A method for preprocessing vibration signals of hydropower units

Abstract

The present invention provides a method for preprocessing vibration signals of a hydropower unit. The present invention proposes five strategies to improve the optimization accuracy of a dung beetle algorithm so that it has good robustness. The improved dung beetle algorithm MSIDBO jumps out of the local optimum through PWLCM chaotic mapping, nonlinear boundary convergence factor, rolling ball behavior integrating dynamic golden sine, sparrow alert mechanism and parameter adaptive adjustment to improve the accuracy of the optimization problem. With the minimum envelope entropy as the fitness function, MSIDBO is used to optimize parameters such as the white noise amplitude weight Nstd and the number of noise additions NE of ICEEMDAN, and the optimized ICEEMDAN is used to perform modal decomposition of the vibration signal of the hydropower unit. The MSIDBO algorithm has a faster convergence speed, higher solution accuracy and better applicability than the standard dung beetle algorithm. Through simulation signal analysis, it is concluded that the decomposition accuracy of the optimized ICEEMDAN is higher than that of the conventional decomposition algorithm, which improves the accuracy of the preprocessing of the vibration signal of the hydropower unit and provides a basis for the intelligent vibration monitoring and diagnosis of the hydropower unit.

Description

Technical Field

[0001] The invention relates to the technical field of hydroelectric generator set signal processing, and in particular to a hydroelectric generator set vibration signal preprocessing method. Background Art

[0002] As part of my country's energy structure, hydropower has the advantages of fast start-up speed, high power generation efficiency and flexible operation and scheduling, and its role in the power supply system is becoming more and more obvious. Wind power and photovoltaic power are affected by natural instability factors and pose a threat to the security of the power grid. Therefore, hydropower units with strong regulation capabilities are of great significance to ensure the security of the power grid. Hydropower units are the core equipment for realizing energy conversion. With the intelligent and large-scale development of hydropower units, their operating conditions have become more and more complex. The coupling between the various components of the unit due to hydraulic, mechanical, electrical and other factors has led to increasingly prominent vibration problems of the unit, affecting the safe and stable operation of the entire power system.

[0003] Vibration is a common problem in hydroelectric generator sets. It is easy to produce in the components of each device of the unit, and each type of hydroelectric generator set has vibration, but the characteristics and amplitude of the vibration are different. Vibration may induce resonance in the power plant building or pressure pipeline of the hydropower station. In extreme cases, vibration will cause fluctuations in the unit output, thereby hindering the stable operation of the hydropower unit. According to relevant statistics, among the various faults that occur in hydropower units, many faults can be reflected in the vibration signal. Therefore, the study of the characteristic information of the vibration signal of the hydropower unit can effectively realize the monitoring of the operating status of the unit and the analysis of the fault situation, so as to help the staff of the hydropower station to discover the faults in the unit as early as possible, thereby preventing the further deterioration of the fault. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a method for preprocessing vibration signals of a hydropower unit to at least solve the above problems.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A method for preprocessing vibration signals of a hydropower unit, the method comprising the following steps:

[0007] Step 1: Use a vibration sensor to obtain the vibration signal of the hydropower unit;

[0008] Step 2: Take the minimum envelope entropy as the optimization objective function and set the optimization range of the white noise amplitude weight and the number of noise addition parameters of the ICEEMDAN algorithm;

[0009] Step 3: Input the two parameters of the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm into the improved dung beetle algorithm based on PWLCM chaotic mapping, nonlinear boundary convergence factor, rolling ball behavior fused with dynamic golden sine, sparrow sentinel mechanism and parameter adaptive adjustment strategy for iterative optimization, and output the optimal parameters;

[0010] Step 4: Input the optimized parameters into ICEEMDAN for modal decomposition of vibration signals.

[0011] Furthermore, in step 2, the minimum envelope entropy is used as the optimization objective function, and the optimization ranges of parameters such as the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm are set, wherein the optimization ranges of parameters such as the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm are set to [0.15, 0.6] and [20, 100] respectively; the original vibration signal x(j) (j = 1, 2, ..., n) is converted into a probability distribution sequence p j , which is used to describe the complexity of the signal, that is, the irregularity of the signal envelope. The envelope entropy expression is as follows:

[0012]

[0013] A1=min{E f} (twenty three)

[0014] Where a(j) is the sequence of envelope signals after Hilbert demodulation; p j is to normalize the envelope signal sequence; H[x(j)] represents the Hilbert transform of the signal; A1 is the objective function,

[0015] The expression for parameter optimization is as follows:

[0016]

[0017] Where lb is the lower bound of the parameter to be optimized, ub is the upper bound of the parameter to be optimized, Nstd is the white noise amplitude weight, and NE is the number of noise additions.

