Measuring instrument, measurement system, measurement calculation device, measurement calculation method, and measurement calculation program

Abstract

To increase the rate of restoration of an original signal while reducing measurement data.SOLUTION: A random measuring unit 1 that randomly measures, in synchronization with a rotational position, a measured signal s(t) that changes in response to information on a rotational phase θ of a rotating electric machine 30 based on a first random matrix Φ, a measuring instrument 10 that transmits a random measurement vector y, which is a vector representation of a random measurement value measured at random, and the first random matrix Φ to a measurement calculation device 50, and the measurement calculation device 50 having an estimation unit 61 that estimates coefficients x={xi} of an n×n orthogonal basis matrix ψ with basis vectors {Ψi} as columns, using the random measurement vector y by using a LASSO technique are communicatively connected.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Rotating machinery inevitably fails due to material defects, fatigue, aging, and other factors. Failures can lead to equipment downtime and economic losses. Therefore, it is important to diagnose rotating machinery faults and keep it in good condition. Fault diagnosis of rotating machinery is performed using a variety of 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). Compressed sensing theory is a technology that can accurately restore a signal from much less measurement data than is normally required, provided that the measurement data is sparse with respect to a certain basis (e.g., Fourier basis) and the basis is incoherent (random sampling is performed). As a result, the technology in Non-Patent Document 1 reduces the amount of 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 Compressed Sensing," 19th Symposium on Evaluation and Diagnosis, Japan Society of Mechanical Engineers, December 2-3, 2021 (Event Date) [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 have sparsity with respect to the Fourier basis. However, the frequency spectrum may become broad (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 aims to provide a measuring instrument, a measurement system, a measurement calculation device, a measurement calculation method, and a measurement calculation program that can increase the restoration rate of the original signal while reducing measurement data. [Means for solving the problem]

[0008] The above-mentioned problems of the present invention can be solved by the following means. A measuring device characterized by comprising: a rotation information acquisition unit that acquires information on the rotational position or rotational speed of a rotating machine (including a rotating electric machine); and a measurement unit that randomly measures a measured signal that changes in response to the rotational position or rotational speed in synchronization with the rotational position or rotational speed based on a predetermined first random matrix Φ. [Effects 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 explanation of the drawings]

[0010] [Figure 1] 1 is a configuration diagram of a measurement system according to a first embodiment of the present invention. [Figure 2] 10A and 10B are diagrams for explaining the difference in the signal under measurement depending on the rotation speed. [Figure 3A] FIG. 4 is a diagram showing a change over time of a signal under measurement. [Figure 3B] FIG. 2 is a diagram showing a change in the rotation phase of a rotary machine over time. [Figure 3C] FIG. 10 is a diagram illustrating the relationship between a signal under test and a rotation phase. [Figure 4] 4 is a flowchart for explaining the operation of the measurement system according to the first embodiment of the present invention. [Figure 5] FIG. 1 is a configuration diagram of a measurement system according to a first comparative example of the present invention. [Figure 6] 1 shows the frequency characteristics of normal propeller vibration measured using the measurement system of the first comparative example. [Figure 7] 1 shows the order characteristics of normal propeller vibration measured by a measurement system according to an embodiment of the present invention. [Figure 8] 1 shows the frequency characteristics of vibration of a propeller with one blade partially damaged, measured using the measurement system of the first comparative example. [Figure 9] 1 shows the order characteristics of vibration of a propeller with one blade partially broken, measured by the measurement system according to the first embodiment of the present invention. [Figure 10] 1 shows the frequency characteristics of vibration of a propeller in which one blade has been completely lost, measured using the measurement system of the first comparative example. [Figure 11] 1 shows the order characteristics of vibration of a propeller in which one blade has been completely lost, measured using the measurement system according to the first embodiment of the present invention. [Figure 12] FIG. 10 is a configuration diagram of a measurement system according to a second comparative example of the present invention. [Figure 13] FIG. 10 is a configuration diagram of a measurement system according to a second embodiment of the present invention. [Figure 14] FIG. 10 is a diagram illustrating frequency characteristics of a general-purpose microphone and a measurement microphone. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments 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. Furthermore, in each drawing, common or similar components are given the same reference numerals, and redundant explanations thereof will be omitted.

