Underwater rotating machinery vibration feature extraction method based on time-frequency energy analysis

Abstract

The invention discloses an underwater rotating machinery vibration feature extraction method based on time-frequency energy analysis. The method comprises the following steps: preprocessing an original mechanical vibration signal of the underwater rotating machine to obtain a filtered vibration signal; performing Fourier synchronous compression transformation and then constructing a time-frequency distribution matrix; establishing an optimal path model containing a smooth constraint term, and obtaining an instantaneous frequency trajectory so as to estimate an optimal time-varying rotating speed; performing time domain-angle domain mapping on the filtered vibration signal, constructing an angle domain vibration signal, and further constructing a time-order distribution matrix; and determining a dominant order component and an optimal operation condition of vibration from two dimensions of an order domain and a time domain. According to the method, the vibration signal characteristics of the underwater rotating machinery under the variable working condition can be extracted with high precision, the rotating speed time mapping relation is estimated under the condition of no rotating speed sensor, the excitation characteristics and the vibration intensity are extracted through the order and rotating speed energy distribution characteristics, and the method has important engineering application value in vibration monitoring and optimization design.

Description

Technical Field

[0001] The present invention relates to a method for extracting mechanical vibration characteristics, and relates to the technical field of vibration monitoring of underwater rotating machinery, in particular to a method for extracting vibration characteristics of underwater rotating machinery based on time-frequency energy analysis. Background Art

[0002] With the rapid development of marine resource development and underwater equipment technology, vibration characteristic analysis of underwater rotating machinery (such as propulsion motors, pumps, and turbines) has become critical to ensuring smooth equipment operation and optimized design. Traditional methods for monitoring vibration of underwater rotating machinery rely on time-domain analysis or frequency-domain analysis, but these methods have significant limitations. On the one hand, they require the equipment to operate at a constant speed, which is inconsistent with the wide speed variation commonly found in complex underwater environments, resulting in a limited dimension of vibration signal information obtained by traditional methods. On the other hand, underwater machinery and equipment generally have design features such as compact structure, strong sealing, and lightweight, and most use open-loop control strategies without speed sensors. This further increases the difficulty of extracting and analyzing vibration signal features related to the operating conditions, making it difficult to accurately and comprehensively assess the vibration state of the equipment during operation.

[0003] Therefore, developing advanced underwater rotating machinery vibration monitoring technology and realizing the analysis of underwater machinery vibration characteristics under variable working conditions without speed sensors has important engineering application value. However, to achieve effective analysis of the vibration characteristics of underwater rotating machinery under variable working conditions, the following three key issues still need to be solved:

[0004] 1) Low analysis accuracy for non-stationary vibration signals under variable operating conditions: Due to the significant non-stationary nature of rotating machinery vibration signals under variable operating conditions, their statistical characteristics evolve dynamically over time. While traditional time-frequency analysis methods (such as short-time Fourier transforms) can characterize the signal's time-frequency characteristics to a certain extent, they have inherent limitations in terms of resolution and accuracy. In particular, under the influence of factors such as spectral aliasing and noise interference, severe frequency ambiguity can occur, making it difficult to accurately capture the dynamic characteristics of non-stationary vibration signals.

[0005] 2) Difficulties in estimating speed from vibration signals alone: ​​As a key parameter characterizing the performance of rotating machinery, accurate speed estimation is crucial. Existing speed estimation methods are mostly based on the assumption of steady-state operating conditions. When the speed changes dynamically, traditional threshold segmentation or peak tracking algorithms struggle to continuously extract instantaneous frequency traces due to the diffuse time-frequency energy distribution. This results in insufficient accuracy in the obtained speed-time mapping, which in turn affects the reliability of vibration characteristic analysis results.

[0006] 3) Complex analysis of vibration characteristics under variable speed conditions: During dynamic speed changes, the operating state of the mechanical system continuously evolves, causing the frequency and amplitude characteristics of the vibration signal to exhibit time-varying characteristics, significantly increasing the complexity of signal analysis. Furthermore, speed changes can cause coupling between the system's excitation frequency and natural frequency, inducing resonance. These factors lead to significant nonlinearity in the system's vibration characteristics, making traditional vibration characteristic analysis methods difficult to effectively apply. Summary of the Invention

[0007] In order to solve the problems existing in the background technology, the present invention provides a method for extracting vibration features of underwater rotating machinery based on time-frequency energy analysis.

[0008] The technical solution adopted in the present invention is:

[0009] The method for extracting vibration characteristics of underwater rotating machinery based on time-frequency energy analysis of the present invention comprises:

[0010] Step a: obtaining an original mechanical vibration signal of the underwater rotating machinery when it is running underwater, and preprocessing the original mechanical vibration signal to obtain a filtered vibration signal.