[0018] Furthermore, in step 3, the two parameters of the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm are input into the improved dung beetle algorithm based on PWLCM chaotic mapping, nonlinear boundary convergence factor, rolling ball behavior fused with dynamic golden sine, sparrow sentinel mechanism and parameter adaptive adjustment strategies to iteratively search for the optimal parameters and output the optimal parameters. The improved dung beetle algorithm process is as follows:

[0019] (1) PWLCM chaotic map initialization population

[0020] The PWLCM chaotic mapping is described as follows:

[0021]

[0022] In the formula, the control parameter p∈(0,0.5) is taken as 0.4; the chaotic random value x(t)∈(0,1) is used to divide the piecewise function;

[0023] (2) Nonlinear boundary convergence factor

[0024] The reproduction and foraging behaviors of the dung beetle algorithm are restricted to a certain area, that is, the spawning area and foraging area can be defined:

[0025]

[0026]

[0027] Among them, R = 1-t / T, T is the maximum number of iterations, Lb * and Ub * They define the lower and upper bounds of the spawning area, respectively. * is the optimal position of the current population; X b represents the global optimal position, Lb b and Ub b Respectively represent the optimal lower and upper limits of the foraging area; the spawning and foraging areas are dynamically adjusted with the number of iterations, and the reset boundary convergence factor R is expressed as follows:

[0028]

[0029] Among them, t is the number of iterations of the current population, and T is the maximum number of iterations of the population;

[0030] (3) Rolling behavior integrating dynamic golden sine

[0031] The golden sine strategy is introduced to use the sine function to comprehensively search for potential areas, improve the convergence speed, and get rid of the limitation of local optimality. The update formula of the improved dung beetle rolling behavior is as follows:

[0032]

[0033]

[0034] In the formula, R1 determines the moving distance of the current individual, which is a random number between [0, 2π]; R2 determines the update direction of the current individual position in the next iteration, which is a random number between [0, π]; X a is the position of the current optimal individual, X c is the position in the i-th dimension, x i(t) represents the position information of the ith dung beetle in the tth iteration; k∈(0,0.2) is the perturbation coefficient, α is -1 or 1, b is a random number between (0,1), δ is a random number between [0,1], x1 and x2 are the golden section coefficients; τ is the golden section ratio, which is 0.618033;

[0035] In addition to improving the movement path of the rolling dung beetle, in order to further balance the exploration and development capabilities of the algorithm in iteration, the inertia weight w is introduced into the golden sine formula:

[0036]

[0037] Among them, w min is the minimum inertia weight, w max is the maximum inertia weight, t is the current iteration number, and T is the maximum iteration number;

[0038] (4) Sparrow Sentinel Mechanism

[0039] The vigilant strategy of the sparrow search algorithm has the function of reminding the population to take anti-predation actions. It is introduced into the dung beetle algorithm. At the same time, the shrinkage factor is introduced. In order to balance the exploration and development capabilities and enable the algorithm to have flexible search capabilities at different stages, the update formula of this strategy is as follows:

[0040]

[0041] In the formula, is the current global optimal position, T is the maximum number of iterations, and t is the current number of iterations; β is a normally distributed random number with mean 0 and variance 1, which is used to control the step length; K is the step length control parameter of the sparrow's moving direction; ε is the minimum constant to avoid the denominator being 0; f i is the fitness value of the i-th sparrow; f w 、f g are the worst fitness value and the current best fitness value respectively;

[0042] (5) Parameter Adaptive Adjustment

[0043] The small step length and long jump mechanism of Levy flight and the wide trajectory movement of Brownian motion, this combined strategy effectively prevents the thieving dung beetles from quickly approaching the optimal individual in the early stage of iteration, and keeps the thieving dung beetles active in the later stage of iteration.