[0012] (First embodiment) FIG. 1 is a configuration diagram of a measurement system according to a first embodiment of the present invention. The measurement system 100 measures the characteristics of a rotating electric machine 30 using a sensor 20, and is configured by connecting a measuring device 10 and a measurement and calculation device 50 so that they can communicate with each other. The rotating electric machine 30 is a rotating machine in which a propeller 32 is attached to a motor 31. A normal propeller 32 has multiple (e.g., seven) blades 32a, but in this embodiment, the characteristics are measured in a state in which one of the blades is missing, resulting in a missing portion 32b. The motor 31 rotates the propeller 32 and is driven by, for example, receiving power from the measuring device 10. The sensor 20 is, for example, a microphone, and detects the sound pressure generated by the propeller 32 via wind pressure.

[0013] The measuring device 10 randomly samples the measured signal s(t) output by the sensor 20 in synchronization with the rotation phase θ that indicates the rotational position of the rotating electrical machine 30. The measuring device 10 is configured with 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. The random measurement unit 1 synchronizes the signal under test s(t) with the rotation phase θ and outputs (transmits) to the measurement calculation device 50 a signal of random measurement vector y=Φs, which is a vector representation of random measurement values ​​randomly sampled according to a first random matrix Φ. Here, the first random matrix Φ includes a matrix Φr indicating that all time-series signals (discrete measurement signals s) synchronized with the signal under test s(t) with the rotation phase θ have been randomly thinned out, and a matrix Φrsu indicating that measurements are performed multiple times with the measurement start timing of the discrete measurement signals s randomized. The matrix Φrsu is a matrix representing the random measurement vector y=Φs obtained by randomly sampling the signal under test s(t) at a constant angular frequency ω rsu (ωrsu<< sampling angular frequency ωs, e.g., 2πf rsu = 2π × 0.3Hz) or a constant frequency f rsu M p points (e.g., M p = 27 points). As a result, the number of samples that the random measurement unit 1 takes of the signal under test s(t) is smaller than the number of discrete measurement signals s. The random measurement unit 1 can be manufactured more cheaply than the unit that samples the signal under test s(t) in synchronization with the rotation phase θ and outputs a sequence of all time-series signals (discrete measurement signals s).

[0014] 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 used 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 electric machine 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.

[0015] The measurement and calculation device 50 restores the discrete measurement signal s from the random measurement values ​​(random measurement vector y=Φs) received from the measurement device 10. In other words, the measurement and calculation device 50 restores all data (discrete measurement signal s) from the small amount of data (y=Φs) received from the measurement device 10. Here, the discrete measurement signal s is calculated based on the basis vector {Ψ i} is the column of the n×n orthogonal basis matrix ψ, the coefficient x is expressed as s=ψx. i} is, for example, a Fourier basis vector with the rotation phase θ as a variable. One period of the Fourier basis vector corresponds to one rotation of the rotary electric machine 30.

[0016] The measurement calculation device 50 is a PC (Personal Computer) comprising a receiving unit 55, a control unit 60, and a transmitting unit 56. The receiving unit 55 receives data of the random measurement vector y=Φs from the measuring 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.

[0017] The estimation unit 61 estimates the coefficient x of the n×n orthogonal basis matrix ψ using the random measurement vector y=Φs received by the measurement calculation device 50. Specifically, using the LASSO (Least Absolute Shrinkage and Selection Operator) method, the coefficient x={x i} is estimated.

number

[0018] Here, the norms in the formulas are defined in the following formulas (2) and (3).

number

number

[0019] Σ|x i | is x from i=1 to N i means adding the absolute values ​​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 the error.

