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 measured signal a(t) that changes in response to information on the rotation phase θ of a rotating device 30, a random measurement unit 1 that randomly measures a preprocessed signal s(t) preprocessed by the preprocessing unit 40 in synchronization with the rotation position based on a first random matrix Φ, a random measurement device 10 that transmits a random measurement vector y that is a vector representation of the random measurement values ​​measured at random and the first random matrix Φ to a measurement calculation device 50, and a random measurement vector y that is a vector representation of the random measurement values ​​measured at random and the first random matrix Φ to a measurement calculation device 50. i} is the coefficient x={x i} and a measurement and calculation device 50 having the same are communicatively connected.

Description

[Technical field]

[0001] The present invention relates to a measuring device, a measuring system, a measuring method, and a measuring program, and relates to a measuring device that measures characteristics of a rotating device, for example. [Background technology]

[0002] Rotating equipment inevitably fails due to material defects, fatigue, aging, etc. Failures lead to equipment downtime and economic losses. Therefore, it is important to diagnose faults in rotating equipment and keep it in good condition. Fault diagnosis of rotating equipment is 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. a pre-processing unit that pre-processes a time-varying signal under test; and a measuring unit that randomly measures the pre-processed signal pre-processed by the pre-processing unit based on a predetermined first random matrix Φ. a rotation information acquisition unit that acquires information on a rotation position or a rotation speed of the rotating device; The pre-processing unit downsamples an envelope signal that indicates an envelope of a sampled signal obtained by sampling the signal under measurement. and passing a specific frequency component corresponding to the rotational position or the rotational speed of the downsampled signal. A measuring device characterized by: 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. 2 is a configuration diagram showing a measurement target facility for measuring a measurement target. [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] 6A to 6C are diagrams illustrating an example of waveforms at each part in the pre-processing unit. [Figure 6] 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 7] 10A and 10B are diagrams for explaining a difference in a measurement signal depending on whether a rotation speed is high or low. [Figure 8A] FIG. 4 is a diagram showing a change in a signal under measurement over time. [Figure 8B] FIG. 4 is a diagram showing a change over time in the rotational phase of a rotating device. [Figure 8C] FIG. 4 is a diagram showing the relationship between a measurement signal and a rotation phase. [Figure 9] 4 is a flowchart for explaining the operation of the measurement system according to the first embodiment of the present invention. [Figure 10] FIG. 13 is a diagram showing a coefficient x={xi} calculated by an estimation unit. [Figure 11] FIG. 13 is a diagram showing a coefficient x={xi} calculated by an estimation unit. [Figure 12] FIG. 13 is a diagram showing an order ratio analysis of a downsampling signal. [Figure 13] FIG. 13 is a diagram showing an order ratio analysis of a downsampling signal. [Figure 14] FIG. 11 is a configuration diagram of a measurement system according to a second embodiment of the present invention. [Figure 15] FIG. 1 illustrates the POA of amplitude in orders. [Figure 16] FIG. 1 illustrates the POA of amplitude in orders. [Figure 17] FIG. 11 is a configuration diagram of a measurement system according to a third embodiment of the present invention. [Figure 18] FIG. 11 is a configuration diagram (1) of a pre-processing unit used in a measurement system according to a third embodiment of the present invention. [Figure 19] FIG. 13 is a configuration diagram (2) of a pre-processing unit used in a measurement system according to a third embodiment of the present invention. [Figure 20] FIG. 13 is a configuration diagram of a measurement system according to a fourth embodiment of the present invention. [Figure 21] FIG. 4 is a diagram showing frequency characteristics of a general-purpose microphone and a measurement microphone. [Figure 22] FIG. 13 is a configuration diagram of a measurement system according to a fifth embodiment of the present invention. [Figure 23] FIG. 13 is a configuration diagram of a measurement system according to a sixth embodiment of the present invention. [Figure 24] FIG. 13 is a configuration diagram of a preprocessing unit used in a measurement system according to a sixth embodiment of the present invention. [Diagram 25] FIG. 1 is a configuration diagram of a measurement system that is a comparative example of the present invention. [Figure 26] 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) of a rotating device 30 using a sensor 20, and the random measurement device 10 and a measurement calculation device 50 are communicably connected. The rotating device 30 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 vibration of the rotating device 30. In addition, the rotation angle sensor 35 outputs a rotation phase θ indicating the rotation position of the rotating device 30.

