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CN107956708B - A method for detecting potential cavitation faults of pumps based on fast spectral kurtosis analysis - Google Patents

A method for detecting potential cavitation faults of pumps based on fast spectral kurtosis analysis Download PDF

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CN107956708B
CN107956708B CN201711146384.8A CN201711146384A CN107956708B CN 107956708 B CN107956708 B CN 107956708B CN 201711146384 A CN201711146384 A CN 201711146384A CN 107956708 B CN107956708 B CN 107956708B
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signal
frequency
cavitation
kurtosis
time
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CN107956708A (en
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余天义
初宁
宁岳
唐川荃
吴大转
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Zhejiang University ZJU
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D15/00—Control, e.g. regulation, of pumps, pumping installations or systems
    • F04D15/0088—Testing machines

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  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention discloses a kind of potential cavitation fault detection methods of pump based on quick spectrum kurtosis analysis, comprising: step 1, acquisition vibration acceleration signal carries out noise reduction, as signal to be processed；Step 2, according to the data volume size of signal, the decomposition order of signal processing is determined；Step 3, according to quick spectrum kurtosis algorithm calculated result, optimal carrier frequency and bandwidth are selected；Step 4, Fourier transformation is carried out to the signal in the carrier frequency of selection and bandwidth, obtains spectrum envelope figure；Step 5, original signal time-domain diagram, the signal time-domain diagram for quickly being composed kurtosis filtering processing and the spectrum envelope figure after selection area Fourier transformation are compared, time and the frequecy characteristic of cavitation fault-signal are analyzed.More cavitation instantaneous signals are able to detect that using this method of the present invention, are made the information in terms of time domain and frequency domain see to be more clear obviously, can obviously be told the normal condition and cavitation condition of pump.

Description

A kind of potential cavitation fault detection method of pump based on quick spectrum kurtosis analysis
Technical field
The invention belongs to field of signal processing more particularly to a kind of real-time status based on quick spectrum kurtosis analysis pump and The method for detecting its potential cavitation failure.
Background technique
High performance centrifugal pump is widely applied on today's society and demand is huge.Since work is in the complicated item such as high-voltage high-speed Under part, the cavitation failure of centrifugal pump occurs again and again, causes vibration frequency aggravation, noise to increase, corrosion of blade, seriously restricts pump Performance and service life.Traditional detection method is in the pump cavitation newborn period, for the signals such as the flow and lift of pump, vibration and noise The detection of variation is simultaneously insensitive；But when cavitation signal significant changes, cavitation failure has rapidly developed quite serious Degree.
The acoustical signal bandwidth span of cavitation bubble is big, instantaneity is strong, processing difficulty is higher；Vibration signal caused by cavitation is past It is modulated strongly toward by blade rotation.
The common fault-signal detection method of field of signal processing mainly has Short Time Fourier Transform and wavelet transformation at present Two kinds.Short Time Fourier Transform is a kind of most common Time-Frequency Analysis Method, it is indicated by the segment signal in time window The signal characteristic at a certain moment.During Short Time Fourier Transform, the length of window determines the temporal resolution and frequency of spectrogram Rate resolution ratio, window is longer, and the signal of interception is longer, and signal is longer, and frequency resolution is higher after Fourier transformation, time resolution Rate is poorer；On the contrary, window length is shorter, the signal of interception is shorter, and frequency resolution is poorer, and temporal resolution is better, that is to say, that In Short Time Fourier Transform, it cannot get both between temporal resolution and frequency resolution, it should be accepted or rejected according to specific requirements. Short Time Fourier Transform is serious to receive the influence of time domain and frequency domain resolution, its effect is caused to be restricted.Moreover, For vibration acceleration signal caused by cavitation, Short Time Fourier Transform can not analyze specific information.