[0020] Before estimating the coefficient x of the n×n orthogonal basis matrix ψ, the regularization coefficient setting unit 62 sets the value of the regularization coefficient λ0 in advance so that it becomes the maximum value within the standard deviation of cross-validation for the regularization coefficient λ with the smallest error. At this time, the initial value (second random matrix Φ0) stored in the random matrix setting unit 2 of the measuring device 10 is used as the first random matrix Φ, but it may be changed as appropriate.

[0021] The reconstruction unit 63 uses the n×n orthogonal basis matrix ψ and the coefficients x={x i} to restore the discrete measurement signal s=ψx. In other words, the restoration unit 63 can restore the entire discrete measurement signal s using the randomly sampled random measurement vector y=Φs.

[0022] 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 if the regularization coefficient λ0 is changed, the transmission unit 56 transmits the changed first random matrix Φ to the measuring device 10. As a result, the value of the random matrix setting unit 2 of the measuring device 10 is updated from the second random matrix Φ0 to the first random matrix Φ.

[0023] FIG. 2 is a diagram for explaining the difference in the number of samples of the signal under measurement s(t) depending on the rotation speed. The horizontal axis is time t, and the vertical axis in the diagram below is the signal under test s(t). The signal under test s(t) is a sine wave signal, with the first cycle from time 0 to t1 being a high rotation speed, and the second cycle from time t1 to t2 being a low rotation speed. In the diagram below, white circles (○) indicate points where the signal under test s(t) is sampled in synchronization with the rotation phase. For example, in the bottom diagram of Figure 2, white circles (○) are placed every 40° of the rotation phase. Additionally, vertical lines of the same timing are drawn on the horizontal lines in the diagram above. The number of samples per cycle is the same whether it is in high rotation speed region A or low rotation speed region B.

[0024] The phenomenon that occurs once per rotation is defined as one cycle, and the nth-order component is defined as the first-order component, and the nth-order component is defined as the nth-order component. The analysis performed by plotting the order on the X-axis and the magnitude of the vibration noise of the order component on the Y-axis is called "rotation order ratio analysis."

[0025] Fig. 3A is a diagram showing changes over time in the signal under test. The horizontal axis represents time t, and the vertical axis represents the signal under test s(t). Fig. 3B is a diagram showing changes over time in the rotation phase of the rotating machine (rotating electrical machine 30). The horizontal axis is time t, and the vertical axis is rotation phase θ(t). Here, the signal under test s(t) is assumed to be a sine wave signal with a gradually increasing frequency. Black circles (●) are marked at multiple measurement points a, b, c, and d of the signal under test s(t) and rotation phase θ(t).

[0026] In Figure 3B, at time t = 0, the rotation phase θ(0) = 0, and the rotation phase θ(t) monotonically increases as time passes (measurement points a → b → c → d). For example, at point b (Figure 3A), the rotation phase is less than (360 × 4)° because it is just before the third cycle. At point c (Figure 3A), the rotation phase is greater than (360 × 4)° because it is just before the fifth cycle. At point d (Figure 3A), the rotation phase is between (360 × 4)° and (360 × 8)° after six cycles.

[0027] FIG. 3C is a diagram showing the relationship between the signal under test and the rotation phase. The horizontal axis represents the rotation phase θ, and the vertical axis represents the signal under test s(θ). Between cd, the time T1 (FIG. 3A) is short, but the rotational phase width Θ1 is expressed as long. In other words, as shown in FIG. 3C, if the rotational phase θ is a variable, the measured signal s(θ) can be evaluated as a sine wave even if the rotational speed of the rotating electrical machine 30 changes.

[0028] FIG. 4 is a flowchart for explaining the operation of the measurement system according to an embodiment of the present invention. This flow is first started when the measurement target is changed. As a result, the measurement calculation device 50 determines parameters (regularization coefficient λ and first random matrix Φ) in advance and starts measurement. Furthermore, a random matrix Φ=Φ0 is set (stored) in advance in the random matrix setting unit 2.

