51, the abnormality degree calculation unit 57 calculates the abnormality degree and determines whether or not the abnormality degree exceeds the threshold value α (step S31). If the abnormality degree is equal to or less than the threshold value α (below the threshold value in step S31), the measurement calculation device 51 returns the process to step S29 and continues receiving the random measurement vector y=Φs. On the other hand, if the abnormality degree exceeds the threshold value α (above the threshold value in step S31), the measurement calculation device 51 causes the notification unit 58 to issue a notification (step S32).

[0053] FIG. 9 is a diagram showing a frequency spectrum measured by the measurement system 101 according to the second embodiment of the present invention. The horizontal axis is frequency [Hz], and the vertical axis is the speed [m / s] at which the measurement target 30 vibrates. The thick solid line is the result of ZoomCS. The thin solid line is the result of ZoomDFT, and the dashed line is the result of ZoomFFT. The open circles (○) are the results of ZoomDFT after correction, and the black circles (●) are the results of ZoomCS after correction. In other words, the speed [m / s] on the vertical axis is the thick solid line with the coefficient x = {x i}, and the thin solid and dashed lines represent the Fourier coefficients xf = {xf i}.

[0054] ZoomFFT is a method of downsampling the signal under test a(t) with a first sampling frequency fs1=256kHz at a second sampling frequency fs2=640Hz, and performing FFT calculations on a total of 512 points of data. In other words, the frequency resolution is 640Hz / 512=1.25Hz. ZoomDFT is a method of downsampling the signal under test a(t) with a first sampling frequency fs1=256kHz at a second sampling frequency fs2=640Hz for 40 seconds, and performing DFT calculations on a total of 25600 points of data. In other words, the frequency resolution is 640Hz / 25600=0.025Hz.

[0055] ZoomCS randomly samples a total of 25,600 points of data over 40 seconds at 640 / 50 = 12.8 Hz intervals. In other words, ZoomCS downsamples 512 points of data at 640 Hz, then compresses and samples them to 1 / 50. In other words, the frequency resolution is (640 Hz / 512) / 50 = 0.025 Hz. As a result, ZoomCS estimates 25,600 points (40 s) of 640 Hz data using 512 points, which is 1 / 50 of the 25,600 points (40 s) of data.

[0056] ZoomCS (thick solid line) and ZoomDFT (thin solid line) both have a frequency resolution of 0.025 Hz, and the frequency spectrum of the vibration velocity matches very well. Therefore, the restoration error calculation unit 54 can confirm that the restoration error r is within a predetermined range. In other words, the restoration error calculation unit 54 calculates the sum of squares of the difference between the Fourier coefficient xf = {xfi} by ZoomDFT and the coefficient x = {xi} by ZoomCS, and can confirm that the restoration error r (equation (7)) obtained by dividing the calculation result (power difference) by the sum of squares of the coefficient x is within a predetermined range.

[0057] By the way, the frequency spectrum of ZoomFFT (dashed line) does not match ZoomCS and ZoomDFT. This is because the frequency resolution of ZoomFFT is 1.25 Hz, which is significantly different from ZoomCS and ZoomDFT. Therefore, we perform correction to change the frequency resolution of the frequency spectra of ZoomDFT and ZoomCS.

[0058] ZoomDFT (○) and ZoomCS (●) are bundled on the frequency axis. Bundling is a frequency domain resampling process that uses DFT and bundles using the compression ratio. This bundling process results in ZoomDFT (○) and ZoomCS (●) that have been corrected and ZoomFFT (dashed line) that are nearly identical. In other words, if the frequency resolution is the same, the frequency spectrum of ZoomFFT will nearly match the frequency spectrum of ZoomCS and ZoomDFT.

[0059] As described above, according to this embodiment, the restoration error calculation unit 54 (FIG. 7) can confirm that the restoration error r falls within a predetermined range (S26 (FIG. 8)). This allows the determined regularization coefficient λ=λ0 (S24 (FIG. 8)) to be determined to be appropriate.