The practicability of wavelet transformation is significantly stronger than Short Time Fourier Transform, it inherits and developed short time discrete Fourier transform office The thought in portion, while overcoming the disadvantages of window size does not change with frequency again, be capable of providing one with frequency shift " when M- frequency " window is the ideal tools for carrying out signal time frequency analysis and processing.Industrial production uses discrete wavelet transformer in practice It changes more.But not unique, wavelet parameter is chosen there are still wavelet basis and combines unstable deficiency.Also have in conjunction with supporting vector simultaneously Machine and Artificial Neural Network improve wavelet decomposition transform, using pressure fluctuation signal and cavitation field distributed image, to wink The cavitation characterization extraction of state variation is largely effective, but algorithm complexity is high, parameter setting still needs experience interpretation.In addition, It complements one another, is based on multi dimensional analysis (impeller-guide vane-load-sound and vibration signal spectrum) with multiscale analysis such as wavelet transformations Cavitation diagnostic method, successfully develop highly sensitive, high reliability Turbine Cavitation Testing monitoring and fault diagnosis system, but this is more Dimensional analysis method fails to obtain high-resolution dynamic spectrum texture, is unfavorable for characterization and turns from sheet to cloud cavitation etc. is crucial Twist process.
Summary of the invention
The present invention provides a kind of potential cavitation fault detection methods of pump based on quick spectrum kurtosis analysis, are able to detect that More instantaneous signals make the information in terms of time domain and frequency domain see to be more clear obviously, can obviously tell the normal shape of pump State and cavitation condition not only possess bigger frequency detection range, but also easy to operate.
A kind of potential cavitation fault detection method of pump based on quick spectrum kurtosis analysis, comprising the following steps:
Step 1, noise reduction is carried out to the vibration acceleration signal of acquisition, as experiment signal to be processed；
Step 2, according to the data volume size for testing signal to be processed, the highest quick spectrum kurtosis algorithm of fitting degree is determined Decompose decomposition order of the order as signal processing；
Step 3, according to quick spectrum kurtosis algorithm calculated result, be chosen so that the maximum carrier frequency of signal kurtosis and Respective bandwidth；
Step 4, Fourier transformation is carried out to the signal after the carrier frequency of selection and respective bandwidth, obtains spectrum envelope Figure；
Step 5, signal time-domain diagram and the selection area Fourier of kurtosis processing are composed according to original signal time-domain diagram, quickly Transformed spectrum envelope figure analyzes time and the frequecy characteristic of fault-signal.
In step 1, the noise-reduction method is, in processing routine, using pre -whitening processing noise, drops to signal It makes an uproar processing, pre -whitening processing is in MATLAB software are as follows:
X=x-mean (x)；
Na=100；
A=lpc (x, Na)；
X=fftfilt (a, x)；
X=x (Na+1:end)；
Wherein x is the signal of processing.
The detailed process of step 2 are as follows:
In MATLAB software, according to actual amount of data, a preliminary exposition order is arranged in step 2-1；
Step 2-2, under the order, observation observes the frequency by quickly composing the carrier frequency and bandwidth that kurtosis algorithm obtains Spectrum envelope figure within the scope of rate after Fourier transformation；
Step 2-3 is determined according to spectrum envelope figure peak feature and is decomposed order.
Decomposing the principle that order is established is adjusted according to the density at the spectrum envelope figure peak of processing result, if peak is close Degree is too low, then reduces decomposition order；It is on the contrary then increase.
In step 3, the quick spectrum kurtosis algorithm is in MATLAB software for one with original signal, decomposition order (nlevel) and sample frequency be independent variable function.
In step 5, the time of fault-signal and the process of frequecy characteristic are analyzed specifically:
Step 5-1 whether there is apparent impact signal according on the signal time-domain diagram of quick spectrum kurtosis processing, and determination is It is no that there are cavitation failures to determine the time of cavitation fault-signal according to position of the impact signal on time-domain diagram；
Step 5-2 determines cavitation event according to the axis frequency and leaf frequency information on the frequency envelope figure after Fourier transformation Hinder the frequecy characteristic of signal.
The present invention provides a kind of methods of quickly spectrum kurtosis frequency spectrum texture analysis, by quickly composing kurtosis function, to pump Vibration signal handled.The present invention is chosen so that it includes the most optimal carrier frequencies of prompting message and bandwidth to carry out Signal filtering obtains frequency domain information by carrying out Fourier transformation to this section of time-domain information, and then detects to pump state, right Specific cavitation failure-frequency is analyzed.