[0029] The measuring device 10 acquires a measured signal s(t) from the sensor 20 in advance for a certain period of time (step S1). The measuring device 10 also receives a signal of a rotational phase θ(t) from the rotating electrical machine 30. As a result, the measuring device 10 acquires a discrete measurement signal s. The measuring device 10 calculates a random measurement vector y=Φs using the previously sampled discrete measurement signal s (step S2). After processing step S2, the measuring device 10 transmits the random measurement vector y to the measurement and calculation device 50 (step S3). The measurement and calculation device 50 receives the random measurement vector y (step S4) and determines a regularization coefficient λ=λ0 so that the regularization coefficient λ=λ0 is 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 a random matrix Φ=Φ0. However, the regularization coefficient λ0 may be determined after appropriately modifying the first random matrix Φ.

[0030] After processing in step S5, the measurement calculation device 50 transmits the appropriately modified first random matrix Φ to the measuring device 10 so that the modified first random matrix Φ can be set therein (step S6). The measuring device 10 receives the first random matrix Φ and resets the second random matrix Φ set in the random matrix setting unit 2 to the received first random matrix Φ (step S7). This completes the preparations for the measuring device 10 to start measurement.

[0031] The measuring device 10 randomly acquires the measured 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 S7. The measuring device 10 also receives a signal of the rotational phase θ(t) from the rotating electric machine 30. During execution of step S8, the measuring device 10 uses the randomly acquired measured signal s(t) to calculate a random measurement vector y=Φs (step S9). After processing step S9, the 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 measurement signal s=ψx.

[0032] FIG. 5 is a configuration diagram of a measurement system according to a first comparative example of the present invention. The measurement system 101 is configured by a measuring device 11 and a measurement calculation device 51 connected to each other so that they can communicate with each other. The measuring device 11 includes a sequential measurement unit 5 that sequentially measures the measured signal s(t) of the sensor 20 at a predetermined sampling time and outputs a discrete measurement signal s. That is, the measuring device 11 differs from the measuring device 10 of the above embodiment in that it does not perform random sampling and is not synchronized with the rotational phase θ(t) from the rotating electric machine 30.

[0033] The measurement calculation device 51 is configured to include a receiving unit 55, an x ​​calculation unit 64, and a reconstruction unit 65. The receiving unit 55 receives a discrete measurement signal s from the measuring device 11. The x calculation unit 64 calculates the coefficient of the Fourier basis vector ψf (Fourier coefficient xf). Here, the variable of the Fourier basis vector ψf is time. In other words, the x calculation unit 64 performs a discrete Fourier transform on the discrete measurement signal s. The reconstruction unit 65 reconstructs the discrete measurement signal s = ψf xf using the Fourier basis vector ψf and the Fourier coefficient xf.

[0034] Furthermore, the measuring device 11 sequentially acquires the signal under test s(t) at predetermined sampling times. Therefore, the measurement and calculation device 51 is capable of performing a fast Fourier transform. However, in the above-described embodiment, the signal under test s(t) is randomly sampled, and therefore a discrete Fourier transform cannot be performed. However, in the measurement and calculation device 50 of the above-described embodiment, the coefficient x of the n×n orthogonal basis matrix ψ is made sparse by solving L1 regularization using LASSO (Least Absolute Shrinkage and Selection Operator).

[0035] 6 shows the frequency characteristics of vibration of a normal propeller measured by the measurement system 101 of the first comparative example. That is, the propeller 32 has no missing portions 32b (FIG. 1) and is composed of seven normal blades 32a. The horizontal axis represents frequency [Hz], and the vertical axis represents the Fourier coefficient xf. The rotation frequency fp of the propeller 32 is set to fp=40 Hz. That is, since the propeller 32 has seven blades 32a, the propeller passing frequency fpp is fpp=7×fp=280 Hz. Peaks are seen at around 40 Hz and 280 Hz. The spectrum is broad at the peak around 280 Hz.