[0060] Third embodiment In the measurement system of each of the above embodiments, during the second period T2, the measurement calculation devices 50 and 51 use the random measurement vector y=Φs sequentially received to calculate the coefficient x={x i However, the random measurement vector y=Φs sequentially received by the measurement calculation device can be stored in a memory unit, and batch processing can be performed using the random measurement vector y=Φs stored in the memory unit.

[0061] FIG. 10 is a configuration diagram of a measurement system according to a third embodiment of the present invention. The measurement system 103 includes a random measurement device 11, a measurement and calculation device 52, and a sequential measurement device 71. The random measurement device 11 and the sequential measurement device 71 have the same configurations as those in the second embodiment. The measurement and calculation device 52 is different from the measurement and calculation device 51 (FIG. 7) in the second embodiment in that it includes a storage unit 59.

[0062] The storage unit 59 stores the random measurement vector y=Φs for the second period T2 (FIG. 5). As a result, the measurement calculation device 52 determines the regularization coefficient λ=λ0 using the received first random measurement vector y1=Φs in the first period T1, and the random measurement vector y=Φs is stored in the storage unit 59 in the second period T2. After that, the restoration unit 63 restores the discrete-time signal s=ψx in batch processing.

[0063] (Fourth embodiment) In the second embodiment, during the first period T1 (FIG. 5), the random measuring device 10 (FIG. 1) randomly acquired the preprocessed signal s(t). However, the preprocessed signal s(t) may also be measured sequentially at a predetermined second sampling frequency fs2 and selected randomly from the sequentially measured data.

[0064] FIG. 11 is a configuration diagram of a measurement system according to the fourth embodiment of the present invention. The measurement system 104 includes a random measurement device 11 , a sequential measurement device 71 , and a measurement and calculation device 53 . The random measuring device 11 and the sequential measuring device 71 have the same configuration as in the second embodiment.

[0065] Like the measurement calculation device 52 of the second embodiment, the measurement calculation device 53 includes a restoration error calculation unit 54, an abnormality degree calculation unit 57, a notification unit 58, an estimation unit 61, a regularization coefficient setting unit 62, and a restoration unit 63. However, the measurement calculation device 51 differs from the measurement calculation device 52 of the above embodiment in that it includes a random selection unit 67 and a switch 68.

[0066] The random selection unit 67 randomly selects a discrete-time signal s sequentially output by the sequential measurement unit 73 in the first period T1 according to the first random matrix Φ. In other words, the random selection unit 67 also functions as a random measurement value output unit, and outputs a first random measurement vector y1=Φs that is a vector representation of the random measurement value. The switch 68 is a one-circuit two-contact switch that sets the terminal j to either the terminal h or the terminal i. The i terminal is connected to the output of the random selection unit 67, the h terminal is connected to the output of the random measurement unit 1, and the j terminal is connected to the input of the estimation unit 61. The estimation unit 61 selects the Fourier basis vector {Ψ i The variable in} is time t.

[0067] In the first period T1, the estimation unit 61 inputs the first random measurement vector y1=Φs output by the random selection unit 67, as in the first embodiment, and estimates the coefficient x of the n×n orthogonal basis matrix ψ. At this time, the value of the regularization coefficient λ0 is set to be the maximum value within the standard deviation of the cross-validation at λ with the smallest error. In addition, the recovery error calculation unit 54 resets the regularization coefficient λ0 when the recovery error r is not within a predetermined range. Note that the random measurement unit 1 also outputs the first random measurement vector y1=Φs, but is blocked by the switch 68.

[0068] In the second period T2, the estimation unit 61 uses the regularization coefficient λ0 set in the first period T1 to input the random measurement vector y=Φs output by the random measurement unit 1 and estimates the coefficient x of the n×n orthogonal basis matrix ψ. Furthermore, as in the second and third embodiments, the restoration unit 63 restores the discrete-time signal s.

[0069] Fifth embodiment Although the measurement system in each of the above embodiments measures the vibration of an ultrasonic horn or the like, it is also possible to measure a rotating device 33 (FIG. 12) such as a bearing. In this case, vibration analysis can be performed using the rotational order.