[0044] The position of the thief dung beetle after the introduction of Brownian motion is updated as follows:

[0045] x i (t+1)=X b +S·g·(|x i (t)-X *|+|x i (t)-X b |)+R BM (34)

[0046] Where g is a 1×D random vector, S is a constant, and R BM is the Brownian motion strategy;

[0047] The position of the thief dung beetle after the introduction of Levy's flight has been updated to:

[0048] x i (t+1)=X b +S·g·(|x i (t)-X * |+|x i (t)-X b |)+R LF (35)

[0049] Among them, R LF Flying strategy for Levy;

[0050] The improved dung beetle algorithm is used to optimize the parameters of ICEEMDAN to achieve adaptive decomposition of fault signals. The specific parameter optimization steps are as follows:

[0051] ① Initialize the population size, maximum number of iterations, search ranges ub and lb of NE and Nstd, maximum and minimum inertia weights, proportion of sparrows aware of danger, basic parameters of Levy flight and Brownian motion step length, and determine the fitness function;

[0052] ② Use PWLCM chaotic mapping to initialize the population of the dung beetle algorithm;

[0053] ③ Perform ICEEMDAN decomposition on the vibration signal to obtain the modal components of the signal, and calculate the envelope entropy value according to formula (1);

[0054] ④ Taking the envelope entropy as the fitness function, the improved dung beetle rolling behavior, stealing behavior, foraging and reproduction behavior are used to perform global and local searches, calculate the fitness value of each dung beetle individual, and select the individual with the smallest fitness as the current best individual to find the minimum envelope entropy value;

[0055] ⑤ The dung beetle population performs anti-predation behavior, updates the individual optimal and global optimal positions, then calculates the envelope entropy value, and selects the dung beetle individual with a smaller fitness, thereby obtaining the minimum envelope entropy value;

[0056] ⑥ Repeat steps ④ to ⑥ until the minimum envelope entropy value is determined or the set number of iterations is reached, and the optimal parameters NE and Nstd are output.

[0057] Furthermore, in step 4, the optimized parameters are input into ICEEMDAN to perform modal decomposition of the vibration signal. The specific ICEEMDAN decomposition is as follows:

[0058] Add i groups of Gaussian white noise to the original signal to form a new signal. Use EMD to calculate the average value of the upper and lower envelopes to obtain the first-order residual and modal components. The decomposition process is as follows:

[0059] x i t=x(t)+β0E1(ξ i (t)) (36)

[0060] r1=M(x i (t)) (37)

[0061] β0=δ0std(x) / std(E1(ξ i (t))) (38)

[0062]

[0063] Among them, ξ i (t) represents Gaussian white noise with mean and unit variance of 0 and 1 respectively, which is used to adjust the required signal-to-noise ratio (SNR) between the added noise and the residual after adding the noise. When k ≥ 1, β k =δ0std(r k ), δ0 represents the inverse of the required SNR between the input signal sequence and the first added noise, std() is the noise standard deviation operator, and the operator E k () represents the kth modal component obtained by EMD decomposition, the operator M() represents the local mean of the signal envelope, and <> represents the average calculation;

[0064] Construct a new decomposition signal r1(t)+ω1E2(ξ i (t)), the second-order residual is estimated by calculating the local mean through EMD decomposition, and the second modal component is defined as:

[0065]

[0066] Calculate the remaining order modal components k=3,4…,K and obtain the k-order residual:

[0067] r k (t)= <M(r k-1 (t)+ω k-1 E k (ξ i (t)))> (41)

[0068] Calculate the k-order modal component:

[0069]

[0070] Repeat the above steps to get the next modal component. When the residual r k When it is a monotonic function, the calculation is terminated.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] In this technical solution, although the dung beetle algorithm has a simple structure and good stability, it is easy to fall into the local optimal value when solving complex problems, resulting in low solution accuracy. Therefore, a dung beetle algorithm integrating PWLCM chaotic mapping, improved convergence factor, dynamic golden sine, sparrow sentinel mechanism and parameter adaptive strategy is proposed. The decomposition performance of the ICEEMDAN decomposition algorithm is greatly affected by parameters such as the white noise amplitude weight and the number of noise additions. The MSIDBO-ICEEMDAN decomposition method is proposed and simulated signal tests are carried out. The results show that the decomposition accuracy of this method is high, ensuring the accuracy of the hydropower unit signal analysis. In practical applications, by analyzing the collected vibration signals of the hydropower unit using the above method, the vibration signals of the hydropower unit can be accurately processed. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0074] Figure 1 The figure is a schematic diagram of the improved dung beetle algorithm flow chart of the present invention.

