Hydraulic turbine set amplitude monitoring method and system based on low-frequency vibration sensor

Abstract

The invention relates to the technical field of water turbine generator sets, in particular to a water turbine set amplitude monitoring method and system based on a low-frequency vibration sensor, and the method comprises the steps: obtaining vibration signals of a top cover of a water turbine set in different induction directions through a three-axis acceleration sensor; performing digital filtering on the vibration signals in different induction directions based on a filter, and extracting target frequency characteristics; performing low-frequency compensation on the target frequency features based on an adaptive compensation algorithm, and extracting low-frequency-band feature signals; performing digital integration on the compensated signal based on Simpson rules, and extracting an amplitude monitoring result; and constructing a hydraulic turbine set top cover model, analyzing the amplitude monitoring result in real time, and outputting a fault result. The purpose of the invention is to improve the accuracy of amplitude monitoring of the hydro-generator, solve the problem of measurement instability of a traditional vibration sensor caused by low-frequency vibration impact of the top cover part of the hydro-generator, and realize stable measurement of the amplitude of the top cover of the hydro-generator.

Description

Technical Field

[0001] The invention relates to the technical field of hydro-turbine generator sets, and in particular to a method and system for monitoring the amplitude of a hydro-turbine generator set based on a low-frequency vibration sensor. Background Art

[0002] Amplitude monitoring of hydro-turbine generator sets is one of the key technologies to ensure the safe and stable operation of hydropower stations. Specifically, it is through real-time monitoring and analysis of the amplitude of hydro-turbine generator sets to promptly discover potential faults or abnormal conditions, so as to take effective preventive measures, reduce unplanned downtime, extend equipment life, and ensure the continuity and reliability of power production. The amplitude of hydro-turbine generator sets is mainly caused by factors such as mechanical imbalance, bearing wear, and blade cavitation. These factors will not only lead to a decrease in unit efficiency, but may also cause more serious equipment damage. Therefore, amplitude monitoring technology is crucial for predictive maintenance. It can help operation and maintenance personnel identify these problems at an early stage, thereby avoiding greater economic losses and safety risks.

[0003] At present, traditional hydro-turbine generators are usually equipped with real-time amplitude monitoring systems, including sensors, data acquisition units, and data analysis software. The amplitude monitoring system can continuously collect amplitude data and conduct preliminary analysis to obtain monitoring results; however, the traditional amplitude monitoring system will experience amplitude jumps and measurement instability during the monitoring process, resulting in unreliable output information. In particular, when the top cover of the hydro-turbine generator is impacted by the low-frequency vibration of the tailwater vortex (about 0.5Hz for a 100r / min unit), it is easy to cause the vibration amplitude output by the sensor to jump and measurement instability, resulting in unreliable unit fault information given by the online vibration monitoring system. To this end, we propose a method for monitoring the amplitude of a hydro-turbine unit. Summary of the invention

[0004] In order to improve the accuracy of amplitude monitoring of hydro-turbine generators, solve the problem of instability of traditional vibration sensors caused by low-frequency vibration impact on the top cover of hydro-turbine generators, and achieve stable measurement of the amplitude of the top cover of hydro-turbine generators, the present invention provides a method and system for monitoring the amplitude of a hydro-turbine unit based on a low-frequency vibration sensor. The technical solutions adopted are as follows:

[0005] The technical solution of the first aspect of the present invention provides a method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor, the method comprising:

[0006] The triaxial acceleration sensor is used to obtain the vibration signals of the turbine head cover in different sensing directions;

[0007] Based on the filter, the vibration signals in different sensing directions are digitally filtered to extract the target frequency characteristics;

[0008] Based on the adaptive compensation algorithm, low-frequency compensation is performed on the target frequency characteristics to extract the low-frequency characteristic signal;

[0009] The compensated signal is digitally integrated based on the Simpson rule to extract the amplitude monitoring result;

[0010] Construct a turbine cover model, perform real-time analysis on the amplitude monitoring results, and output fault results.