The method of the present invention significant increase signal enhancing ability, can enhance cavitation signal, simultaneously from the leaf frequency of rotation The related data that pump can clearly be told also has an apparent resolution for normal condition and cavitation condition.
Detailed description of the invention
Fig. 1 is that the present invention is based on the pump real-time state monitorings of quick spectrum kurtosis frequency spectrum texture analysis and potential cavitation failure to examine The flow diagram of the method for survey；
Fig. 2 is using quickly spectrum kurtosis to the analysis and processing result schematic diagram under rated condition；
Fig. 3 a is original signal time-domain diagram under rated condition；
Fig. 3 b is the signal time-domain diagram quickly composed after kurtosis filtering processing under rated condition；
Fig. 3 c is the spectrum envelope figure under rated condition after selection area Fourier transformation；
Fig. 4 is using quickly spectrum kurtosis to the analysis and processing result schematic diagram under pump cavitation state；
Fig. 5 a is original signal time-domain diagram under pump cavitation state；
Fig. 5 b is the signal time-domain diagram quickly composed after kurtosis filtering processing under pump cavitation state；
Fig. 5 c is the spectrum envelope figure under pump cavitation state after selection area Fourier transformation.
Specific embodiment
Quickly spectrum kurtosis is a kind of fourth order spectrum analysis tool.It is defined asWherein H (n, It f) is complex envelope of the signal x (n) in frequency f.<>is the operator averaged.Quickly spectrum kurtosis can be very good to analyze unstable Process, such as instantaneous signal, and the kurtosis numerical value of the instantaneous signal of height unstable state depends on the frequency resolution (Δ of estimator F), each transient phenomena corresponds to a kind of optimal frequency band { f, Δ f }.Therefore, in actual analytic process, it should look for To the information of optimal frequency and frequency resolution, so that kurtosis reaches maximum value in this section, it can find correlation Transient state information.
Pump is under many states, such as cavitation and deformable blade, and the vibration of pump can all be made to mutate, thus Generate a large amount of transient state information.In this way, quickly composing the good detection transient state information of kurtosis algorithm and good noise resisting ability A kind of good tool is provided to the cavitation fault detection and diagnosis of pump.
In order to more specifically describe the present invention, with reference to the accompanying drawing and specific embodiment is to technical solution of the present invention It is described in detail.
As shown in Figure 1, the pump incipient fault detection method based on quick spectrum kurtosis analysis includes the following steps:
S01 is collected normal load pump respectively by vibration acceleration sensor and the vibration letter of the pump of cavitation phenomenon occurs Number, and import data in processing routine.
In processing routine, using the method for pre -whitening processing noise, noise reduction process is carried out to signal.In MATLAB software In, the sentence of prewhitening are as follows:
X=x-mean (x)；
Na=100；
A=lpc (x, Na)；
X=fftfilt (a, x)；
X=x (Na+1:end)；
Wherein x is the signal of processing.
S02 calculates the signal that noise reduction process obtains using quick spectrum kurtosis function, and quickly composing kurtosis function is one It is a using original signal, decompose order (nlevel) and sample frequency as the function of independent variable.
According to the size of data volume, chooses suitable calculates and decompose order.Herein, the principle that order is established is decomposed It is to be determined according to the density at the spectrum envelope figure peak of processing result, if peak density is too low, reduces decomposition order；It is on the contrary Then increase.In this test data, test frequency acquisition be 40960Hz, test data substantially between 640,000 to 650,000, because This uses the analysis of six ranks.
S03 finds the frequency for possessing maximum spectrum kurtosis and corresponding frequency band in the quickly analysis result figure of spectrum kurtosis It is wide.Under normally loaded condition quick spectrum kurtosis analysis result as shown in Fig. 2, its optimal carrier frequency be 2400Hz, Frequency bandwidth adds the quotient of first power for the order of sample frequency and 2, therefore its frequency bandwidth is 320Hz.It is fast under cavitation condition For speed spectrum kurtosis analysis result as shown in figure 4, its optimal carrier frequency is 19626.6667Hz, frequency bandwidth is 1706.6667.