[0075] Figure 2 This is a schematic diagram of the improvement of the boundary convergence factor R described in the present invention.

[0076] Figure 3 This is a schematic diagram of the simulation signal and its composition described in the present invention.

[0077] Figure 4 Schematic diagram of the comparison of ICEEMDAN iterations optimized by different algorithms described in the present invention.

[0078] Figure 5 It is a schematic diagram of the decomposition result of the simulation signal ICEEMDAN according to the present invention.

[0079] Figure 6 It is a schematic diagram of the LMD decomposition result of the simulation signal according to the present invention.

[0080] Figure 7It is a schematic diagram of the EEMD decomposition result of the simulation signal according to the present invention.

[0081] Figure 8 Schematic diagram of reconstruction error of three decomposition methods of simulation signals described in the present invention. DETAILED DESCRIPTION

[0082] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The enumerated embodiments are only used to explain the present invention but not to limit the scope of the present invention.

[0083] Reference Figure 1 The present invention provides a method for preprocessing vibration signals of a hydropower unit, comprising the following steps:

[0084] Step 1: The present invention uses the following simulation signal to implement the method, the simulation signal and its composition are as follows: Figure 3 shown.

[0085]

[0086] Among them, x(t) is the sum of the signals, x1(t) to x6(t) are sinusoidal signals with different amplitudes and frequencies, and x7(t) is the trend term.

[0087] Step 2: Take the minimum envelope entropy as the optimization objective function and set the optimization range of parameters such as the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm.

[0088] Step 3: Input the two parameters of the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm into the improved dung beetle algorithm based on PWLCM chaotic mapping, nonlinear boundary convergence factor, rolling ball behavior fused with dynamic golden sine, sparrow sentinel mechanism and parameter adaptive adjustment strategies to iteratively search and output the optimal parameters.

[0089] Step 4: Input the optimized parameters into ICEEMDAN for modal decomposition of vibration signals.

[0090] In step 2, the minimum envelope entropy is used as the objective function for optimization, and the optimization range of parameters such as the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm is set. Among them, the optimization ranges of parameters such as the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm are set to [0.15, 0.6] and [20, 100] respectively; the smaller the envelope entropy value, the richer the effective features contained in the signal, and the more effective the fault information in the decomposed modal components can be. The envelope entropy is converted into a probability distribution sequence p by Hilbert demodulation. j, which is used to describe the complexity of the signal, that is, the irregularity of the signal envelope. The envelope entropy expression is as follows:

[0091]

[0092] A1=min{E f} (44)

[0093] Where a(j) is the sequence of envelope signals after Hilbert demodulation; p j is to normalize the envelope signal sequence; H[x(j)] represents the Hilbert transform of the signal; A1 is the objective function.

[0094] The expression for parameter optimization is as follows:

[0095]

[0096] Where lb is the lower bound of the parameter to be optimized, ub is the upper bound of the parameter to be optimized, Nstd is the white noise amplitude weight, and NE is the number of noise additions.

[0097] In step 3, the two parameters of the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm are input into the improved dung beetle algorithm based on PWLCM chaotic mapping, nonlinear boundary convergence factor, rolling ball behavior fused with dynamic golden sine, sparrow vigilant mechanism and parameter adaptive adjustment strategies to iteratively search and output the optimal parameters. The improved dung beetle algorithm process is as follows:

[0098] (1) PWLCM chaotic mapping initialization population

[0099] The PWLCM chaotic mapping, with its randomness, non-repetitiveness and ergodicity, makes the population distribution more uniform, which is better than the random method. The PWLCM chaotic mapping is described as follows:

[0100]

[0101] In the formula: The control parameter p∈(0,0.5) is generally taken as 0.4; the chaotic random value x(t)∈(0,1) is used to divide the piecewise function.

[0102] (2) Nonlinear boundary convergence factor

[0103] The reproduction and foraging behaviors of the dung beetle algorithm are restricted to a certain area, that is, the egg-laying area and the foraging area can be defined.

