A method and system for denoising vibration signals of nuclear power circulating water pump impellers

Abstract

A method for denoising the vibration signal of a nuclear power circulating water pump impeller is disclosed. In the method, the vibration signal of the nuclear power circulating water pump impeller is collected; an improved seagull algorithm uses logistic chaos mapping to initialize the seagull population, uses the tanh function to design a nonlinear decreasing inertia weight, adds a Lévy flight mechanism in the seagull attack phase, and constructs a fitness function using correlation coefficient and permutation entropy. The improved seagull algorithm is used to optimize the variational mode decomposition parameters; the vibration signal is subjected to variational mode decomposition according to the variational mode decomposition parameters optimized by the improved seagull algorithm to obtain eigenmode components, and the evaluation score of each eigenmode component is calculated using the constructed screening index; the noise level of each eigenmode component is determined based on the evaluation score, and corresponding denoising is performed. The signal is reconstructed using the denoised eigenmode components, and the evaluation index is set to evaluate the result.

Description

Technical Field

[0001] The present invention belongs to the technical field of nuclear power circulating water pump impeller vibration, and in particular to a nuclear power circulating water pump impeller vibration signal denoising method and system. Background Art

[0002] Circulating water pumps are the main pumps in a nuclear power plant's three-circuit circulating water system, providing cooling water for the plant's steam turbine condensers and auxiliary cooling systems. The unstable flow of fluid within the circulating water pump is characterized by strong randomness and pulsation, and the presence of multiple vortex systems. This unsteady fluid pressure pulsation acts on the impeller inside the casing, causing vibration excitation and generating the impeller's passing frequency.

[0003] As a critical component of a circulating water pump, impeller failure can cause significant events such as unit power reduction, shutdown, and reactor shutdown. Therefore, it is essential to diagnose the type and severity of impeller failures in the early stages. Vibration signal acquisition is the primary method for monitoring the condition of circulating water pump impellers. Vibration sensors indirectly extract the impeller's characteristic frequencies by collecting vibration signals from the casing. However, the acquisition of impeller vibration signals is susceptible to interference from water flow impact noise, and the characteristic frequencies are easily lost in the noise. The pass-through frequency is a crucial characteristic frequency for identifying impeller failure and its type. Therefore, feature enhancement and denoising methods are required to identify weak pass-through frequencies.

[0004] Variational modal decomposition (VMD) is a signal decomposition method proposed by KD et al. in 2014. It has been widely used in fields such as signal processing and fault diagnosis. However, traditional VMD methods require manual parameter setting, making it difficult to effectively decompose signals and resulting in poor denoising. Therefore, it is necessary to adaptively optimize the key VMD parameters to improve the denoising effect of vibration signals, identify the impeller's passing frequency, and provide high-quality monitoring data for subsequent impeller fault diagnosis.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The present invention proposes a method and system for denoising the vibration signal of a nuclear power circulating water pump impeller, which improves the denoising effect of the vibration signal and identifies the passing frequency of the impeller.

[0007] The method for denoising the vibration signal of the impeller of a nuclear power circulating water pump includes the following steps:

[0008] The first step is to collect the vibration signal of the impeller of the nuclear power circulating water pump;

[0009] In the second step, the improved Seagull algorithm uses Logistic chaos mapping to initialize the seagull population, uses the tanh function to design a nonlinear decreasing inertia weight, incorporates the Lévy flight mechanism during the seagull attack phase, constructs the fitness function using the correlation coefficient and permutation entropy, and uses the improved Seagull algorithm to optimize the variational mode decomposition parameters.

[0010] The third step is to perform variational modal decomposition on the vibration signal according to the variational modal decomposition parameters optimized by the improved Seagull algorithm to obtain the intrinsic modal components. The evaluation score of each intrinsic modal component is calculated using the constructed screening index.

[0011] The fourth step is to determine the noise level of each eigenmode component based on the evaluation score, perform corresponding denoising, reconstruct the signal using the denoised eigenmode components, and set evaluation indicators to evaluate the results.

[0012] In the method, in the first step, collecting the vibration signal of the nuclear power circulating water pump impeller includes arranging three acceleration sensors on the impeller housing of the circulating water pump test bench to collect the vibration signals of the impeller in the x-axis, y-axis and z-axis directions.