[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) in synchronization with the rotation phase θ. 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, a rotation information acquisition unit 3, 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 a measurement target facility for inspecting a measurement target. The equipment 150 to be measured includes a rotating device 30 to be measured, a motor 32, a sensor 20, and a rotation angle sensor 35. The rotating device 30 is, for example, a bearing including an inner ring 30a and an outer ring 30b. The rotating device 30 to be evaluated has scratches on the outer ring 30b. For comparison, the rotating device 30 includes a rotating device with a large scratch and a rotating device with a small scratch. The inner ring 30a of the rotating device 30 is rotated by the motor 32. The rotation angle sensor 35 detects the rotation position of the inner ring 30a and outputs a rotation phase θ. The sensor 20 is an acceleration sensor that detects vibration of the rotating device 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 includes a sampling unit 41, a BPF (Band Pass Filter) 42, an absolute value detection unit 43, an LPF (Low Pass Filter) 44, a down-sampling unit 45, and an inverse BRF (Band Rejection Filter) processing unit (order ratio inverse BRF processing unit 46).

[0016] The sampling unit 41 digitally converts the measured signal a(t) at a first sampling frequency fs1 (for example, fs1=25.6 kHz) and outputs a sampling signal SP(t). The BPF 42 passes frequency components of the sampling signal SP(t) in a predetermined range (for example, 1 kHz to 10 kHz) and outputs a band-pass signal BPF(t). The absolute value detection unit 43 performs absolute value detection on the band-pass signal BPF(t) and outputs an absolute value signal ABS(t). The LPF 44 blocks high frequency components of the detection signal ABS(t) and outputs a low-pass signal LPF(t) as an envelope signal. The downsampling unit 45 samples the low-pass signal LPF(t) at a second sampling frequency fs2 (for example, fs2=1.28 kHz) lower than the first sampling frequency fs1 (for example, fs1=25.6 kHz) and outputs a downsampling signal DNSP(t). The order ratio inverse BRF processing unit 46 is a filter processing unit that passes a specific frequency component (specific order ratio component) corresponding to the order ratio, and outputs a pre-processed signal s(t). The specific frequency (specific order) of this specific frequency component (specific order ratio component) corresponds to the rotation position (rotation phase θ) or the rotation speed. Note that the order ratio inverse BRF processing unit 46 may instead be provided with a BPF processing unit (filter processing unit) that passes a band including the specific frequency component (specific order ratio component).

[0017] 4 and 5 are diagrams showing examples of waveforms at each part in the pre-processing part. FIG. 5 shows the damage to the outer ring 32b on a larger scale than that shown in FIG.

[0018] 4 and 5 is the sampling signal SP(t) obtained by sampling unit 41 sampling the measured signal a(t). The second waveform is the band-pass signal BPF(t). The third waveform is the low-pass signal LPF(t) that has passed through absolute value detection unit 43 and LPF 44. The low-pass signal LPF(t) represents the envelope of the output signal of absolute value detection unit 43. The fourth waveform is the down-sampled signal DNSP(t), and the fifth waveform is the pre-processed signal s(t) that has been subjected to order ratio inverse BRF processing.

[0019] Returning to the explanation of Fig. 1, the random measurement unit 1 synchronizes the preprocessing signal s(t) with the rotation phase θ, and outputs (transmits) a signal of a random measurement vector y = Φs, which is a vector representation of random measurement values ​​randomly sampled 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.

[0020] FIG. 6 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. The first period T1 is from t=0 to t1, and the second period T2 is from t=t2 onwards. Note that the period from t1 to t2 is a measurement interruption period.

[0021] 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 time series signal strings (discrete time signals s) in which the pre-processed signal s(t) is synchronized with the rotation phase θ are randomly thinned out, or a matrix Φrsu indicating that measurements are performed 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.

[0022] The matrix Φrsu is a constant angular frequency ω rsu (ω rsu <<sampling angular frequency ωs) or constant frequency f rsu M p Assume that data of points are 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 can be manufactured more inexpensively than the unit that samples the preprocessed signal s(t) in synchronization with the rotation phase θ and outputs a sequence of all time-series signals (discrete-time signals s).