[0070] FIG. 12 is a configuration diagram of a measurement system according to the fifth embodiment of the present invention. The measurement system 105 measures the characteristics (e.g., vibration) of the rotating device 33 using the sensor 21, and the random measurement device 12 and the measurement calculation device 50 are communicably connected. The rotating device 33 is, for example, a bearing, and includes a rotating electric machine such as a motor or a generator. The sensor 20 is, for example, an acceleration sensor, and detects the vibration of the rotating device 33. Furthermore, the rotation angle sensor 39 outputs a rotation phase θ indicating the rotation position of the rotating device 33.

[0071] The random measuring device 12 preprocesses the measured signal a(t) output by the sensor 21, and randomly samples the preprocessed signal s(t) in synchronization with the rotation phase θ. The random measuring device 12 includes a preprocessing unit 40, a random matrix setting unit 2 as a setting unit, and a receiving unit 4, similar to the random measuring device 10 (FIG. 1) of the first embodiment. However, the random measuring device 12 differs from the random measuring device 10 in that it includes a random measuring unit 5 instead of the random measuring unit 1, and further includes a rotation information acquiring unit 3. The rotation information acquiring unit 3 detects the rotation position of the rotating device 33 and outputs it to the random measuring unit 5 as a signal of the rotation phase θ. The random measuring unit 5 synchronizes the preprocessed signal s(t) with the rotation phase θ, and outputs (transmits) to the measurement calculation device 50 a signal of a random measurement vector y=Φs that is a vector representation of the random measurement values ​​randomly sampled according to the first random matrix Φ.

[0072] FIG. 13 is a configuration diagram showing a measurement target facility for inspecting a measurement target. The measurement target equipment 151 includes a rotating device 33 to be measured, a motor 38, a sensor 21, and a rotation angle sensor 39. The rotating device 33 is, for example, a bearing including an inner ring 33a and an outer ring 33b. The rotating device 33 to be evaluated has a scratched outer ring 31b. The inner ring 33a of the rotating device 33 is rotated by the motor 38. The rotation angle sensor 39 detects the rotation position of the inner ring 33a and outputs a rotation phase θ. The sensor 21 is an acceleration sensor that detects vibration of the rotating device 33. The random measurement unit 1 synchronizes the preprocessed signal s(t) with the rotation phase θ, and outputs (transmits) to the measurement calculation device 50 a signal of a random measurement vector y=Φs, which is a vector representation of the random measurement values ​​randomly sampled according to a first random matrix Φ.

[0073] FIG. 14 is a diagram for explaining the difference in the number of samples of the preprocessed signal s(t) depending on whether the rotation speed is high or low. The horizontal axis is time t, and the vertical axis in the diagram below is the pre-processed signal s(t). The pre-processed signal s(t) is a sine wave signal, with the first period from time 0 to t1 being a high rotation speed, and the second period from time t1 to t2 being a low rotation speed. In the diagram below, the points where the pre-processed signal s(t) is sampled in synchronization with the rotation phase are indicated by white circles (○). For example, in the lower diagram of Figure 14, white circles (○) are placed every 40° of the rotation phase. In addition, vertical lines of the same timing are drawn on the horizontal lines in the diagram above. Whether in high rotation speed region A or low rotation speed region B, the number of samples per period is the same.

[0074] In addition, one rotation is one cycle, and a phenomenon that occurs once is defined as the first-order rotation component, and n times that is defined as the nth-order rotation component. An analysis in which the X-axis represents the order and the Y-axis represents the magnitude of the vibration noise of the order component is called a "rotational order ratio analysis."

[0075] Fig. 15A is a diagram showing a change over time in a measurement signal, where the horizontal axis is time t and the vertical axis is the preprocessed signal s(t), and Fig. 15B is a diagram showing a change over time in the rotation phase of a rotating device. The horizontal axis is time t, and the vertical axis is the rotation phase θ(t). Here, the preprocessed signal s(t) is assumed to be a sine wave signal whose frequency gradually increases. Black circles (●) are marked at multiple measurement points a, b, c, and d of the preprocessed signal s(t) and the rotation phase θ(t).