[0011] Furthermore, based on the filter, the vibration signals in different sensing directions are digitally filtered respectively to extract the target frequency characteristics, including:

[0012] According to the vibration characteristics of the turbine head cover in different induction directions, the target frequency range is extracted;

[0013] Initialize the second-order filter coefficients and system functions based on the target frequency range;

[0014] The vibration signals of different sensing directions are digitally filtered using a second-order system cascade, and the filtered acceleration vector is extracted;

[0015] The power spectrum density in different sensing directions is calculated based on the acceleration vector, and the energy distribution in different sensing directions is extracted.

[0016] Furthermore, based on the filter, the vibration signals in different sensing directions are digitally filtered respectively to extract the target frequency characteristics, and the method also includes:

[0017] The recursive least squares algorithm is used to dynamically adjust the filter coefficients of the second-order sections of the filter in different sensing directions.

[0018] Furthermore, based on the adaptive compensation algorithm, low-frequency compensation is performed on the target frequency characteristics to extract the low-frequency band characteristic signal, including:

[0019] According to the preset low-frequency compensation frequency threshold, the digital series correction method is used to perform low-frequency compensation on the energy distribution in different sensing directions and extract the low-frequency characteristic signal.

[0020] Furthermore, the expression of the low-frequency compensation transfer function is:

[0021]

[0022] Where C(z) represents the low-frequency compensation transfer function; ξ0 represents the damping ratio of the second-order system before low-frequency compensation; ω0 represents the natural frequency of the second-order system before low-frequency compensation; ξ1 represents the damping ratio of the second-order system after low-frequency compensation; ω1 represents the natural frequency of the second-order system after low-frequency compensation; z represents the frequency response characteristics of the second-order system; and T represents the sampling period.

[0023] Furthermore, the compensated signal is digitally integrated based on the Simpson rule to extract the amplitude monitoring results, including:

[0024] The compensated signal is divided into intervals, each interval including three adjacent sampling points;

[0025] Apply Simpson's rule to each subinterval to calculate the integral value of the interval, and accumulate the integral results of all subintervals to obtain the total integral value;

[0026] The velocity signal and position signal are extracted separately by point-by-point integration.

[0027] Furthermore, the expression of Simpson's rule is:

[0028]

[0029] In the formula, a represents the starting point of integration; b represents the end point of integration; [a, b] represents the integration interval; h represents the width of each interval, f(x) represents the integrand; f0, f1, and f2 represent the values ​​of the integrand at three adjacent sampling points, respectively.

[0030] Furthermore, a turbine cover model is constructed to analyze the amplitude monitoring results in real time and output fault results, including:

[0031] Construct a multi-degree-of-freedom vibration model based on relevant parameters of the turbine head cover;

[0032] Obtain the normal operating benchmark data of the turbine head cover in different sensing directions, and output the fault results based on the speed signal and displacement signal.

[0033] Furthermore, the expression of the multi-degree-of-freedom vibration model is:

[0034]

[0035] In the formula, M represents the mass matrix; C represents the damping matrix; K represents the stiffness matrix; x represents the displacement vector; represents the velocity vector; represents the acceleration vector; F(t) represents the external force vector, including external excitation and internal unbalanced force.

[0036] The technical solution of the second aspect of the present invention provides a hydraulic turbine amplitude monitoring system based on a low-frequency vibration sensor, the system comprising:

[0037] A data acquisition module is configured to obtain vibration signals of a top cover of a water turbine unit in different sensing directions using a triaxial acceleration sensor;

[0038] A digital filtering module is configured to digitally filter the vibration signals in different sensing directions based on the filter to extract the target frequency characteristics;

[0039] A low-frequency compensation module is configured to perform low-frequency compensation on the target frequency characteristics based on an adaptive compensation algorithm to extract a low-frequency band characteristic signal;

[0040] a digital integration module configured to digitally integrate the compensated signal based on the Simpson rule and extract an amplitude monitoring result;

[0041] The fault detection module is configured to construct a turbine head cover model, perform real-time analysis on the amplitude monitoring results, and output fault results.