[0023] 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 rotation information acquisition unit 3 detects the rotational position of the rotating device 30 and outputs it to the random measurement unit 1 as a signal of the rotation phase θ. The receiving unit 4 receives update data (first random matrix Φ) of the random matrix setting unit 2 from the measurement calculation device 50.

[0024] In the second period T2 (FIG. 6), 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 the rotation phase θ as a variable. Note that one period of the Fourier basis vector corresponds to one rotation of the rotating device 30.

[0025] 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.

[0026] 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

[0027] Here, the norms in the formulas are defined in the following formulas (2) and (3).

number

number

[0028] 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.

[0029] Before estimating the coefficient x of the n×n orthogonal basis matrix ψ in the second period T2, 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.

[0030] 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.

[0031] 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.

[0032] FIG. 7 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 7, 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.

[0033] 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."

[0034] Fig. 8A 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. 8B is a diagram showing a change over time in the rotational 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).

[0035] In FIG. 8B, 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. 8A), the rotation phase is less than (360×4)° because it is just before the third period. At point c (FIG. 8A), the rotation phase is more than (360×4)° because it is just before the fifth period. At point d (FIG. 8A), the rotation phase is between (360×4)° and (360×8)° after six periods.

[0036] FIG. 8C 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. 8A) is short, but the rotational phase width Θ1 is expressed as long. In other words, as shown in FIG. 8C, if the rotational phase θ is a variable, the preprocessed signal s(θ) can be evaluated as a sine wave even if the rotational speed of the rotating device 30 changes.

[0037] 9 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 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. 6), and starts measurement in the second period T2 (FIG. 6). In addition, a random matrix Φ=Φ0 is set (stored) in advance in the random matrix setting unit 2.

[0038] In the first period T1, the preprocessing unit 40 (FIGS. 1 and 3) of the random measuring device 10 preprocesses the measured signal a(t) from the sensor 20 and outputs a preprocessed signal s(t). The random measuring unit 1 (FIG. 1) of the random measuring device 10 acquires the preprocessed signal s(t) in advance (step S1). The random measuring device 10 also receives a signal of the rotation phase θ(t) from the rotating device 30. As a result, the random measuring device 10 acquires a discrete-time signal s. The random measuring 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 measuring 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 the 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.

[0039] After the process of step S5, the measurement calculation device 50 transmits the modified first random matrix Φ so that the appropriately modified first random matrix Φ can be set in the random measurement device 10 (step S6). 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). This completes the preparation for starting measurement in the random measurement device 10.

[0040] In the second period T2, the random measuring device 10 randomly acquires a preprocessing 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. The random measuring device 10 also receives a signal of the rotation phase θ(t) from the rotating device 30. During execution of step S8, the random measuring device 10 uses the randomly acquired preprocessing signal s(t) to calculate a random measurement vector y=Φs (step S9). After processing in step S9, the random measuring 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 (step S13).

[0041] 10 and 11 show the coefficient x={x i The vertical axis is the coefficient x [m / s 2 ], and the horizontal axis is the order. The maximum value of the vertical axis in Figure 10 is 0.15[m / s 2 ], and the maximum value on the vertical axis of Figure 11 is 6 [m / s 2 FIG. 11 shows the characteristics of a sample in which the outer ring 32b has been scratched more severely than in FIG. For the sample with small scratches (Fig. 10), the first-order rotation component X1 = 0.08647 [m / s 2 ], and the second order component of rotation X2 = 0.0998 [m / s 2 In the case of the sample with a large scratch (Fig. 11), the first rotation component X1 is ≒ 0.4 [m / s 2 ], and the second rotation component X2 = 4.3741 [m / s 2 In other words, in the sample with small scratches (Fig. 10), the first-order rotational component and the second-order rotational component appear to be at the same level, but in the sample with large scratches (Fig. 11), the second-order rotational component increases more significantly than the first-order rotational component.

[0042] 12 and 13 are graphs showing the order ratio analysis of the downsampling signal DNSP(t). The vertical axis shows the acceleration amplitude [m / s 2 ], and the horizontal axis is the order. The maximum value of the vertical axis in Figure 12 is 0.125[m / s 2 ], and the maximum value on the vertical axis of Figure 13 is 4 [m / s 2 ]. The results are similar to those in Figures 10 and 11.