[0076] In Fig. 15B, at time t = 0, the rotation phase θ(0) = 0, and the rotation phase θ(t) monotonically increases with time (measurement points a → b → c → d). For example, at point b (Fig. 15A), the rotation phase is less than (360 × 4)° because it is just before the third period. At point c (Fig. 15A), the rotation phase is more than (360 × 4)° because it is just before the fifth period. At point d (Fig. 15A), it is six periods later, and the rotation phase is between (360 × 4)° and (360 × 8)°.

[0077] FIG. 15C is a diagram showing the relationship between the measurement signal and the rotation phase. The horizontal axis represents the rotation phase θ, and the vertical axis represents the preprocessed signal s(θ). Between cd, the time T3 (FIG. 15A) is short, but the rotation phase width Θ1 is expressed as long. In other words, as shown in FIG. 15C, if the rotation phase θ is a variable, the preprocessed signal s(θ) can be evaluated as a sine wave even if the rotation speed of the rotating device 33 changes.

[0078] As described above, according to the measurement system 105 (FIG. 12) of this embodiment, the random measurement unit 1 performs measurement in synchronization with the rotation phase θ of the rotating device 33. As a result, the estimation unit 61 of the measurement calculation device 50 (FIG. 12) calculates the coefficient x for each frequency, x={x i}, but the coefficient for each order ratio is x={x i} is estimated. Also, the basis vectors {Ψ i} is, for example, a Fourier basis vector with the rotation phase θ as a variable, and one period of the Fourier basis vector corresponds to one rotation of the rotating device 33.

[0079] As described above, the measurement system 105 of this embodiment can perform an order ratio analysis with a reduced number of measurement points. That is, the measurement system 105 can zoom in and measure a frequency component (specific frequency component) at an order ratio (specific order ratio) specified based on the rotational angular velocity (ω=dθ / dt) measured in the first time period T1.

[0080] Sixth embodiment FIG. 16 is a configuration diagram of a measurement system according to the sixth embodiment of the present invention. The measurement system 106 is configured to include a random measurement device 10 and a measurement calculation device 77, similar to the measurement system 100 (FIG. 1) of the above embodiment. However, the random measurement device 13 is connected to a general-purpose sensor 22 instead of the sensor 20 (FIG. 1). In other words, the general-purpose sensor 22 is connected to a pre-processing unit 40 included in the random measurement device 13. The general-purpose sensor 22 is, for example, a general-purpose microphone made of MEMS, and has poor frequency characteristics compared to when a measurement sensor is used as the sensor 21 (FIG. 12).

[0081] FIG. 17 is a diagram showing frequency characteristics of a measurement microphone, a high-frequency measurement microphone, and a general-purpose microphone made of MEMS. The horizontal axis is frequency [Hz]. The vertical axis is decibels, with 0 decibels at a reference frequency of 1 kHz. Measurement microphones maintain flat frequency characteristics from about 10 Hz to about 20 kHz. In contrast, general-purpose microphones made from MEMS have a drop in output at low frequencies from about 20 Hz to 200 Hz. High-frequency measurement microphones can be used from about 10 Hz to 100 kHz.

[0082] The measurement and calculation device 77 (FIG. 16) differs from the measurement and calculation device 50 (FIG. 1) in that it includes a frequency correction unit 69. The frequency correction unit 69 corrects the frequency characteristics of the random measurement vector y received by the receiving unit 55. Specifically, the frequency correction unit 69 increases the gain at low frequencies of about 20 Hz to 200 Hz, and makes the frequency characteristics including the general-purpose sensor 22 similar to those of the measurement sensor.

[0083] Seventh embodiment FIG. 18 is a configuration diagram of a measurement system according to the seventh embodiment of the present invention. The measurement system 104, like the measurement system 103 (FIG. 10) of the fourth embodiment, includes a random measurement device 13, a measurement calculation device 78, and a sequential measurement device 71. The random measurement device 13 differs from the random measurement device 11 (FIG. 10) in that it further includes a switch 7 that switches to either the general-purpose sensor 22 or the measurement sensor 23 and connects to the pre-processing unit 40.