[0011] Step b: Perform Fourier synchronous compression transform on the filtered vibration signal, improve the time-frequency resolution through the compression operator, thereby constructing the time-frequency distribution matrix of the filtered vibration signal and accurately capturing the instantaneous characteristics of the vibration signal.

[0012] Step c: Based on the time-frequency distribution matrix of the filtered vibration signal, an optimal path model including a smoothness constraint term is established to extract the optimal time-frequency path distribution with concentrated energy and smooth waveform in the filtered vibration signal. In the optimal time-frequency path distribution, the instantaneous frequency trajectory of the filtered vibration signal changing with the speed is obtained, thereby obtaining the estimated optimal time-varying speed, that is, the speed-time mapping relationship.

[0013] Step d: Perform time-domain-angle-domain mapping on the filtered vibration signal in step a according to the estimated optimal time-varying speed to construct an angle-domain vibration signal. Then, use the same Fourier synchronous compression transform as in step b to construct a time-order distribution matrix to extract the order characteristics of the underwater rotating machinery vibration related to the speed.

[0014] Step e: The time-order distribution matrix is ​​subjected to order slicing processing to obtain the order energy spectrum of the angular domain vibration signal, that is, the energy distribution on the entire order axis, so as to determine the dominant order component causing the vibration in the original mechanical vibration signal, that is, extract the excitation characteristics based on the order energy distribution characteristics; at the same time, the time-order distribution matrix is ​​subjected to time slicing processing to obtain the speed energy spectrum of the angular domain vibration signal, that is, the energy distribution in the entire speed range, and extract the vibration intensity based on the speed energy distribution characteristics, so as to obtain the optimal speed, and control the speed of the underwater rotating machinery so that it operates under the optimal operating conditions.

[0015] In the step a, the original mechanical vibration signal is preprocessed based on the fractional resampling method, specifically by sequentially performing upsampling and zero-interpolation processing, bidirectional anti-aliasing filtering processing based on a Butterworth filter, and downsampling and decimation processing.

[0016] In the step b, the Fourier synchronous compression transform adopts the synchronous compression transform based on the short-time Fourier transform.

[0017] In step c, the optimal path model including the smoothness constraint term is as follows:

[0018]

[0019] Among them, U(t,f c,t ) and U(t-1,f c,t-1 ) are the optimal time-frequency path distributions of the filtered vibration signal at time t and time t-1, respectively, and are used to search for the time-frequency path in the vibration signal that changes with the speed; f c,t and f c,t-1 are the frequencies of the filtered vibration signal at time t and time t-1 respectively; F(t,f c ) is the time-frequency distribution matrix; Δ is the maximum allowed frequency change step size, which implements the frequency change constraint; λ is a trade-off parameter, λ>0, which controls the smoothing strength.

[0020] According to the frequency f of the filtered vibration signal at each moment t c,t Obtain the instantaneous frequency trajectory f of the filtered vibration signal at each moment t as the speed changes c (t), and thus the estimated optimal time-varying speed data is obtained as follows:

[0021] ω(t)=60·f c (t)

[0022] Where ω(t) is the estimated optimal time-varying speed.

[0023] In the step d, the filtered vibration signal in step a is mapped from the time domain to the angle domain according to the estimated optimal time-varying speed. Specifically, the estimated optimal time-varying speed ω(t) is first integrated to obtain the cumulative rotation angle θ(t); then the filtered vibration signal y[n] is mapped to the angle domain using a linear interpolation method, thereby constructing the angle domain vibration signal y[θ k ],as follows:

[0024]

[0025] θ k =kΔθ

[0026] θ n ≤θ k ≤θ n+1

[0027] Among them, θ k is the angle at the kth sampling point, k=0,1,2,...,θ n and θ n+1 are the angles at the nth and n+1th sampling points, respectively; y[n] and y[n+1] are the filtered vibration signals at the nth and n+1th sampling points, respectively; Δθ is the preset angle domain sampling interval, which is determined by the accumulated rotation angle θ(t) and the number of samples k.

[0028] In the step e, the time-order distribution matrix is ​​subjected to order slicing processing, specifically as follows:

[0029] e11) First, divide the order range and determine the upper limit of the target order range. max as follows:

[0030]

[0031] Among them, P and Q are interpolation multiples and decimation multiples respectively; f s is the sampling rate of the original mechanical vibration signal; f0 is the maximum fundamental frequency of the vibration signal after filtering.

[0032] Then set 0 to the upper limit of the target order range O max The orders between are divided into several equally spaced order slices according to the preset order resolution ΔO, thereby generating an order sequence {O i |i=1,2,…,N},O i is the i-th order slice, and N is the total number of order slices.