[0043] As described above, according to the measurement system 100 of this embodiment, downsampling is performed in the preprocessing unit 40 and random sampling is performed in the random measurement unit 1, so the number of samples is reduced in a cumulative manner. Therefore, the number of data of the random measurement vector y that the random measuring device 10 transmits to the measurement calculation device 50 becomes extremely small.

[0044] 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 (FIG. 9)). In the present embodiment, the error is calculated as the difference between the sum of the products of the power calculated by CS (Compressed Sensing) and the sum of the products of the power calculated by DFT (Discrete Fourier Transform).

[0045] FIG. 14 is a configuration diagram of a measurement system according to the second embodiment of the present invention. The measurement system 101 is configured by connecting a random measurement device 11, a sequential measurement device 70, and a measurement and calculation device 51 so as to be capable of communicating with each other.

[0046] The random measurement device 11 is configured to include the pre-processing unit 40, the random measurement unit 1 as a random measurement value output unit, and the rotation information acquisition unit 3. The random measurement unit 1 randomly acquires the pre-processed signal s(t) pre-processed by the pre-processing unit 40 in synchronization with the rotation phase θ. Note that the timing of random acquisition may or may not be synchronized with the clock of the second sampling frequency fs2 of the downsampling unit 45 (FIG. 3) of the pre-processing unit 40.

[0047] The sequential measurement device 70 includes a preprocessing unit 40, a sequential measurement unit 73, a DFT calculation unit 74, and a PLL 75. The PLL (Phase Locked Loop) 75 receives a signal with a rotation phase θ and generates a sampling angular frequency ω s =Nω. The generated phase synchronization signal is also used as a clock for the second sampling frequency fs2 of the downsampling unit 45 (FIG. 3) of the preprocessing unit 40. The sequential measurement unit 73 sequentially measures the preprocessed signal s(t) preprocessed by the preprocessing unit 40 at a sampling angular frequency ωs (ωs=2πfs2) synchronized with the rotation phase θ, and outputs a discrete-time signal s. In particular, the matrix Φ indicating a random start rsu When , the sampling angular frequency ωs = Nω rsu It becomes.

[0048] A DFT (Discrete Fourier Transform) calculation unit 74 performs a discrete Fourier transform on the discrete time signal s and outputs the calculation result (Fourier coefficients xf={xf i}) to the measurement calculation device 50. The sequential measurement device 70 randomly selects points that are not to be measured and a sampling angular frequency ωs=Nω rsu It differs from the random measuring device 10 (FIG. 1) in that

[0049] 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 calculation device 50 checks 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. Then, the measurement calculation device 50 sets a parameter (regularization coefficient λ).

[0050] In addition, in the second period T2, the measurement and calculation device 51, like the measurement and calculation device 50 (Figure 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.

[0051] 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.

[0052] 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 (7)). 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.

[0053]

number

[0054] 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.

[0055] 15 and 16 are diagrams showing the POA of acceleration in the order. The vertical axis is POA [m / s 2], and the horizontal axis is the order. Note that POA (Partial Overall) refers to the sum (sum of products) of the power within a specified spectrum interval. Note that "overall" refers to the sum (sum of products) of the power of all frequencies. Also, there are three types of parameters in Figures 15 and 16: CS (white circle "○"), DFT (diamond "◇"), and FFT (square "□"). Here, DFT is calculated with a frame length of 10 seconds at the second sampling frequency (e.g., 1.28 kHz), and FFT is calculated by dividing the 10-second interval into 10 10 The difference is that it is divided into 1024 frame lengths and all frame lengths are averaged.

[0056] For a sample with small scratches (Fig. 15), the POA of the second order rotation by CS is CS2 = 0.100017, and the POA of the second order rotation by FFT is FFT2 = 0.13381. For a sample with large scratches (Fig. 16), the POA of the second order rotation by CS is CS2 = 4.3847, and the POA of the second order rotation by FFT is FFT2 = 5.59547. In the sample with small scratches (Fig. 15), the POA of the first rotation is slightly less than that of the second rotation. However, in the sample with large scratches (Fig. 16), the POA of the second rotation is significantly more prevalent, and therefore the POA of the first rotation is significantly less than that of the second rotation.