That segment signal for possessing maximum spectrum kurtosis is carried out the transformation assay of time-domain and frequency-domain, uses Fourier transformation by S04 The state for obtaining the envelope diagram of frequency, and then being pumped by analyzing frequency come detection and diagnosis.Wherein, Fig. 3 a, Fig. 3 b and Fig. 3 c divide It Wei not original signal time-domain diagram, signal time-domain diagram and Fourier transformation after quickly composing kurtosis filtering processing under normally loaded condition Spectrum envelope figure afterwards；Fig. 5 a, Fig. 5 b and Fig. 5 c are respectively original signal time-domain diagram under cavitation condition, quickly compose kurtosis filtering processing Spectrum envelope figure after rear signal time-domain diagram and Fourier transformation.
S05, using processing result map analysis comparison pump under normally loaded condition with the information under cavitation condition.From original Signal can not analyze the relevant information of pump, as shown in Fig. 3 a and Fig. 5 a.It can be found that the vibration letter of pump in normal state In number, the data after having passed through quick spectrum kurtosis filtering processing are still more smooth in the time domain, can not find out in the time domain bright Aobvious impact, as shown in Figure 3b.Spectrum envelope figure after Fourier transformation can significantly find axis frequency in Fig. 3 c (24.61Hz) and relevant leaf frequency harmonic information (75Hz, 100Hz etc.).In this trial, the revolving speed of pump is that 1375 circles are every Minute, then its axis frequency is 25Hz.And under cavitation condition, it, can be in time-domain after have passed through quickly spectrum kurtosis filtering processing Obviously impact signal is clearly found, such as Fig. 5 b.On its frequency envelope figure after Fourier transformation, occur a large amount of High-frequency information, range has even arrived 1600Hz or more, and leaf frequency signal (172Hz) is especially prominent, illustrates that pump cavitation phenomenon produces Raw bubble has superposition to leaf frequency, bubble high-frequency Ground shock waves blade caused by cavitation, such as Fig. 5 c.It is therefore evident that spectrum kurtosis point Analysis can be detected and analyzed the cavitation condition of pump.
This example has apparent frequency information under specified normal condition using the vibration acceleration data of pump, And under cavitation condition, there is apparent impact signal at specific time point, on frequency map, can also find cavitation phenomenon Performance, there is a large amount of high-frequency information, show quickly to compose kurtosis algorithm for the Vibration Condition Monitoring and failure point of pump Analysis has good effect.
Technical solution of the present invention and beneficial effect is described in detail in above-described specific embodiment, Ying Li Solution is not intended to restrict the invention the foregoing is merely presently most preferred embodiment of the invention, all in principle model of the invention Interior done any modification, supplementary, and equivalent replacement etc. are enclosed, should all be included in the protection scope of the present invention.