[0013] In the method, in the second step,

[0014] The Logistic chaos initialization method is used to initialize the seagull population. The Logistic chaos mapping formula is:

[0015] z k+1 =μz k (1-z k ),

[0016] Where k is the number of iterations, z k Represents the current value of the chaotic sequence, z k+1 Represents the value of the next iterative chaotic sequence, μ is an adjustable parameter, and μ∈[0, 4],

[0017] The tanh function is used to design the nonlinear inertia weight of the seagull algorithm during the migration phase:

[0018]

[0019] Among them, the value A is an additional variable that controls the position update of the individual seagull, that is, the designed nonlinear inertia weight, f c is the coefficient that controls the frequency of A value decrease, k is the number of iterations, K MAx is the maximum number of iterations, γ is the parameter that controls the slope of the tanh function,

[0020] Introducing the Levy flight mechanism into the seagull's prey attack phase to enhance the algorithm's ability to escape from local optimal solutions:

[0021] P s (k)=(D s (k)×x×y×z×Levi(β))+P bs (k),

[0022] Among them, P s (k) is the new position of the seagull after attacking the prey, P bs (k) is the optimal individual position of the seagull group, D s (k) is the relative position of the current seagull individual and the optimal individual, x, y, z are the spatial parameters of the seagull in the attack prey stage, β is the random parameter of Lévy flight, and β∈[0,2],

[0023] The Levy flight mechanism is expressed as:

[0024]

[0025] Among them, u and v are parameters that obey the random normal distribution, which are specifically expressed as:

[0026]

[0027] in, Indicates that μ obeys the mean of 0 and the variance is The normal distribution, σ μ is the standard deviation of the parameter μ, and the calculation formula is as follows:

[0028]

[0029] Where β is the random parameter of the Lévy flight, and β∈[0,2], Γ(x)=(x-1)!.

[0030] In the method described above, in the second step, the penalty factor α and the mode number K of the variational mode decomposition are optimized using the improved seagull algorithm, and the fitness function constructed is:

[0031]

[0032] Where x is the original signal, X is the reconstructed signal, r(X, x) is the correlation coefficient between the reconstructed signal X and the original signal x, and P k is the permutation entropy of the kth eigenmode component, β1 is the weight coefficient,

[0033] The calculation formula for the permutation entropy of the kth eigenmode component is as follows:

[0034]

[0035] Where M is the length of the sequence, IMF k is the kth eigenmode component, and the eigenmode component time series is reconstructed in phase space, Pj (·) represents each row of the reconstruction matrix, that is, the probability of each sequence occurring.

[0036] In the method described above, in the second step, the iterative process of optimizing the variational mode decomposition parameters using the improved Seagull algorithm is as follows:

[0037] Step 1: Initialize the size of the seagull population, set the problem-solving dimension, the number of algorithm iterations, and give the search range of the parameter K to be optimized [K min , K max ], given the search range of the parameter to be optimized α [α min , α max ],

[0038] Step 2: Set the initialization parameter combination and calculate the fitness value of each seagull individual.

[0039] Step 3: Update the current individual position of the seagull and the optimal individual position of the group. If the fitness value of the current position of the seagull is less than the fitness value of the last iteration, then update the optimal individual position and optimal fitness value of the seagull group. Otherwise, the optimal individual position and optimal fitness value remain unchanged.

[0040] Step 4: Repeat steps 2 and 3 until the optimal variational mode decomposition parameter combination is obtained, or the current number of iterations is greater than the set number of iterations.

[0041] In the method, in the third step,

[0042] By integrating the correlation coefficient and permutation entropy, the screening index of the eigenmode components is constructed, that is, the evaluation score calculation formula of each eigenmode component is:

[0043] I=β2r(IMF k ,x)+(1-β2)P k ,

[0044] Among them, x is the original signal, r(IMF k , x) is the correlation coefficient between the kth IMF component and the original signal x, β2 is the weight coefficient,

[0045] An evaluation score of each eigenmode component is calculated based on the screening index.

[0046] In the fourth step of the method, the noise level of each eigenmode component is determined, and the components are divided into useful components, low-noise components, and high-noise components. The IMF component with the highest evaluation score is defined as the useful component, and the evaluation score I of each IMF component is compared with the evaluation score I of the maximum IMF component. max For comparison, if The corresponding modal components are defined as high-noise components, and the rest are defined as low-noise components.

[0047] Perform wavelet threshold denoising on the low-noise component, discard the high-noise component, and reconstruct the denoised low-noise component and the useful component to obtain the denoising result. The wavelet threshold formula is:

[0048]

[0049] Where n is the signal length, j = 1, 2, ... is the current layer number, λ j is the wavelet threshold of the jth layer, σ is the signal variance,

[0050] Setting evaluation indicators to evaluate denoising effects includes:

[0051] For simulated signals, the signal-to-noise ratio is used to evaluate the denoising effect:

[0052]

[0053] Among them, n is the signal length, x is the original signal, X is the denoised signal, t is the time,

[0054] For the simulation signal, the root mean square error is used to evaluate the denoising effect:

[0055]

[0056] Among them, RMSE is the root mean square error, n is the signal length, x is the original signal, X is the denoised signal, t is the time,

[0057] For the measured vibration signal of the impeller, the denoising effect is evaluated using the noise reduction rate:

[0058]

[0059] Where NRR is the noise reduction rate, σ x is the standard deviation of the original signal, σ X is the standard deviation of the denoised signal.