[0042] The present invention has the following beneficial effects:

[0043] The method and system for monitoring the amplitude of a turbine unit based on a low-frequency vibration sensor provided by the present invention effectively monitors the low-frequency vibration of the turbine unit top cover in different sensing directions under complex operating conditions through a three-axis acceleration sensor, solving the problem of measurement instability caused by the traditional magnetoelectric velocity sensor being susceptible to impact in the low-frequency band; by embedding digital filtering and low-frequency compensation algorithms, the target frequency characteristics can be accurately extracted and effective low-frequency compensation can be performed, ensuring the stability and accuracy of the signal. In addition, the digital integration technology of the Simpson rule flexibly realizes the conversion from acceleration to velocity and then to displacement, further enhancing the flexibility and accuracy of data processing, improving the anti-interference ability in the amplitude monitoring process, and improving the reliability of vibration monitoring of the turbine unit top cover and the accuracy of fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A method flow chart of a method for monitoring amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor provided by an embodiment of the present invention;

[0046] Figure 2 A schematic structural diagram of a hydraulic turbine amplitude monitoring system based on a low-frequency vibration sensor provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the method and system for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor according to the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] The specific scheme of a method and system for monitoring amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor provided by the present invention is described in detail below in conjunction with the accompanying drawings.

[0050] See also Figure 1 , which shows a method flow chart of a method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor provided by an embodiment of the present invention, the method comprising:

[0051] Step S100: using a three-axis acceleration sensor to obtain vibration signals of the turbine cover in different sensing directions; specifically, the three-axis acceleration sensor selects an acceleration sensor based on a micro-electromechanical system (MEMS), and the silicon micro-sensitive chip inside the sensor adopts a comb-tooth structure to form a group of differential detection capacitors with a completely symmetrical structure; during the movement, the relative positions between the movable comb teeth and the mass block and the fixed comb teeth change, thereby causing a change in the capacitance value, and then the variable capacitance is proportionally converted into a variable voltage, the output voltage is proportional to the input acceleration, and the polarity of the voltage reflects the direction of the input acceleration; in this embodiment, the three-dimensional coordinates of the turbine cover are defined, x and z are the sensing directions of the plane horizontal and vertical coordinate axes, and y is the sensing direction of the spatial height coordinate axis. Sampling is performed according to a preset sampling frequency to obtain a time series of a three-dimensional vector, which can be expressed as:

[0052]

[0053] Where a(n) represents the acceleration vector at the nth sampling moment; a x (n), a y (n), a z (n) respectively represents the acceleration values ​​on the x, y, and z axes at the nth sampling moment; the three-axis acceleration sensor based on MEMS technology can monitor the acceleration signals of the turbine top cover in different sensing directions; more comprehensive vibration information can be obtained, which is helpful to more accurately evaluate the vibration state of the top cover;

[0054] The MEMS-based low-frequency vibration velocity sensor can replace the existing magnetoelectric low-frequency velocity vibration sensor, solving the problems of single-direction measurement, low-frequency measurement interference and short service life, and better realizing the stable measurement and detection of the vibration amplitude of the top cover of the unit; different from the magnetoelectric vibration sensor, the spring oscillator mechanical model is easily affected by lateral impact force in low-frequency applications, which can easily cause irreversible elastic deformation of the spring damping, resulting in jumps in the vibration amplitude output by the sensor and measurement instability; the three-axis MEMS acceleration sensor can effectively solve the problem of measuring the vibration information of the top cover of the hydraulic turbine generator set in three sensing directions under the complex operating conditions of the mixed flow hydraulic turbine generator set.

[0055] Step S200: digitally filtering the vibration signals in different sensing directions based on the filter to extract the target frequency characteristics;

[0056] Step S200 specifically includes:

[0057] Step S210: extracting a target frequency range according to the vibration characteristics of the turbine head cover in different sensing directions; specifically, the main vibration frequency range of the turbine head cover under different operating conditions can be determined according to historical data;

[0058] Step S220: Initializing the second-order filter coefficients and system function based on the target frequency range;

[0059] Step S230: digitally filter the vibration signals in different sensing directions using a second-order system cascade to extract the filtered acceleration vector; wherein the second-order system cascade can be expressed as:

[0060]