[0104]

[0105] Among them, R = 1-t / T, T is the maximum number of iterations, Lb * and Ub *They define the lower and upper bounds of the spawning area, respectively. * is the optimal position of the current population; X b represents the global optimal position, Lb b and Ub b Respectively represent the optimal lower and upper limits of the foraging area; the spawning and foraging areas are dynamically adjusted with the number of iterations, and the reset boundary convergence factor R is expressed as follows:

[0106]

[0107] Among them, t is the number of iterations of the current population, and T is the maximum number of iterations of the population. The iterative comparison of the boundary convergence factor R before and after the improvement is shown in Figure 2 shown.

[0108] (3) Rolling behavior integrating dynamic golden sine

[0109] The golden sine strategy is introduced to use the sine function to comprehensively search for potential areas, improve the convergence speed, and get rid of the limitation of local optimality. The update formula of the improved dung beetle rolling behavior is as follows:

[0110]

[0111] In the formula, R1 determines the moving distance of the current individual, which is a random number between [0, 2π]; R2 determines the update direction of the current individual position in the next iteration, which is a random number between [0, π]; X a is the position of the current optimal individual, X c is the position in the i-th dimension, x i (t) represents the position information of the i-th dung beetle in the t-th iteration; k∈(0,0.2) is the perturbation coefficient, α is -1 or 1, b is a random number between (0,1), δ is a random number between [0,1], x1 and x2 are the golden section coefficients; τ is the golden section ratio, which is approximately 0.618033.

[0112] In addition to improving the movement path of the rolling dung beetle, in order to further balance the exploration and development capabilities of the algorithm in iteration, the inertia weight w is introduced into the golden sine formula:

[0113]

[0114]

[0115] Among them, w min is the minimum inertia weight, w max is the maximum inertia weight, t is the current iteration number, and T is the maximum iteration number.

[0116] (4) Sparrow Sentinel Mechanism

[0117] The vigilant strategy of the sparrow search algorithm has the function of reminding the population to take anti-predation actions, which is introduced into the dung beetle algorithm. At the same time, the shrinkage factor is introduced In order to balance the exploration and development capabilities, the algorithm has flexible search capabilities at different stages. The update formula of this strategy is as follows:

[0118]

[0119] Where: is the current global optimal position, T is the maximum number of iterations, and t is the current number of iterations; β is a normally distributed random number with mean 0 and variance 1, which is used to control the step length; K is the step length control parameter of the sparrow's moving direction; ε is the minimum constant to avoid the denominator being 0; f i is the fitness value of the i-th sparrow; f w 、f g are the worst fitness value and the current best fitness value respectively.

[0120] (5) Parameter Adaptive Adjustment

[0121] The small step length and long jump mechanism of Levy flight and the wide trajectory movement of Brownian motion, this combined strategy can effectively prevent the thieving dung beetle from quickly approaching the optimal individual in the early stage of iteration, and enable the thieving dung beetle to maintain a certain activity in the later stage of iteration.

[0122] The position of the thief dung beetle after the introduction of Brownian motion is updated as follows:

[0123] x i (t+1)=X b +S·g·(|x i (t)-X * |+|x i (t)-X b |)+R BM (55)

[0124] Where g is a 1×D random vector, S is a constant, and R BM is the Brownian motion strategy;

[0125] The position of the thief dung beetle after the introduction of Levy's flight has been updated to:

[0126] x i (t+1)=X b +S·g·(|x i (t)-X * |+|x i (t)-X b |)+R LF (56)

[0127] Among them, R LFFlying strategy for Levy.

[0128] The improved dung beetle algorithm is used to optimize the parameters of ICEEMDAN. The optimization iterations are compared as follows: Figure 4 The specific parameter optimization steps are as follows:

[0129] ① Initialize the basic parameters such as population size, maximum number of iterations, search ranges ub and lb of NE and Nstd, maximum and minimum inertia weights, proportion of sparrows aware of danger, step length of Levy flight and Brownian motion, and determine the fitness function;

[0130] ② Use PWLCM chaotic mapping to initialize the population of the dung beetle algorithm;

[0131] ③ Perform ICEEMDAN decomposition on the vibration signal to obtain the modal components of the signal, and calculate the envelope entropy value according to formula (1);