[0084] The measurement and calculation device 78 differs from the measurement and calculation device 52 (FIG. 10) in that it further includes a frequency correction unit 69 and a switch 68. The sequential measurement device 72 also connects the measurement sensor 23 instead of the sensor 21 (FIG. 12). The measurement sensor 23 may be a measurement microphone (FIG. 17). The measurement sensor 23 may also be a high-frequency measurement microphone (FIG. 17) capable of measuring high frequencies (up to 100 kHz). The measurement sensor 23 may also be a Doppler vibrometer. The frequency correction unit 69 is connected to the storage unit 59 and corrects the frequency characteristics of the data (random measurement vector y=Φs) of the general-purpose sensor 22 stored in the storage unit 59. As a result, the general-purpose sensor 22 and the frequency correction unit 69 become equivalent to the measurement sensor 23 as a whole.

[0085] The switches 7 and 68 are two-contact switches that connect either the terminal a or b to the terminal c. The switches 7 and 68 are connected to the terminal b in the first period T1 (FIG. 5), and are connected to the terminal a in the second period T2 (FIG. 5). That is, the switch 2 connects the measurement sensor 23 to the pre-processing unit 40 in the first period T1, and connects the general-purpose sensor 21 to the pre-processing unit 40 in the second period T2. The switch 68 connects the storage unit 59 to the estimation unit 61 and the regularization coefficient setting unit 62 in the first period T1, and connects the frequency correction unit 69 to the estimation unit 61 and the regularization coefficient setting unit 62 in the second period T2.

[0086] (Comparative Example) In each of the above embodiments, the preprocessed signal s(t) is restored by compressive sampling (CS), but the preprocessed signal s(t) may be restored by DFT transformation without compressive sampling.

[0087] FIG. 19 is a configuration diagram of a measurement system that is a comparative example of the present invention. The measurement system 106 is configured by connecting the measurement device 14 and the measurement calculation device 70 so as to be able to communicate with each other. The measurement device 14 includes the preprocessing unit 40 and the sequential measurement unit 6. The preprocessing unit 40 preprocesses the measured signal a(t) of the sensor 20 and outputs a preprocessed signal s(t). The sequential measurement unit 6 sequentially acquires the preprocessed signal s(t) at a second sampling frequency fs2 and outputs a discrete-time signal s. In other words, the measurement device 14 differs from the random measurement devices 10 (FIG. 1), 11 (FIGS. 7, 10, 11), and 12 (FIG. 12) of the above-mentioned embodiments in that it does not randomly sample.

[0088] The measurement calculation device 79 includes a receiving unit 55, an x ​​calculation unit 64, and a restoration unit 65. The receiving unit 55 receives a discrete-time signal s from the measuring device 14. The x calculation unit 64 calculates a coefficient (Fourier coefficient xf) of a Fourier basis vector ψf. Here, the variable of the Fourier basis vector ψf is time. That is, the x calculation unit 64 performs a discrete Fourier transform on the discrete-time signal s, similar to the DFT calculation unit 74 (FIGS. 7, 10, and 11). The restoration unit 65 restores the discrete-time signal s=ψf·xf using the Fourier basis vector ψf and the Fourier coefficient xf.

[0089] In addition, since the sequential measurement unit 6 sequentially acquires the preprocessed signal s(t) at the second sampling frequency fs2, the measurement calculation device 53 can perform a fast Fourier transform, but since the preprocessed signal s(t) is randomly sampled in the above-mentioned embodiment, a discrete Fourier transform cannot be performed. However, in the measurement calculation devices 50 (FIG. 1), 51 (FIG. 7), 52 (FIG. 10), and 53 (FIG. 11) of the above-mentioned embodiments, the coefficient x of the n×n orthogonal basis matrix ψ is made sparse by solving L1 regularization by LASSO (Least Absolute Shrinkage and Selection Operator).