[0061] Where H(z) represents the second-order filter system function; A represents the constant gain; β 1k , β 2k Represents the coefficient of the second-order system numerator polynomial, which is used to determine the zero position of the filter; α 1k , α 2k Represents the denominator polynomial coefficient of the second-order system, which is used to determine the pole position of the filter; H k (z) represents the transfer function of the kth second-order filter; z -1 、z -2They represent the operation of delaying one sampling cycle and two sampling cycles respectively; the system function H(z) describes the frequency response characteristics of the second-order filter, and is designed based on the original acceleration signal obtained in step S100. By selecting appropriate filter parameters and using an adaptive algorithm, the target frequency characteristics can be effectively extracted from the original signal, thereby improving the accuracy and reliability of amplitude monitoring; multiple second-order filters are cascaded to form a high-order filter to meet more complex frequency response requirements. The filter is applied to the acceleration signal of each sensing direction to obtain the filtered acceleration vector, which can be expressed as:

[0062]

[0063] Where af(n) represents the acceleration vector after filtering at the nth sampling moment;

[0064] Step S240: Calculate the power spectrum density in different sensing directions based on the acceleration vector, and extract the energy distribution in different sensing directions; specifically, use the fast Fourier transform (FFT) to convert the time domain signal into the frequency domain, and calculate the power spectrum density (PSD) in each sensing direction. The power spectrum density reflects the energy distribution of different frequency components, which helps to identify the main vibration source and fault characteristics;

[0065] Step S250: dynamically adjust the filter coefficients of the second-order sections of the filter in different sensing directions using a recursive least squares algorithm; specifically, the recursive least squares algorithm can update the filter coefficients in real time to better match the actual signal characteristics, and by continuously adjusting the filter coefficients, make the output of the filter as close to the desired signal as possible.

[0066] Step S300: low-frequency compensation is performed on the target frequency characteristics based on the adaptive compensation algorithm to extract the low-frequency characteristic signal; specifically, the filter in step S200 can dynamically adjust the parameters to optimize the filtering effect, but in the low-frequency band, due to signal noise and nonlinear characteristics, the filter may not be able to fully retain all useful signals, so through low-frequency compensation, the characteristic signal of the low-frequency band can be further enhanced to improve the sensitivity and accuracy of monitoring;

[0067] Step S300 specifically includes:

[0068] Step S310: According to the preset low-frequency compensation frequency threshold, a digital series correction method is used to perform low-frequency compensation on the energy distribution in different sensing directions, and a low-frequency band characteristic signal is extracted; wherein the expression of the low-frequency compensation transfer function is:

[0069]

[0070] Wherein, C(z) represents the low-frequency compensation transfer function; ξ0 represents the damping ratio of the second-order system before low-frequency compensation; ω0 represents the natural frequency of the second-order system before low-frequency compensation; ξ1 represents the damping ratio of the second-order system after low-frequency compensation; ω1 represents the natural frequency of the second-order system after low-frequency compensation; z represents the frequency response characteristics of the second-order system; T represents the sampling period; according to the preset low-frequency compensation frequency threshold and the required compensation effect, the damping ratios ξ0, ξ1 and the natural frequencies ω0, ω1 before and after low-frequency compensation are calculated. These parameters can be determined by theoretical analysis or experimental data to ensure that the compensated system has better low-frequency response characteristics; in this embodiment, the low-frequency compensation transfer function The number C(z) is corrected in series with the acceleration vector af(n) filtered in step S200, and the compensated acceleration vector afc(n)=C(z)·af(n) is obtained by inputting the filtered signal into the low-frequency compensator; the characteristic signal of the low-frequency band is enhanced by low-frequency compensation, and the sensitivity and accuracy of monitoring are improved; the pre-compensation system refers to the system that has been processed by step S200 before low-frequency compensation, and its characteristics are determined by the filter transfer function applied in step S200; the compensation transfer function combines the filter characteristics in step S200 and the new compensation characteristics to achieve the enhancement of the low-frequency band characteristic signal.