[0033] e12) According to each order slice, the order domain integration interval is set within its full time range as follows:

[0034]

[0035] Among them, Ω i is the i-th order slice O i The order domain integration interval of .

[0036] Based on the order domain integration interval, the cumulative energy of each order slice is obtained as follows:

[0037]

[0038] Among them, E(O i ) is the i-th order slice O i The cumulative energy of; F(O,t) is the time-order distribution matrix under the order slice O at time t.

[0039] e13) After obtaining the cumulative energy of each order slice, it is expressed as an order energy distribution sequence {(O1, E(O1)), (O2, E(O2)), ..., (O N ,E(O N ))}, the cumulative energy of each order slice is summed and normalized to obtain the order energy spectrum of the angle domain vibration signal, that is, the relative order energy distribution is as follows:

[0040]

[0041] in, represents the order energy spectrum.

[0042] In the step e, the time-order distribution matrix is ​​subjected to time slicing processing to obtain the speed energy spectrum of the original mechanical vibration signal, as follows:

[0043] Step e21: Based on the optimal time-varying speed estimated in step c, determine the speed range ω0~ω max ,ω max and ω0 are the maximum and minimum values ​​of the estimated optimal time-varying speed respectively; then the speed range is divided into several equally spaced speed slices according to the preset speed resolution Δω, thereby generating a speed sequence {ω j |j=1,2,…,M},ω j is the jth speed slice, M is the total number of speed slices; then the time series {T j |j=1,2,…,M},T j is the time corresponding to the j-th speed slice.

[0044] Step e22: Set the time domain integration interval according to the time series as follows:

[0045]

[0046] Among them, Φ jis the j-th speed slice ω j The set time domain integration interval; ΔT is the time domain resolution; P and Q are the interpolation multiple and the decimation multiple respectively; f s is the sampling rate of the original mechanical vibration signal; T is the sampling time of the original mechanical vibration signal, in seconds.

[0047] Based on the time domain integration interval, the cumulative energy of each speed slice is obtained as follows:

[0048]

[0049] Among them, E(ω j ) is the j-th speed slice ω j The cumulative energy of; F(O,t) is the time-order distribution matrix under the order slice O at time t.

[0050] Step e23: According to the correspondence between the time domain and the speed domain, the cumulative energy of each speed slice is reordered in ascending order of speed; then, the cumulative energy of each speed slice after reordering is normalized to the maximum value to obtain the speed energy spectrum of the angle domain vibration signal, that is, the relative speed energy distribution is as follows:

[0051]

[0052] in, is the j-th speed slice ω j The speed energy spectrum.

[0053] In step e, the vibration energy variation trend with speed is determined based on the speed energy spectrum. Within a preset speed range, the speed at which the vibration energy is lowest is determined as the optimal speed. The vibration intensity of the underwater rotating machinery is controlled to achieve optimal configuration of the system's operating conditions.

[0054] The electronic device of the present invention comprises: a memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method described above.

[0055] The computer-readable storage medium of the present invention stores program data thereon, and when the program data is executed by a processor, the method described above is implemented.

[0056] The method of the present invention is based on a fractional resampling signal pre-processing method and an improved time-frequency analysis method to achieve high-precision extraction of time-varying vibration signal characteristics of underwater rotating machinery under variable working conditions; establishes a speed estimation model based on vibration signals, breaking through the traditional method's dependence on speed sensors; proposes a vibration feature extraction method based on angle and order energy distribution characteristics to obtain vibration feature parameters with clear physical meaning and engineering interpretability, providing a new technical approach for vibration characteristic analysis and monitoring of underwater rotating machinery.

[0057] The beneficial effects of the present invention are:

[0058] The proposed method can accurately extract the vibration signal characteristics of underwater rotating machinery under variable operating conditions. It also estimates the speed-time mapping relationship of the rotating machinery without a speed sensor. Furthermore, it uses the order and speed energy distribution characteristics to extract the excitation characteristics and vibration intensity under variable operating conditions. This method has important engineering applications in vibration monitoring and optimized design of underwater rotating machinery. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of the method of the present invention;

[0060] Figure 2 This is a time-frequency characteristic diagram of a vibration signal according to an embodiment of the present invention;

[0061] Figure 3 This is a speed-time mapping diagram according to an embodiment of the present invention;

[0062] Figure 4 This is a vibration signal order characteristic diagram according to an embodiment of the present invention;

[0063] Figure 5 This is an order energy distribution diagram of an embodiment of the present invention;

[0064] Figure 6 This is a speed energy distribution diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. However, this embodiment and the accompanying drawings do not limit the scope of protection of the present invention. Obviously, the described embodiment is only one embodiment of the implementation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0066] like Figure 1 As shown, the underwater rotating machinery vibration feature extraction method based on time-frequency energy analysis of the present invention is as follows:

[0067] Step a: The original mechanical vibration signal of the underwater rotating machinery during its underwater operation is obtained by installing a vibration signal sensor on the equipment housing of the underwater rotating machinery, and the original mechanical vibration signal is pre-processed to obtain a filtered vibration signal. In specific implementation, the sensor arrangement of the present invention is as follows: a piezoelectric acceleration sensor (sensitivity: 10mV / m·s) is installed at the radial position of the deep-sea plunger pump housing of the underwater electro-hydraulic actuator. 2 The signal acquisition system uses a 24-bit high-precision data acquisition card with a sampling frequency of 50kHz to continuously collect vibration signals from the pump under variable speed conditions (at an external pressure of 40MPa, the speed increases from approximately 1000RPM to 2000RPM).

[0068] The raw mechanical vibration signal is preprocessed using fractional resampling. This involves upsampling with zero insertion, bidirectional anti-aliasing filtering using a Butterworth filter, and downsampling with decimation. The target sampling rate for the fractional resampling vibration signal is set at 10 kHz, and the anti-aliasing filter uses a 10-7 kHz bandpass filter.

[0069] The fractional resampling method is as follows:

[0070] a1) Upsampling and zero interpolation: The original mechanical vibration signal x[m] is upsampled by P times interpolation to generate the intermediate signal x l [n], the upsampling zero-interpolation function is as follows:

[0071]

[0072] Among them, n and m are the sampling points of the vibration signal after zero insertion and the original mechanical vibration signal respectively; x l [n] is the original mechanical vibration signal after upsampling and zero insertion; x[m] is the original mechanical vibration signal currently being processed; in specific implementation, P=1.

[0073] a2) Bidirectional anti-aliasing filtering: A third-order Butterworth filter is used to filter the upsampled zero-inserted vibration signal x l [n] Perform forward and reverse filtering at 10-7kHz to maintain the signal phase and suppress spectrum aliasing. After filtering, the bidirectional filtered signal x is obtained. f [n], the bidirectional filtering function is as follows:

[0074]

[0075] Where h[n] is the Butterworth filter response function, and h[-n] is the time-reversed response function of h[n].

[0076] A bidirectional anti-aliasing filtering method is applied through a Butterworth filter to filter the original mechanical vibration signal to eliminate spectrum aliasing and preserve the phase characteristics of the signal.

[0077] a3) Downsampling: for the bidirectional filtered signal x f [n] Downsample with Q as the decimation factor to generate the output signal. The downsampling decimation function is as follows:

[0078] y[n]=x f [Qn]

[0079] Wherein, y[n] is the vibration signal after filtering; P and Q are the interpolation multiple and the decimation multiple, respectively; Q is an integer greater than 1 and satisfies P / Q to form an irreducible fraction. In a specific implementation, Q=5.

[0080] The sampling rate of the original mechanical vibration signal is f s Convert to target sampling rate P·f s / Q, obtains vibration signals in a specific frequency range, compresses the signals to reduce the sampling frequency, and improves the post-processing speed.

[0081] The above converts the original signal sampling rate from 50kHz to the target sampling rate of 10kHz, obtains a vibration signal in the frequency range of 10-7kHz, and compresses the number of sampling points of the original vibration signal to 1 / 5 of the original, thereby reducing the signal sampling frequency and improving the post-processing speed.

[0082] Step b: Perform Fourier synchronous compression transform on the filtered vibration signal, improve the time-frequency resolution through the compression operator, and thus construct the time-frequency distribution matrix of the filtered vibration signal to accurately capture the instantaneous characteristics of the vibration signal. The Fourier synchronous compression transform uses a synchronous compression transform based on the short-time Fourier transform. The Fourier synchronous compression transform time-frequency analysis method is as follows:

[0083] F(t,f c )=∫F(t,f)δ(ω(t,f)-f c )df

[0084]

[0085] Among them, F(t,f c ) is the time-frequency distribution matrix, t is the time, f cis the frequency variable after compression; F(t,f) is the short-time Fourier transform result, which is the time-frequency representation of the signal, and f is the frequency variable before compression; δ(·) is the Dirac function, which concentrates the energy at the instantaneous frequency; ω(t,f) is the phase transformation function, which redistributes the frequency variable and frequency point of the transformation result F(t,f) in the time-frequency domain according to the mapping relationship of (t,f)→(t,ω(t,f)); y(τ) is the continuous time representation of the filtered vibration signal, and τ is the time variable; g(τ-t) is the window function of Fourier transform. In the specific implementation, the Kaiser window is adopted, the window shape coefficient is set to 5, the window length is 2000, and the frequency domain resolution is 2000; i is the imaginary unit.