[0060] A nuclear power circulating water pump impeller vibration signal denoising system includes:

[0061] The acceleration sensor in the x-axis direction is set on the circumference of the impeller housing.

[0062] The acceleration sensor in the y-axis direction is arranged on the circumference of the impeller housing and is arranged at 90 degrees to the acceleration sensor in the x-axis direction.

[0063] The z-axis acceleration sensor is installed on the top of the impeller housing.

[0064] A signal acquisition and processing unit is electrically connected to the acceleration sensor in the x-axis direction, the acceleration sensor in the y-axis direction, and the acceleration sensor in the z-axis direction to execute the nuclear power circulating water pump impeller vibration signal denoising method.

[0065] Beneficial effects

[0066] The present invention constructs a fitness function that combines the correlation coefficient, energy ratio, and permutation entropy of each eigenmodal component with the original signal after variational modal decomposition, performs adaptive optimization of key parameters of variational modal decomposition through an improved Seagull algorithm, then performs variational modal decomposition on the original signal according to the optimized parameters, and performs corresponding denoising steps on the eigenmodal components, which not only effectively improves the adaptability of the algorithm, but also retains useful information as much as possible while removing noise, and has a better noise reduction effect. It solves the problem that the difficulty in selecting VMD parameters leads to the difficulty in effectively decomposing the signal and the poor denoising effect, improves the denoising effect of the impeller vibration signal, and identifies the passing frequency of the impeller. In addition, the present invention can also be used as a pre-processing part of mechanical equipment fault diagnosis, expanding its practical value in engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a schematic diagram of the steps of an implementation method of a nuclear power circulating water pump impeller vibration signal denoising method according to the present invention;

[0068] Figure 2 This is a diagram of the arrangement of acceleration sensors for collecting vibration signals of a nuclear power circulating water pump impeller in one embodiment of a nuclear power circulating water pump impeller vibration signal denoising system of the present invention;

[0069] Figure 3 This is a graph showing how the fitness curve of an embodiment of a method for denoising vibration signals of a nuclear power circulating water pump impeller in the present invention changes with the number of iterations;

[0070] Figure 4 This is a graph showing how key parameters of variational mode decomposition change with the number of iterations in one embodiment of a method for denoising vibration signals of a nuclear power circulating water pump impeller in the present invention;

[0071] Figure 5 It is a permutation entropy diagram of each eigenmode component after variational mode decomposition in one embodiment of a nuclear power circulating water pump impeller vibration signal denoising method of the present invention;

[0072] Figure 6 This is a time domain diagram of each eigenmode component after variational mode decomposition of an embodiment of a nuclear power circulating water pump impeller vibration signal denoising method of the present invention;

[0073] Figure 7(a) to Figure 7(c) This is a comparison diagram of the denoising effects of an embodiment of a nuclear power circulating water pump impeller vibration signal denoising method in the present invention, wherein Figure 7(a), Figure 7(b), and Figure 7(c) are respectively the envelope spectrum of the original signal, the envelope spectrum obtained by applying the method of the present invention, and the envelope spectrum obtained by applying the wavelet threshold denoising method.

[0074] Various other advantages and benefits of the present invention will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are intended only to illustrate preferred embodiments and are not to be construed as limiting the present invention. It should be understood that the drawings described below are merely examples of the present invention, and that those skilled in the art will be able to derive other drawings from these drawings without inventive effort. Throughout the drawings, identical reference numerals are used to denote identical components. DETAILED DESCRIPTION

[0075] Specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although specific embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0076] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the present invention, but the description is based on the general principles of the specification and is not intended to limit the scope of the invention. The scope of protection of the present invention shall be as defined in the attached claims.

[0077] To facilitate understanding of the embodiments of the present invention, further explanation will be given below using specific embodiments as examples in conjunction with the accompanying drawings, and the accompanying drawings do not constitute a limitation on the embodiments of the present invention.

[0078] like Figures 1 to 7(c) As shown in FIG, the method for denoising the vibration signal of the impeller of a nuclear power circulating water pump includes the following steps:

[0079] The first step is to collect the vibration signal of the impeller of the nuclear power circulating water pump;

[0080] In the second step, the improved Seagull algorithm uses Logistic chaos mapping to initialize the seagull population, uses the tanh function to design a nonlinear decreasing inertia weight, incorporates the Lévy flight mechanism during the seagull attack phase, constructs the fitness function using the correlation coefficient and permutation entropy, and uses the improved Seagull algorithm to optimize the variational mode decomposition parameters.

