CN116070135B - Plunger pump fault diagnosis method based on synchronous extraction standard S transformation - Google Patents
Plunger pump fault diagnosis method based on synchronous extraction standard S transformation Download PDFInfo
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- CN116070135B CN116070135B CN202310086018.7A CN202310086018A CN116070135B CN 116070135 B CN116070135 B CN 116070135B CN 202310086018 A CN202310086018 A CN 202310086018A CN 116070135 B CN116070135 B CN 116070135B
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 10
- 230000010349 pulsation Effects 0.000 claims abstract description 52
- 238000012544 monitoring process Methods 0.000 claims abstract description 15
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04B—POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS
- F04B51/00—Testing machines, pumps, or pumping installations
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
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Abstract
The application discloses a plunger pump fault diagnosis method based on synchronous extraction standard S transformation, which comprises the following steps: collecting and classifying pressure signals of a plunger pump outlet, and generating a pressure signal database; processing the pressure signal database to obtain a pressure pulsation signal; performing standard S conversion on the pressure pulsation signal to obtain a time-frequency spectrogram; performing multi-ridge line identification on the time-frequency spectrogram by utilizing synchronous extraction transformation to generate a time-frequency ridge line; based on the time-frequency ridge line, performing time domain extraction on the pressure pulsation signal to generate pressure pulsation time domain waveform characteristics; and monitoring the waveform characteristics of the pressure pulsation time domain, judging the occurrence degree of the faults of the plunger pump based on the monitoring result, and classifying according to the degree to obtain a classification result. The application can accurately identify the instantaneous frequency of the non-stationary signal. It only retains the instantaneous frequency information most relevant to the time-varying characteristics of the acquired signal. The acquisition of the time-frequency ridge line is an important intermediate link of time-frequency analysis and time-frequency filtering. Line-pass filtering of the signal can be achieved.
Description
Technical Field
The application belongs to the field of hydraulic element state monitoring and fault diagnosis, and particularly relates to a plunger pump fault diagnosis method based on synchronous extraction standard S transformation.
Background
The axial plunger pump is widely applied to hydraulic equipment due to the characteristics of compact structure, high power density, high volumetric efficiency and the like. Because of the special structure of the axial plunger pump, oil backward flow and impact can be generated in the working process, so that the output flow and the pressure of the axial plunger pump are periodically pulsed. The pressure pulsation signal of the pump is a product of dynamic coupling of the multiple energy domains of the hydraulic system, is an important information source for monitoring the running state of the system, and carries the dynamic information of the running of equipment.
In the process of extracting the pressure pulsation signal of the plunger pump, due to the global coupling characteristic of a hydraulic system, the frequency spectrum components of the pressure signal are complex, and particularly, the interaction between pump control motors brings difficulty to the monitoring diagnosis and fault diagnosis of the operation state of the plunger pump based on the pressure pulsation. Accurate acquisition of pressure pulsation signal characteristics is key to follow-up fault diagnosis and quantification, and is important to ensure that highly reliable early fault characteristics are extracted, especially in hydraulic systems with high noise and interference.
Disclosure of Invention
The application aims to provide a plunger pump fault diagnosis method based on synchronous extraction standard S transformation, so as to solve the problems in the prior art.
In order to achieve the above object, the present application provides a method for diagnosing faults of a plunger pump based on synchronous extraction standard S transformation, comprising:
collecting and classifying pressure signals of a plunger pump outlet, and generating a pressure signal database;
processing the pressure signal database to obtain a pressure pulsation signal;
performing standard S transformation on the pressure pulsation signal to obtain a time-frequency spectrogram;
performing multi-ridge line identification on the time-frequency spectrogram to generate a time-frequency ridge line;
performing time domain extraction on the pressure pulsation signal based on the time-frequency ridge line to generate pressure pulsation time domain waveform characteristics;
and monitoring the pressure pulsation time domain waveform characteristics, judging the occurrence degree of the fault of the plunger pump based on the monitoring result, and classifying according to the degree to obtain a classification result.
Preferably, the process of generating the pressure signal database includes:
and acquiring the pressure signal at the outlet of the plunger pump on line through a pressure sensor, classifying the pressure signal at the outlet of the plunger pump, and generating the pressure signal database.
Preferably, the process of obtaining the pressure pulsation signal includes:
and removing trend items in the pressure signal database based on empirical mode decomposition to obtain the pressure pulsation signal.
Preferably, the process of performing the standard S transform includes:
performing linear time-frequency conversion processing on the pressure pulsation signal to generate a time-frequency conversion kernel function;
and carrying out Fourier transformation on the time-frequency transformation kernel function, judging, and if the time-frequency transformation kernel function meets the preset condition, successfully finishing standard S transformation of the pressure pulsation signal to generate a time-frequency spectrogram.
