Water pump pressure pulsation diagnosis method based on variational mode decomposition and chaos theory

Abstract

The invention discloses a water pump pressure pulsation diagnosis method based on variational mode decomposition and chaos theory, which belongs to the technical field of water conservancy equipment pressure pulsation fault diagnosis, and comprises the following steps: setting a pressure pulsation monitoring point, and collecting a pressure signal; decomposing the pressure signal at each monitoring point by adopting variational mode decomposition (VMD); performing spectrum analysis on each modal function of the pressure signal decomposed by the VMD, and extracting spectrum features; frequency components and pressure amplitude are extracted, and internal flow field state information is obtained; in combination with a chaos theory, obtaining different modal attractors and the maximum value lyapunov exponent lymax; and introducing the modal attractors obtained in the VMD and chaos theory analysis and the lymax into a weighted decision model, and obtaining pressure fluctuation information at different monitoring points in combination with influence weights. According to the water pump pressure pulsation diagnosis method based on the variational mode decomposition and chaos theory, the pressure pulsation occurring in the operation of the water pump is monitored, and damage possibly caused by abnormal pressure pulsation is prevented.

Description

Technical Field

[0001] This invention relates to the field of pressure pulsation fault diagnosis technology for hydraulic equipment, and in particular to a method for diagnosing pressure pulsation in water pumps based on variational mode decomposition and chaos theory. Background Technology

[0002] In the field of water conservancy engineering, water pumps are the core components of pumping stations. The safe and stable operation of these units is affected by many factors, and pressure pulsations that frequently occur during operation can easily lead to unpredictable shutdowns or even serious malfunctions, causing irreversible damage to the pumping station and power system. Traditional fault diagnosis for pressure pulsations in pumping station units only monitors and measures the peak values ​​of pressure fluctuations at individual points within the unit. It cannot detect and mitigate abnormal pressure pulsations within the unit in a timely manner, lacking timeliness, and cannot effectively and comprehensively assess the location and cause of abnormal pressure pulsations. Summary of the Invention

[0003] The purpose of this invention is to provide a water pump pressure pulsation diagnosis method based on variational mode decomposition and chaos theory. This method involves scientifically arranging pressure monitoring points within the water pump's flow channel, extracting time-domain information of pressure fluctuations obtained at these monitoring points, and then decomposing the pulsation information based on variational mode decomposition (VMD) and chaos theory. This allows for accurate diagnosis of internal pressure pulsation during water pump operation, tracing the source of pressure pulsation, promptly identifying the cause of abnormal pressure pulsation in the unit, and resolving the problem in a timely manner.

[0004] To achieve the above objectives, this invention provides a method for diagnosing water pump pressure pulsation based on variational mode decomposition and chaos theory, comprising the following steps:

[0005] S1. Install real-time pressure pulsation monitoring points and a pressure sensing system in the water pump unit;

[0006] S2. Collect pressure signals from the pressure pulsation monitoring points in S1 to form a pressure time-domain signal, and collect and calculate the pressure peak-to-peak value.

[0007] S3. Variational mode decomposition (VMD) is used to decompose the pressure signal at each monitoring point to obtain the frequency and amplitude variation information corresponding to different modes at different points.

[0008] S4. Perform spectral analysis on the mode functions of the pressure signals at different locations obtained from VMD decomposition to extract the spectral characteristics of the internal flow field pressure signals of the pump unit.

[0009] S5. Based on the spectrum analysis results, extract key characteristic parameters to obtain information on the internal flow field state of the unit. Key characteristic parameters include frequency components and pressure amplitude.

[0010] S6. Combining chaos theory, we obtain the maximum value of different modal attractors and the Lyapunov exponent lymax.

[0011] S7. The modal attractor and lymax obtained from VMD and chaos theory analysis are introduced into the weighted decision model. Combined with the influence weights of different key characteristic parameters, the pressure fluctuation information at different pressure monitoring points is obtained to evaluate the operating status of the water pump unit.

