Adaptive estimation method for rotating speed of rotating machine based on maximum criterion of harmonic energy and proportion

Abstract

A rotating machine rotating speed self-adaptive estimation method based on harmonic energy and a maximum proportion criterion comprises the steps that firstly, multi-channel vibration signals are obtained through sampling, a channel signal with the maximum effective value is obtained to serve as a target signal for rotating speed estimation, and the target signal is pre-processed in a self-adaptive mode according to the signal type; secondly, Fourier transform is carried out on the preprocessed signals, a maximum value method is adopted to search for main spectrum peaks and frequency positions of the main spectrum peaks in an amplitude spectrum, and harmonic energy and proportions of all frequencies are calculated; and finally, searching the frequency corresponding to the maximum harmonic energy and proportion, performing frequency correction to obtain an accurate rotating frequency, and performing conversion calculation to obtain the rotating speed. The method effectively overcomes the defect that a traditional rotating speed estimation method is easily influenced by the power frequency amplitude, achieves accurate measurement of the steady-state rotating speed of equipment in a rotating speed sensor-free mode, can be suitable for displacement, speed, acceleration and other vibration sensors, and has the advantages of being simple in logic, wide in application range, high in estimation precision and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of mechanical equipment fault diagnosis, and in particular relates to a method for adaptively estimating the rotation speed of a rotating machine based on harmonic energy and maximum proportion criteria. Background Art

[0002] Speed ​​is an extremely important characteristic parameter of rotating machinery and equipment, and many equipment failure characteristics are directly related to speed information. For example, rotor imbalance failure often shows that the power frequency (power frequency = equipment speed / 60) is dominant; oil film vortex failure often shows 0.3-0.48 times the power frequency. Therefore, accurate measurement of equipment speed is crucial for accurate fault identification. At present, equipment speed measurement is mainly achieved by installing speed sensors or key phase sensors on the equipment. However, due to the constraints of installation conditions, price costs and other factors, a large number of equipment are not equipped with corresponding speed sensors when leaving the factory, resulting in the inability to obtain accurate speed information, which will seriously affect the accuracy of fault diagnosis results. To solve the above problems, researchers at home and abroad generally collect vibration signals by using vibration sensors configured on the equipment, and then estimate the equipment speed.

[0003] Equipment vibration sensors mainly include three types: vibration displacement, velocity, and acceleration. According to the motion characteristics of rotating machinery, vibration signals contain a lot of information related to the equipment speed. At present, the conventional method of estimating equipment speed based on vibration signals is to achieve speed estimation by searching for the maximum value in the frequency domain (Ye Xiuxi, Shao Tianzhang, et al. Research on engine speed measurement based on vibration signals [J], Mobile Power and Vehicles, 2011, (04): 28-32). The specific steps are as follows: first, obtain the vibration signal through sampling, and perform preprocessing such as removing the mean value on the signal; then, search for the maximum spectrum peak in the signal spectrum diagram, index the corresponding frequency according to the maximum spectrum peak, and calculate the corresponding speed as the speed estimation value. It is not difficult to see that the accuracy of the equipment speed estimation value is obviously affected by the signal spectrum structure, which makes the speed estimation method have the following shortcomings: on the one hand, when facing different types of vibration signals, the same preprocessing method is used, and it is impossible to make adaptive adjustments according to the signal type; on the other hand, the traditional method is only applicable to the spectrum structure with the largest proportion of power frequency amplitude. For the case where the power frequency amplitude accounts for a small proportion, the accuracy of the speed estimation value is often poor.

[0004] Faced with vibration signals of different types and different spectral structures, how to adaptively complete signal preprocessing and design a set of power frequency estimation criteria with a wider range of applicability and higher accuracy is a key issue that needs to be solved to improve the accuracy of rotating machinery speed estimation. Summary of the invention

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a method for adaptively estimating the speed of a rotating machinery based on the harmonic energy and maximum proportion criteria, which can utilize different types of vibration signals to adaptively estimate the steady-state operating speed of the machinery and realize accurate measurement of the speed of the rotating machinery in a speed sensorless mode.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for adaptively estimating the rotation speed of a rotating machine based on harmonic energy and maximum proportion criterion comprises the following steps:

[0008] 1) Select multiple measuring points of the rotating machinery for sampling, and store the samples sampled at a certain moment in the matrix X M×N Where: M represents the number of channels, N represents the number of sampling points; f s Indicates the sampling frequency;

[0009] 2) Calculate the matrix X M×N The rms value of each line of the signal in and index its maximum value R max Corresponding to channel number h, take X M×N The h-th row signal S(n) is used as the target signal for speed estimation;

[0010] 3) Use u to mark the signal type, perform corresponding preprocessing on S(n) according to the value of u, and obtain the signal v(n). Perform Fourier transform on the signal v(n) to obtain its amplitude spectrum A(ω), where ω represents the frequency, and calculate the total energy E of the amplitude spectrum A(ω);

[0011] 4) Use the maximum value method to search for the first L spectral peaks with the largest amplitudes in the amplitude spectrum A(ω) and their position indexes, sort the position indexes from small to large, and store them in the array k(i). According to the spectrum resolution Δf=f s / N, the frequency value corresponding to the above spectrum peak is obtained as f(i)=k(i)×Δf, i=1,2,3,…,L;

[0012] 5) Traverse each frequency in the array f(i), search for the corresponding first Z-order harmonic components one by one, calculate the energy and E0(i) of the first Z-order harmonic components, and calculate their energy proportion p(i) = E0(i) / E;

[0013] 6) Search for the maximum value of the array p(i) and index the corresponding frequency f0. Use the spectrum correction algorithm to correct the frequency f0 in the amplitude spectrum A(ω) to obtain the rotation frequency f0', and calculate the rotation speed r = f0'×60.

[0014] The value range of u in step 3) is 0, 1, 2, where 0 represents an acceleration signal, 1 represents a velocity signal, and 2 represents a displacement signal.

[0015] The preprocessing rule in step 3) is as follows: when u=0, the signal S(n) is integrated. Δt represents the sampling interval; when u is 1 or 2, v(n)=S(n).

[0016] The value range of L in step 4) is 10-20.

[0017] The value range of Z in step 5) is 10-20.

[0018] The spectrum correction algorithm in step 6) includes but is not limited to a ratio correction method, a phase correction method, etc.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The present invention proposes a method for adaptively measuring the rotation speed of a rotating machine based on harmonic energy and maximum proportion criteria. The method can adaptively perform preprocessing according to the type of vibration signal, and proposes harmonic energy and maximum proportion criteria to estimate the mechanical speed in combination with the characteristics of the vibration signal of the rotating machine, which effectively makes up for the defect that the traditional speed estimation method is easily affected by the power frequency amplitude, and can realize the accurate measurement of the steady-state speed of the machine in the speed sensor-free mode. The method has the advantages of simple logic, wide application range, and high calculation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of an embodiment of the present invention.

[0022] Figure 2 This is the layout diagram of the Bentley rotor test bench and displacement sensor in Example 1.

[0023] Figure 3 It is the waveform diagram of the signal v(n) in Example 1.

[0024] Figure 4 is the amplitude spectrum A(ω) of the signal v(n) in Example 1.

[0025] Figure 5 This is the layout diagram of the INV1216 type test bench and acceleration sensor in Example 2.

[0026] Figure 6 It is a waveform diagram of the signal S(n) in Example 2.

[0027] Figure 7 It is the waveform diagram of the signal v(n) in Example 2.

[0028] Figure 8 is the amplitude spectrum A(ω) of the signal v(n) in Example 2. DETAILED DESCRIPTION

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments:

[0030] In Example 1, the vibration displacement signal and the key phase signal are collected by relying on the Bentley high-speed rotor test bench, and its structural diagram is as follows: Figure 2 As shown: The test bench includes motor A and rotor B. Rotor B is supported by 1# and 2# sliding bearings. The two rotors are connected by a flexible coupling C. A mass disk is arranged in the span of rotor B. The shaft diameter is 10mm, and the span of the front and rear supports is 325mm. Two mutually perpendicular vibration displacement sensors are installed in the X and Y directions of the vibration measuring belt surface of the 2# bearing, marked as 2X and 2Y; a key phase sensor K is installed at the keyway position of motor A. The speed calculated by the key phase sensor is the real speed. The vibration displacement sensor and key phase sensor K both use Bentley eddy current sensors with a diameter of 5mm; during the experiment, the motor operating speed is controlled to be stable at 1000rpm.