Preferably, the process of generating the time-frequency ridge line includes:
acquiring instant phase information in the time-frequency spectrogram, and performing multi-ridge line identification on the instant phase information to generate instant frequency estimation;
and extracting the instant frequency estimation based on SEO to generate a time-frequency ridge line.
Preferably, the process of extracting the instant frequency estimate based on the SEO and generating the time-frequency ridge line includes:
acquiring a time-frequency ridge line in a time-frequency plane based on the instant frequency estimation;
rejecting divergent energy in the time-frequency ridge line in the time-frequency plane based on a delta function to generate a nearby energy time-frequency ridge line;
calculating based on synchronous extraction standard S transformation and the partial derivative of the energy time-frequency ridge line to generate a synchronous extraction operator;
and extracting the instant frequency estimation based on the synchronous extraction operator to generate the time-frequency ridge line.
Preferably, the process of generating the pressure pulsation time domain waveform characteristic includes:
based on the principle of no theory of the standard S transformation, extracting characteristic components in the time-frequency ridge line to generate fault characteristic components;
and performing line-pass filtering on the fault characteristic component to complete time domain extraction and generate the pressure pulsation time domain waveform characteristic.
Preferably, the process of judging the occurrence degree of the fault of the plunger pump based on the monitoring result comprises the following steps:
and monitoring the time domain waveform characteristics of the pressure pulsation after reconstruction in real time, and judging the occurrence degree of the fault of the plunger pump according to whether the amplitude modulation phenomenon occurs.
The application has the technical effects that:
1. the synchronous extraction standard S-transform can determine instantaneous amplitude, frequency and phase without bias. The instantaneous frequency of the non-stationary signal can be accurately identified. It only retains the instantaneous frequency information most relevant to the time-varying characteristics of the acquired signal. The acquisition of the time-frequency ridge line is an important intermediate link of time-frequency analysis and time-frequency filtering. Line-pass filtering of the signal can be achieved.
2. The extracted pressure pulsation signal has strong interference resistance and high signal to noise ratio compared with the vibration signal. The fault characteristic component in the pressure pulsation signal can be extracted from the time-frequency transformation spectrogram unbiased according to the ridge line information by using the principle of no principle.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a flow chart of a processing method in an embodiment of the application;
FIG. 2 is a simplified model of a hydraulic system in an embodiment of the application;
FIG. 3 is a graph of measured pressure signals in an embodiment of the present application;
FIG. 4 is a graph showing the pressure pulsation signal after removal of trend term in an embodiment of the present application;
FIG. 5 is a diagram of a synchronous extraction of a standard S-transform reconstructed pressure pulsation signal during a normal state in an embodiment of the present application;
FIG. 6 is a pressure pulsation signal reconstructed by synchronous extraction of standard S-transitions during a fault condition in an embodiment of the present application;
wherein, 1, a motor; 2. a rotational speed torque meter; 3. a magneto-electric rotation speed sensor; 4. a plunger pump; 5. temperature, pressure, flow sensors; 6. a hydraulic motor; 7. and (3) loading.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
Example 1
As shown in fig. 1, the present embodiment provides a method for diagnosing a fault of a plunger pump based on a synchronous extraction standard S transformation, including:
step 1: the pressure signal at the outlet of the plunger pump is obtained through the pressure sensor, and the data is collected on line by using the Labview software of the upper computer, as shown in FIG. 3, and is classified to form a database.
Step 2: the trend term in the originally acquired pressure signal is removed using Empirical Mode Decomposition (EMD) to obtain a pressure pulsation signal, as shown in fig. 4.
Step 3: and performing standard S conversion on the pressure pulsation signal to obtain a time-frequency spectrogram.
Step 4: and performing multi-ridge line identification from the instant phase information after time-frequency conversion to obtain instant frequency estimation. And obtaining a time-frequency ridge line by using an SEO extraction operator, wherein after a signal is subjected to time-frequency transformation, characteristic component information is required to be extracted according to a time-frequency analysis result, namely, a time-frequency filtering stage.
Step 5: and reconstructing characteristic frequency components of the pressure pulsation according to the time-frequency ridge line by using the principle of nothing. The characteristic components can be filtered out by the ridge line information by utilizing the principle of no principle of standard S transformation, and the extraction of the time domain is completed.
Step 6: and monitoring the time domain waveform characteristics of the pressure pulsation after reconstruction in real time, and judging the occurrence degree of the fault of the plunger pump according to whether the amplitude modulation phenomenon occurs. Fig. 5 is a pressure pulsation signal reconstructed by synchronous extraction standard S-transformation in a normal state. Fig. 6 is a pressure pulsation signal reconstructed by synchronous extraction of standard S-transitions at the time of a fault condition.