[0036] 7 shows the order characteristics of vibration of a normal propeller measured by the measurement system 100 of the first embodiment. The horizontal axis represents the order of the Fourier basis vector ψf, and the vertical axis represents its coefficient x. Since the propeller 32 has seven blades 32a, the seventh-order component is particularly high compared to the first, second, third, fourth, fifth, and sixth-order components. In addition, each order rises sharply, and it can be evaluated as having high sparseness.

[0037] Fig. 8 shows the frequency characteristics of vibration of a propeller with one blade partially damaged, measured by the measurement system 101 of the first comparative example. As in Fig. 6, the horizontal axis represents frequency [Hz] and the vertical axis represents the Fourier coefficient xf. Because one blade is partially damaged, the peak of the fundamental wave, rotational frequency fp = 40 Hz, is higher than in Figure 6 (normal propeller). In addition, 2nd and 3rd harmonic components are present, and there is also a slight 7th harmonic component (280 Hz). However, the 7th harmonic component has a wider spectrum.

[0038] Fig. 9 shows the order characteristics of vibration of a propeller with one blade partially damaged, measured by the measurement system 100 of the first embodiment. As in Fig. 7, the horizontal axis represents the order of the n × n orthogonal basis matrix ψ (Fourier basis), and the vertical axis represents its coefficient x. The first-order component is high, and second-order, third-order, etc. components are also present. The seventh-order component is also present to a certain extent. In addition, the spectrum of each order is narrow and highly sparse.

[0039] FIG. 10 shows the frequency characteristics of vibration of a propeller with one blade completely lost, measured by the measurement system 101 of the first comparative example. As with FIGS. 6 and 8, the horizontal axis represents frequency f [Hz], and the vertical axis represents the Fourier coefficient xf. Because one blade is completely lost, the peak of the fundamental wave, rotational frequency fp = 40 Hz, is higher than in FIG. 8 (partial loss). In addition, second and third harmonic components are present, and the seventh harmonic component (280 Hz) is more prevalent than in FIG. 8. The spectrum of the seventh harmonic component is as wide as in FIG. 8.

[0040] 11 shows the order characteristics of vibration of a propeller with one blade completely lost, measured by the measurement system 100 according to the first embodiment of the present invention. As in FIGS. 7 and 9, the horizontal axis represents the order of the Fourier basis vector ψf, and the vertical axis represents its coefficient x. As in Figure 9, 1st, 2nd, 3rd, and other order components appear, including the 7th order. These peak values ​​are higher than in Figure 9. In addition, the spectrum of each order is narrow and highly sparse.

[0041] (Second Comparative Example) FIG. 12 is a configuration diagram of a measurement system according to a second comparative example of the present invention. The measurement system 102 is configured by connecting a measuring device 12 and a measurement and calculation device 52 so that they can communicate with each other. The measuring device 12 includes a synchronous measurement unit 6 that measures a signal under measurement s(t) in synchronization with a signal of the rotational phase θ of the rotating electric machine 30. In other words, the synchronous measurement unit 6 samples the signal under measurement s(t) at predetermined equal phase intervals. In other words, the synchronous measurement unit 6 differs from the random measurement unit 1 (FIG. 1) of the above embodiment in that it does not randomly sample the signal under measurement s(t). The measurement and calculation device 52 includes a receiving unit 55, an x ​​calculation unit 67, and a restoration unit 68.

[0042] The receiving unit 55 receives the discrete measurement signal s output by the synchronization measurement unit 6. The x calculation unit 67 calculates the Fourier coefficient xf of the Fourier basis vector ψf. Here, the Fourier basis vector ψf has the rotation phase θ as a variable. The reconstruction unit 68 uses the Fourier basis vector ψf and the Fourier coefficient xf to reconstruct the discrete-time signal s = ψf xf.

[0043] In the measurement system 102 of this comparative example, the measuring device 12 samples the signal under test s(t) in synchronization with the signal of rotational phase θ, so the number of samples per period is the same regardless of whether the rotation speed of the rotating electrical machine 30 is fast or slow. Furthermore, the measurement and calculation device 52 receives the discrete measurement signal s at equal phase intervals, so it can calculate the Fourier coefficient xf, similar to the measurement and calculation device 51 of the first comparative example (FIG. 5).