[0086] The phase transformation function ω(t,f) is as follows:

[0087]

[0088] Perform Fourier synchronous compression transform on the preprocessed vibration signal to construct the time-frequency distribution matrix. The obtained time-frequency feature map is as follows: Figure 2 As shown, it can be seen that the time-frequency energy concentration area clearly reflects the instantaneous frequency change of the vibration signal.

[0089] Step c: Based on the time-frequency distribution matrix of the filtered vibration signal, an optimal path model including a smoothness constraint is established to extract the optimal time-frequency path distribution with concentrated energy and smooth waveform in the filtered vibration signal. The instantaneous frequency trajectory of the filtered vibration signal as it changes with speed is obtained from the optimal time-frequency path distribution, thereby obtaining the estimated optimal time-varying speed, that is, the speed-time mapping relationship. The optimal path model including the smoothness constraint is as follows:

[0090]

[0091] Among them, U(t,f c,t ) and U(t-1,f c,t-1 ) are the optimal time-frequency path distributions of the filtered vibration signal at time t and time t-1, respectively, and are used to search for the time-frequency path in the vibration signal that changes with the speed; f c,t and f c,t-1 are the frequencies of the filtered vibration signal at time t and time t-1 respectively; F(t,f c ) is the time-frequency distribution matrix; Δ is the maximum allowed frequency change step size, which implements the frequency change constraint and is selected as Δ=2; λ is a trade-off parameter, λ>0, which controls the smoothing strength and is selected as λ=0.2.

[0092] According to the frequency f of the filtered vibration signal at each moment t c,t Obtain the instantaneous frequency trajectory f of the filtered vibration signal at each moment t as the speed changes c(t), and thus the estimated optimal time-varying speed data is obtained as follows:

[0093] ω(t)=60·f c (t)

[0094] Where ω(t) is the estimated optimal time-varying speed.

[0095] In the optimal path model, the first term is the amplitude term, which ensures that the optimized U(t,f c,t ) approximates the original F(t,f c,t ); The second term constrains the frequency variation range and smoothness, i.e., the smoothness constraint term, to ensure that a continuous and energy-concentrated instantaneous frequency trajectory is extracted.

[0096] The estimated speed-time mapping diagram is as follows: Figure 3 As shown in the figure, the speed change of underwater rotating machinery shows nonlinear characteristics. In 10 seconds, the speed increases from 1024RPM to 1964RPM, and its change trend is similar to Figure 2 The time-frequency feature maps in are highly consistent, further verifying the accuracy of the estimated rotation speed.

[0097] Step d: Map the filtered vibration signal in step a from time to angle based on the estimated optimal time-varying speed to construct an angle-domain vibration signal. Then, use the same Fourier synchronous compression transform as in step b to construct a time-order distribution matrix to extract the order characteristics of the underwater rotating machinery vibration related to the speed. Map the filtered vibration signal in step a from time to angle based on the estimated optimal time-varying speed. Specifically, first integrate the estimated optimal time-varying speed ω(t) to obtain the cumulative rotation angle θ(t). Then, the filtered vibration signal y[n] is mapped to the angle domain using the linear interpolation method, thereby constructing the angle domain vibration signal y[θ k ],as follows:

[0098]

[0099] θ k =kΔθ

[0100] θ n ≤θ k ≤θ n+1

[0101] Among them, θ k is the angle at the kth sampling point, k=0,1,2,...,θ n and θ n+1are the angles at the nth and n+1th sampling points respectively; y[n] and y[n+1] are the filtered vibration signals at the nth and n+1th sampling points respectively; Δθ is the preset angle domain sampling interval, which is determined by the cumulative rotation angle θ(t) and the sampling number k, and is set to Δθ=0.00236 to generate the angle sequence θ k =kΔθ(k=0,1,2,...,10 5 ).

[0102] Construct the time-order distribution matrix, and the resulting vibration signal order characteristic diagram is as follows: Figure 4 As shown, it can be clearly seen that the order characteristic diagram shows obvious energy concentration characteristics on the order axis, reflecting the characteristic that the order of the vibration signal does not change with the speed, and can accurately capture the key frequency components in the vibration signal.

[0103] Step e: The time-order distribution matrix is ​​subjected to order slicing processing to obtain the order energy spectrum of the angular domain vibration signal, that is, the energy distribution on the entire order axis, so as to determine the dominant order component causing the vibration in the original mechanical vibration signal, that is, extract the excitation characteristics based on the order energy distribution characteristics; at the same time, the time-order distribution matrix is ​​subjected to time slicing processing to obtain the speed energy spectrum of the angular domain vibration signal, that is, the energy distribution in the entire speed range, and extract the vibration intensity based on the speed energy distribution characteristics, so as to obtain the optimal speed, and control the speed of the underwater rotating machinery so that it operates under the optimal operating conditions.