Claims (6)

1.一种基于快速谱峭度分析的泵潜在空化故障检测方法，包括：1. A pump potential cavitation fault detection method based on fast spectral kurtosis analysis, comprising: 步骤1，对采集的振动加速度信号进行降噪，作为试验待处理信号；Step 1, noise reduction is performed on the collected vibration acceleration signal as a test signal to be processed; 步骤2，根据试验待处理信号，确定拟合程度最高的快速谱峭度算法分解阶数作为信号处理的分解阶数；Step 2, according to the test signal to be processed, determine the decomposition order of the fast spectral kurtosis algorithm with the highest fitting degree as the decomposition order of signal processing; 步骤3，根据快速谱峭度算法计算结果，选择能够使信号峭度最大的载波频率以及相应带宽；Step 3, according to the calculation result of the fast spectral kurtosis algorithm, select the carrier frequency and the corresponding bandwidth that can maximize the signal kurtosis; 步骤4，对选择的载波频率以及相应带宽后的信号进行傅里叶变换，得到频谱包络图；Step 4: Fourier transform is performed on the selected carrier frequency and the signal after the corresponding bandwidth to obtain a spectrum envelope diagram; 步骤5，根据原信号时域图、经快速谱峭度处理的信号时域图以及选定区域傅里叶变换后的频谱包络图，分析故障信号的时间和频率特征。Step 5: Analyze the time and frequency characteristics of the fault signal according to the original signal time domain diagram, the signal time domain diagram processed by fast spectral kurtosis, and the spectral envelope diagram after Fourier transform of the selected area. 2.根据权利要求1所述的基于快速谱峭度分析的泵潜在空化故障检测方法，其特征在于，步骤1中，所述的降噪方法为，在处理程序中，使用预白化处理噪声，对信号进行降噪处理。2. The method for detecting potential cavitation faults of pumps based on fast spectral kurtosis analysis according to claim 1, wherein in step 1, the noise reduction method is, in the processing procedure, using pre-whitening to process noise , to perform noise reduction processing on the signal. 3.根据权利要求1所述的基于快速谱峭度分析的泵潜在空化故障检测方法，其特征在于，步骤2的具体过程为：3. the pump potential cavitation fault detection method based on fast spectral kurtosis analysis according to claim 1, is characterized in that, the concrete process of step 2 is: 步骤2-1，在MATLAB软件中，根据试验待处理信号的数据量大小，设置一个初步分解阶数；Step 2-1, in the MATLAB software, set a preliminary decomposition order according to the data amount of the experimental signal to be processed; 步骤2-2，在该阶数下，观察由快速谱峭度算法得到的载波频率及带宽，观察该频率范围内傅里叶变换后的频谱包络图；Step 2-2, under this order, observe the carrier frequency and bandwidth obtained by the fast spectral kurtosis algorithm, and observe the spectral envelope after the Fourier transform in this frequency range; 步骤2-3，根据频谱包络图的特征，确定分解阶数。Step 2-3: Determine the decomposition order according to the characteristics of the spectral envelope. 4.根据权利要求1或3所述的基于快速谱峭度分析的泵潜在空化故障检测方法，其特征在于，步骤2中，分解阶数的确定原则是根据试验待处理信号的数据量以及处理结果的频谱包络图峰的密度来调整。4. the pump potential cavitation fault detection method based on fast spectral kurtosis analysis according to claim 1 or 3, is characterized in that, in step 2, the determination principle of decomposition order is according to the data volume of the test signal to be processed and The density of the peaks in the spectral envelope of the processing result is adjusted. 5.根据权利要求1所述的基于快速谱峭度分析的泵潜在空化故障检测方法，其特征在于，步骤3中，所述的快速谱峭度算法在MATLAB中为一个以原信号、分解阶数以及采样频率为自变量的函数。5. the pump potential cavitation fault detection method based on fast spectral kurtosis analysis according to claim 1, is characterized in that, in step 3, described fast spectral kurtosis algorithm in MATLAB is a method based on original signal, decomposition The order and sampling frequency are functions of the independent variables. 6.根据权利要求1所述的基于快速谱峭度分析的泵潜在空化故障检测方法，其特征在于，步骤5中，所述的分析故障信号的时间和频率特征的过程具体为，6. The method for detecting a potential cavitation fault of a pump based on fast spectral kurtosis analysis according to claim 1, wherein in step 5, the process of analyzing the time and frequency characteristics of the fault signal is specifically: 步骤5-1，根据快速谱峭度处理的信号时域图上是否存在明显的空化冲击信号，确定是否存在空化故障，根据冲击信号在时域图上的位置，确定空化故障信号的时间；Step 5-1: Determine whether there is a cavitation fault according to whether there is an obvious cavitation shock signal on the time-domain graph of the signal processed by the fast spectral kurtosis, and determine the cavitation fault signal according to the position of the shock signal on the time-domain graph. time; 步骤5-2，根据经过傅里叶变换后的频率包络图上的轴频和叶频信息，确定空化故障信号的频率特征。Step 5-2: Determine the frequency characteristic of the cavitation fault signal according to the information of the axial frequency and the leaf frequency on the frequency envelope after the Fourier transform.
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