[0081] The third step is to perform variational modal decomposition on the vibration signal according to the variational modal decomposition parameters optimized by the improved Seagull algorithm to obtain the intrinsic modal components. The evaluation score of each intrinsic modal component is calculated using the constructed screening index.

[0082] The fourth step is to determine the noise level of each eigenmode component based on the evaluation score, perform corresponding denoising, reconstruct the signal using the denoised eigenmode components, and set evaluation indicators to evaluate the results.

[0083] In a preferred embodiment of the method, in the first step, collecting the vibration signal of the nuclear power circulating water pump impeller includes arranging three acceleration sensors on the impeller housing of the circulating water pump test bench to collect the vibration signals of the impeller in the x-axis, y-axis and z-axis directions.

[0084] In a preferred embodiment of the method, in the second step,

[0085] The Logistic chaos initialization method is used to initialize the seagull population. The Logistic chaos mapping formula is:

[0086] z k+1 =μz k (1-z k ),

[0087] Where k is the number of iterations, z k Represents the current value of the chaotic sequence, z k+1 Represents the value of the next iterative chaotic sequence, μ is an adjustable parameter, and μ∈[0, 4],

[0088] The tanh function is used to design the nonlinear inertia weight of the seagull algorithm during the migration phase:

[0089]

[0090] Among them, the value A is an additional variable that controls the position update of the individual seagull, that is, the designed nonlinear inertia weight, f c is the coefficient that controls the frequency of A value decrease, k is the number of iterations, K MAx is the maximum number of iterations, γ is the parameter that controls the slope of the tanh function,

[0091] Introducing the Levy flight mechanism into the seagull's prey attack phase to enhance the algorithm's ability to escape from local optimal solutions:

[0092] P s (k)=(D s (k)×x×y×z×Levi(β))+P bs (k),

[0093] Among them, P s(k) is the new position of the seagull after attacking the prey, P bs (k) is the optimal individual position of the seagull group, x, y, z are the spatial parameters of the seagull in the attack prey stage, β is the random parameter of Lévy flight, and β∈[0,2],

[0094] The flight mechanism of Naiwei is described as follows:

[0095]

[0096] Among them, u and v are parameters that obey the random normal distribution, which are specifically expressed as:

[0097]

[0098] in, Indicates that μ obeys the mean of 0 and the variance is The normal distribution, σ μ is the standard deviation of the parameter μ, and the calculation formula is as follows:

[0099]

[0100] Where β is the random parameter of the Lévy flight, and β∈[0,2], Γ(x)=(x-1)!.

[0101] In a preferred embodiment of the method, in the second step, the penalty factor α and the mode number K of the variational mode decomposition are optimized using the improved seagull algorithm, and the fitness function constructed is:

[0102]

[0103] Where x is the original signal, for example, the original signal is the collected vibration signal, X is the reconstructed signal, r(X, x) is the correlation coefficient between the reconstructed signal X and the original signal x, P k is the permutation entropy of the kth eigenmode component, β1 is the weight coefficient,

[0104] The calculation formula for the permutation entropy of the kth eigenmode component is as follows:

[0105]

[0106] Where M is the length of the sequence, IMF k is the kth eigenmode component, and the eigenmode component time series is reconstructed in phase space, P j (·) represents each row of the reconstruction matrix, that is, the probability of each sequence occurring.

[0107] In a preferred embodiment of the method, in the second step, the iterative process of optimizing the variational mode decomposition parameters using the improved Seagull algorithm is as follows:

[0108] Step 1: Initialize the size of the seagull population, set the problem-solving dimension, the number of algorithm iterations, and give the search range of the parameter K to be optimized [K min , K max ], given the search range of the parameter to be optimized α [α min , α max ],

[0109] Step 2: Set the initialization parameter combination and calculate the fitness value of each seagull individual.

[0110] Step 3: Update the current individual position of the seagull and the optimal individual position of the group. If the fitness value of the current position of the seagull is less than the fitness value of the last iteration, then update the optimal individual position and optimal fitness value of the seagull group. Otherwise, the optimal individual position and optimal fitness value remain unchanged.

[0111] Step 4: Repeat steps 2 and 3 until the optimal variational mode decomposition parameter combination is obtained, or the current number of iterations is greater than the set number of iterations.

[0112] In a preferred embodiment of the method, in the third step,

[0113] By integrating the correlation coefficient and permutation entropy, the screening index of the eigenmode components is constructed, that is, the evaluation score calculation formula of each eigenmode component is:

[0114] I=β2r(IMF k ,x)+(1-β2)P k ,

[0115] Among them, x is the original signal, r(IMF k , x) is the correlation coefficient between the kth IMF component and the original signal x, β2 is the weight coefficient,

[0116] An evaluation score of each eigenmode component is calculated based on the screening index.