[0104] The time-order distribution matrix is ​​sliced ​​into orders as follows:

[0105] e11) First, divide the order range and determine the upper limit of the target order range. max as follows:

[0106]

[0107] Among them, P and Q are interpolation multiples and decimation multiples respectively; f s is the sampling rate of the original mechanical vibration signal; f0 is the maximum fundamental frequency of the filtered vibration signal. When implementing it, the upper limit of the target order range is determined. max =150.

[0108] Then set 0 to the upper limit of the target order range O max The orders between are divided into several equally spaced order slices according to the preset order resolution ΔO, thereby generating an order sequence {O i |i=1,2,…,N},O iis the i-th order slice, and N is the total number of order slices. In practical applications, since the energy proportion of high-order orders is very low, the target order range is set to 0 to 100 orders. Secondly, the order axis from 0 to 100 is divided into several equally spaced order slices according to the preset order resolution ΔO = 1, and the order sequence {O i |i=1,2,…,100}.

[0109] e12) According to each order slice, the order domain integration interval is set within its full time range as follows:

[0110]

[0111] Among them, Ω i is the i-th order slice O i The order domain integration interval of .

[0112] Based on the order domain integration interval, the cumulative energy of each order slice is obtained as follows:

[0113]

[0114] Among them, E(O i ) is the i-th order slice O i The cumulative energy of; F(O,t) is the time-order distribution matrix under the order slice O at time t.

[0115] e13) After obtaining the cumulative energy of each order slice, it is expressed as an order energy distribution sequence {(O1, E(O1)), (O2, E(O2)), ..., (O N ,E(O N ))}, the cumulative energy of each order slice is summed and normalized to obtain the order energy spectrum of the angle domain vibration signal, that is, the relative order energy distribution is as follows:

[0116]

[0117] in, represents the order energy spectrum.

[0118] For each order O i Calculate the total cumulative energy over the entire time range and define the order domain integration interval:

[0119]

[0120] Obtain the order energy diagram, analyze the contribution ratio of each order to the total vibration, and identify the dominant order. The obtained order energy diagram is as follows: Figure 5As shown in the figure, the vibration orders of this underwater rotating machinery are mainly concentrated in multiples of 7, which is consistent with the vibration characteristics of the plunger pump with a seven-plunger structure. At the same time, the main order is 35, and the vibration energy generated by the main order accounts for more than 17% of the total vibration energy.

[0121] The relative order energy spectrum provides an intuitive understanding of the contribution of each order to the overall vibration. By setting a preset energy threshold, the dominant order component causing the vibration can be determined, effectively evaluating the vibration source and providing a basis for optimizing the design of mechanical structures.

[0122] The time-order distribution matrix is ​​time-sliced ​​to obtain the speed energy spectrum of the original mechanical vibration signal, as follows:

[0123] Step e21: Based on the optimal time-varying speed estimated in step c, determine the speed range ω0~ω max ,ω max and ω0 are the maximum and minimum values ​​of the estimated optimal time-varying speed respectively; then the speed range is divided into several equally spaced speed slices according to the preset speed resolution Δω, thereby generating a speed sequence {ω j |j=1,2,…,M},ω j is the jth speed slice, M is the total number of speed slices; then the time series {T j |j=1,2,…,M},T j The time corresponding to the j-th speed slice is determined. In the specific implementation, based on the estimated speed range, the speed analysis range is determined to be 1000-2000RPM, and it is divided into several equally spaced speed slices according to the preset speed resolution Δω=2RPM to generate the speed sequence {ω j |j=1,2,…,100};Determine the corresponding time domain resolution ΔT=1000 according to the speed sequence; Similarly, generate the time series {T j |j=1, 2,…, 100}.

[0124] Step e22: Set the time domain integration interval according to the time series as follows:

[0125]

[0126]

[0127] Among them, Φ j is the j-th speed slice ω j The set time domain integration interval; ΔT is the time domain resolution; P and Q are the interpolation multiple and the decimation multiple respectively; f s is the sampling rate of the original mechanical vibration signal; T is the sampling time of the original mechanical vibration signal, in seconds.

[0128] Based on the time domain integration interval, the cumulative energy of each speed slice is obtained as follows:

[0129]

[0130] Among them, E(ω j ) is the j-th speed slice ω j The cumulative energy; F(O,t) is the time-order distribution matrix under the order slice O at time t, and is calculated on the order diagram according to t∈Φ j When performing time slice analysis, the total accumulated energy in the full order range is calculated according to the time domain resolution, and the time domain integration interval Φ is defined. j =[T j -500,T j +500].