[0044] Furthermore, in the measuring device 12 of this comparative example, the signal under test s(t) is sampled at equal phase intervals, resulting in a large number of data samples. In contrast, in the measuring device 10 of the above embodiment, the signal under test s(t) is randomly sampled, resulting in a small number of data samples.

[0045] (Second embodiment) In the first embodiment, no type of microphone was selected for the sensor 20. In this embodiment, the sensor (general-purpose sensor 21 (FIG. 13)) used in the random measurement unit 1 is a general-purpose microphone made of MEMS (Micro Electro Mechanical Systems), which is simpler and smaller than the measurement microphone.

[0046] FIG. 13 is a configuration diagram of a measurement system according to the second embodiment of the present invention. The measurement system 103 is configured to include a measurement device 10 and a measurement calculation device 53, similar to the measurement system 100 of the above embodiment. However, the random measurement unit 1 of the 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 has poor frequency sensitivity compared to when a measurement sensor is used as the sensor 20.

[0047] FIG. 14 is a diagram showing the 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 represents the measurement microphone, and the dashed line represents the general-purpose MEMS microphone. The solid and dashed lines are 0 decibels at a reference frequency of 1 kHz. The measurement microphone maintains a flat frequency response from about 20 Hz to about 20 kHz. In contrast, the output of the general-purpose MEMS microphone drops at low frequencies from about 20 Hz to 200 Hz.

[0048] Measurement and calculation device 53 differs from measurement and calculation device 50 (FIG. 1) in that it includes a frequency corrector 69. Frequency corrector 69 corrects the frequency characteristics of the random measurement vector y received by receiver 55. Specifically, frequency corrector 69 increases the gain at low frequencies of about 20 Hz to 200 Hz, making the frequency characteristics combined with general-purpose sensor 21 comparable to those of the measurement sensor.

[0049] (Variation) 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, as follows. (1) In the measurement system 100 (FIG. 1) of each of the above-described embodiments, the measurement device 10 and the measurement calculation device 50 are connected to each other so that they can communicate with each other. However, they may be configured as an integrated unit. In this case, the random measurement unit 1 outputs a 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.

[0050] (2) 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.

[0051] (3) In the measuring device 10 of the above embodiment, random sampling is performed in synchronization with the signal of the rotational phase θ indicating the rotational position of the rotating electric machine 30. However, even in the case of the rotational speed, the rotational position can be calculated by performing unwrapping and integration processing. In other words, the rotation information acquiring unit 3 acquires information on the rotational position or rotational speed of the rotating electric machine 30, and the measuring device 10 performs random sampling in synchronization with the information on the rotational position or rotational speed of the rotating electric machine 30.

[0052] (3) In the above embodiment, the measured signal s(t) is acquired at equal phase intervals while being synchronized with the rotation phase θ, and the acquired discrete measurement signal s is subjected to a discrete Fourier transform to obtain the Fourier coefficients fk={fki} and the coefficient x={x i} and check in advance that the sum of squares (power difference) is within a predetermined range. After checking in advance, it is preferable that the regularization coefficient setting unit 62 sets the regularization coefficient λ0. i} and calculate the first power of the sum of squares of the Fourier coefficients xf={xf i}, and then calculate the difference between the first power and the second power. In this case, the power difference may be confirmed in advance using a measurement sensor, and a simple and small general-purpose sensor may be used for random sampling of the signal to be measured s(t).