[0012] Preferably, in S3, variational mode decomposition (VMD) decomposes the pressure signal into multiple intrinsic mode functions (IMFs) to achieve fine decomposition of the signal and accurately obtain the frequency domain characteristics of the pressure signal; specifically, it includes the following steps:

[0013] S31. Obtain the corresponding sub-signal u from the pressure signal through Hilbert transform. k The transformation formula for the one-sided spectrum of the corresponding analytic signal is:

[0014]

[0015] u k (t)=A k (t)cos(φ k (t));

[0016] Where δ(t) is the Dirac function; j represents the Hilbert twitch factor; u k (t) is the decomposed intrinsic mode functions (IMFs); A k (t) is the envelope of the intrinsic mode functions and is greater than 0; φ k (t) represents the phase, and the predicted center frequency ω can be obtained by differentiating it. k ;

[0017] S32. Multiply each sub-signal u by its own predicted center frequency using a complex exponential function. k The frequency is adjusted to baseband using the following formula:

[0018]

[0019] S33. Predict each sub-signal u using squared regularization and Gaussian smoothing. k Given the bandwidth of the sub-signal and constraining the sum of the bandwidths of each sub-signal to be minimized, the variational model of the bandwidth of each sub-signal is finally expressed as:

[0020]

[0021] The convergence condition for the iterative loop is:

[0022]

[0023] Where ε represents the set correlation coefficient.

[0024] Preferably, in S6, the chaos theory analysis method is carried out in phase space, specifically including the following steps:

[0025] S61. Determine τ and m using the CC algorithm to obtain the reconstructed phase space. When determining m and τ, determine the length of the time window, i.e., the delay time window τ. w The length of τ is an important parameter that takes into account both τ and m; that is, the determination of τ is not independent of m, but depends on the delay time window τ. w The specific equation is as follows:

[0026] τ w =(m-1)τ;

[0027]

[0028] Where m represents the embedding dimension, τ represents the delay time for reconstructing the phase space, N represents the size of the data set, r' represents the radius from the data point to the far point in the phase space, θ represents the position angle of the data point in the phase space, and e represents the value of the time delay; the correlation integral represents the cumulative distribution function, which represents the probability that the distance between any two points in the phase space is less than the radius r', where the distance between the points represents the infinite norm of the vector difference. The monitoring statistic S1 used to calculate the probability that the distance between two points is less than the radius r' is defined as follows:

[0029] S1(m,N,r',e)=C(m,N,r',e)-C m (1,N,r',e);

[0030]

[0031] Among them, C m x represents the correlation integral of the time series when m = 1; i The time series can be decomposed into τ disjoint subsequences, each of length N. s =N / τ;

[0032] S62. The phase space of each time series is reconstructed using the coordinate delay method, and principal component analysis is performed on time series with an embedding dimension greater than 2 to clearly display the phase space. An embedding dimension greater than 1 indicates that it is meaningful to analyze the chaotic characteristics of the time series signal under study. The time series obtained from real-time monitoring is brought into S1 for analysis, and the attractor dispersion of the relevant pressure monitoring points is obtained through the signal dispersion in the phase diagram.

[0033] S63. The Lyapunov exponent is used to quantitatively describe whether adjacent phase space trajectories diverge or converge exponentially, thereby determining whether the system is in a chaotic state. In calculating the maximum Lyapunov exponent, the linear region of the y(t) ~ t curve is selected, and the least squares method is used to create a regression line. y(t) is a general term for time series signals, and the slope of the line is the final maximum Lyapunov exponent. ymax By quantitatively analyzing the degree of disorder of each parameter, and then through l ymax The values ​​are analyzed and determined.

[0034] Preferably, in S61, the calculation process of S1 is as follows:

[0035] S611. Divide the time series into e disjoint subsequences, each of length N. s =N / e, and after averaging, the monitoring statistic S2 for each subsequence is calculated as follows:

[0036]

[0037] Among them, C s C represents the correlation integral when the monitoring statistic S = 1. ms This represents the correlation integral when the monitoring data S=1 and m=1;

[0038] S612. The two radii r' corresponding to the maximum and minimum values ​​are selected to measure the maximum deviation of all radii, and the difference is defined as:

[0039] ΔS2(m,e)=max{S2(m,r j ',e)}-min{S2(m,r j ',e)};

[0040] The local maximum time is the zero of S2(m,r',e) and the minimum value of ΔS2(m,e); the optimal time delay τ d Corresponding to the first value of these local maxima, the detection statistics for each subsequence are simplified to:

[0041]

[0042] The global minimum value S2(e) is used as the time window length of the target time series, and the embedding dimension is calculated according to the time window length equation.