[0117] In a preferred embodiment of the method, in the fourth step, the noise level of each eigenmode component is determined, and the components are divided into useful components, low-noise components, and high-noise components. The IMF component with the highest evaluation score is defined as the useful component, and the evaluation score I of each IMF component is compared with the evaluation score I of the maximum IMF component. max For comparison, if The corresponding modal components are defined as high-noise components, and the rest are defined as low-noise components.

[0118] Perform wavelet threshold denoising on the low-noise component, discard the high-noise component, and reconstruct the denoised low-noise component and the useful component to obtain the denoising result. The wavelet threshold formula is:

[0119]

[0120] Where n is the signal length, j = 1, 2, ... is the current layer number, λ j is the wavelet threshold of the jth layer, σ is the signal variance,

[0121] Setting evaluation indicators to evaluate denoising effects includes:

[0122] For the simulated signal, the signal-to-noise ratio is used to evaluate the denoising effect:

[0123]

[0124] Among them, n is the signal length, x is the original signal, X is the denoised signal, t is the time,

[0125] For the simulation signal, the root mean square error is used to evaluate the denoising effect:

[0126]

[0127] Among them, RMSE is the root mean square error, n is the signal length, x is the original signal, X is the denoised signal, t is the time,

[0128] For the measured vibration signal of the impeller, the denoising effect is evaluated using the noise reduction rate:

[0129]

[0130] Where NRR is the noise reduction rate, σ x is the standard deviation of the original signal, σ X is the standard deviation of the denoised signal.

[0131] A nuclear power circulating water pump impeller vibration signal denoising system includes:

[0132] The acceleration sensor in the x-axis direction is set on the circumference of the impeller housing.

[0133] The acceleration sensor in the y-axis direction is arranged on the circumference of the impeller housing and is arranged at 90 degrees to the acceleration sensor in the x-axis direction.

[0134] The z-axis acceleration sensor is installed on the top of the impeller housing.

[0135] A signal acquisition and processing unit is electrically connected to the acceleration sensor in the x-axis direction, the acceleration sensor in the y-axis direction, and the acceleration sensor in the z-axis direction to execute the nuclear power circulating water pump impeller vibration signal denoising method as described above.

[0136] In one embodiment, in the second step, an improved seagull algorithm is proposed. Logistic chaotic mapping is used to initialize the seagull population, the tanh function is used to design a nonlinear decreasing inertia weight, the Lévy flight mechanism is added during the seagull attack phase, the correlation coefficient and permutation entropy are used to construct the fitness function, and the improved seagull algorithm is used to optimize the key parameters of variational mode decomposition, including:

[0137] The Logistic chaos initialization method is used to initialize the seagull population. The Logistic chaos mapping formula is:

[0138] z k+1 =μz k (1-z k )

[0139] Where k is the number of iterations, z k Represents the current value of the chaotic sequence, z k+1 Represents the value of the next iterative chaotic sequence, μ is an adjustable parameter with a value of 4.

[0140] The tanh function is used to design the nonlinear inertia weight of the seagull algorithm during the migration phase:

[0141]

[0142] Among them, the value A is an additional variable that controls the position update of the individual seagull, that is, the designed nonlinear inertia weight, f c is the coefficient that controls the frequency of A value decrease, and its value is 1. K is the number of iterations. MAX is the maximum number of iterations, which is set to 15, and γ is the parameter that controls the slope of the tanh function, which is set to 1.

[0143] Introducing the Levy flight mechanism into the seagull's prey attack phase to enhance the algorithm's ability to escape from local optimal solutions:

[0144] P s (k)=(D s (k)×x×y×z×Levi(β))+P bs (k)

[0145] Among them, P s (k) is the new position of the seagull after attacking the prey, D s (k) is the relative position of the current seagull individual and the optimal individual, P bs (k) is the optimal individual position of the seagull group, x, y, and z are the spatial parameters of the seagulls during the prey attack phase, and β is the random parameter of Lévy flight, which is set to 1.5.

[0146] The Levy flight mechanism is expressed as:

[0147]

[0148] Among them, β is the random parameter of Lévy flight, u and v are parameters that obey random normal distribution, which are specifically expressed as:

[0149]

[0150] in, Indicates that μ obeys the mean of 0 and the variance is The normal distribution, σ μ is the standard deviation of the parameter μ, and the calculation formula is as follows:

[0151]

[0152] Where β is the random parameter of the Lévy flight, which is set to 1.5, and Γ(x) = (x-1)!.