[0031] Reference Figure 1 , a rotating machinery speed adaptive estimation method based on harmonic energy and maximum proportion criterion, comprising the following steps:

[0032] 1) Select the measurement points 2X and 2Y of the test bench for sampling, and store the samples sampled at a certain moment in the matrix X M×N In which: the number of channels M = 2, the number of sampling points N = 4096; the sampling frequency f s =2048Hz;

[0033] 2) Calculate the matrix X M×N The rms value of each line of the signal in and index its maximum value R max =165.3μm corresponds to channel number h=2, take X M×N The second row signal S(n) is used as the target signal for speed estimation;

[0034] 3) The signal type is a displacement signal, marked u = 2, according to the corresponding preprocessing rules, the signal v(n) = S(n), Figure 3 is the waveform of the signal v(n), it can be seen that the signal is dominated by the industrial frequency; perform Fourier transform on the signal v(n) to obtain its amplitude spectrum A(ω), where ω represents the frequency, as shown in Figure 4 As shown, it can be seen that the amplitude of the power frequency in the signal is the largest, and the total energy of the amplitude spectrum A(ω) is calculated to be E=2734390;

[0035] 4) Use the maximum value method to search for the first L spectral peaks with the largest amplitudes and their position indexes in the amplitude spectrum A(ω), taking L=10, sorting the position indexes from small to large, and storing them in the array k(i). According to the spectrum resolution Δf=fs / N=0.5Hz, the frequency value corresponding to the above spectral peak is obtained as f(i)=k(i)×0.5, i=1,2,3,…,L;

[0036] 5) Traverse each frequency in the array f(i), take Z=10, search for the corresponding first Z-order harmonic components one by one, calculate the energy and E0(i) of the first Z-order harmonic components, and calculate their energy proportion p(i)=E0(i) / E. The results are shown in Table 1;

[0037] 6) Search for the maximum value of the array p(i) and index its corresponding frequency f0=17.0Hz. Use the proportional correction algorithm to correct the frequency f0 in the amplitude spectrum A(ω) to obtain the rotation frequency f0'=16.76Hz, and calculate the rotation speed r=1006rpm. The estimated result is only 6rpm different from the actual rotation speed of 1000rpm, and the rotation speed error rate is 0.6%. The rotation speed estimation result has high accuracy and fully meets the engineering technical requirements.

[0038] Table 1 Calculation results of harmonic energy and proportion

[0039] Serial number Frequency f(i) / Hz Harmonic energy and proportion p(i) / % 1 17.0 99.59 2 33.5 0.29 3 43.5 0.16 4 50.5 0.07

[0040] In Example 2, the vibration acceleration signal and the key phase signal are collected by relying on the INV1216 type test bench, and its structural diagram is as follows: Figure 5 As shown: The test bench includes motor E and rotor F. Rotor F is supported by 1# and 2# rolling bearings. The two rotors are connected by a flexible coupling D. A mass disk is arranged in the span of rotor F, and the shaft diameter is 10mm. Two mutually perpendicular vibration acceleration sensors are installed in the horizontal H and vertical V directions of the 1# and 2# bearing seats, marked as 1H, 1V, 2H, and 2V respectively; a key phase sensor K is installed at the keyway position of motor E, and the speed calculated by the key phase sensor is the actual speed; the vibration acceleration sensor model is CTC AC192, and its sensitivity is 100mV / g. During the experiment, the motor operating speed is controlled to be stable at about 1000rpm.

[0041] Reference Figure 1 , a rotating machinery speed adaptive estimation method based on harmonic energy and maximum proportion criterion, comprising the following steps:

[0042] 1) Select the test points 1H, 1V, 2H, and 2V of the test bench for sampling, and store the samples sampled at a certain moment in the matrix X M×N In which: the number of channels M = 4, the number of sampling points N = 16384, the sampling frequency fs =8192Hz;

[0043] 2) Calculate the matrix X M×N The rms value of each line of the signal in and index its maximum value R max =0.07m / s2 corresponds to channel number h=3, take matrix X M×N The third row signal S(n) is used as the target signal for speed estimation. Figure 6 is the waveform diagram of signal S(n);