[0057] Third embodiment In the first and second embodiments, the random measurement unit 1 performs random sampling in synchronization with the rotation phase θ, but the measured signal s(t) can also be acquired randomly at a predetermined sampling frequency fs or sampling angular frequency ωs.

[0058] FIG. 17 is a configuration diagram of a measurement system according to the third embodiment of the present invention. The measurement system 102 includes a random measurement device 11, a sequential measurement device 71, and a measurement and calculation device 52. The random measurement device 11 differs from the random measurement device 11 of the second embodiment (FIG. 14) in that it does not have the rotation information acquisition unit 3 and has a pre-processing unit 48 instead of the pre-processing unit 40. In other words, the random measurement unit 1 drives the pre-processing signal s(t) at a predetermined sampling frequency fs or sampling angular frequency ωs without synchronizing with the rotation phase θ. As a result, the random measurement unit 1 randomly acquires the pre-processing signal s(t) and outputs a random measurement vector y=Φs.

[0059] FIG. 18 is a configuration diagram of a preprocessing unit 48 used in a measurement system 102 according to the third embodiment of the present invention. The pre-processing unit 48 is similar to the pre-processing unit 40 of the above embodiment in that it includes a sampling unit 41, a BPF 42, an absolute value detection unit 43, an LPF 44, and a down-sampling unit 45. However, the pre-processing unit 48 differs from the pre-processing unit 40 in that it does not include an order ratio inverse BRF processing unit 46. In other words, the down-sampling signal DNSP(t) is output as the pre-processed signal s(t).

[0060] Returning to the explanation of Fig. 17, the sequential measurement device 71 includes a sequential measurement section 73 and a DFT calculation section 74, but differs from the sequential measurement device 70 (Fig. 14) of the above embodiment in that it has a sampling frequency generator 76 instead of the PLL 75 (Fig. 1) and a pre-processing section 48 instead of the pre-processing section 40. That is, the sequential measurement section 73 sequentially measures the pre-processed signal s(t) at the sampling frequency fs or sampling angular frequency ωs generated by the sampling frequency generator 76. Here, the sequential measurement section 73 is synchronized with the second sampling frequency fs2 that drives the downsampling section 45 (Fig. 18) of the pre-processing section 48.

[0061] Like the measurement calculation device 50 of the first embodiment, the measurement calculation device 52 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 52 differs from the measurement calculation device 51 of the second embodiment (FIG. 14) in that it includes a random selection unit 67 and a switch 68.

[0062] 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 of { is time t.} This point differs from the second embodiment in which the rotation phase θ is a variable.

[0063] 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.

[0064] 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 embodiment, the restoration unit 63 restores the discrete-time signal s.

[0065] FIG. 19 is a configuration diagram of another pre-processing unit used in the measurement system 102 according to the third embodiment of the present invention. The pre-processing unit 49 is used instead of the pre-processing unit 48 (FIG. 17). The pre-processing unit 49 includes a sampling unit 41, a BPF 42, an absolute value detection unit 43, an LPF 44, and a down-sampling unit 45, similar to the pre-processing unit 48 of the above embodiment. However, the pre-processing unit 49 differs in that it includes a feature frequency filter 47 after the down-sampling unit 45. The feature frequency filter 47 is a filter that passes a frequency component (a component of a feature frequency) having a peak in the frequency domain. This feature frequency is measured in advance in the first period T1 (FIG. 6). The feature frequency filter 47 performs frequency inverse BRF processing or frequency BPF processing.

[0066] (Fourth embodiment) FIG. 20 is a configuration diagram of a measurement system according to the fourth embodiment of the present invention. The measurement system 103 is configured to include a random measurement device 10 and a measurement calculation device 53, similar to the measurement system 100 (FIG. 1) of the above embodiment. However, the random measurement unit 1 of the random measurement device 10 is connected to a general-purpose sensor 21 instead of the sensor 20 (FIG. 1). The general-purpose sensor 21 is, for example, a general-purpose sensor made of MEMS, and is inexpensive but has poor frequency characteristics compared to when a measurement sensor is used as the sensor 20.