[0153] In this embodiment, in the second step, the energy and information loss of the signal after variational mode decomposition and the randomness of each intrinsic mode component are fully considered to construct a new fitness function. The penalty factor α and the mode number K of the variational mode decomposition are optimized using the improved seagull algorithm. The constructed new fitness function is:

[0154]

[0155] Where x is the original signal, X is the reconstructed signal, r(X, x) is the correlation coefficient between the reconstructed signal X and the original signal x, and P k is the permutation entropy of the kth eigenmode component, β1 is the weight coefficient, and its value is 0.6.

[0156] The calculation formula for the permutation entropy of the kth eigenmode component is as follows:

[0157]

[0158] Where M is the length of the sequence, IMF k is the kth eigenmode component, and the eigenmode component time series is reconstructed in phase space, P j (·) represents each row of the reconstruction matrix, that is, the probability of each sequence occurring.

[0159] In this embodiment, in the second step, the iterative process of optimizing the variational mode decomposition parameters using the improved Seagull algorithm is as follows:

[0160] Step 1: Initialize the seagull population size n = 15, set the problem solving dimension d = 2, the algorithm iteration number N = 15, and give the search range of the parameter K to be optimized [K min , K max ], given the search range of the parameter to be optimized α [α min, α max ].

[0161] Step 2: Set the initialization parameter combination and calculate the fitness value of each seagull individual.

[0162] Step 3: Update the current individual position of the seagull and the optimal individual position of the group. If the fitness value of the seagull's current position is less than the fitness value of the previous iteration, then update the optimal individual position and optimal fitness value of the seagull group. Otherwise, the optimal individual position and optimal fitness value remain unchanged.

[0163] Step 4: Repeat steps 2 and 3 until the optimal variational mode decomposition parameter combination is obtained, or the current number of iterations is greater than the set number of iterations.

[0164] In this embodiment, in the third step, the original signal is subjected to variational mode decomposition according to the parameters optimized by the improved Seagull algorithm to obtain a series of intrinsic mode components. The evaluation score of each intrinsic mode component is calculated using the constructed screening index, including:

[0165] By integrating the correlation coefficient and permutation entropy, the screening index of the eigenmode components is constructed, that is, the evaluation score calculation formula of each eigenmode component is:

[0166] I=β2r(IMF k ,x)+(1-β2)P k ,

[0167] Among them, x is the original signal, r(IMF k , x) is the correlation coefficient between the kth IMF component and the original signal x, β2 is the weight coefficient,

[0168] Based on the screening index constructed above, the evaluation score of each eigenmode component is calculated.

[0169] In this embodiment, in the fourth step, the noise level of each eigenmode component is determined based on the evaluation score, and a corresponding denoising step is performed. The signal is reconstructed using the denoised eigenmode components, and an evaluation index is set to evaluate the denoising result, including:

[0170] Determine the noise level of each intrinsic mode component and distinguish them into useful components, low-noise components and high-noise components. The specific distinction method is: (1) define the IMF component with the highest evaluation score as the useful component. (2) Compare the evaluation score I of each IMF component with the evaluation score I of the maximum IMF component. max Compare. If The corresponding modal components are defined as high-noise components, and the rest are defined as low-noise components.

[0171] Perform wavelet threshold denoising on the low-noise component, discard the high-noise component, and reconstruct the denoised low-noise component and the useful component to obtain the denoising result. The wavelet threshold formula is:

[0172]

[0173] Where n is the signal length, j = 1, 2, ... is the current layer number, λ j is the wavelet threshold of the jth layer, and σ is the signal variance.

[0174] Set evaluation indicators to evaluate the denoising effect, including:

[0175] For the simulated signal, the signal-to-noise ratio is used to evaluate the denoising effect:

[0176]

[0177] Among them, n is the signal length, x is the original signal, X is the denoised signal, t is the time,

[0178] For the simulation signal, the root mean square error is also used to evaluate the denoising effect:

[0179]

[0180] Among them, RMSE is the root mean square error, n is the signal length, x is the original signal, X is the denoised signal, t is the time,

[0181] For the measured vibration signal of the impeller, the denoising effect is evaluated using the noise reduction rate:

[0182]

[0183] Where NRR is the noise reduction rate, σ x is the standard deviation of the original signal, σ X is the standard deviation of the denoised signal.

[0184] To further illustrate the method of the present invention, an enhanced nuclear power circulating water pump impeller vibration signal denoising method of the present invention is explained below with a specific implementation method and in combination with the accompanying drawings, which is not intended to limit the present invention.