[0043] Preferably, S7 specifically includes the following steps:

[0044] S71. Construct a weighted decision function to evaluate the pressure pulsation at multiple monitoring points of the unit. First, construct a weighted decision function for a single point:

[0045]

[0046] In the formula, the subscripts i and j represent the number of modes and the number of points, respectively; This represents the normalized peak-to-peak pressure value at a single detection point. This indicates that the amplitude values ​​of different modes obtained after decomposing the pressure signal using VMD are normalized, -l ymax w represents the negative value of the maximum Lyapunov exponent; p w A and w l These represent the weighting factors for different parameters, where w p The value is 0.5, w A The value is 0.2, w l The value is 0.3;

[0047] S72. Construct a weighted decision function for pressure pulsation detection points in different locations within the overall unit:

[0048] F (P) =w1F (P1) +w2F (P2) +w3F (P3) +……+w 14 F (P14) +w 15 F (P15) +w 16 F (P16) ;

[0049] In the formula, F (P1) To F (P16) The weighted decision function for each monitoring point; w1 to w 16 The corresponding weighting factors are defined according to the importance of different points, with w1 valued at 2%, w2 to w5 at 3%, and w6 to w9 at 4%. 10 to w 13 The value is 6%, w 14 w 15 The value is 20%, w 16 The value is 5%.

[0050] Therefore, this invention employs the aforementioned pump pressure pulsation diagnosis method based on variational mode decomposition and chaos theory to effectively monitor pressure pulsations occurring during pump operation, preventing and mitigating potential damage from abnormal pressure pulsations. First, pressure monitoring points are scientifically arranged within the pump's flow channel, and the time-domain information of pressure fluctuations obtained at these points is extracted. Then, based on the variational mode decomposition (VMD) method and chaos theory, the pulsation information at the monitoring points is decomposed, thereby enabling accurate diagnosis of internal pressure pulsations during pump operation. The source of the pressure pulsations is traced, allowing for timely identification of the causes of abnormal pressure pulsations in the unit.

[0051] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0052] Figure 1 This is a flowchart of an embodiment of the pump pressure pulsation diagnosis method based on variational mode decomposition and chaos theory of the present invention;

[0053] Figure 2 This is a schematic diagram of the monitoring point location in an embodiment of the pump pressure pulsation diagnosis method based on variational mode decomposition and chaos theory of the present invention. Detailed Implementation

[0054] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0055] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0056] Example 1

[0057] This invention provides a method for diagnosing water pump pressure pulsation based on variational mode decomposition and chaos theory, the process of which is as follows: Figure 1 As shown, the following steps are included:

[0058] S1. Real-time pressure pulsation monitoring points are arranged in the water pump unit, and a pressure sensing system is installed. The monitoring points set in this embodiment are as follows: Figure 2 As shown, monitoring point 1 is located near the wall at the inlet section of the water pump; monitoring points 2, 5, 9, 10, and 12 are located near the wall of the flow channel's central axis, with monitoring point 5 located more than 1.0Dm directly below the water pump impeller; detection point 3 is located on the pipe wall directly below the impeller's axis; detection points 4 and 6 are on the same horizontal plane, 1.0Dm below the impeller; and detection points 7 and 8 are on the same horizontal plane, 1.2Dm above the impeller.

[0059] S2. Collect pressure signals from the pressure pulsation monitoring points in S1 to form a pressure time-domain signal, and collect and calculate the pressure peak-to-peak value.

[0060] S3. Variational Mode Decomposition (VMD) is used to decompose the pressure signal at each monitoring point to obtain the frequency and amplitude variation information corresponding to different modes at different locations. VMD decomposes the pressure signal into multiple intrinsic mode functions (IMFs) to achieve fine decomposition of the signal and accurately obtain the frequency domain characteristics of the pressure signal; specifically, it includes the following steps:

[0061] S31. Obtain the corresponding sub-signal u from the pressure signal through Hilbert transform. k The transformation formula for the one-sided spectrum of the corresponding analytic signal is:

[0062]

[0063] u k (t)=A k (t)cos(φ k (t));

[0064] Where δ(t) is the Dirac function; j represents the Hilbert twitch factor; u k (t) is the decomposed intrinsic mode functions (IMFs); A k (t) is the envelope of the intrinsic mode functions and is greater than 0; φ k (t) represents the phase, and the predicted center frequency ω can be obtained by differentiating it. k .

[0065] S32. Multiply each sub-signal u by its own predicted center frequency using a complex exponential function. k The frequency is adjusted to baseband using the following formula:

[0066]

[0067] S33. Predict each sub-signal u using squared regularization and Gaussian smoothing. k Given the bandwidth of the sub-signal and constraining the sum of the bandwidths of each sub-signal to be minimized, the variational model of the bandwidth of each sub-signal is finally expressed as:

[0068]

[0069] The convergence condition for the iterative loop is:

[0070]

[0071] Where ε represents the set correlation coefficient.