[0071] Step S400: digitally integrate the compensated signal based on the Simpson rule to extract the amplitude monitoring result;

[0072] Step S400 specifically includes:

[0073] Step S410: Divide the compensated signal into intervals, each interval including three adjacent sampling points; for the acceleration signal afc(n) after low-frequency compensation, arrange the signal in time sequence to form a discrete time series; and construct an N-order polynomial through the Lagrange interpolation polynomial so that the polynomial passes through all given points;

[0074] Step S420: Apply the Simpson's rule to each subinterval to calculate the integral value of the interval, and accumulate the integral results of all subintervals to obtain a total integral value; specifically, the Simpson's rule is applicable to approximate the integral value of a function in a certain interval. By dividing the signal into multiple subintervals and applying the Simpson's rule, the integral error can be effectively reduced and the precision of the integral can be improved; the expression of the Simpson's rule is:

[0075]

[0076] In the formula, a represents the starting point of integration; b represents the end point of integration; [a, b] represents the integration interval; h represents the width of each interval, f(x) represents the integrand; f0, f1, and f2 represent the values ​​of the integrand at three adjacent sampling points. For example, when the Nth degree polynomial is 2, there are three points (x0, f0), (x1, f1), and (x2, f2). If these three points are uniformly distributed in the interval [a, b], the integral of each point can be calculated based on the Lagrange basis function, for example:

[0077]

[0078] In the formula, a0 represents; L0(x) represents the Lagrangian basis function. By integrating the Lagrangian basis function on the interval [a, b], the integral value of each basis function on the interval can be obtained; x represents a point in the interval [a, b]. The integral formula based on the Lagrangian basis function provides an effective numerical integration method, which approximates the integral value of a complex function through discrete points. This method can improve the accuracy and reliability of vibration signal calculation;

[0079] Step S430: extracting the velocity signal and the position signal respectively by point-by-point integration;

[0080] Step S500: construct a turbine head cover model, perform real-time analysis on the amplitude monitoring results, and output fault results;

[0081] Step S500 specifically includes:

[0082] Step S510: construct a multi-degree-of-freedom vibration model according to relevant parameters of the turbine unit top cover; the expression of the multi-degree-of-freedom vibration model is:

[0083]

[0084] In the formula, M represents the mass matrix; C represents the damping matrix; K represents the stiffness matrix; x represents the displacement vector; represents the velocity vector; represents the acceleration vector; F(t) represents the external force vector, including external excitation and internal unbalanced force;

[0085] Step S520: outputting the fault result according to the speed signal and the displacement signal; specifically, according to the speed signal and the position signal obtained in step S400, inputting these signals into the multi-degree-of-freedom vibration model, using the Kalman filter in combination with the multi-degree-of-freedom vibration model, estimating the state variables (displacement, velocity and acceleration) of the system, and detecting whether there is a deviation by comparing the estimated state variables with the actual measured values; if the characteristic value of a certain sensing direction exceeds the normal range, triggering the abnormality detection mechanism; judging the fault type according to the specific manifestation of the abnormal characteristics, combining historical data and expert knowledge, the fault type includes at least imbalance, looseness and cracks, and finally outputting the fault result; this embodiment can more accurately simulate and predict the dynamic behavior of the turbine head cover through the multi-degree-of-freedom vibration model; combining the speed and displacement signals, the vibration characteristics can be more comprehensively analyzed, thereby improving the accuracy of fault diagnosis.

[0086] See also Figure 2 , which shows a schematic structural diagram of a hydraulic turbine amplitude monitoring system based on a low-frequency vibration sensor provided by an embodiment of the present invention, the system comprising:

[0087] A data acquisition module is configured to obtain vibration signals of a top cover of a water turbine unit in different sensing directions using a triaxial acceleration sensor;

[0088] A digital filtering module is configured to digitally filter the vibration signals in different sensing directions based on the filter to extract the target frequency characteristics;

[0089] A low-frequency compensation module is configured to perform low-frequency compensation on the target frequency characteristics based on an adaptive compensation algorithm to extract a low-frequency band characteristic signal;

[0090] a digital integration module configured to digitally integrate the compensated signal based on the Simpson rule and extract an amplitude monitoring result;

[0091] The fault detection module is configured to construct a turbine head cover model, perform real-time analysis on the amplitude monitoring results, and output fault results.

[0092] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor, characterized in that: The method comprises: The triaxial acceleration sensor is used to obtain the vibration signals of the turbine head cover in different sensing directions; Based on the filter, the vibration signals in different sensing directions are digitally filtered to extract the target frequency characteristics; Based on the adaptive compensation algorithm, low-frequency compensation is performed on the target frequency characteristics to extract the low-frequency characteristic signal; The compensated signal is digitally integrated based on the Simpson rule to extract the amplitude monitoring result; Construct a turbine cover model, perform real-time analysis on the amplitude monitoring results, and output fault results.