[0185] Figure 1 This is a schematic diagram of the steps of an implementation method of an enhanced nuclear power circulating water pump impeller vibration signal denoising method in the present invention. Figure 2 This is a diagram of the arrangement of acceleration sensors for collecting vibration signals of a circulating pump impeller, in accordance with one embodiment of an enhanced nuclear power circulating water pump impeller vibration signal denoising method of the present invention. Figure 3 This is a graph showing how the fitness curve of an embodiment of an enhanced nuclear power circulating water pump impeller vibration signal denoising method in the present invention changes with the number of iterations. Figure 4This is a graph showing how key parameters of variational modal decomposition vary with the number of iterations in one embodiment of an enhanced nuclear power circulating water pump impeller vibration signal denoising method in the present invention, wherein the key parameters are the penalty factor α and the modal number K. Figure 5 It is a permutation entropy diagram of each eigenmode component after variational modal decomposition in one embodiment of an enhanced nuclear power circulating water pump impeller vibration signal denoising method in the present invention. Figure 6 This is a time domain diagram of each eigenmode component after variational mode decomposition in an embodiment of an enhanced nuclear power circulating water pump impeller vibration signal denoising method in the present invention. Figure 7(a) to Figure 7(c) This is a comparison diagram of the denoising effects of an embodiment of an enhanced nuclear power circulating water pump impeller vibration signal denoising method in the present invention, wherein Figure 7(a), Figure 7(b), and Figure 7(c) are respectively the envelope spectrum of the original signal, the envelope spectrum obtained by applying the method of the present invention, and the envelope spectrum obtained by applying the wavelet threshold denoising method, and the impeller passing frequency is marked in the elliptical box.

[0186] According to an enhanced nuclear power circulating water pump impeller vibration signal denoising method of the present invention, an exemplary implementation is provided as follows:

[0187] In the first step, three acceleration sensors were placed on the impeller housing of the nuclear power circulating water pump test bench to collect vibration signals in the x-axis, y-axis, and z-axis directions of the impeller. The circulating water pump speed is 341r / min, the number of impeller blades is 4, the shaft rotation frequency is 5.68Hz, and the impeller passing frequency is 22.72Hz.

[0188] In the second step, if Figure 4 ,By improving the Seagull optimization algorithm to optimize the key parameters of ,variational modal decomposition, the value of the penalty factor α gradually stabilizes after the 10th round, and takes a value of 12840. The value of the modal number K also gradually stabilizes after 10 rounds, and takes a value of 6.

[0189] In the third step, as shown in Table 1, the evaluation scores of each eigenmode component are calculated.

[0190] Table 1 Evaluation scores of each eigenmode component

[0191]

[0192] In the fourth step, as shown in Table 1, of the six eigenmode components after variational mode decomposition, the third eigenmode component has the highest evaluation score and is not subjected to denoising. The remaining eigenmode components all have evaluation scores greater than half of the highest evaluation score and are therefore subjected to wavelet denoising. The third eigenmode component is reconstructed with the remaining eigenmode components after denoising to obtain the denoised signal.

[0193] The denoising results of the present invention were compared with those of direct wavelet threshold processing, which verified the effectiveness of the method of the present invention. Figure 7(a) to Figure 7(c) The figure shows a comparison of denoising effects. From the comparison of Figure 7(a) and Figure 7(c), it can be seen that the signal after direct wavelet threshold denoising cannot identify the impeller passing frequency, and the spectrum has many interference components, and the denoising effect is very poor. From the comparison of Figure 7(a) and Figure 7(b), it can be seen that the method of the present invention can not only clearly distinguish the impeller passing frequency, but also enhance the rotation frequency and its multiple amplitude to a certain extent. The spectrum is relatively clean and has fewer interference components. Table 2 is a comparison of the denoising rate of the method of the present invention and that after direct wavelet threshold processing. It can be seen from Table 2 that the denoising effect of the method of the present invention is better, and the denoising rate is significantly improved compared with direct wavelet threshold processing, thus verifying the effectiveness of the method proposed in the present invention.

[0194] Table 2 Comparison of denoising performance of the two methods

[0195]

[0196] This disclosure also provides an enhanced nuclear power circulating water pump impeller vibration signal denoising system, comprising at least three acceleration sensors and a signal acquisition and processing module. The z-axis acceleration sensor is mounted on top of the impeller housing, while the x- and y-axis acceleration sensors are positioned around the impeller housing at 90° angles. The three acceleration sensors are electrically connected to the signal acquisition and processing module.

[0197] Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields. The above-mentioned specific embodiments are merely illustrative and instructive, and are not restrictive. A person skilled in the art, guided by this specification and without departing from the scope of protection of the claims of the present invention, may also devise various forms, all of which fall within the scope of protection of the present invention.