[0090] (Modification) The present invention is not limited to the above-described embodiments, and modifications can be made without departing from the spirit of the present invention. For example, the following modifications are possible. (1) In the pre-processing unit 40 (FIG. 3) of the above embodiment, a zoom function is performed, but an envelope process can be added. A pre-processing unit 49 shown in FIG. 20 includes a sampling unit 41, a BPF 45a, an absolute value detection unit 47, a BPF 45b, a frequency shift 42, a low-pass filter 43, a down-sampling unit 44, and a BPF processing unit 46. As a result, the pre-processing unit 49 converts the frequency so that a specific frequency range f1 to f2 is displayed in the range of 0 to f (frequency range Hz).

[0091] The sampling section 41 samples the signal under test a(t) at a first sampling frequency of 51.2 kHz. The BPF 45a passes frequency components of the sampling signal SP(t) in a predetermined range (for example, 10 kHz to 15 kHz) and outputs a band-pass signal BPF(t). The absolute value detection section 47 performs absolute value detection on the band-pass signal BPF(t) and outputs an absolute value signal ABS(t). The BPF 45b passes frequency components of the absolute value signal ABS(t) in a predetermined range and outputs a band-pass signal BPF(t).

[0092] The frequency shifter 42 performs a frequency shift of a specific frequency (fm=2 kHz) on the bandpass signal BPF(t) output by the BPF 45b. That is, the frequency shifter 42 is a multiplier that multiplies the sampling signal SP(t) of the first sampling frequency fs1=51.2 kHz by the sine wave signal M(t) of the specific frequency (fm=2 kHz). This multiplier outputs both signals (output signals) of the signal component FSh(t) of the upper frequency (Fs1 / 2+fm=25.6 kHz+2 kHz) and the signal component FSl(t) of the lower frequency (Fs1 / 2-fm=25.6 kHz-2 kHz).

[0093] That is, the signal component FSh(t) is a bandpass signal BPF(t) that is frequency-shifted upward by a specific frequency (fm = 2 kHz), and the signal component FSl(t) is a bandpass signal BPF(t) that is frequency-shifted downward by a specific frequency (fm).

[0094] As in the pre-processing unit 40 (FIG. 3) of the first embodiment, the low-pass filter 43 extracts a signal component FSl(t) that has been frequency-shifted downward. The down-sampling unit 44 samples the output signal LPF(t) of the low-pass filter 43 at a second sampling frequency of 1.28 kHz, and outputs a down-sampling signal DNSP(t). The BPF processing unit 46 outputs a pre-processed signal s(t). That is, the pre-processing unit 49 of this modification performs envelope processing after realizing a zoom function. Note that in the case of FIG. 12 (fifth embodiment), since it is synchronized with the rotation phase θ, an order ratio BPF processing unit (not shown) is used instead of the BPF processing unit 46 of the pre-processing unit 49.

[0095] (2) In the measurement systems 100 (Fig. 1), 101 (Fig. 7), 103 (Fig. 10), and 104 (Fig. 11) of the above-mentioned embodiments, the random measuring devices 10 (Fig. 1), 11 (Figs. 7, 10, 11) and the measurement and calculation devices 50 (Fig. 1), 51 (Fig. 7), 52 (Fig. 10), and 53 (Fig. 11) are connected to each other so as to be able to communicate with each other, but they may be configured as an integrated unit. In this case, the random measurement unit 1 outputs the random measurement vector y, which is serial data, to the receiving unit 55 (Fig. 1), and the receiving unit 55 inputs the random measurement vector y, which is also serial data.

[0096] (3) In the above embodiment, the LASSO method is used, but the problem can be solved using various computationally efficient algorithms such as a greedy method, a method based on convex optimization, or a method based on belief propagation.

[0097] (4) In each of the above embodiments, the discrete-time signal s is subjected to a discrete Fourier transform, and the Fourier coefficients fk={fki} and the coefficients x={x i} and the square sum (power difference) is confirmed to be within a predetermined range. i} and calculate the first power of the sum of squares of Fourier coefficients xf={xf i}, and then a difference between the first power and the second power may be calculated.