[0131] Step e23: According to the correspondence between the time domain and the speed domain, the accumulated energy of each speed slice is reordered in the ascending order of the speed, and the speed energy distribution sequence {(ω1, E(ω1)), (ω2, E(ω2)), ..., (ω M ,E(ω M ))}. Then, the cumulative energy of each reordered speed slice is normalized to its maximum value to obtain the speed energy spectrum of the angle domain vibration signal, that is, the relative speed energy distribution is as follows:

[0132]

[0133] in, is the j-th speed slice ω j The speed energy spectrum.

[0134] Based on the speed energy spectrum, the trend of vibration energy changing with speed is obtained. Within the preset speed range, the speed at which the vibration energy is lowest is obtained as the optimal speed. The vibration intensity of the underwater rotating machinery is controlled to achieve the optimal configuration of the system operating conditions. The speed energy distribution diagram is obtained, and the trend of vibration intensity changing with speed is analyzed to select the optimal operating conditions of the system. The speed energy diagram obtained is as follows: Figure 6 As shown in the figure, it can be seen that the vibration energy of the underwater rotating machinery is relatively small and stable in the speed range of 1350-1750 RPM, and this speed range can meet general working requirements. Therefore, the optimal operating conditions of the system can be determined.

[0135] Based on the time-order distribution matrix, a comprehensive evaluation of the system's excitation characteristics and vibration intensity is conducted from both the order and time domain dimensions: first, through order slicing analysis, the energy distribution characteristics on the entire order axis are obtained, and the dominant order components causing the vibration are determined; second, time slicing analysis is performed, and the data on the time axis is rearranged in order from low to high speed to obtain the energy distribution characteristics within the entire speed range, revealing the trend of the system's vibration intensity changing with speed, thereby determining the system's optimal operating conditions.

[0136] This example verifies the effectiveness of the method of the present invention in analyzing the vibration characteristics of underwater rotating machinery under variable speed working conditions, as shown in the following:

[0137] 1) Without a speed sensor, the speed estimation error is small and can meet the analysis requirements.

[0138] 2) Through order energy analysis, the main vibration order components of underwater machinery under variable speed conditions were identified.

[0139] 3) Through speed energy analysis, the optimal operating conditions of the system in different speed ranges were obtained.

[0140] The method of the present invention can be widely used in vibration monitoring and design optimization of rotating machinery such as underwater thrusters, underwater motors, and pumps.

[0141] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for extracting vibration features of underwater rotating machinery based on time-frequency energy analysis, characterized in that: include: Step a: obtaining an original mechanical vibration signal of the underwater rotating machinery when it is running underwater, and preprocessing the original mechanical vibration signal to obtain a filtered vibration signal; Step b: performing Fourier synchronous compression transform on the filtered vibration signal to construct a time-frequency distribution matrix of the filtered vibration signal; Step c: Based on the time-frequency distribution matrix of the filtered vibration signal, an optimal path model including a smoothing constraint term is established to extract the optimal time-frequency path distribution of the filtered vibration signal. The instantaneous frequency trajectory of the filtered vibration signal as it changes with the speed is obtained from the optimal time-frequency path distribution, thereby obtaining the estimated optimal time-varying speed; Step d: performing time-domain-angle-domain mapping on the filtered vibration signal in step a according to the estimated optimal time-varying speed to construct an angle-domain vibration signal, and then using the same Fourier synchronous compression transform as in step b to construct a time-order distribution matrix; Step e: The time-order distribution matrix is ​​subjected to order slicing processing to obtain the order energy spectrum of the angular domain vibration signal, thereby determining the dominant order component causing vibration in the original mechanical vibration signal; at the same time, the time-order distribution matrix is ​​subjected to time slicing processing to obtain the speed energy spectrum of the angular domain vibration signal, thereby obtaining the optimal speed, and controlling the speed of the underwater rotating machinery so that it operates under the optimal operating conditions.

2. The method for extracting underwater rotating machinery vibration characteristics based on time-frequency energy analysis according to claim 1, characterized in that: In the step a, the original mechanical vibration signal is preprocessed based on the fractional resampling method, specifically by sequentially performing upsampling and zero-interpolation processing, bidirectional anti-aliasing filtering processing based on a Butterworth filter, and downsampling and decimation processing.

3. The method for extracting vibration features of underwater rotating machinery based on time-frequency energy analysis according to claim 1, characterized in that: In the step b, the Fourier synchronous compression transform adopts the synchronous compression transform based on the short-time Fourier transform.