[0072] S4. Perform spectral analysis on the mode functions of the pressure signals at different locations obtained from VMD decomposition to extract the spectral characteristics of the internal flow field pressure signals of the water pump unit.

[0073] S5. Based on the spectrum analysis results, extract key characteristic parameters to obtain information on the internal flow field state of the unit. Key characteristic parameters include frequency components, pressure amplitude, etc.

[0074] S6. Combining chaos theory, we obtain the attractors of different modes and the maximum value of the Lyapunov exponent, lymax. The chaos theory analysis method is carried out in phase space and specifically includes the following steps:

[0075] S61. Determine τ and m using the CC algorithm to obtain the reconstructed phase space. When determining m and τ, determine the length of the time window, i.e., the delay time window τ. w The length of τ is an important parameter that takes into account both τ and m; that is, the determination of τ is not independent of m, but depends on the delay time window τ. w The specific equation is as follows:

[0076] τ w =(m-1)τ;

[0077]

[0078] Where m represents the embedding dimension, τ represents the delay time for reconstructing the phase space, N represents the size of the data set, r' represents the radius from the data point to the far point in the phase space, θ represents the position angle of the data point in the phase space, and e represents the value of the time delay; the correlation integral represents the cumulative distribution function, which represents the probability that the distance between any two points in the phase space is less than the radius r', where the distance between the points represents the infinite norm of the vector difference. The monitoring statistic S1 used to calculate the probability that the distance between two points is less than the radius r' is defined as follows:

[0079] S1(m,N,r',e)=C(m,N,r',e)-C m (1,N,r',e);

[0080]

[0081] Among them, C m x represents the correlation integral of the time series when m = 1; i The time series can be decomposed into τ disjoint subsequences, each of length N. s =N / τ.

[0082] The calculation process for S1 is as follows:

[0083] S611. Divide the time series into e disjoint subsequences, each of length N. s=N / e, and after averaging, the monitoring statistic S2 for each subsequence is calculated as follows:

[0084]

[0085] Among them, C s C represents the correlation integral when the monitoring statistic S = 1. ms This represents the correlation integral when the monitoring data S=1 and m=1;

[0086] S612. The two radii r' corresponding to the maximum and minimum values ​​are selected to measure the maximum deviation of all radii, and the difference is defined as:

[0087] ΔS2(m,e)=max{S2(m,r j ',e)}-min{S2(m,r j ',e)};

[0088] The local maximum time is the zero of S2(m,r',e) and the minimum value of ΔS2(m,e); the optimal time delay τ d Corresponding to the first value of these local maxima, the detection statistics for each subsequence are simplified to:

[0089]

[0090] The global minimum value S2(e) is used as the time window length of the target time series, and the embedding dimension is calculated according to the time window length equation.

[0091] S62. The phase space of each time series is reconstructed using the coordinate delay method, and principal component analysis is performed on time series with an embedding dimension greater than 2 to clearly display the phase space. An embedding dimension greater than 1 indicates that it is meaningful to analyze the chaotic characteristics of the time series signal under study. The time series obtained from real-time monitoring is brought into S1 for analysis, and the attractor dispersion of the relevant pressure monitoring points is obtained through the signal dispersion in the phase diagram.

[0092] S63. The Lyapunov exponent is used to quantitatively describe whether adjacent phase space trajectories diverge or converge exponentially, thereby determining whether the system is in a chaotic state. In calculating the maximum Lyapunov exponent, the linear region of the y(t) ~ t curve is selected, and the least squares method is used to create a regression line. y(t) is a general term for time series signals, and the slope of the line is the final maximum Lyapunov exponent. ymax The choice of the linear interval directly affects the accuracy of the maximum Lyapunov exponent in characterizing chaotic signals. This is achieved by quantifying the degree of disorder of each parameter and then using l... ymax The values ​​are analyzed and determined.

[0093] S7. Introduce the modal attractor and lymax obtained from VMD and chaos theory analysis into the weighted decision model, and combine the influence weights of different key characteristic parameters to comprehensively obtain pressure fluctuation information at different pressure monitoring points, and evaluate the operating status of the water pump unit. Specifically, this includes the following steps:

[0094] S71. Construct a weighted decision function to evaluate the pressure pulsation at multiple monitoring points of the unit. First, construct a weighted decision function for a single point:

[0095]

[0096] In the formula, the subscripts i and j represent the number of modes and the number of points, respectively; This represents the normalized peak-to-peak pressure value at a single detection point. This indicates that the amplitude values ​​of different modes obtained after decomposing the pressure signal using VMD are normalized, -l ymax w represents the negative value of the maximum Lyapunov exponent; p w A and w l These represent the weighting factors for different parameters, where w p The value is 0.5, w A The value is 0.2, w l The value is 0.3.