[0053] (4) In each of the above embodiments, the sound pressure of a rotating electric machine 30 having a propeller 32 attached to a motor 31 is measured, but the vibration and sound pressure of a rotating machine (including a rotating electric machine) such as a bearing can also be measured. [Explanation of symbols]

[0054] 1 Random measurement section (measurement section) 2 Random matrix setting section (setting section) 3 Rotation information acquisition unit 4. Receiving section 10,11,12 Measuring equipment 20 Sensors (measurement sensors) 21 General-purpose sensors 30 Rotating Electric Machines (Rotating Machines) 50, 51, 52, 53 Measurement and calculation device 56 Transmission unit (setting unit) 60 Control Unit (Computer) 61 Estimation part 62 Regularization coefficient setting unit 63, 65, 68 Restoration section 69 Frequency corrector 100,101,102,103 Measurement system s(t),s(θ) Signal under test s Discrete measurement signal θ rotation phase (rotation position) y random measurement vector Φ first random matrix Φ0 second random matrix λ regularization coefficient x coefficient ψ orthogonal basis matrix

Claims

1. a rotation information acquisition unit that acquires information on a rotational position or a rotational speed of the rotating machine; a measurement unit that randomly measures a measurement target signal that changes in response to the rotational position or the rotational speed based on a predetermined first random matrix Φ in synchronization with the rotational position or the rotational speed; A measuring device comprising:

2. a measuring device including a rotation information acquisition unit that acquires information on a rotational position or a rotational speed of a rotating machine, a measurement unit that randomly measures a measured signal that changes in response to the rotational position or the rotational speed based on a predetermined first random matrix Φ in synchronization with the rotational position or the rotational speed, 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 calculation device; 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 receiving unit that receives from a measuring device random measurement values ​​obtained by randomly measuring a measured signal that changes in response to a rotational position or a rotational speed of a rotating machine based on a predetermined first random matrix Φ in synchronization with the rotational position or the rotational speed; A random measurement vector y, which is a vector representation of the random measurement values, is expressed as a function of the first random matrix Φ and the 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 (6) defined in formulas (4) and (5) is minimized. ｉ }; and A measurement and calculation device comprising: [0045] [0050] [006]

4. A regularization coefficient setting unit that predetermines the regularization coefficient λ 0 based on the random measurement vector y and an arbitrary second random matrix before the estimation unit estimates the coefficient x, so that the regularization coefficient λ 0 becomes a maximum value within the 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 and calculation device according to claim 3 , further comprising:

5. 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.

4. The measurement and calculation device according to claim 3.

6. The basis vectors {Ψ ｉ } is a Fourier basis vector with the rotational position as a variable, The coefficient x is a Fourier coefficient.

4. The measurement and calculation device according to claim 3.

7. a receiving step of receiving from a measuring device a random measurement value obtained by randomly measuring a measured signal that changes in response to a rotational position or a rotational speed of the rotating machine based on a predetermined first random matrix Φ in synchronization with the rotational position or the rotational speed; A random measurement vector y, which is a vector representation of the random measurement values, is expressed as a function of the first random matrix Φ and the basis vector {Ψ ｉ When the n × n orthogonal basis matrix ψ having columns {} is expressed as the product Φψx of the matrix and its coefficient x, the regularization coefficient λ0 is expressed as the formula (9) defined in formula (7) and formula (8). The coefficients x of the orthogonal basis matrix ψ are set to {x ｉ }; and A measurement and calculation method comprising the steps of: [0070] [0080] [0090]

8. a receiving step of receiving from a measuring device a random measurement value obtained by randomly measuring a measured signal that changes in response to a rotational position or a rotational speed of the rotating machine based on a predetermined first random matrix Φ in synchronization with the rotational position or the rotational speed; A random measurement vector y, which is a vector representation of the random measurement values, is expressed as a function of the first random matrix Φ and the basis vector {Ψ ｉ When the n × n orthogonal basis matrix ψ having columns {{{x}}} is expressed as the product Φψx of the matrix and its coefficient x, the regularization coefficient λ0 is set so that the formula (12) defined in the formulas (10) and (11) is minimized as follows: Coefficient x of the orthogonal basis matrix ψ = {x ｉ }; and A measurement calculation program that causes a computer to execute the above steps. [0089] ##EQU00011## ##EQU00012##

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