4. The method for extracting vibration features of underwater rotating machinery based on time-frequency energy analysis according to claim 1, characterized in that: In step c, the optimal path model including the smoothness constraint term is as follows: Among them, U(t,f c,t ) and U(t-1,f c,t-1 ) are the optimal time-frequency path distributions of the filtered vibration signal at time t and time t-1, respectively, and f c,t and f c,t-1 are the frequencies of the filtered vibration signal at time t and time t-1 respectively; F(t,f c ) is the time-frequency distribution matrix; Δ is the maximum frequency change step; λ is the trade-off parameter; According to the frequency f of the filtered vibration signal at each moment t c,t Obtain the instantaneous frequency trajectory f of the filtered vibration signal at each moment t as the speed changes c (t), and thus the estimated optimal time-varying speed data is obtained as follows: ω(t)=60·f c (t) Where ω(t) is the estimated optimal time-varying speed.

5. The method for extracting underwater rotating machinery vibration characteristics based on time-frequency energy analysis according to claim 1, characterized in that: In the step d, the filtered vibration signal in step a is mapped from the time domain to the angle domain according to the estimated optimal time-varying speed. Specifically, the estimated optimal time-varying speed ω(t) is first integrated to obtain the cumulative rotation angle θ(t); then the filtered vibration signal y[n] is mapped to the angle domain using a linear interpolation method, thereby constructing the angle domain vibration signal y[θ k ],as follows: i k =kΔθ i n ≤θ k ≤θ n+1 Among them, θ k is the angle at the kth sampling point, k=0,1,2,...,θ n and θ n+1 are the angles at the nth and n+1th sampling points respectively; y[n] and y[n+1] are the filtered vibration signals at the nth and n+1th sampling points respectively; Δθ is the preset angle domain sampling interval.

6. The method for extracting underwater rotating machinery vibration characteristics based on time-frequency energy analysis according to claim 1, characterized in that: In the step e, the time-order distribution matrix is ​​subjected to order slicing processing, specifically as follows: e11) First, divide the order range and determine the upper limit of the target order range. max as follows: Among them, P and Q are interpolation multiples and decimation multiples respectively; f s is the sampling rate of the original mechanical vibration signal; f0 is the maximum fundamental frequency of the vibration signal after filtering; Then set 0 to the upper limit of the target order range O max The orders between are divided into several equally spaced order slices according to the preset order resolution ΔO, thereby generating an order sequence {O i |i=1,2,…,N},O i is the i-th order slice, and N is the total number of order slices; e12) Set the order domain integration interval according to each order slice as follows: Among them, Ω i is the i-th order slice O i The order domain integration interval of ; Based on the order domain integration interval, the cumulative energy of each order slice is obtained as follows: Among them, E(O i ) is the i-th order slice O i The cumulative energy; F(O,t) is the time-order distribution matrix under the order slice O at time t; e13) After obtaining the cumulative energy of each order slice, the cumulative energy of each order slice is summed and normalized to obtain the order energy spectrum of the angle domain vibration signal as follows: in, represents the order energy spectrum.

7. The method for extracting underwater rotating machinery vibration characteristics based on time-frequency energy analysis according to claim 1, characterized in that: In the step e, the time-order distribution matrix is ​​subjected to time slicing processing to obtain the speed energy spectrum of the original mechanical vibration signal, as follows: Step e21: Based on the optimal time-varying speed estimated in step c, determine the speed range ω0~ω max ,ω max and ω0 are the maximum and minimum values ​​of the estimated optimal time-varying speed, respectively; Then the speed range is divided into several equally spaced speed slices according to the preset speed resolution Δω, thereby generating a speed sequence {ω j |j=1,2,…,M},ω j is the jth speed slice, M is the total number of speed slices; then the time series {T j |j=1,2,…,M},T j is the time corresponding to the j-th speed slice; Step e22: Set the time domain integration interval according to the time series as follows: Among them, Φ j is the j-th speed slice ω j The set time domain integration interval; ΔT is the time domain resolution; P and Q are the interpolation multiple and the decimation multiple respectively; f s is the sampling rate of the original mechanical vibration signal; T is the sampling time of the original mechanical vibration signal; Based on the time domain integration interval, the cumulative energy of each speed slice is obtained as follows: Among them, E(ω j ) is the j-th speed slice ω j The cumulative energy; F(O,t) is the time-order distribution matrix under the order slice O at time t; Step e23: According to the correspondence between the time domain and the speed domain, the cumulative energy of each speed slice is reordered in ascending order of speed; then, the cumulative energy of each speed slice after reordering is normalized to the maximum value, and the speed energy spectrum of the angle domain vibration signal is obtained as follows: in, is the j-th speed slice ω j The speed energy spectrum.

8. The method for extracting underwater rotating machinery vibration characteristics based on time-frequency energy analysis according to claim 7, characterized in that: In the step e, based on the speed energy spectrum, the variation trend of the vibration energy with the speed is obtained, and within the preset speed range, the speed at which the vibration energy is lowest is obtained as the optimal speed.

9. An electronic device, characterized in that: include: A memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having program data stored thereon, characterized in that: When the program data is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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