[0132] ④ Taking the envelope entropy as the fitness function, the improved dung beetle rolling behavior, stealing behavior, foraging and reproduction behavior are used to perform global and local searches, calculate the fitness value of each dung beetle individual, and select the individual with the smallest fitness as the current best individual to find the minimum envelope entropy value;

[0133] ⑤ The dung beetle population performs anti-predation behavior, updates the individual optimal and global optimal positions, then calculates the envelope entropy value, and selects the dung beetle individual with a smaller fitness, thereby obtaining the minimum envelope entropy value;

[0134] ⑥ Repeat steps ④ to ⑥ until the minimum envelope entropy value is determined or the set number of iterations is reached, and the optimal parameters NE and Nstd are output;

[0135] In step 4, the optimized parameters are input into ICEEMDAN to perform modal decomposition of the vibration signal. The specific ICEEMDAN decomposition is as follows:

[0136] Add i groups of Gaussian white noise to the original signal to form a new signal, and use EMD to calculate the average value of the upper and lower envelopes to obtain the first-order residual and modal components. The decomposition process is as follows:

[0137] x i t=x(t)+β0E1(ξ i (t)) (57)

[0138] r1=M(x i (t)) (58)

[0139] β0=δ0std(x) / std(E1(ξ i (t))) (59)

[0140]

[0141] Among them, ξ i (t) represents Gaussian white noise with mean and unit variance of 0 and 1 respectively, which is used to adjust the required signal-to-noise ratio (SNR) between the added noise and the residual after adding the noise. When k ≥ 1, β k =δ0std(r k ), δ0 represents the inverse of the required SNR between the input signal sequence and the first added noise, and std() is the noise standard deviation operator. Operator E k () represents the kth modal component obtained by EMD decomposition, the operator M() represents the local mean of the signal envelope, and <> represents the average calculation.

[0142] Construct a new decomposition signal r1(t)+ω1E2(ξ i (t)), the second-order residual is estimated by calculating the local mean through EMD decomposition, and the second modal component is defined as:

[0143]

[0144] Calculate the remaining order modal components k=3,4…,K and obtain the k-order residual:

[0145] r k (t)= <M(r k-1 (t)+ω k-1 E k (ξ i (t)))> (62)

[0146] Calculate the k-order modal component:

[0147]

[0148] Repeat the above steps to get the next modal component. When the residual r k When it is a monotonic function, the calculation is terminated.

[0149] In order to conduct comparative analysis, the x(t) simulation signal is input into four decomposition methods: LMD, EEMD, and ICEEMDAN, and each modal component diagram is obtained. The results obtained by each algorithm are as follows: Figure 5-Figure 7 The best parameters of MSIDBO for optimizing ICEEMDAN, the white noise amplitude weight (Nstd) and the number of noise additions (NE), are [0.30858, 78]. The white noise amplitude weight (Nstd) and the number of noise additions (NE) of EEMD and ICEEMDAN are set to [0.30858, 78].

[0150] from Figure 5The analysis of ICEEMDAN decomposition results shows that the waveform decomposed by this method has a high similarity with the original signal, the decomposition effect is good, and the basic characteristics of the original analog signal can be better maintained.

[0151] from Figure 6 The analysis of the LMD decomposition results shows that the signal consists of 7 components in total, and LMD decomposes 3 modal components, which means that the signal is under-decomposed and there is modal aliasing.

[0152] from Figure 7 The analysis of EEMD decomposition results shows that the total analog signal consists of 7 components, 5 of which are decomposed by EEMD, resulting in incomplete decomposition and modal aliasing.

[0153] The reconstruction error can reflect the decomposition accuracy of various decomposition algorithms, thereby evaluating their effectiveness. Figure 8 From the analysis, it can be seen that the reconstruction error of LMD is relatively large, EEMD is the second largest, and ICEEMDAN has the smallest error, so the decomposition accuracy of ICEEMDAN is the highest, which shows that the vibration signal preprocessing method of the hydropower unit of the present invention is effective and has high accuracy.