Claims

1. A method for denoising vibration signals of a nuclear power circulating water pump impeller, characterized in that: It includes the following steps: The first step is to collect the vibration signal of the impeller of the nuclear power circulating water pump; In the second step, the improved Seagull algorithm uses Logistic chaos mapping to initialize the seagull population, uses the tanh function to design a nonlinear decreasing inertia weight, incorporates the Lévy flight mechanism during the seagull attack phase, constructs the fitness function using the correlation coefficient and permutation entropy, and uses the improved Seagull algorithm to optimize the variational mode decomposition parameters. The third step is to perform variational modal decomposition on the vibration signal according to the variational modal decomposition parameters optimized by the improved Seagull algorithm to obtain the intrinsic modal components. The evaluation score of each intrinsic modal component is calculated using the constructed screening index. The fourth step is to determine the noise level of each eigenmode component based on the evaluation score, perform corresponding denoising, reconstruct the signal using the denoised eigenmode components, and set evaluation indicators to evaluate the results; in, In the second step, the penalty factor of variational mode decomposition is optimized using the improved Seagull algorithm. and modal number , the constructed fitness function is: ； in, is the original signal, To reconstruct the signal, To reconstruct the signal and the original signal The correlation coefficient of For the The permutation entropy of the eigenmode components, is the weight coefficient, No. The calculation formula of the permutation entropy of an eigenmode component is as follows: ； in, is the length of the sequence, For the eigenmode components, and reconstruct the phase space of the eigenmode component time series. Represents each row of the reconstruction matrix, that is, the probability of each sequence occurring.

2. The method according to claim 1, characterized in that In the first step, the vibration signal of the nuclear power circulating water pump impeller is collected by arranging three acceleration sensors on the impeller housing of the circulating water pump test bench. axis, axis, Vibration signal in the axis direction.

3. The method according to claim 1, characterized in that In the second step, The Logistic chaos initialization method is used to initialize the seagull population. The Logistic chaos mapping formula is: ， in, is the number of iterations, represents the current value of the chaotic sequence, Represents the value of the next iterative chaotic sequence, is an adjustable parameter, and , The tanh function is used to design the nonlinear inertia weight of the seagull algorithm during the migration phase: ， in, The value is an additional variable that controls the position update of the individual seagull, that is, the designed nonlinear inertia weight. For control The coefficient of the frequency of value decrease, is the number of iterations, is the maximum number of iterations, is the parameter that controls the slope of the tanh function, Introducing the Levy flight mechanism into the seagull's prey attack phase to enhance the algorithm's ability to escape from local optimal solutions: ， in, The new position of the seagull after attacking the prey. is the relative position of the current seagull individual and the optimal individual, is the optimal individual position of the seagull group, 、 、 is the spatial parameter of the seagull during the attack on prey stage, are random parameters of the Lévy flight, and , The Levy flight mechanism is expressed as: ， in, are the random parameters of the Levy flight, and is the parameter that obeys the random normal distribution, which is specifically expressed as: ， in, express The mean is 0 and the variance is The normal distribution of For parameters The standard deviation is calculated as follows: ， in, are random parameters of the Lévy flight, and , .

4. The method according to claim 1, wherein In the second step, the iterative process of optimizing the variational mode decomposition parameters using the improved Seagull algorithm is as follows: Step 1: Initialize the size of the seagull population, set the problem-solving dimension, the number of algorithm iterations, and the parameters to be optimized. Search scope , given the parameters to be optimized Search scope[ ], Step 2: Set the initialization parameter combination and calculate the fitness value of each seagull individual. Step 3: Update the current individual position of the seagull and the optimal individual position of the group. If the fitness value of the current position of the seagull is less than the fitness value of the last iteration, then update the optimal individual position and optimal fitness value of the seagull group. Otherwise, the optimal individual position and optimal fitness value remain unchanged. Step 4: Repeat steps 2 and 3 until the optimal variational mode decomposition parameter combination is obtained, or the current number of iterations is greater than the set number of iterations.

5. The method according to claim 1, wherein In the third step, By integrating the correlation coefficient and permutation entropy, the screening index of the eigenmode components is constructed, that is, the evaluation score calculation formula of each eigenmode component is: ， in, is the original signal; For the indivual Component and original signal The correlation coefficient of is the weight coefficient, An evaluation score of each eigenmode component is calculated based on the screening index.

6. A nuclear power circulating water pump impeller vibration signal denoising system, characterized in that: It includes, Axial acceleration sensor, which is set on the circumference of the impeller housing, The axial acceleration sensor is arranged on the circumference of the impeller housing and is The axial acceleration sensors are arranged at 90°. Axial acceleration sensor, which is installed on the top of the impeller housing, Signal acquisition and processing unit, which is electrically connected Axis acceleration sensor, Axis acceleration sensor, An axial acceleration sensor is used to perform the nuclear power circulating water pump impeller vibration signal denoising method as described in any one of claims 1 to 5.

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