[0098] (5) In each of the above embodiments, the pre-processing unit 40 receives the signal under measurement a(t), and the sampling unit 41 (FIG. 3) outputs the sampling signal SP(t). This is not limiting, and the pre-processing unit 40 may receive the sampling signal SP(t). In any case, the pre-processing unit 40 pre-processes the signal under measurement a(t).

[0099] (6) In the second embodiment, the pre-processing unit 40 (FIG. 3) is provided inside the random measurement device 11 (FIG. 7) and the sequential measurement device 71. However, the present invention is not limited to this. The pre-processing unit 40 (FIG. 3) may be provided only inside the random measurement device 11, and the sequential measurement device 71 may not be provided with the pre-processing unit 40, and the measured signal a(t) output by the sensor 20 may be input to the sequential measurement unit 73. In this case, the DFT calculation unit 74 (FIG. 7) performs DFT calculation on the non-downsampled signal FSl(t) (FIG. 3). [Explanation of symbols]

[0100] 1 Random measurement section (measurement section) 2 Random matrix setting section (setting section) 3. Rotation information acquisition section 4. Receiving section 5 Random measurement section 6 Sequential measurement section 7. Switch 10, 11, 12, 13 Random measuring equipment (measuring equipment) 14 Measuring equipment 20 Sensor (Doppler Vibrometer) 21 Sensor (accelerometer) 22 General-purpose sensors 23 Measurement Sensors 30 Measurement Object 33 Rotating Equipment 40 Pretreatment section 41 Sampling section 42 Frequency Shift 43 Low-pass filter 44 Downsampling section 45 Bandpass Filter 46 BPF processing section 47 Absolute value detector 50, 51, 52, 53 Measurement and calculation device 54 Restoration error calculation section 55 Receiving section 56 Transmitter 57 Abnormality calculation section 58 Notification Department 59 Memory section 60 Control Unit (Computer) 61 Estimation part 62 Regularization coefficient setting unit 63,65 Restoration section 64 x calculation section 65 Restoration Department 67 Random Selection Section 68 Switch 69 Frequency correction section 71 Sequential measuring equipment 73 Sequential measurement section 74 DFT calculation section 76 Sampling Frequency Generator 77, 78, 79 Measurement and calculation device 100, 101, 103, 104, 105, 106 Measurement system (measurement calculation system) a(t) Signal under test SP(t) Sampling signal s(t) Preprocessed signal (downsampled signal) y random measurement vector (random measurement y, second random measurement vector) y1 First random measurement vector Φ First random matrix (random matrix) λ regularization coefficient x coefficient ψ Orthogonal basis matrix (orthogonal basis) ψf Fourier basis vectors

Claims

1. a pre-processing unit that pre-processes a time-varying signal under test; a random measurement unit that randomly measures the preprocessed signal preprocessed by the preprocessing unit based on a predetermined first random matrix Φ; The pre-processing unit down-samples the signal under test to a signal obtained by shifting the signal under test downward by a specific frequency. A measuring device characterized by:

2. The preprocessing unit includes a bandpass filter that passes the signal under test in a predetermined bandwidth, a multiplier that frequency-shifts the output signal of the bandpass filter up or down by a specific frequency, an LPF that passes only signal components of lower frequencies of the output signal of the multiplier, and a downsampling unit that downsamples the output signal of the LPF.

2. The measuring device according to claim 1 .

3. A rotation information acquisition unit that acquires information on a rotation position or a rotation speed of the rotating device, The random measurement unit performs random measurements in synchronization with the rotational position or the rotational speed.

3. The measuring device according to claim 2.

4. a random measurement unit that randomly measures the preprocessed signal preprocessed by the preprocessing unit based on a predetermined first random matrix Φ; and a transmission unit that transmits a random measurement vector y that is a vector representation of the random measurement values ​​measured randomly and the first random matrix Φ to a measurement and calculation device, wherein the preprocessing unit downsamples the measured signal to a signal that is frequency-shifted downward by a specific frequency; a receiver for receiving the random measurement vector y from the measurement device; and a receiver for computing the random measurement vector y in relation to the first random matrix Φ and basis vector {Ψ ｉ When the n×n orthogonal basis matrix ψ with columns {x} is expressed as the product Φψx of its coefficient x, the regularization coefficient λ0 is determined so that the formula (3) defined in formulas (1) and (2) is minimized. ｉ }; and a measurement calculation device having an estimation unit for estimating A measurement system in which the above components are connected so as to be capable of communicating with each other. [0010] [0025] [0030]