[0067] FIG. 21 is a diagram showing frequency characteristics of a general-purpose microphone and a measurement microphone. The horizontal axis is frequency [Hz] and the vertical axis is decibels. The solid line indicates the measurement microphone and the dashed line indicates the general-purpose microphone made of MEMS. The solid and dashed lines are 0 decibels at a reference frequency of 1 kHz. The measurement microphone maintains a flat frequency characteristic from about 20 Hz to about 20 kHz. In contrast, the output of the general-purpose microphone made of MEMS drops at low frequencies from about 20 Hz to 200 Hz.

[0068] The measurement calculation device 53 differs from the measurement 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, so that the frequency characteristics combined with the general-purpose sensor 21 are similar to those of the measurement sensor. The frequency correction unit 69 outputs the correction data to the estimation unit 61 and the regularization coefficient setting unit 62.

[0069] Fifth embodiment FIG. 22 is a configuration diagram of a measurement system according to the fifth embodiment of the present invention. The measurement system 104 includes a random measurement device 12, a measurement calculation device 59, and a sequential measurement device 70, similar to the measurement system 101 (FIG. 14) of the fourth embodiment. The random measurement device 12 differs from the random measurement device 11 (FIG. 14) in that the random measurement device 12 further includes a switch 8 that switches between the general-purpose sensor 21 and the measurement sensor 22. The measurement calculation device 59 differs from the measurement calculation device 51 (FIG. 14) in that the measurement calculation device 59 further includes a frequency correction unit 69 and a switch 9. The sequential measurement device 70 also connects the measurement sensor 22 instead of the sensor 20 (FIG. 14).

[0070] The switches 8 and 9 are two-contact switches that connect either the terminal a or b to the terminal c. The switches 8 and 9 are connected to the terminal b in the first period T1 (FIG. 6), and are connected to the terminal a in the second period T2 (FIG. 6). That is, the switch 8 connects the measurement sensor 22 to the random measurement unit 1 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 9 connects the random measurement unit 1 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.

[0071] Sixth embodiment In the fifth embodiment, the frequency correction unit 69 is provided inside the measurement and calculation device 59, but it can also be provided inside the pre-processing unit .

[0072] FIG. 23 is a configuration diagram of a measurement system according to the sixth embodiment of the present invention. The measurement system 105 includes a random measurement device 13, a measurement calculation device 51, and a sequential measurement device 70, similar to the measurement system 101 (FIG. 14) of the fifth embodiment. The random measurement device 13 differs from the random measurement device 11 (FIG. 14) in that it includes a switch 8 and a pre-processing unit 49 instead of the pre-processing unit 40. The switch 8 switches between the general-purpose sensor 21 and the measurement sensor 22, similar to the random measurement device 12 (FIG. 23).

[0073] FIG. 24 is a configuration diagram of a preprocessing unit 49 used in the measurement system according to the sixth embodiment of the present invention. The pre-processing unit 49 is the same as the pre-processing unit 40 of the first embodiment in that it includes a sampling unit 41, a BPF 42, an absolute value detection unit 43, an LPF 44, a downsampling unit 45, and an inverse BRF processing unit (order ratio inverse BRF processing unit 46). However, the pre-processing unit 49 differs in that a frequency correction unit 69 is inserted between the sampling unit 41 and the BPF 42.

[0074] (Comparative Example) In each of the above embodiments, the pre-processed signal s(t) is restored by compressive sampling (CS) in synchronization with the rotation phase of the rotating device 30. However, the pre-processed signal s(t) may be restored by DFT transformation without compressive sampling.

[0075] FIG. 25 is a configuration diagram of a measurement system which is a comparative example of the present invention. The measurement system 106 is configured by connecting the measuring device 14 and a measurement calculation device 79 so as to be able to communicate with each other. The measuring device 14 includes a preprocessing unit 48 and a sequential measurement unit 5. The preprocessing unit 48 preprocesses the measured signal a(t) of the sensor 20 and outputs a preprocessed signal s(t). The sequential measurement unit 5 sequentially measures the preprocessed signal s(t) at a predetermined sampling time and outputs a discrete-time signal s. In other words, the measuring device 14 differs from the random measuring device 11 of the second embodiment in that it does not randomly sample and is not synchronized with the rotation phase θ(t) from the rotating device 30.