The specific process of the embodiment is as follows:
step 1: the pressure sensor is used for acquiring a pressure signal at the outlet of the plunger pump, and the upper computer Labview software is used for realizing data on-line acquisition and classifying to form a database.
Step 2: the trend term in the originally acquired pressure signal is removed by Empirical Mode Decomposition (EMD) to obtain a pressure pulsation signal s (t).
Step 3: and performing standard S conversion on the pressure pulsation signal to obtain a time-frequency spectrogram.
The pressure pulsation signal s (t) ∈c, the linear time-frequency transformation of which can be expressed as:
where t is the sampling time of the signal, τ is the instantaneous shift factor, ω is the frequency,is the instantaneous frequency, "-" indicates the conjugate, ">Is a kernel function of standard time-frequency transformation. If the fourier transform of the time-frequency transform kernel function:
satisfying equation (3), such a linear time-frequency transform is defined as a standard time-frequency transform (NTFT).
The typical kernel function expression of NTFT is:
in the method, in the process of the application,the time-frequency resolution regulator is NTFT, is a real function, and can be any value or expression which is not 0. w (t) is a window function. Standard Gauss windows are typically used as window functions, i.e
Where σ represents the scale factor of the window function. Let σ=1f, whenThe time-frequency transformation mode is standard S transformation. Where λ and p are two adjustment factors and f represents frequency.
Step 4: and performing multi-ridge line identification from the instant phase information after time-frequency conversion to obtain instant frequency estimation. And obtaining a time-frequency ridge line by using an SEO extraction operator, wherein after a signal is subjected to time-frequency transformation, characteristic component information is required to be extracted according to a time-frequency analysis result, namely, a time-frequency filtering stage.
The standard S-transform of the pressure pulsation signal S (t) can be written as
Where i is an imaginary unit. Order theFrom Fourier transform properties, rules on scale transformation and translation and Parseval theorem are available
Wherein,representation function->Complex conjugate of->Is the fourier transform of the signal s (t). f (f) a Is the frequency of the pressure pulsation signal.
For a certain single component of the pressure pulsation signal, there is:wherein A is the amplitude of the component, f 0 For the frequency of this component, δ (t) is a pulse function. Then
Thus, the instantaneous frequency of the signal s (t) is defined as:
wherein the method comprises the steps ofIs a partial guide symbol. With delta functions common in mathematics, only energy near the time-frequency ridge in the time-frequency plane is retained, and the rest of divergent energy statistics are rejected, thus defining a synchronous extraction criterion S transformation as:
Te(f,τ)＝NST(f,τ)·δ(f-f x (f,τ))(11)
substituting (12) into (11) can obtain:
equation (13) is a Synchronous Extraction Operator (SEO), then the SEO can be written as:
from the delta function, the SEO can be calculated by
Step 5: the non-trivial principle is an important property of standard S transformation. The essence is that a certain characteristic component in the pressure pulsation signal can be extracted from the time-frequency transformation spectrogram without bias according to the ridge line information by utilizing the principle of no theory.
Assuming that a certain fault signature component can be expressed as
Wherein A is h For the magnitude of the fault signature, beta h For the frequency of the fault signature component,is the initial phase of the fault signature. The signal (16) is subjected to standard S transformation, and two properties expressed by the formula (17) are satisfied.
When (when)Taking the maximum value of 1>
The method has the characteristic of line pass in time-frequency filtering.
Step 6: and monitoring the time domain waveform characteristics of the pressure pulsation after reconstruction in real time, and judging the occurrence degree of the fault of the plunger pump according to whether the amplitude modulation phenomenon occurs.