[0154] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for preprocessing vibration signals of a hydropower unit, characterized in that: The method comprises the following steps: Step 1: Use a vibration sensor to obtain the vibration signal of the hydropower unit; Step 2: Take the minimum envelope entropy as the optimization objective function and set the optimization range of the white noise amplitude weight and the number of noise addition parameters of the ICEEMDAN algorithm; Step 3: Input the two parameters of the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm into the improved dung beetle algorithm based on PWLCM chaotic mapping, nonlinear boundary convergence factor, rolling ball behavior fused with dynamic golden sine, sparrow sentinel mechanism and parameter adaptive adjustment strategy for iterative optimization, and output the optimal parameters; Step 4: Input the optimized parameters into ICEEMDAN for modal decomposition of vibration signals.

2. A method for preprocessing vibration signals of a hydroelectric unit according to claim 1, characterized in that: In step 2, the minimum envelope entropy is used as the optimization objective function, and the optimization ranges of parameters such as the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm are set, where the optimization ranges of parameters such as the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm are set to [0.15, 0.6] and [20, 100] respectively; the original vibration signal x(j) (j = 1, 2, ..., n) is converted into a probability distribution sequence p j , which is used to describe the complexity of the signal, that is, the irregularity of the signal envelope. The envelope entropy expression is as follows: A1=min{E f } (2) Where a(j) is the sequence of envelope signals after Hilbert demodulation; p j is to normalize the envelope signal sequence; H[x(j)] represents the Hilbert transform of the signal; A1 is the objective function, The expression for parameter optimization is as follows: Where lb is the lower bound of the parameter to be optimized, ub is the upper bound of the parameter to be optimized, Nstd is the white noise amplitude weight, and NE is the number of noise additions.

3. A method for preprocessing vibration signals of a hydroelectric unit according to claim 1, characterized in that: In step 3, the two parameters of the white noise amplitude weight and the number of noise additions of the ICEEMDAN algorithm are input into the improved dung beetle algorithm based on PWLCM chaotic mapping, nonlinear boundary convergence factor, rolling ball behavior fused with dynamic golden sine, sparrow guard mechanism and parameter adaptive adjustment strategies for iterative optimization, and the optimal parameters are output. The improved dung beetle algorithm process is as follows: (1) PWLCM chaotic map initialization population The PWLCM chaotic mapping is described as follows: In the formula, the control parameter p∈(0,0.5) is taken as 0.4; the chaotic random value x(t)∈(0,1) is used to divide the piecewise function; (2) Nonlinear boundary convergence factor The reproduction and foraging behaviors of the dung beetle algorithm are restricted to a certain area, that is, the spawning area and foraging area can be defined: Among them, R = 1-t / T, T is the maximum number of iterations, Lb * and Ub * They define the lower and upper bounds of the spawning area, respectively. * is the optimal position of the current population; X b represents the global optimal position, Lb b and Ub b Respectively represent the optimal lower and upper limits of the foraging area; the spawning and foraging areas are dynamically adjusted with the number of iterations, and the reset boundary convergence factor R is expressed as follows: Among them, t is the number of iterations of the current population, and T is the maximum number of iterations of the population; (3) Rolling behavior integrating dynamic golden sine The golden sine strategy is introduced to use the sine function to comprehensively search for potential areas, improve the convergence speed, and get rid of the limitation of local optimality. The update formula of the improved dung beetle rolling behavior is as follows: In the formula, R1 determines the moving distance of the current individual, which is a random number between [0, 2π]; R2 determines the update direction of the current individual position in the next iteration, which is a random number between [0, π]; X a is the position of the current optimal individual, X c is the position in the i-th dimension, x i (t) represents the position information of the ith dung beetle in the tth iteration; k∈(0,0.2) is the perturbation coefficient, α is -1 or 1, b is a random number between (0,1), δ is a random number between [0,1], x1 and x2 are the golden section coefficients; τ is the golden section ratio, which is 0.618033; In addition to improving the movement path of the rolling dung beetle, in order to further balance the exploration and development capabilities of the algorithm in iteration, the inertia weight w is introduced into the golden sine formula: Among them, w min is the minimum inertia weight, w max is the maximum inertia weight, t is the current iteration number, and T is the maximum iteration number; (4) Sparrow Sentinel Mechanism The vigilant strategy of the sparrow search algorithm has the function of reminding the population to take anti-predation actions. It is introduced into the dung beetle algorithm. At the same time, the shrinkage factor is introduced. In order to balance the exploration and development capabilities and enable the algorithm to have flexible search capabilities at different stages, the update formula of this strategy is as follows: In the formula, is the current global optimal position, T is the maximum number of iterations, and t is the current number of iterations; β is a normally distributed random number with mean 0 and variance 1, which is used to control the step length; K is the step length control parameter of the sparrow's moving direction; ε is the minimum constant to avoid the denominator being 0; f i is the fitness value of the i-th sparrow; f w 、f g are the worst fitness value and the current best fitness value respectively; (5) Parameter Adaptive Adjustment The small step length and long jump mechanism of Levy flight and the wide trajectory movement of Brownian motion, this combined strategy effectively prevents the thieving dung beetles from quickly approaching the optimal individual in the early stage of iteration, and keeps the thieving dung beetles active in the later stage of iteration. The position of the thief dung beetle after the introduction of Brownian motion is updated as follows: x i (t+1)=X b +S·g·(x i (t)-X * |+|x i (t)-X b )+R BM (13) Where g is a 1×D random vector, S is a constant, and R BM is the Brownian motion strategy; The position of the thief dung beetle after the introduction of Levy's flight has been updated to: x i (t+1)=X b +S·g·(x i (t)-X * |+|x i (t)-X b )+R LF (14) Among them, R LF Flying strategy for Levy; The improved dung beetle algorithm is used to optimize the parameters of ICEEMDAN to achieve adaptive decomposition of fault signals. The specific parameter optimization steps are as follows: ① Initialize the population size, maximum number of iterations, search ranges ub and lb of NE and Nstd, maximum and minimum inertia weights, proportion of sparrows aware of danger, basic parameters of Levy flight and Brownian motion step length, and determine the fitness function; ② Use PWLCM chaotic mapping to initialize the population of the dung beetle algorithm; ③ Perform ICEEMDAN decomposition on the vibration signal to obtain the modal components of the signal, and calculate the envelope entropy value according to formula (1); ④ Taking the envelope entropy as the fitness function, the improved dung beetle rolling behavior, stealing behavior, foraging and reproduction behavior are used to perform global and local searches, calculate the fitness value of each dung beetle individual, and select the individual with the smallest fitness as the current best individual to find the minimum envelope entropy value; ⑤ The dung beetle population performs anti-predation behavior, updates the individual optimal and global optimal positions, then calculates the envelope entropy value, and selects the dung beetle individual with a smaller fitness, thereby obtaining the minimum envelope entropy value; ⑥ Repeat steps ④ to ⑥ until the minimum envelope entropy value is determined or the set number of iterations is reached, and the optimal parameters NE and Nstd are output.