[0097] S72. Construct a weighted decision function for pressure pulsation detection points in different locations within the overall unit:

[0098] F (P) =w1F (P1) +w2F (P2) +w3F (P3) +……+w 14 F (P14) +w 15 F (P15) +w 16 F (P16) ;

[0099] In the formula, F (P1) To F (P16) The weighted decision function for each monitoring point; w1 to w 16 The corresponding weighting factors are defined according to the importance of different points, with w1 valued at 2%, w2 to w5 at 3%, and w6 to w9 at 4%. 10 to w 13 The value is 6%, w 14 w 15 The value is 20%, w 16 The value is 5%.

[0100] Therefore, this invention employs the aforementioned pump pressure pulsation diagnosis method based on variational mode decomposition and chaos theory to effectively monitor pressure pulsations during pump operation, prevent and mitigate potential damage from abnormal pressure pulsations. First, pressure monitoring points are scientifically arranged within the pump's flow channel, and the time-domain information of pressure fluctuations at these points is extracted. Then, based on the variational mode decomposition (VMD) method and chaos theory, the pulsation information at the monitoring points is decomposed, thereby enabling accurate diagnosis of internal pressure pulsations during pump operation. The source of pressure pulsations is traced to identify the cause of abnormal pressure pulsations in the unit, and the problem is resolved promptly. This reduces the occurrence of accidents such as unit vibration and leakage caused by pressure pulsations, lowers the economic and safety risks of pump station losses due to pressure pulsations, and improves the safety and stability of the pump station and power grid operation.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for diagnosing water pump pressure pulsation based on variational mode decomposition and chaos theory, characterized in that: The following steps are involved: S1. Install real-time pressure pulsation monitoring points and a pressure sensing system in the water pump unit; S2. Collect pressure signals from the pressure pulsation monitoring points in S1 to form a pressure time-domain signal, and collect and calculate the pressure peak-to-peak value. S3. Variational mode decomposition (VMD) is used to decompose the pressure signal at each monitoring point to obtain the frequency and amplitude variation information corresponding to different modes at different points. S4. Perform spectral analysis on the mode functions of the pressure signals at different locations obtained from VMD decomposition to extract the spectral characteristics of the internal flow field pressure signals of the pump unit. S5. Based on the spectrum analysis results, extract key characteristic parameters to obtain information on the internal flow field state of the unit. Key characteristic parameters include frequency components and pressure amplitude. S6. Combining chaos theory, we obtain the maximum value of different modal attractors and the Lyapunov exponent lymax. S7. The modal attractor and lymax obtained from VMD and chaos theory analysis are introduced into the weighted decision model. Combined with the influence weights of different key characteristic parameters, the pressure fluctuation information at different pressure monitoring points is obtained to evaluate the operating status of the water pump unit.

2. The method for diagnosing water pump pressure pulsation based on variational mode decomposition and chaos theory according to claim 1, characterized in that: In S3, Variational Mode Decomposition (VMD) decomposes the pressure signal into multiple Intrinsic Mode Functions (IMFs) to achieve fine decomposition of the signal and accurately obtain the frequency domain characteristics of the pressure signal; specifically, it includes the following steps: S31. Obtain the corresponding sub-signal u from the pressure signal through Hilbert transform. k The transformation formula for the one-sided spectrum of the corresponding analytic signal is: u k (t)=A k (t)cos(φ k (t)); Where δ(t) is the Dirac function; j represents the Hilbert twitch factor; u k (t) is the decomposed intrinsic mode functions (IMFs); A k (t) is the envelope of the intrinsic mode functions and is greater than 0; φ k (t) represents the phase, and the predicted center frequency ω can be obtained by differentiating it. k ; S32. Multiply each sub-signal u by its own predicted center frequency using a complex exponential function. k The frequency is adjusted to baseband using the following formula: S33. Predict each sub-signal u using squared regularization and Gaussian smoothing. k Given the bandwidth of the sub-signal and constraining the sum of the bandwidths of each sub-signal to be minimized, the variational model of the bandwidth of each sub-signal is finally expressed as: The convergence condition for the iterative loop is: Where ε represents the set correlation coefficient.