[0044] 3) The signal type is acceleration signal, mark u = 0, and the signal S(n) is integrated. Sampling interval Δt = 1 / f s =0.00012s, Figure 7 is the waveform of the signal v(n). It can be seen that the low frequency of the speed signal after integration is more obvious. Perform Fourier transform on the signal v(n) to obtain its amplitude spectrum A(ω), where ω represents the frequency, as shown in Figure 8 As shown, it can be seen that the power frequency amplitude of the speed signal is not the largest, and the total energy of the calculated amplitude spectrum A(ω) is E=0.040;

[0045] 4) Use the maximum value method to search for the first L spectral peaks with the largest amplitudes and their position indexes in the amplitude spectrum A(ω), taking L=10, sorting the position indexes from small to large, and storing them in the array k(i). According to the spectrum resolution Δf=fs / N=0.5Hz, the frequency value corresponding to the above spectral peak is obtained as f(i)=k(i)×0.5, i=1,2,3,…,L;

[0046] 5) Traverse each frequency in the array f(i), take Z=10, search for the corresponding first Z-order harmonic components one by one, calculate the energy and E0(i) of the first Z-order harmonic components, and calculate their energy proportion p(i)=E0(i) / E. The results are shown in Table 2;

[0047] 6) Search for the maximum value of the array p(i) and index its corresponding frequency f0=16.5Hz, use the proportional correction algorithm to correct the frequency f0 in the amplitude spectrum A(ω) to obtain the rotation frequency f0'=16.79Hz, and calculate the rotation speed r=1008rpm; the estimated result is only 8rpm different from the actual rotation speed of 1000rpm, and the rotation speed error rate is 0.8%. The rotation speed estimation result has high accuracy and fully meets the engineering technical requirements.

[0048] Table 2 Calculation results of harmonic energy and proportion

[0049] Serial number Frequency f(i) / Hz Harmonic energy and proportion p(i) / % 1 13.0 34.74 2 16.5 50.52 3 96.5 1.58 4 103.5 28.4 5 106.5 0.92 6 113.0 0.88 7 116.5 1.38

Claims

1. A method for adaptively estimating the rotation speed of a rotating machine based on the harmonic energy and maximum proportion criterion, characterized in that: The following steps are involved: 1) Select multiple measuring points of the rotating machinery for sampling, and store the samples sampled at a certain moment in the matrix X M×N In which: M represents the number of channels, N represents the number of sampling points; f s Indicates the sampling frequency; 2) Calculate the matrix X M×N The rms value of each line of the signal in and index its maximum value R max Corresponding to channel number h, take X M×N The h-th row signal S(n) is used as the target signal for speed estimation; 3) Use u to mark the signal type, perform corresponding preprocessing on S(n) according to the value of u, and obtain the signal v(n). Perform Fourier transform on the signal v(n) to obtain its amplitude spectrum A(ω), where ω represents the frequency, and calculate the total energy E of the amplitude spectrum A(ω); 4) Use the maximum value method to search for the first L spectral peaks with the largest amplitudes in the amplitude spectrum A(ω) and their position indexes, sort the position indexes from small to large, and store them in the array k(i). According to the spectrum resolution Δf=fs / N, the frequency value corresponding to the above spectral peak is obtained as f(i)=k(i)×Δf, i=1,2,3,…,L; 5) Traverse each frequency in the array f(i), search for the corresponding first Z-order harmonic components one by one, calculate the energy and E0(i) of the first Z-order harmonic components, and calculate their energy proportion p(i) = E0(i) / E; 6) Search for the maximum value of the array p(i) and index the corresponding frequency f0. Use the spectrum correction algorithm to correct the frequency f0 in the amplitude spectrum A(ω) to obtain the rotation frequency f0', and calculate the rotation speed r = f0'×60.

2. The method according to claim 1, characterized in that The value range of u in step 3) is 0, 1, 2, where 0 represents an acceleration signal, 1 represents a velocity signal, and 2 represents a displacement signal.

3. The method according to claim 2, characterized in that The preprocessing rule in step 3) is as follows: when u=0, the signal S(n) is integrated. Δt represents the sampling interval; when u is 1 or 2, v(n)=S(n).

4. The method according to claim 1, characterized in that The value range of L in step 4) is 10-20.

5. The method according to claim 1, characterized in that The value range of Z in step 5) is 10-20.

6. The method according to claim 1, characterized in that The spectrum correction algorithm in step 6) includes a ratio correction method and a phase correction method.

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