2. The method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor according to claim 1, characterized in that: Based on the filter, the vibration signals in different sensing directions are digitally filtered to extract the target frequency characteristics, including: According to the vibration characteristics of the turbine head cover in different induction directions, the target frequency range is extracted; Initialize the second-order filter coefficients and system functions based on the target frequency range; The vibration signals of different sensing directions are digitally filtered using a second-order system cascade, and the filtered acceleration vector is extracted; The power spectrum density in different sensing directions is calculated based on the acceleration vector, and the energy distribution in different sensing directions is extracted.

3. The method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor according to claim 2, characterized in that: Based on the filter, the vibration signals in different sensing directions are digitally filtered to extract the target frequency characteristics, which also includes: The recursive least squares algorithm is used to dynamically adjust the filter coefficients of the second-order sections of the filter in different sensing directions.

4. The method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor according to claim 1, characterized in that: Based on the adaptive compensation algorithm, low-frequency compensation is performed on the target frequency characteristics to extract the low-frequency characteristic signals, including: According to the preset low-frequency compensation frequency threshold, the digital series correction method is used to perform low-frequency compensation on the energy distribution in different sensing directions and extract the low-frequency characteristic signal.

5. The method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor according to claim 4, characterized in that: The expression of low frequency compensation transfer function is: Where C(z) represents the low-frequency compensation transfer function; ξ0 represents the damping ratio of the second-order system before low-frequency compensation; ω0 represents the natural frequency of the second-order system before low-frequency compensation; ξ1 represents the damping ratio of the second-order system after low-frequency compensation; ω1 represents the natural frequency of the second-order system after low-frequency compensation; z represents the frequency response characteristics of the second-order system; and T represents the sampling period.

6. The method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor according to any one of claims 1 to 5, characterized in that: The compensated signal is digitally integrated based on the Simpson rule to extract the amplitude monitoring results, including: The compensated signal is divided into intervals, each interval including three adjacent sampling points; Apply Simpson's rule to each subinterval to calculate the integral value of the interval, and accumulate the integral results of all subintervals to obtain the total integral value; The velocity signal and position signal are extracted separately by point-by-point integration.

7. The method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor according to claim 6, characterized in that: The expression of Simpson's rule is: In the formula, a represents the starting point of integration; b represents the end point of integration; [a, b] represents the integration interval; h represents the width of each interval, f(x) represents the integrand; f0, f1, and f2 represent the values ​​of the integrand at three adjacent sampling points, respectively.

8. The method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor according to claim 6, characterized in that: Construct a turbine cover model, perform real-time analysis on the amplitude monitoring results, and output fault results, including: Construct a multi-degree-of-freedom vibration model based on relevant parameters of the turbine head cover; Obtain the normal operating benchmark data of the turbine head cover in different sensing directions, and output the fault results based on the speed signal and displacement signal.

9. The method for monitoring the amplitude of a hydraulic turbine unit based on a low-frequency vibration sensor according to claim 8, characterized in that: The expression of the multi-degree-of-freedom vibration model is: In the formula, M represents the mass matrix; C represents the damping matrix; K represents the stiffness matrix; x represents the displacement vector; represents the velocity vector; represents the acceleration vector; F(t) represents the external force vector, including external excitation and internal unbalanced force.

10. A hydraulic turbine amplitude monitoring system based on a low-frequency vibration sensor is characterized in that: The system comprises: A data acquisition module is configured to obtain vibration signals of a top cover of a water turbine unit in different sensing directions using a triaxial acceleration sensor; A digital filtering module is configured to digitally filter the vibration signals in different sensing directions based on the filter to extract the target frequency characteristics; A low-frequency compensation module is configured to perform low-frequency compensation on the target frequency characteristics based on an adaptive compensation algorithm to extract a low-frequency band characteristic signal; a digital integration module configured to digitally integrate the compensated signal based on the Simpson rule and extract an amplitude monitoring result; The fault detection module is configured to construct a turbine head cover model, perform real-time analysis on the amplitude monitoring results, and output fault results.

Images