4. A method for preprocessing vibration signals of a hydroelectric unit according to claim 1, characterized in that: In step 4, the optimized parameters are input into ICEEMDAN for modal decomposition of the vibration signal. The specific ICEEMDAN decomposition is as follows: Add i groups of Gaussian white noise to the original signal to form a new signal. Use EMD to calculate the average value of the upper and lower envelopes to obtain the first-order residual and modal components. The decomposition process is as follows: x i t=x(t)+β0E1(ξ i (t))(15) r1=M(x i (t))(16) β0=δ0std(x) / std(E1(ξ i (t)))(17) Among them, ξ i (t) represents Gaussian white noise with mean and unit variance of 0 and 1 respectively, which is used to adjust the required signal-to-noise ratio (SNR) between the added noise and the residual after adding the noise. When k ≥ 1, β k =δ0std(r k ), δ0 represents the inverse of the required SNR between the input signal sequence and the first added noise, std() is the noise standard deviation operator, and the operator E k () represents the kth modal component obtained by EMD decomposition, the operator M() represents the local mean of the signal envelope, and <> represents the average calculation; Construct a new decomposition signal r1(t)+ω1E2(ξ i (t)), the second-order residual is estimated by calculating the local mean through EMD decomposition, and the second modal component is defined as: Calculate the remaining order modal components k=3,4…,K and obtain the k-order residual: r k (t)= <M(r k-1 (t)+ω k-1 E k (x) i (t)))〉 (20) Calculate the k-order modal component: Repeat the above steps to get the next modal component. When the residual r k When it is a monotonic function, the calculation is terminated.

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