[0076] 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. The restoration unit 65 restores the discrete-time signal s=ψf·xf using the Fourier basis vector ψf and the Fourier coefficient xf.

[0077] In addition, since the sequential measurement unit 5 sequentially acquires the preprocessed signal s(t) at each predetermined sampling time, the measurement calculation device 79 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) and 51 (FIG. 14) 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).

[0078] (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 first embodiment, only envelope processing is performed, but a zoom function can be added. A pre-processing unit 78 shown in FIG. 26 includes a sampling unit 41, a BPF 42a, an absolute value detection unit 43, a BPF 42b, a frequency shift 77, an LPF 44, a down-sampling unit 45, and a BPF processing unit (order ratio BPF processing unit 66).

[0079] The sampling section 41 samples the signal a(t) to be measured at a first sampling frequency of 51.2 kHz. The BPF 42a passes frequency components in a predetermined range (for example, 10 kHz to 15 kHz) of the sampling signal SP(t) and outputs a band-pass signal BPF(t). The absolute value detection section 43 performs absolute value detection on the band-pass signal BPF(t) and outputs an absolute value signal ABS(t). The BPF 42b passes frequency components in a predetermined range of the absolute value signal ABS(t) and outputs a band-pass signal BPF(t). The frequency shifter 77 performs a frequency shift equivalent to a specific order ratio (fm=2 kHz, which corresponds to the 40th order component at a rotation speed of 3000 r / min) on the band-pass signal BPF(t) output by the BPF 42b. That is, the frequency shifter 77 is a multiplier that multiplies the sampling signal SP(t) of the first sampling frequency fs1=51.2kHz by the sine wave signal M(t) of a specific frequency (fm=2kHz). This multiplier outputs both the signal component FSh(t) of the upper frequency (Fs1 / 2+fm=25.6kHz+2kHz) and the signal component FSl(t) of the lower frequency (Fs1 / 2-fm=25.6kHz-2kHz) (output signal).

[0080] 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).

[0081] As in the pre-processing unit 40 (FIG. 3) of the first embodiment, the LPF 44 extracts a signal component FSl(t) that has been frequency-shifted downward. The downsampling unit 45 samples the output signal LPF(t) of the LPF 44 at a second sampling frequency of 1.28 kHz, and outputs a downsampling signal DNSP(t). The order ratio BPF processing unit 66 outputs a pre-processed signal s(t). That is, the pre-processing unit 78 of this modification performs envelope processing after realizing a zoom function. Note that in the case of FIG. 17 (third embodiment), since it is not synchronized with the rotation phase θ, a BPF processing unit (not shown) is used instead of the order ratio BPF processing unit 66 of the pre-processing unit 78.

[0082] (2) In the measurement system 100 (FIG. 1) of each of the above embodiments, the random measuring device 10 and the measurement calculation device 50 are connected 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, and the receiving unit 55 inputs the random measurement vector y, which is also serial data.

[0083] (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.

[0084] (4) In the random measuring device 10 of the above embodiment, random sampling is performed in synchronization with the signal of the rotation phase θ indicating the rotation position of the rotating device 30, but even in the case of the rotation speed, the rotation position can be calculated by unwrapping and integrating. In other words, the rotation information acquisition unit 3 acquires information on the rotation position or rotation speed of the rotating device 30, and the random measuring device 10 performs random sampling in synchronization with the information on the rotation position or rotation speed of the rotating device 30.

[0085] (5) In the second embodiment, 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 the Fourier coefficients xf={xf i}, and then a difference between the first power and the second power may be calculated.

[0086] (6) 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).

[0087] (7) In the second embodiment, the pre-processing unit 40 (FIG. 14) is provided inside the random measuring device 11 (FIG. 14), and the sequential measuring device 70 is not provided with the pre-processing unit 40. However, the present invention is not limited to this, and the pre-processing unit 40 may be provided between the random measuring device (same configuration as the random measuring device 10 (FIG. 1)) and the sequential measuring device 70, and the sensor 20. In this case, the DFT calculation unit 74 (FIG. 14) performs DFT calculation on the pre-processed signal s(t) that has been pre-processed. [Explanation of symbols]