5. A regularization coefficient setting unit that predetermines the regularization coefficient λ 0 in advance based on the random measurement vector y and an arbitrary second random matrix in a first period before the estimation unit estimates the coefficient x so that the regularization coefficient λ 0 becomes a maximum value within a standard deviation of cross-validation at the regularization coefficient λ with the smallest error; A setting unit that sets the second random matrix as the first random matrix Φ in the measurement device; The measurement system of claim 4 further comprising:

6. the signal to be measured during the first period is a signal to be measured by a measurement sensor, In a second period following the first period, the measured signal randomly measured by the random measurement unit is measured by a general-purpose sensor having a frequency characteristic worse than that of the measurement sensor, Further comprising a frequency correction unit that corrects the random measurement vector y output by the random measurement unit during the second period using a frequency characteristic of the general-purpose sensor; The estimation unit estimates coefficients x={xi} of the orthogonal basis matrix ψ using output data of the frequency correction unit during the second period.

6. The measurement system according to claim 5.

7. the first random matrix Φ is a matrix indicating that the signal under test is randomly decimated, The method further includes a regularization coefficient setting unit that determines the regularization coefficient λ0 that is the maximum value within the standard deviation of cross-validation for the regularization coefficient λ with the smallest error.

5. The measurement system according to claim 4.

8. a sequential measurement unit that sequentially measures a time-varying signal under test at a predetermined sampling frequency during a first period; a DFT calculation unit that performs a discrete Fourier transform on the discrete time signal measured by the sequential measurement unit and outputs Fourier coefficients; a pre-processing unit that pre-processes the signal under test or the discrete-time signal; a random measurement value output unit that outputs a first random measurement value that is randomly measured or selected with a predetermined first random matrix Φ using the preprocessed signal preprocessed by the preprocessing unit; A first random measurement vector y1, which is a vector representation of the first random measurement value, is expressed as a first random measurement vector y1 using the first random matrix Φ and the basis vector {Ψ ｉ The orthogonal basis matrix ψ of n×n, whose columns are n, is expressed as a product Φψx of its coefficient x, and the regularization coefficient is λ0. Then, the coefficient x of the orthogonal basis matrix ψ is calculated by multiplying the regularization coefficient λ0 by the product Φψx, so that the formula (6) defined by the formulas (4) and (5) is minimized. ｉ }; and A regularization coefficient setting unit that determines the regularization coefficient λ 0 that is maximum within a standard deviation of cross-validation at a regularization coefficient λ with the smallest error based on the first random measurement vector y 1 and the first random matrix Φ; The pre-processing unit down-samples the signal under test to a signal obtained by shifting the signal under test downward in frequency by a specific frequency, the error is a difference in power between the discrete-time signal and the first random measurement value, calculated using the coefficient x estimated by the estimation unit and the Fourier coefficient; The difference is within a predetermined range A measurement system comprising: [0045] [0050] [006]

9. The power difference is the sum of the squares of the differences between the coefficient x and the Fourier coefficients 9. The measurement system according to claim 8.

10. The power difference is the difference between the power calculated using the coefficient x and the power calculated using the Fourier coefficients.

9. The measurement system according to claim 8.

11. a pre-processing step of pre-processing a time-varying signal under test; A random measurement process for randomly measuring the preprocessed signal preprocessed in the preprocessing process based on a predetermined first random matrix Φ; The pre-processing step down-samples the measured signal by shifting the signal downward by a specific frequency. A measuring method comprising:

12. a pre-processing step of pre-processing a time-varying signal under test; a random measurement step of randomly measuring the preprocessed signal preprocessed in the preprocessing step based on a predetermined first random matrix Φ; The pre-processing step down-samples the measured signal by shifting the signal downward by a specific frequency. A measurement program comprising:

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