3. The method for diagnosing water pump pressure pulsation based on variational mode decomposition and chaos theory according to claim 1, characterized in that: In S6, the chaos theory analysis method is carried out in phase space, specifically including the following steps: S61. Determine τ and m using the CC algorithm to obtain the reconstructed phase space. When determining m and τ, determine the length of the time window, i.e., the delay time window τ. w The length of τ is an important parameter that takes into account both τ and m; that is, the determination of τ is not independent of m, but depends on the delay time window τ. w The specific equation is as follows: t w =(m-1)τ; Where m represents the embedding dimension, τ represents the delay time for reconstructing the phase space, N represents the size of the data set, r' represents the radius from the data point to the far point in the phase space, θ represents the position angle of the data point in the phase space, and e represents the value of the time delay; the correlation integral represents the cumulative distribution function, which represents the probability that the distance between any two points in the phase space is less than the radius r', where the distance between the points represents the infinite norm of the vector difference. The monitoring statistic S1 used to calculate the probability that the distance between two points is less than the radius r' is defined as follows: S1(m,N,r',e)=C(m,N,r',e)-C m (1,N,r',e); Among them, C m x represents the correlation integral of the time series when m = 1; i The time series can be decomposed into τ disjoint subsequences, each of length N. s =N / τ; S62. The phase space of each time series is reconstructed using the coordinate delay method, and principal component analysis is performed on time series with an embedding dimension greater than 2 to clearly display the phase space. An embedding dimension greater than 1 indicates that it is meaningful to analyze the chaotic characteristics of the time series signal under study. The time series obtained from real-time monitoring is brought into S1 for analysis, and the attractor dispersion of the relevant pressure monitoring points is obtained through the signal dispersion in the phase diagram. S63. The Lyapunov exponent is used to quantitatively describe whether adjacent phase space trajectories diverge or converge exponentially, thereby determining whether the system is in a chaotic state. In calculating the maximum Lyapunov exponent, the linear region of the y(t) ~ t curve is selected, and the least squares method is used to create a regression line. y(t) is a general term for time series signals, and the slope of the line is the final maximum Lyapunov exponent. ymax By quantitatively analyzing the degree of disorder of each parameter, and then by using l ymax The values ​​are analyzed and determined.

4. The method for diagnosing pump pressure pulsation based on variational mode decomposition and chaos theory according to claim 3, characterized in that: In S61, the calculation process of S1 is as follows: S611. Divide the time series into e disjoint subsequences, each of length N. s =N / e, and after averaging, the monitoring statistic S2 for each subsequence is calculated as follows: Among them, C s C represents the correlation integral when the monitoring statistic S = 1. ms This represents the correlation integral when the monitoring data S=1 and m=1; S612. The two radii r' corresponding to the maximum and minimum values ​​are selected to measure the maximum deviation of all radii, and the difference is defined as: ΔS2(m,e)=max{S2(m,r j ’,e)}-min{S2(m,r j ’,e)}; The local maximum time is the zero of S2(m,r',e) and the minimum value of ΔS2(m,e); the optimal time delay τ d Corresponding to the first value of these local maxima, the detection statistics for each subsequence are simplified to: The global minimum value S2(e) is used as the time window length of the target time series, and the embedding dimension is calculated according to the time window length equation.

5. The method for diagnosing pump pressure pulsation based on variational mode decomposition and chaos theory according to claim 3, characterized in that: S7 specifically includes the following steps: S71. Construct a weighted decision function to evaluate the pressure pulsation at multiple monitoring points of the unit. First, construct a weighted decision function for a single point: In the formula, the subscripts i and j represent the number of modes and the number of points, respectively; This represents the normalized peak-to-peak pressure value at a single detection point. This indicates that the amplitude values ​​of different modes obtained after decomposing the pressure signal using VMD are normalized, -l ymax w represents the negative value of the maximum Lyapunov exponent; p w A and w l These represent the weighting factors for different parameters, where w p The value is 0.5, w A The value is 0.2, w l The value is 0.3; S72. Construct a weighted decision function for pressure pulsation detection points in different locations within the overall unit: F (P) =w1F (P1) +w2F (P2) +w3F (P3) +……+w 14 F (P14) +w 15 F (P15) +w 16 F (P16) ; In the formula, F (P1) To F (P16) The weighted decision function for each monitoring point; w1 to w 16 The corresponding weighting factors are defined according to the importance of different points, with w1 valued at 2%, w2 to w5 at 3%, and w6 to w9 at 4%. 10 to w 13 The value is 6%, w 14 w 15 The value is 20%, w 16 The value is 5%.

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