[0088] 1 Random measurement section (measurement section) 2 Random matrix setting section (setting section) 3. Rotation information acquisition section 4. Receiving section 5 Sequential measurement section 8,9 Switch 10,11,12,13 Random measuring equipment 14 Measuring equipment 21 General-purpose sensors 22 Measurement Sensors 30 Rotating equipment (bearings) 40,48,49,78 Pre-processing section 41 Sampling section 42 Bandpass Filter 43 Absolute value detector 44 LPF 45 Downsampling section 46 Order ratio inverse BRF processing section (filter processing section) 47 Feature Frequency Filters 50, 51, 52, 53, 59, 79 Measurement and calculation device 54 Restoration error calculation section 55 Receiving section 56 Transmitter 57 Abnormality calculation section 58 Notification Department 60 Control Unit (Computer) 61 Estimation part 62 Regularization coefficient setting unit 63,65 Restoration section 64 x operation section 66 Order ratio BPF processing section 67 Random Selection Section 68 Switch 69 Frequency correction section 73 Sequential measurement section 74 DFT calculation section 75 PLL 77 Frequency Shift 100, 101, 102, 103, 104, 105, 106 Measurement system (measurement calculation system) 150 Equipment to be measured a(t) Signal under test SP(t) Sampling signal LPF(t) Low-pass signal (envelope signal) DNSP(t) Downsampled signal s(t) Preprocessed signal s discrete time signal θ Rotational phase (rotational position) y random measurement vector (random measurement y, second random measurement vector) y1 First random measurement vector Φ First 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 measurement unit that randomly measures the preprocessed signal preprocessed by the preprocessing unit based on a predetermined first random matrix Φ; a rotation information acquisition unit that acquires information on a rotation position or a rotation speed of the rotating device, The pre-processing unit down-samples an envelope signal indicating an envelope of a sampled signal obtained by sampling the signal under test, and passes a specific frequency component corresponding to the rotational position or the rotational speed through the down-sampled signal. A measuring device characterized by:

2. The present invention relates to a measurement device that includes a pre-processing unit that pre-processes a time-varying signal under test, a measurement unit that randomly measures the pre-processed signal pre-processed by the pre-processing 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 at random and the first random matrix Φ to a measurement and calculation device, wherein the pre-processing unit down-samples an envelope signal that indicates an envelope of the signal under test, 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]

3. 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 2 further comprising:

4. 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 preprocessed by the preprocessing unit is measured by a general-purpose sensor having a frequency characteristic worse than that of the measurement sensor, A frequency correction unit is further provided to correct the random measurement vector y output by the 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.

4. The measurement system according to claim 3.

5. 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 preprocessed by the preprocessing unit is measured by a general-purpose sensor having a frequency characteristic worse than that of the measurement sensor, The pre-processing unit pre-processes a corrected signal obtained by correcting a measurement signal measured by the general-purpose sensor using a frequency characteristic of the general-purpose sensor, The estimation unit estimates coefficients x={xi} of the orthogonal basis matrix ψ using the measured signal whose frequency characteristics have been corrected during the second period.

4. The measurement system according to claim 3.

6. 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.

3. The measurement system according to claim 2.

7. a sequential measurement unit that sequentially measures a time-varying signal under test at a first sampling frequency or a sampling angular 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 according to a predetermined 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 vector using the 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 random matrix Φ; the pre-processing unit down-samples an envelope signal indicating an envelope of the signal under measurement, the predetermined first sampling frequency is equal to a second sampling frequency when the pre-processing unit downsamples, 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]

8. The pre-processing unit further includes a feature frequency filter that passes a feature frequency component measured in the first period for the downsampled signal.

8. The measurement system according to claim 7.

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 7 or 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 7 or 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 by the preprocessing process based on a predetermined first random matrix Φ; a rotation information acquisition step of acquiring information on a rotation position or a rotation speed of the rotating device, The pre-processing step includes down-sampling an envelope signal indicating an envelope of the signal under test, and passing a specific frequency component corresponding to the rotational position or the rotational speed through the down-sampled signal. 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; a rotation information acquisition step of acquiring information on a rotation position or a rotation speed of the rotating device; The pre-processing step includes down-sampling an envelope signal indicating an envelope of the signal under test, and passing a specific frequency component corresponding to the rotational position or the rotational speed through the down-sampled signal. A measurement program comprising:

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