Example two
Taking a plunger pump fault detection test bed in a hydraulic system as an example, a simplified model of the test bed is shown in fig. 2. The number of plungers of the plunger pump for experiments is 7, the working pressure of the plunger pump under the normal state of the valve plate is set to be 10MPa, and the temperature of oil is controlled to be 35+/-0.5 ℃. And (5) carrying out experimental acquisition on the pump outlet pressure signal. Each set of experimental setup was measured three times and the sampling time was set to 60s. And then replacing the normal components, respectively replacing the components with serious abrasion faults of the valve plates, and repeating the experiment under the same working condition.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (5)
1. The plunger pump fault diagnosis method based on the synchronous extraction standard S transformation is characterized by comprising the following steps of:
collecting and classifying pressure signals of a plunger pump outlet, and generating a pressure signal database;
processing the pressure signal database to obtain a pressure pulsation signal;
performing standard S transformation on the pressure pulsation signal to obtain a time-frequency spectrogram;
performing multi-ridge line identification on the time-frequency spectrogram to generate a time-frequency ridge line;
performing time domain extraction on the pressure pulsation signal based on the time-frequency ridge line to generate pressure pulsation time domain waveform characteristics;
monitoring the pressure pulsation time domain waveform characteristics, judging the occurrence degree of the faults of the plunger pump based on the monitoring result, and classifying according to the degree to obtain a classification result;
the process of performing the standard S transformation includes:
performing linear time-frequency conversion processing on the pressure pulsation signal to generate a time-frequency conversion kernel function;
performing Fourier transformation on the time-frequency transformation kernel function, judging, and if the time-frequency transformation kernel function meets a preset condition, successfully finishing standard S transformation of the pressure pulsation signal to generate a time-frequency spectrogram;
the process for generating the time-frequency ridge line comprises the following steps:
acquiring instant phase information in the time-frequency spectrogram, and performing multi-ridge line identification on the instant phase information to generate an instant frequency estimated value;
extracting the instant frequency estimation value based on SEO to generate a time-frequency ridge line;
extracting the instant frequency estimation value based on SEO, and generating a time-frequency ridge line comprises the following steps:
acquiring a time-frequency ridge line in a time-frequency plane based on the instant frequency estimation;
rejecting divergent energy in the time-frequency ridge line in the time-frequency plane based on a delta function to generate a nearby energy time-frequency ridge line;
calculating based on synchronous extraction standard S transformation and the partial derivative of the energy time-frequency ridge line to generate a synchronous extraction operator;
and extracting the instant frequency estimation based on the synchronous extraction operator to generate the time-frequency ridge line.
2. The method for diagnosing a fault in a plunger pump based on the S-transformation of the synchronous extraction standard according to claim 1, wherein the process of generating the pressure signal database comprises:
and acquiring the pressure signal at the outlet of the plunger pump on line through a pressure sensor, classifying the pressure signal at the outlet of the plunger pump, and generating the pressure signal database.
3. The method for diagnosing a fault in a plunger pump based on the S-transformation of the synchronous extraction standard according to claim 1, wherein the process of obtaining the pressure pulsation signal includes:
and removing trend items in the pressure signal database based on empirical mode decomposition to obtain the pressure pulsation signal.
4. The method for diagnosing faults of a plunger pump based on synchronous extraction standard S transformation according to claim 1, wherein the process of generating the time domain waveform characteristics of the pressure pulsation comprises the following steps:
based on the principle of no theory of the standard S transformation, extracting characteristic components in the time-frequency ridge line to generate fault characteristic components;
and performing line-pass filtering on the fault characteristic component to complete time domain extraction and generate the pressure pulsation time domain waveform characteristic.
5. The method for diagnosing a fault in a plunger pump based on the S-transformation of the synchronous extraction standard according to claim 1, wherein the process of judging the degree of occurrence of the fault in the plunger pump based on the monitoring result comprises:
and monitoring the reconstructed pressure pulsation time domain waveform characteristics in real time, judging the occurrence degree of the fault of the plunger pump according to whether the amplitude modulation phenomenon occurs, and classifying according to the degree to obtain a classification result.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108694392A (en) * | 2018-05-22 | 2018-10-23 | 成都理工大学 | A kind of high-precise synchronization extraction generalized S-transform Time-Frequency Analysis Method |
| CN112431753A (en) * | 2021-01-25 | 2021-03-02 | 赛腾机电科技（常州）有限公司 | Multiple quantitative diagnosis method for shoe loosening fault of axial plunger pump |
| CN113378329A (en) * | 2021-07-06 | 2021-09-10 | 长沙理工大学 | Axial plunger pump state monitoring method based on digital twinning |
| CN114359663A (en) * | 2021-12-27 | 2022-04-15 | 江苏大学 | Hydraulic plunger pump intelligent fault diagnosis method based on pressure signals |
| CN114460459A (en) * | 2021-11-10 | 2022-05-10 | 青岛农业大学 | Signal capturing method based on standard time-frequency transformation |
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| JP6759864B2 (en) * | 2016-08-30 | 2020-09-23 | 株式会社デンソー | Spark plug |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108694392A (en) * | 2018-05-22 | 2018-10-23 | 成都理工大学 | A kind of high-precise synchronization extraction generalized S-transform Time-Frequency Analysis Method |
| CN112431753A (en) * | 2021-01-25 | 2021-03-02 | 赛腾机电科技（常州）有限公司 | Multiple quantitative diagnosis method for shoe loosening fault of axial plunger pump |
| CN113378329A (en) * | 2021-07-06 | 2021-09-10 | 长沙理工大学 | Axial plunger pump state monitoring method based on digital twinning |
| CN114460459A (en) * | 2021-11-10 | 2022-05-10 | 青岛农业大学 | Signal capturing method based on standard time-frequency transformation |
| CN114359663A (en) * | 2021-12-27 | 2022-04-15 | 江苏大学 | Hydraulic plunger pump intelligent fault diagnosis method based on pressure signals |
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